From f68b79f55f4cdf7c0374726907acbd10f9a0bac1 Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Mon, 13 Jul 2026 15:28:18 +0200 Subject: [PATCH 01/10] feature: Add lint checks to all CI workflows --- .github/workflows/hyperforge_conditional_workflow.yaml | 4 ++++ .github/workflows/hyperforge_external_workflow.yaml | 4 ++++ .github/workflows/hyperforge_generate_workflow.yaml | 4 ++++ .github/workflows/hyperforge_google_workflow.yaml | 4 ++++ .github/workflows/hyperforge_historical_workflow.yaml | 4 ++++ .github/workflows/hyperforge_http_workflow.yaml | 4 ++++ .github/workflows/hyperforge_mcp_workflow.yaml | 4 ++++ .github/workflows/hyperforge_nucliadb_workflow.yaml | 4 ++++ .github/workflows/hyperforge_passthrough_workflow.yaml | 4 ++++ .github/workflows/hyperforge_perplexity_search_workflow.yaml | 4 ++++ .github/workflows/hyperforge_perplexity_workflow.yaml | 4 ++++ .github/workflows/hyperforge_related_workflow.yaml | 4 ++++ .github/workflows/hyperforge_remi_workflow.yaml | 4 ++++ .github/workflows/hyperforge_rephrase_workflow.yaml | 4 ++++ .github/workflows/hyperforge_restart_workflow.yaml | 4 ++++ .github/workflows/hyperforge_restricted_workflow.yaml | 4 ++++ .github/workflows/hyperforge_smart_workflow.yaml | 4 ++++ .github/workflows/hyperforge_static_string_workflow.yaml | 4 ++++ .github/workflows/hyperforge_static_workflow.yaml | 4 ++++ .github/workflows/hyperforge_summarize_workflow.yaml | 4 ++++ .github/workflows/hyperforge_workflow.yaml | 4 ++++ 21 files changed, 84 insertions(+) diff --git a/.github/workflows/hyperforge_conditional_workflow.yaml b/.github/workflows/hyperforge_conditional_workflow.yaml index 0bc972f..7c12fcd 100644 --- a/.github/workflows/hyperforge_conditional_workflow.yaml +++ b/.github/workflows/hyperforge_conditional_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/conditional + run: make lint + - name: Run tests working-directory: agents/conditional run: make test diff --git a/.github/workflows/hyperforge_external_workflow.yaml b/.github/workflows/hyperforge_external_workflow.yaml index 76f23e1..53b1cb6 100644 --- a/.github/workflows/hyperforge_external_workflow.yaml +++ b/.github/workflows/hyperforge_external_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/external + run: make lint + - name: Run tests working-directory: agents/external run: make test diff --git a/.github/workflows/hyperforge_generate_workflow.yaml b/.github/workflows/hyperforge_generate_workflow.yaml index 9421362..32dac5f 100644 --- a/.github/workflows/hyperforge_generate_workflow.yaml +++ b/.github/workflows/hyperforge_generate_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/generate + run: make lint + - name: Run tests working-directory: agents/generate run: make test diff --git a/.github/workflows/hyperforge_google_workflow.yaml b/.github/workflows/hyperforge_google_workflow.yaml index 1a99c02..58a2633 100644 --- a/.github/workflows/hyperforge_google_workflow.yaml +++ b/.github/workflows/hyperforge_google_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/google + run: make lint + - name: Run tests working-directory: agents/google run: make test diff --git a/.github/workflows/hyperforge_historical_workflow.yaml b/.github/workflows/hyperforge_historical_workflow.yaml index 97f6d04..979c9e1 100644 --- a/.github/workflows/hyperforge_historical_workflow.yaml +++ b/.github/workflows/hyperforge_historical_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/historical + run: make lint + - name: Run tests working-directory: agents/historical run: make test diff --git a/.github/workflows/hyperforge_http_workflow.yaml b/.github/workflows/hyperforge_http_workflow.yaml index 343670e..1c94ce6 100644 --- a/.github/workflows/hyperforge_http_workflow.yaml +++ b/.github/workflows/hyperforge_http_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/http + run: make lint + - name: Run tests working-directory: agents/http run: make test diff --git a/.github/workflows/hyperforge_mcp_workflow.yaml b/.github/workflows/hyperforge_mcp_workflow.yaml index 9f7cff7..243ffa8 100644 --- a/.github/workflows/hyperforge_mcp_workflow.yaml +++ b/.github/workflows/hyperforge_mcp_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/mcp + run: make lint + - name: Run tests working-directory: agents/mcp run: make test diff --git a/.github/workflows/hyperforge_nucliadb_workflow.yaml b/.github/workflows/hyperforge_nucliadb_workflow.yaml index eb7ee95..a99d8ad 100644 --- a/.github/workflows/hyperforge_nucliadb_workflow.yaml +++ b/.github/workflows/hyperforge_nucliadb_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/nucliadb + run: make lint + - name: Run tests working-directory: agents/nucliadb run: make test diff --git a/.github/workflows/hyperforge_passthrough_workflow.yaml b/.github/workflows/hyperforge_passthrough_workflow.yaml index 291a48a..6fb7004 100644 --- a/.github/workflows/hyperforge_passthrough_workflow.yaml +++ b/.github/workflows/hyperforge_passthrough_workflow.yaml @@ -46,6 +46,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/passthrough + run: make lint + - name: Run tests working-directory: agents/passthrough run: make test diff --git a/.github/workflows/hyperforge_perplexity_search_workflow.yaml b/.github/workflows/hyperforge_perplexity_search_workflow.yaml index 350d277..7ba8995 100644 --- a/.github/workflows/hyperforge_perplexity_search_workflow.yaml +++ b/.github/workflows/hyperforge_perplexity_search_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/perplexity_search + run: make lint + - name: Run tests working-directory: agents/perplexity_search run: make test diff --git a/.github/workflows/hyperforge_perplexity_workflow.yaml b/.github/workflows/hyperforge_perplexity_workflow.yaml index 272ea12..9ea7900 100644 --- a/.github/workflows/hyperforge_perplexity_workflow.yaml +++ b/.github/workflows/hyperforge_perplexity_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/perplexity + run: make lint + - name: Run tests working-directory: agents/perplexity run: make test diff --git a/.github/workflows/hyperforge_related_workflow.yaml b/.github/workflows/hyperforge_related_workflow.yaml index 0ab3192..769b19c 100644 --- a/.github/workflows/hyperforge_related_workflow.yaml +++ b/.github/workflows/hyperforge_related_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/related + run: make lint + - name: Run tests working-directory: agents/related run: make test diff --git a/.github/workflows/hyperforge_remi_workflow.yaml b/.github/workflows/hyperforge_remi_workflow.yaml index 63601d2..a615c50 100644 --- a/.github/workflows/hyperforge_remi_workflow.yaml +++ b/.github/workflows/hyperforge_remi_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/remi + run: make lint + - name: Run tests working-directory: agents/remi run: make test diff --git a/.github/workflows/hyperforge_rephrase_workflow.yaml b/.github/workflows/hyperforge_rephrase_workflow.yaml index 0d99f3a..b23906d 100644 --- a/.github/workflows/hyperforge_rephrase_workflow.yaml +++ b/.github/workflows/hyperforge_rephrase_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/rephrase + run: make lint + - name: Run tests working-directory: agents/rephrase run: make test diff --git a/.github/workflows/hyperforge_restart_workflow.yaml b/.github/workflows/hyperforge_restart_workflow.yaml index bc7e0a6..47d1af7 100644 --- a/.github/workflows/hyperforge_restart_workflow.yaml +++ b/.github/workflows/hyperforge_restart_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/restart + run: make lint + - name: Run tests working-directory: agents/restart run: make test diff --git a/.github/workflows/hyperforge_restricted_workflow.yaml b/.github/workflows/hyperforge_restricted_workflow.yaml index 1358ca3..1aa85e8 100644 --- a/.github/workflows/hyperforge_restricted_workflow.yaml +++ b/.github/workflows/hyperforge_restricted_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/restricted + run: make lint + - name: Run tests working-directory: agents/restricted run: make test diff --git a/.github/workflows/hyperforge_smart_workflow.yaml b/.github/workflows/hyperforge_smart_workflow.yaml index d58fa9b..2faa38d 100644 --- a/.github/workflows/hyperforge_smart_workflow.yaml +++ b/.github/workflows/hyperforge_smart_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/smart + run: make lint + - name: Run tests working-directory: agents/smart run: make test diff --git a/.github/workflows/hyperforge_static_string_workflow.yaml b/.github/workflows/hyperforge_static_string_workflow.yaml index 4741cc9..f78d4db 100644 --- a/.github/workflows/hyperforge_static_string_workflow.yaml +++ b/.github/workflows/hyperforge_static_string_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/static_string + run: make lint + - name: Run tests working-directory: agents/static_string run: make test diff --git a/.github/workflows/hyperforge_static_workflow.yaml b/.github/workflows/hyperforge_static_workflow.yaml index abbbd60..3b3360a 100644 --- a/.github/workflows/hyperforge_static_workflow.yaml +++ b/.github/workflows/hyperforge_static_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/static + run: make lint + - name: Run tests working-directory: agents/static run: make test diff --git a/.github/workflows/hyperforge_summarize_workflow.yaml b/.github/workflows/hyperforge_summarize_workflow.yaml index 9834109..25e4472 100644 --- a/.github/workflows/hyperforge_summarize_workflow.yaml +++ b/.github/workflows/hyperforge_summarize_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: agents/summarize + run: make lint + - name: Run tests working-directory: agents/summarize run: make test diff --git a/.github/workflows/hyperforge_workflow.yaml b/.github/workflows/hyperforge_workflow.yaml index 91c50fc..f20bd2d 100644 --- a/.github/workflows/hyperforge_workflow.yaml +++ b/.github/workflows/hyperforge_workflow.yaml @@ -47,6 +47,10 @@ jobs: - name: Install package dependencies with uv run: uv sync --group dev + - name: Lint + working-directory: hyperforge + run: make lint + - name: Run tests working-directory: hyperforge run: make test From f4ec640e3c2a6819c70053492ae8ad64302a1a3a Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Mon, 13 Jul 2026 15:45:08 +0200 Subject: [PATCH 02/10] simplify ruff extends --- Makefile | 45 ++++++++++++++----------- agents/conditional/pyproject.toml | 3 ++ agents/external/pyproject.toml | 3 ++ agents/generate/pyproject.toml | 3 ++ agents/google/pyproject.toml | 3 ++ agents/historical/pyproject.toml | 3 ++ agents/http/pyproject.toml | 3 ++ agents/mcp/pyproject.toml | 3 ++ agents/nucliadb/pyproject.toml | 3 ++ agents/passthrough/pyproject.toml | 3 ++ agents/perplexity/pyproject.toml | 3 ++ agents/perplexity_search/pyproject.toml | 3 ++ agents/related/pyproject.toml | 3 ++ agents/remi/pyproject.toml | 3 ++ agents/rephrase/pyproject.toml | 3 ++ agents/restart/pyproject.toml | 3 ++ agents/restricted/pyproject.toml | 3 ++ agents/smart/pyproject.toml | 3 ++ agents/static/pyproject.toml | 3 ++ agents/static_string/pyproject.toml | 3 ++ agents/summarize/pyproject.toml | 3 ++ hyperforge.mk | 14 +++++--- hyperforge/Makefile | 1 + hyperforge/pyproject.toml | 3 ++ 24 files changed, 99 insertions(+), 24 deletions(-) diff --git a/Makefile b/Makefile index b4b5cb7..e5f2509 100644 --- a/Makefile +++ b/Makefile @@ -1,3 +1,26 @@ +COMPONENTS := \ + hyperforge \ + agents/conditional \ + agents/external \ + agents/generate \ + agents/google \ + agents/historical \ + agents/http \ + agents/mcp \ + agents/nucliadb \ + agents/passthrough \ + agents/perplexity \ + agents/perplexity_search \ + agents/related \ + agents/remi \ + agents/rephrase \ + agents/restart \ + agents/restricted \ + agents/smart \ + agents/static \ + agents/static_string \ + agents/summarize + install: uv sync @@ -5,16 +28,13 @@ install-test: uv sync --group dev fmt: - uv run ruff format hyperforge agents - uv run ruff check hyperforge agents --select I --fix + @for dir in $(COMPONENTS); do $(MAKE) -C $$dir format || exit 1; done extract-openai: uv run arag-extract-openapi $(DOCS_FILE) $(API_VERSION) $(HASH) lint: - uv run ruff check hyperforge agents - uv run ruff format --check hyperforge agents - uv run mypy hyperforge agents + @for dir in $(COMPONENTS); do $(MAKE) -C $$dir lint || exit 1; done start_local_db: brew services start postgresql @@ -47,17 +67,4 @@ dockers: docker build -t arag . -f HYPERFORGE.Dockerfile all-tests: - make -C hyperforge test - make -C agents/smart test - make -C agents/perplexity test - make -C agents/perplexity_search test - make -C agents/google test - make -C agents/conditional test - make -C agents/external test - make -C agents/generate test - make -C agents/nucliadb test - make -C agents/passthrough test - make -C agents/remi test - make -C agents/smart test - make -C agents/summarize test - make -C agents/static test + @for dir in $(COMPONENTS); do $(MAKE) -C $$dir test || exit 1; done diff --git a/agents/conditional/pyproject.toml b/agents/conditional/pyproject.toml index 5f56f33..9a95a1d 100644 --- a/agents/conditional/pyproject.toml +++ b/agents/conditional/pyproject.toml @@ -47,3 +47,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/external/pyproject.toml b/agents/external/pyproject.toml index 6f8f722..f0a0e83 100644 --- a/agents/external/pyproject.toml +++ b/agents/external/pyproject.toml @@ -46,3 +46,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/generate/pyproject.toml b/agents/generate/pyproject.toml index 4e57091..42ff9dd 100644 --- a/agents/generate/pyproject.toml +++ b/agents/generate/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/google/pyproject.toml b/agents/google/pyproject.toml index 1be2662..09e9c16 100644 --- a/agents/google/pyproject.toml +++ b/agents/google/pyproject.toml @@ -41,3 +41,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/historical/pyproject.toml b/agents/historical/pyproject.toml index 60a6d1f..4629b73 100644 --- a/agents/historical/pyproject.toml +++ b/agents/historical/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/http/pyproject.toml b/agents/http/pyproject.toml index e37726a..1f0f607 100644 --- a/agents/http/pyproject.toml +++ b/agents/http/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/mcp/pyproject.toml b/agents/mcp/pyproject.toml index b02b0a7..a61a154 100644 --- a/agents/mcp/pyproject.toml +++ b/agents/mcp/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/nucliadb/pyproject.toml b/agents/nucliadb/pyproject.toml index f66ac14..566bf27 100644 --- a/agents/nucliadb/pyproject.toml +++ b/agents/nucliadb/pyproject.toml @@ -43,3 +43,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/passthrough/pyproject.toml b/agents/passthrough/pyproject.toml index f0f9164..eee2809 100644 --- a/agents/passthrough/pyproject.toml +++ b/agents/passthrough/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/perplexity/pyproject.toml b/agents/perplexity/pyproject.toml index 8b0dd8d..4564b3c 100644 --- a/agents/perplexity/pyproject.toml +++ b/agents/perplexity/pyproject.toml @@ -45,3 +45,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/perplexity_search/pyproject.toml b/agents/perplexity_search/pyproject.toml index 45af11b..b76fb17 100644 --- a/agents/perplexity_search/pyproject.toml +++ b/agents/perplexity_search/pyproject.toml @@ -46,3 +46,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/related/pyproject.toml b/agents/related/pyproject.toml index e11228d..f850e58 100644 --- a/agents/related/pyproject.toml +++ b/agents/related/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/remi/pyproject.toml b/agents/remi/pyproject.toml index 0c6dd9b..3e63aed 100644 --- a/agents/remi/pyproject.toml +++ b/agents/remi/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/rephrase/pyproject.toml b/agents/rephrase/pyproject.toml index 48c4b49..ea92252 100644 --- a/agents/rephrase/pyproject.toml +++ b/agents/rephrase/pyproject.toml @@ -43,3 +43,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/restart/pyproject.toml b/agents/restart/pyproject.toml index 3ba8e15..4cc1f75 100644 --- a/agents/restart/pyproject.toml +++ b/agents/restart/pyproject.toml @@ -42,3 +42,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/restricted/pyproject.toml b/agents/restricted/pyproject.toml index 9272605..124ad5d 100644 --- a/agents/restricted/pyproject.toml +++ b/agents/restricted/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/smart/pyproject.toml b/agents/smart/pyproject.toml index 384f895..b8a4f98 100644 --- a/agents/smart/pyproject.toml +++ b/agents/smart/pyproject.toml @@ -44,3 +44,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/static/pyproject.toml b/agents/static/pyproject.toml index 3102f90..1e98caf 100644 --- a/agents/static/pyproject.toml +++ b/agents/static/pyproject.toml @@ -42,3 +42,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/static_string/pyproject.toml b/agents/static_string/pyproject.toml index a4730d9..8965097 100644 --- a/agents/static_string/pyproject.toml +++ b/agents/static_string/pyproject.toml @@ -37,3 +37,6 @@ dev = [ "pytest", "pytest-asyncio", ] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/agents/summarize/pyproject.toml b/agents/summarize/pyproject.toml index 385e734..837fec4 100644 --- a/agents/summarize/pyproject.toml +++ b/agents/summarize/pyproject.toml @@ -45,3 +45,6 @@ asyncio_mode = "auto" # Exclude specific files from being packaged source-exclude = ["tests/**", "*.pyc"] + +[tool.ruff] +extend = "../../ruff.toml" diff --git a/hyperforge.mk b/hyperforge.mk index d719462..53d4031 100644 --- a/hyperforge.mk +++ b/hyperforge.mk @@ -2,6 +2,10 @@ # # include path/to/hyperforge.mk # +# Set REPO_ROOT before including if the default (../..) doesn't match your depth. +# REPO_ROOT is used to locate mypy.ini. + +REPO_ROOT ?= ../.. pytest_flags := -s -rfE -v --tb=native pytest_extra_flags := @@ -14,16 +18,16 @@ PYTEST := pytest $(pytest_flags) $(pytest_extra_flags) .PHONY: format format: - uv run ruff check --fix --config=../../ruff.toml . - uv run ruff format --config=../../ruff.toml . + uv run ruff check --fix . + uv run ruff format . .PHONY: lint lint: - uv run ruff check --config=../../ruff.toml . - uv run ruff format --check --config=../../ruff.toml . + uv run ruff check . + uv run ruff format --check . uv run ty check . - uv run mypy --config-file=../../mypy.ini src + uv run mypy --config-file=$(REPO_ROOT)/mypy.ini src .PHONY: test test: diff --git a/hyperforge/Makefile b/hyperforge/Makefile index fbd6d0c..0f03e3c 100644 --- a/hyperforge/Makefile +++ b/hyperforge/Makefile @@ -1,3 +1,4 @@ +REPO_ROOT := .. include ../hyperforge.mk .PHONY: test-cov diff --git a/hyperforge/pyproject.toml b/hyperforge/pyproject.toml index 5001183..b7a26f9 100644 --- a/hyperforge/pyproject.toml +++ b/hyperforge/pyproject.toml @@ -89,3 +89,6 @@ hyperforge-sandbox = "hyperforge.server:sandbox.run" hyperforge-extract-openapi = "hyperforge.api.commands:extract_openapi" hyperforge-workflows-cleanup-cronjob = "hyperforge.agents.workflow_cleanup:run" hyperforge-standalone = "hyperforge.standalone:run.run" + +[tool.ruff] +extend = "../ruff.toml" From fe6c323128342b19150d6365f84f6509c2447a4e Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Mon, 13 Jul 2026 15:56:01 +0200 Subject: [PATCH 03/10] bring back mypy --- hyperforge.mk | 6 +- pyproject.toml | 3 +- uv.lock | 157 +++++++++---------------------------------------- 3 files changed, 32 insertions(+), 134 deletions(-) diff --git a/hyperforge.mk b/hyperforge.mk index 53d4031..05cf160 100644 --- a/hyperforge.mk +++ b/hyperforge.mk @@ -24,9 +24,9 @@ format: .PHONY: lint lint: - uv run ruff check . 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agents/generate/tests/test_generate.py | 1 + agents/google/tests/test_google.py | 3 ++- agents/historical/tests/test_historical.py | 5 ++--- agents/mcp/src/hyperforge_mcp/agent.py | 2 +- agents/mcp/src/hyperforge_mcp/http.py | 2 +- agents/mcp/src/hyperforge_mcp/multi.py | 2 +- agents/mcp/tests/test_mcp_oauth.py | 7 ++++--- .../src/hyperforge_nucliadb/advanced_ask_agent.py | 2 +- .../src/hyperforge_nucliadb/ask/analysis.py | 2 +- agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py | 2 +- .../nucliadb/src/hyperforge_nucliadb/ask/hydrate.py | 2 +- .../nucliadb/src/hyperforge_nucliadb/ask/multi.py | 2 +- .../src/hyperforge_nucliadb/ask/query_analysis.py | 2 +- .../nucliadb/src/hyperforge_nucliadb/ask/rerank.py | 3 +-- .../nucliadb/src/hyperforge_nucliadb/ask_utils.py | 3 +-- .../src/hyperforge_nucliadb/basic_ask_agent.py | 2 +- agents/nucliadb/src/hyperforge_nucliadb/driver.py | 2 +- agents/nucliadb/tests/test_nucliadb.py | 13 +++++++------ agents/perplexity/tests/test_perplexity.py | 3 ++- .../tests/test_perplexity_search.py | 3 ++- agents/related/src/hyperforge_related/agent.py | 2 +- agents/related/tests/test_related.py | 1 + agents/remi/src/hyperforge_remi/agent.py | 2 +- agents/remi/tests/test_remi.py | 1 + agents/rephrase/src/hyperforge_rephrase/agent.py | 2 +- agents/restart/src/hyperforge_restart/agent.py | 2 +- agents/restart/tests/test_restart.py | 2 +- .../restricted/src/hyperforge_restricted/agent.py | 2 +- agents/restricted/tests/test_sandbox.py | 1 + agents/smart/tests/test_smart_mcp_perplexity.py | 1 + agents/static/tests/test_static.py | 1 + agents/static_string/tests/test_static_string.py | 1 + agents/summarize/src/hyperforge_summarize/agent.py | 2 +- hyperforge/src/hyperforge/alembic/env.py | 5 +++-- hyperforge/src/hyperforge/api/session.py | 2 +- hyperforge/src/hyperforge/broker/redis.py | 1 - hyperforge/src/hyperforge/fixtures.py | 2 +- hyperforge/src/hyperforge/server/session.py | 3 +-- hyperforge/tests/api/test_api.py | 3 ++- hyperforge/tests/api/test_chat_history_workflow.py | 1 + hyperforge/tests/api/test_interaction_basic.py | 9 +++++---- hyperforge/tests/api/test_interaction_e2e.py | 3 ++- hyperforge/tests/api/test_session.py | 7 ++++--- hyperforge/tests/api/test_workflow_api.py | 3 ++- hyperforge/tests/models/test_memory.py | 7 ++++--- hyperforge/tests/models/test_utils.py | 6 +++--- hyperforge/tests/server/test_session_manager.py | 1 + hyperforge/tests/standalone/test_in_memory_cache.py | 1 + hyperforge/tests/standalone/test_standalone.py | 9 +++++---- hyperforge/tests/test_convert_arag_answer.py | 5 +++-- hyperforge/tests/test_mcp_interaction.py | 7 ++++--- hyperforge/tests/test_next.py | 1 + .../tests/unit/arag/agents/test_step_title.py | 3 ++- .../tests/unit/arag/test_mcp_oauth_callback.py | 1 + .../tests/unit/arag/test_memory_chat_history.py | 3 ++- 58 files changed, 98 insertions(+), 75 deletions(-) diff --git a/agents/conditional/src/hyperforge_conditional/conditional.py b/agents/conditional/src/hyperforge_conditional/conditional.py index a5e42de..28f98e8 100644 --- a/agents/conditional/src/hyperforge_conditional/conditional.py +++ b/agents/conditional/src/hyperforge_conditional/conditional.py @@ -2,6 +2,7 @@ from time import time from typing import Any, Dict, List, Literal, Optional, cast +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.agent import Agent, AgentConfig from hyperforge.configure import get_agent_config_klass, get_agent_klass from hyperforge.manager import Manager @@ -10,8 +11,6 @@ from hyperforge.utils import WidgetType from pydantic import BaseModel, Field, field_serializer, field_validator -from hyperforge import PROMPT_ENVIRONMENT - CONDITIONAL_AGENT = """ Given a prompt that indicates a condition , assess whether a given text fulfills it or not. # PROMPT: diff --git a/agents/external/src/hyperforge_external/agent.py b/agents/external/src/hyperforge_external/agent.py index cb31f6a..5f0d345 100644 --- a/agents/external/src/hyperforge_external/agent.py +++ b/agents/external/src/hyperforge_external/agent.py @@ -1,6 +1,7 @@ from copy import deepcopy from time import time +from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.manager import Manager @@ -9,7 +10,6 @@ from hyperforge.utils import check_dns from hyperforge.utils.http import safe_http_client -from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge_external.config import ExternalCallAgentConfig EXTERNAL_CALL_PROMPT = """ diff --git a/agents/generate/src/hyperforge_generate/agent.py b/agents/generate/src/hyperforge_generate/agent.py index 3eb163b..8c34feb 100644 --- a/agents/generate/src/hyperforge_generate/agent.py +++ b/agents/generate/src/hyperforge_generate/agent.py @@ -1,6 +1,7 @@ from time import time from uuid import uuid4 +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.manager import Manager @@ -8,7 +9,6 @@ from hyperforge.trace import trace_agent from nuclia.lib.nua_responses import ChatModel, UserPrompt -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_generate.config import GenerateAgentConfig GENERATE_PROMPT = """ diff --git a/agents/generate/tests/test_generate.py b/agents/generate/tests/test_generate.py index 6a6a34e..559dce4 100644 --- a/agents/generate/tests/test_generate.py +++ b/agents/generate/tests/test_generate.py @@ -6,6 +6,7 @@ from hyperforge.memory import Chunk, Context from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules + from hyperforge_generate.agent import GenerateAgent from hyperforge_generate.config import GenerateAgentConfig diff --git a/agents/google/tests/test_google.py b/agents/google/tests/test_google.py index 6ea4936..47d1a17 100644 --- a/agents/google/tests/test_google.py +++ b/agents/google/tests/test_google.py @@ -8,9 +8,10 @@ from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import MemoryConfig, Rules +from nuclia.lib.nua import AsyncNuaClient + from hyperforge_google.agent import GoogleAgent from hyperforge_google.config import GoogleAgentConfig -from nuclia.lib.nua import AsyncNuaClient NUA_KEY = os.environ.get( "NUA_KEY", diff --git a/agents/historical/tests/test_historical.py b/agents/historical/tests/test_historical.py index 352417f..c079840 100644 --- a/agents/historical/tests/test_historical.py +++ b/agents/historical/tests/test_historical.py @@ -1,12 +1,11 @@ from unittest.mock import AsyncMock, patch -import pytest -from hyperforge.manager import Manager from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules +from nucliadb_models.search import KnowledgeboxFindResults + from hyperforge_historical.agent import HistoricalAgent from hyperforge_historical.config import HistoricalAgentConfig -from nucliadb_models.search import KnowledgeboxFindResults def make_agent(all_sessions: bool = False) -> HistoricalAgent: diff --git a/agents/mcp/src/hyperforge_mcp/agent.py b/agents/mcp/src/hyperforge_mcp/agent.py index be84615..ec743dc 100644 --- a/agents/mcp/src/hyperforge_mcp/agent.py +++ b/agents/mcp/src/hyperforge_mcp/agent.py @@ -10,6 +10,7 @@ import mcp.shared.exceptions as exceptions import mcp.types as types from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import ContextAgent @@ -32,7 +33,6 @@ from nuclia_models.predict.generative_responses import GenerativeFullResponse from pydantic import FileUrl -from hyperforge import logger from hyperforge_mcp.config import MCPAgentConfig, Transport from hyperforge_mcp.http import MCPHTTPDriver from hyperforge_mcp.stdio import MCPStdioDriver diff --git a/agents/mcp/src/hyperforge_mcp/http.py b/agents/mcp/src/hyperforge_mcp/http.py index af8cef4..d17842a 100644 --- a/agents/mcp/src/hyperforge_mcp/http.py +++ b/agents/mcp/src/hyperforge_mcp/http.py @@ -13,6 +13,7 @@ import httpx from cryptography.fernet import Fernet from httpx import Auth, Timeout +from hyperforge import logger from hyperforge.configure import driver from hyperforge.driver import Driver from hyperforge.interaction import ( @@ -34,7 +35,6 @@ ) from pydantic import AnyUrl, BaseModel -from hyperforge import logger from hyperforge_mcp.config_driver import MCPHTTPDriverConfig, MCPHTTPInnerConfig # --------------------------------------------------------------------------- diff --git a/agents/mcp/src/hyperforge_mcp/multi.py b/agents/mcp/src/hyperforge_mcp/multi.py index 6ab450c..872a275 100644 --- a/agents/mcp/src/hyperforge_mcp/multi.py +++ b/agents/mcp/src/hyperforge_mcp/multi.py @@ -1,6 +1,7 @@ from time import time from typing import Any, Dict, List, Optional, Tuple +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.context.agent import ContextAgent from hyperforge.interaction import Feedback, PromptFeedbackSchema @@ -12,7 +13,6 @@ from mcp import types from nuclia.lib.nua_responses import Image, Message -from hyperforge import logger from hyperforge_mcp.agent import EXIT_LOOP_TOOLS, MCPAgent from hyperforge_mcp.config import MCPAgentConfig, MultiMCPAgentConfig from hyperforge_mcp.tools import ( diff --git a/agents/mcp/tests/test_mcp_oauth.py b/agents/mcp/tests/test_mcp_oauth.py index 274dac1..4d4b699 100644 --- a/agents/mcp/tests/test_mcp_oauth.py +++ b/agents/mcp/tests/test_mcp_oauth.py @@ -7,6 +7,10 @@ import httpx import pytest +from mcp.client.auth import PKCEParameters +from mcp.shared.auth import OAuthClientInformationFull, OAuthClientMetadata +from pydantic import AnyUrl + from hyperforge_mcp.http import ( _MCP_OAUTH_SINGLE_FLIGHT_LOCKS, MCPOAuthRoutingParams, @@ -14,9 +18,6 @@ _RoutedOAuthClientProvider, handle_callback, ) -from mcp.client.auth import PKCEParameters -from mcp.shared.auth import OAuthClientInformationFull, OAuthClientMetadata -from pydantic import AnyUrl def _make_provider(**kwargs) -> _RoutedOAuthClientProvider: diff --git a/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py b/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py index 5faba7f..351d05c 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py @@ -2,6 +2,7 @@ from time import time from typing import Any, Dict, List, Optional, cast +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import ContextAgent @@ -10,7 +11,6 @@ from nucliadb_models import filters as ndb_filters from nucliadb_models.search import AskRequest -from hyperforge import logger from hyperforge_nucliadb.advanced_ask_config import ( AdvancedAskAgentConfig, ) diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/analysis.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/analysis.py index 6239d55..506e671 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/analysis.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/analysis.py @@ -3,6 +3,7 @@ from time import time from typing import Any, Dict, List +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.manager import Manager from hyperforge.memory.memory import QuestionMemory, Source from nuclia_models.predict.run_agents import RunTextAgentsRequest @@ -10,7 +11,6 @@ from nucliadb_models import RelationNodeType from nucliadb_models.search import Filter, KnowledgeGraphEntity -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_nucliadb.ask.config import AskAgentConfig from hyperforge_nucliadb.ask.kb_analysis import get_knowledge_base_analysis from hyperforge_nucliadb.ask.models import Analysis diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py index e03d4b7..f5b1668 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py @@ -2,6 +2,7 @@ from time import time from typing import Any, ClassVar, Dict, List, Optional, cast +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import ContextAgent, trace_agent @@ -9,7 +10,6 @@ from hyperforge.manager import Manager from hyperforge.memory.memory import Context, QuestionMemory, Source -from hyperforge import logger from hyperforge_nucliadb.ask.analysis import question_analysis from hyperforge_nucliadb.ask.config import AskAgentConfig from hyperforge_nucliadb.ask.hydrate import hydrate diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/hydrate.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/hydrate.py index 05444ff..a030e59 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/hydrate.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/hydrate.py @@ -3,6 +3,7 @@ from enum import Enum from typing import Dict, List, Tuple +from hyperforge import logger from hyperforge.models import Chunk, Context, FieldTypes from nuclia.lib.nua_responses import Image from nucliadb_models import PagePositions @@ -24,7 +25,6 @@ from nucliadb_models.resource import FileFieldExtractedData, LinkFieldExtractedData from nucliadb_models.search import KnowledgeboxFindResults -from hyperforge import logger from hyperforge_nucliadb.ask.models import NDBChunk, SearchResults from hyperforge_nucliadb.driver import NucliaDBDriver diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/multi.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/multi.py index 4549ead..6fd0e25 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/multi.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/multi.py @@ -2,11 +2,11 @@ from time import time from typing import List, cast +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.manager import Manager from hyperforge.memory.memory import QuestionMemory from hyperforge.models import Source -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_nucliadb.driver import NucliaDBDriver from hyperforge_nucliadb.sync.driver import SyncDriver diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/query_analysis.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/query_analysis.py index e94fbcf..09b5cb7 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/query_analysis.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/query_analysis.py @@ -2,9 +2,9 @@ from dataclasses import dataclass from typing import Optional +from hyperforge import logger from lingua import LanguageDetectorBuilder -from hyperforge import logger from hyperforge_nucliadb.ask.config import AskAgentConfig from hyperforge_nucliadb.ask.kb_analysis import KnowledgeBoxInfo diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/rerank.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/rerank.py index 9703a90..be48530 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/rerank.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/rerank.py @@ -1,9 +1,8 @@ +from hyperforge import logger from hyperforge.manager import Manager from hyperforge.models import Context from nuclia.lib.nua_responses import RerankModel -from hyperforge import logger - async def rerank(context: Context, manager: Manager, top_k: int) -> None: """ diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask_utils.py b/agents/nucliadb/src/hyperforge_nucliadb/ask_utils.py index 92ccc9c..612e4dc 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask_utils.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask_utils.py @@ -1,11 +1,10 @@ from typing import Literal, Optional +from hyperforge import logger from nucliadb_models import filters as ndb_filters from nucliadb_models.labels import LABEL_QUERY_ALIASES as LABEL_ALIASES from nucliadb_models.search import SyncAskResponse -from hyperforge import logger - def get_chunk_text(ask_response: SyncAskResponse, chunk_id: str) -> str: ids = chunk_id.split("/") diff --git a/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py b/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py index a7441a2..abc30dd 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py @@ -4,6 +4,7 @@ from time import time from typing import Any, ClassVar, Dict, List, Literal, Optional, cast +from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import ContextAgent @@ -36,7 +37,6 @@ SyncAskResponse, ) -from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge_nucliadb.ask.multi import choose_source from hyperforge_nucliadb.ask_utils import ( combine_catalog_filter_expressions, diff --git a/agents/nucliadb/src/hyperforge_nucliadb/driver.py b/agents/nucliadb/src/hyperforge_nucliadb/driver.py index e245f77..3e01b06 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/driver.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/driver.py @@ -4,6 +4,7 @@ from typing import Dict, List, Optional, Union import httpx +from hyperforge import logger from hyperforge.configure import driver from hyperforge.driver import Driver from hyperforge.models import Facets @@ -37,7 +38,6 @@ ) from nucliadb_sdk.v2 import NucliaDBAsync -from hyperforge import logger from hyperforge_nucliadb.driver_config import ( ManagerConnection, NucliaDBConfig, diff --git a/agents/nucliadb/tests/test_nucliadb.py b/agents/nucliadb/tests/test_nucliadb.py index 5ef5fdd..4e1e951 100644 --- a/agents/nucliadb/tests/test_nucliadb.py +++ b/agents/nucliadb/tests/test_nucliadb.py @@ -8,12 +8,6 @@ from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import Context from hyperforge.utils.http import PrivateUrlError -from hyperforge_nucliadb.advanced_ask_agent import build_ask_request -from hyperforge_nucliadb.advanced_ask_config import AdvancedAskAgentConfig -from hyperforge_nucliadb.ask.hydrate import hydrate_images -from hyperforge_nucliadb.driver_config import ( - NucliaDBConnection, -) from nucliadb_models import filters as ndb_filters from nucliadb_models.hydration import ( Hydrated, @@ -25,6 +19,13 @@ ) from nucliadb_models.search import AskRequest, Image +from hyperforge_nucliadb.advanced_ask_agent import build_ask_request +from hyperforge_nucliadb.advanced_ask_config import AdvancedAskAgentConfig +from hyperforge_nucliadb.ask.hydrate import hydrate_images +from hyperforge_nucliadb.driver_config import ( + NucliaDBConnection, +) + NUA_KEY = os.environ.get( "NUA_KEY", ) or cassette_nua_key("https://europe-1.nuclia.cloud/") diff --git a/agents/perplexity/tests/test_perplexity.py b/agents/perplexity/tests/test_perplexity.py index 4a5160c..8ed4faa 100644 --- a/agents/perplexity/tests/test_perplexity.py +++ b/agents/perplexity/tests/test_perplexity.py @@ -8,9 +8,10 @@ from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import MemoryConfig, Rules +from nuclia.lib.nua import AsyncNuaClient + from hyperforge_perplexity.config import PerplexityAgentConfig from hyperforge_perplexity.perplexity import PerplexityAgent -from nuclia.lib.nua import AsyncNuaClient NUA_KEY = os.environ.get( "NUA_KEY", diff --git a/agents/perplexity_search/tests/test_perplexity_search.py b/agents/perplexity_search/tests/test_perplexity_search.py index 7537e00..bc695a1 100644 --- a/agents/perplexity_search/tests/test_perplexity_search.py +++ b/agents/perplexity_search/tests/test_perplexity_search.py @@ -8,9 +8,10 @@ from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import MemoryConfig, Rules +from nuclia.lib.nua import AsyncNuaClient + from hyperforge_perplexity_search.agent import PerplexitySearchAgent from hyperforge_perplexity_search.config import PerplexitySearchAgentConfig -from nuclia.lib.nua import AsyncNuaClient NUA_KEY = os.environ.get( "NUA_KEY", diff --git a/agents/related/src/hyperforge_related/agent.py b/agents/related/src/hyperforge_related/agent.py index e477433..4dbf1ef 100644 --- a/agents/related/src/hyperforge_related/agent.py +++ b/agents/related/src/hyperforge_related/agent.py @@ -1,11 +1,11 @@ from time import time +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.agent import Agent from hyperforge.manager import Manager from hyperforge.memory import QuestionMemory from hyperforge.trace import trace_agent -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_related.config import RelatedAgentConfig ASK_JSON_SCHEMA = { diff --git a/agents/related/tests/test_related.py b/agents/related/tests/test_related.py index 62f02ed..a95533c 100644 --- a/agents/related/tests/test_related.py +++ b/agents/related/tests/test_related.py @@ -4,6 +4,7 @@ from hyperforge.manager import Manager from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules + from hyperforge_related.agent import RelatedAgent from hyperforge_related.config import RelatedAgentConfig diff --git a/agents/remi/src/hyperforge_remi/agent.py b/agents/remi/src/hyperforge_remi/agent.py index b154414..3e3d241 100644 --- a/agents/remi/src/hyperforge_remi/agent.py +++ b/agents/remi/src/hyperforge_remi/agent.py @@ -1,5 +1,6 @@ import asyncio +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.manager import Manager @@ -7,7 +8,6 @@ from hyperforge.trace import trace_agent from nuclia_models.predict.remi import RemiResponse -from hyperforge import logger from hyperforge_remi.config import ( ContextGranularity, RemiAgentConfig, diff --git a/agents/remi/tests/test_remi.py b/agents/remi/tests/test_remi.py index 8e73b26..bf10c4d 100644 --- a/agents/remi/tests/test_remi.py +++ b/agents/remi/tests/test_remi.py @@ -4,6 +4,7 @@ import pytest from hyperforge.engine import main as arag_main from hyperforge.minimal_fixtures import cassette_nua_key + from hyperforge_remi.config import ContextGranularity NUA_KEY = os.environ.get( diff --git a/agents/rephrase/src/hyperforge_rephrase/agent.py b/agents/rephrase/src/hyperforge_rephrase/agent.py index 253206d..4d679e1 100644 --- a/agents/rephrase/src/hyperforge_rephrase/agent.py +++ b/agents/rephrase/src/hyperforge_rephrase/agent.py @@ -1,6 +1,7 @@ from time import time from typing import Any, Dict, Optional, cast +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.manager import Manager @@ -9,7 +10,6 @@ from hyperforge.trace import trace_agent from hyperforge_nucliadb.driver import NucliaDBDriver -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_rephrase.config import RephraseAgentConfig REPHRASE_PROMPT = """ diff --git a/agents/restart/src/hyperforge_restart/agent.py b/agents/restart/src/hyperforge_restart/agent.py index 2d8e575..af8239e 100644 --- a/agents/restart/src/hyperforge_restart/agent.py +++ b/agents/restart/src/hyperforge_restart/agent.py @@ -1,12 +1,12 @@ from time import time +from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge.agent import Agent from hyperforge.exceptions import MaxRetries from hyperforge.manager import Manager from hyperforge.memory import QuestionMemory from hyperforge.trace import trace_agent -from hyperforge import PROMPT_ENVIRONMENT, logger from hyperforge_restart.config import RestartAgentConfig REPHRASE_PROMPT = """ diff --git a/agents/restart/tests/test_restart.py b/agents/restart/tests/test_restart.py index db75550..3f45cc0 100644 --- a/agents/restart/tests/test_restart.py +++ b/agents/restart/tests/test_restart.py @@ -1,11 +1,11 @@ from unittest.mock import AsyncMock, MagicMock -from uuid import uuid4 import pytest from hyperforge.exceptions import MaxRetries from hyperforge.manager import Manager from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules + from hyperforge_restart.agent import RestartAgent from hyperforge_restart.config import RestartAgentConfig diff --git a/agents/restricted/src/hyperforge_restricted/agent.py b/agents/restricted/src/hyperforge_restricted/agent.py index 2f47544..c79dab5 100644 --- a/agents/restricted/src/hyperforge_restricted/agent.py +++ b/agents/restricted/src/hyperforge_restricted/agent.py @@ -8,6 +8,7 @@ from time import time from typing import Any, Dict, List, Optional, cast +from hyperforge import logger from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import ContextAgent, build_context_agent @@ -27,7 +28,6 @@ from opentelemetry import trace from RestrictedPython.Guards import safe_builtins # type: ignore -from hyperforge import logger from hyperforge_restricted.config import PythonAgentConfig from hyperforge_restricted.decision import ( CHOOSE_AGENT_TEMPLATE, diff --git a/agents/restricted/tests/test_sandbox.py b/agents/restricted/tests/test_sandbox.py index b69684a..e7e6169 100644 --- a/agents/restricted/tests/test_sandbox.py +++ b/agents/restricted/tests/test_sandbox.py @@ -4,6 +4,7 @@ import pytest from hyperforge.definition import FunctionDefinition + from hyperforge_restricted import sandbox from hyperforge_restricted.model import ( RestrictedPythonTask, diff --git a/agents/smart/tests/test_smart_mcp_perplexity.py b/agents/smart/tests/test_smart_mcp_perplexity.py index 4a306ba..6db4e77 100644 --- a/agents/smart/tests/test_smart_mcp_perplexity.py +++ b/agents/smart/tests/test_smart_mcp_perplexity.py @@ -7,6 +7,7 @@ from hyperforge.memory.memory import EphemeralSessionMemory, MemoryConfig from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import Rule, Rules + from hyperforge_smart.agent import SmartAgent from hyperforge_smart.config import SmartAgentConfig diff --git a/agents/static/tests/test_static.py b/agents/static/tests/test_static.py index a79ef5e..5df57ea 100644 --- a/agents/static/tests/test_static.py +++ b/agents/static/tests/test_static.py @@ -5,6 +5,7 @@ from hyperforge.manager import Manager from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules + from hyperforge_static.agent import StaticAgent from hyperforge_static.config import StaticAgentConfig diff --git a/agents/static_string/tests/test_static_string.py b/agents/static_string/tests/test_static_string.py index 6bc6af8..b805560 100644 --- a/agents/static_string/tests/test_static_string.py +++ b/agents/static_string/tests/test_static_string.py @@ -5,6 +5,7 @@ from hyperforge.manager import Manager from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import MemoryConfig, Rules + from hyperforge_static_string.agent import StaticStringAgent, StaticStringAgentConfig pytestmark = pytest.mark.asyncio diff --git a/agents/summarize/src/hyperforge_summarize/agent.py b/agents/summarize/src/hyperforge_summarize/agent.py index 891cc05..ca46bbf 100644 --- a/agents/summarize/src/hyperforge_summarize/agent.py +++ b/agents/summarize/src/hyperforge_summarize/agent.py @@ -1,6 +1,7 @@ from time import time from typing import List, Optional, overload +from hyperforge import PROMPT_ENVIRONMENT from hyperforge.agent import Agent from hyperforge.configure import agent from hyperforge.context.agent import generate_ctx_block_id @@ -11,7 +12,6 @@ from nuclia.lib.nua_responses import ChatModel, Tool, ToolChoiceAuto, UserPrompt from nuclia_models.predict.generative_responses import ToolCall -from hyperforge import PROMPT_ENVIRONMENT from hyperforge_summarize.config import SummarizeAgentConfig from hyperforge_summarize.prompts import MARKDOWN_TWO_LEVELS_CITATIONS_PROMPT_ADJUSTMENT diff --git a/hyperforge/src/hyperforge/alembic/env.py b/hyperforge/src/hyperforge/alembic/env.py index cc25296..9f24b42 100644 --- a/hyperforge/src/hyperforge/alembic/env.py +++ b/hyperforge/src/hyperforge/alembic/env.py @@ -1,11 +1,12 @@ import os from logging.config import fileConfig -import hyperforge.db.agents # noqa from alembic import context +from sqlalchemy import engine_from_config, pool + +import hyperforge.db.agents # noqa from hyperforge.database import metadata from hyperforge.db.settings import DataManagerSettings -from sqlalchemy import engine_from_config, pool # this is the Alembic Config object, which provides # access to the values within the .ini file in use. diff --git a/hyperforge/src/hyperforge/api/session.py b/hyperforge/src/hyperforge/api/session.py index 204a9a5..5a82668 100644 --- a/hyperforge/src/hyperforge/api/session.py +++ b/hyperforge/src/hyperforge/api/session.py @@ -1,7 +1,7 @@ """Session management functions for ARAG agents with NucliaDB memory.""" -from uuid import UUID from typing import Optional +from uuid import UUID from nucliadb_models import ( CreateResourcePayload, diff --git a/hyperforge/src/hyperforge/broker/redis.py b/hyperforge/src/hyperforge/broker/redis.py index 68b1a36..14984b7 100644 --- a/hyperforge/src/hyperforge/broker/redis.py +++ b/hyperforge/src/hyperforge/broker/redis.py @@ -15,7 +15,6 @@ from hyperforge.pubsub import AgentMessage, StartInteraction from hyperforge.redis_utils import ManualStreamKeysRedisCluster - _REDIS_CONNECT_TIMEOUT_SECONDS = 5 _REDIS_HEALTH_CHECK_INTERVAL_SECONDS = 30 _REDIS_RETRY_ATTEMPTS = 3 diff --git a/hyperforge/src/hyperforge/fixtures.py b/hyperforge/src/hyperforge/fixtures.py index b257157..635abeb 100644 --- a/hyperforge/src/hyperforge/fixtures.py +++ b/hyperforge/src/hyperforge/fixtures.py @@ -33,7 +33,7 @@ drop_database, ) -import hyperforge # noqa: F401 +import hyperforge from hyperforge.api.app import HTTPApplication from hyperforge.api.settings import Settings from hyperforge.broker.redis import RedisBroker diff --git a/hyperforge/src/hyperforge/server/session.py b/hyperforge/src/hyperforge/server/session.py index 081ef8e..5151597 100644 --- a/hyperforge/src/hyperforge/server/session.py +++ b/hyperforge/src/hyperforge/server/session.py @@ -10,7 +10,6 @@ import nucliadb_telemetry.context import nucliadb_telemetry.metrics import prometheus_client -from hyperforge.server.web import WebServer from lru import LRU from nucliadb_telemetry import errors from nucliadb_telemetry.utils import get_telemetry @@ -42,7 +41,7 @@ from hyperforge.server.cache import Cache from hyperforge.server.settings import Settings from hyperforge.server.utils import get_memory -from hyperforge.server.web import start_health_check +from hyperforge.server.web import WebServer, start_health_check HOSTNAME = os.environ.get("HOSTNAME", "arag-server").encode() diff --git a/hyperforge/tests/api/test_api.py b/hyperforge/tests/api/test_api.py index 7c826dd..0c0a216 100644 --- a/hyperforge/tests/api/test_api.py +++ b/hyperforge/tests/api/test_api.py @@ -1,10 +1,11 @@ import pytest from httpx import AsyncClient -from hyperforge.db.encryption import decrypt_data from nucliadb_models.resource import KnowledgeBoxObj from sqlalchemy import text from sqlalchemy.engine.base import Connection +from hyperforge.db.encryption import decrypt_data + pytestmark = [ pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["test"]), pytest.mark.asyncio, diff --git a/hyperforge/tests/api/test_chat_history_workflow.py b/hyperforge/tests/api/test_chat_history_workflow.py index af5de7c..efd095f 100644 --- a/hyperforge/tests/api/test_chat_history_workflow.py +++ b/hyperforge/tests/api/test_chat_history_workflow.py @@ -2,6 +2,7 @@ from copy import deepcopy import pytest + from hyperforge.engine import main as arag_main from hyperforge.minimal_fixtures import cassette_nua_key from hyperforge.models import HistoryQuestionAnswer diff --git a/hyperforge/tests/api/test_interaction_basic.py b/hyperforge/tests/api/test_interaction_basic.py index 8dd5144..75f8f49 100644 --- a/hyperforge/tests/api/test_interaction_basic.py +++ b/hyperforge/tests/api/test_interaction_basic.py @@ -3,9 +3,13 @@ from typing import Any, List, Optional from unittest.mock import patch -import hyperforge.engine import pytest from httpx import AsyncClient +from nucliadb_models.resource import KnowledgeBoxObj +from websockets.asyncio.client import connect +from websockets.exceptions import ConnectionClosed + +import hyperforge.engine from hyperforge.agent import Agent from hyperforge.api.app import HTTPApplication from hyperforge.api.models import InteractionOperation, InteractionRequest @@ -21,9 +25,6 @@ from hyperforge.pubsub import UserToAgentInteraction from hyperforge.retrieval.agent import RetrievalAgent from hyperforge.server.session import SessionManager -from nucliadb_models.resource import KnowledgeBoxObj -from websockets.asyncio.client import connect -from websockets.exceptions import ConnectionClosed pytestmark = [ pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["test", "127.0.0.1"]), diff --git a/hyperforge/tests/api/test_interaction_e2e.py b/hyperforge/tests/api/test_interaction_e2e.py index 4de9551..cb952b8 100644 --- a/hyperforge/tests/api/test_interaction_e2e.py +++ b/hyperforge/tests/api/test_interaction_e2e.py @@ -2,10 +2,11 @@ import pytest from httpx import AsyncClient +from nucliadb_models.resource import KnowledgeBoxObj + from hyperforge.api.models import InteractionRequest from hyperforge.interaction import AragAnswer from hyperforge.server.session import SessionManager -from nucliadb_models.resource import KnowledgeBoxObj pytestmark = [ pytest.mark.vcr( diff --git a/hyperforge/tests/api/test_session.py b/hyperforge/tests/api/test_session.py index 89abcfd..b5327f6 100644 --- a/hyperforge/tests/api/test_session.py +++ b/hyperforge/tests/api/test_session.py @@ -2,9 +2,12 @@ from typing import Any, Optional from unittest.mock import AsyncMock, patch -import hyperforge.engine import pytest from httpx import AsyncClient +from nucliadb_models.resource import KnowledgeBoxObj +from websockets.asyncio.client import connect + +import hyperforge.engine from hyperforge.agent import Agent from hyperforge.api.models import InteractionOperation from hyperforge.context.agent import ContextAgent, trace_agent @@ -17,8 +20,6 @@ from hyperforge.memory import QuestionMemory from hyperforge.retrieval.agent import RetrievalAgent from hyperforge.server.session import SessionManager -from nucliadb_models.resource import KnowledgeBoxObj -from websockets.asyncio.client import connect pytestmark = [ pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["test", "127.0.0.1"]), diff --git a/hyperforge/tests/api/test_workflow_api.py b/hyperforge/tests/api/test_workflow_api.py index fb53ce8..78964cf 100644 --- a/hyperforge/tests/api/test_workflow_api.py +++ b/hyperforge/tests/api/test_workflow_api.py @@ -2,13 +2,14 @@ import pytest from httpx import AsyncClient +from nucliadb_models.resource import KnowledgeBoxObj + from hyperforge.db.agents import ( WORKFLOW_PURGE_RETENTION, retrieval_agent_generation, retrieval_agent_workflow, ) from hyperforge.db.workflow_cleanup import cleanup_deleted_workflows -from nucliadb_models.resource import KnowledgeBoxObj pytestmark = [ pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["test"]), diff --git a/hyperforge/tests/models/test_memory.py b/hyperforge/tests/models/test_memory.py index faa6819..3c237e6 100644 --- a/hyperforge/tests/models/test_memory.py +++ b/hyperforge/tests/models/test_memory.py @@ -1,11 +1,12 @@ -from hyperforge.api.models import INFO_FIELD_ID -from hyperforge.memory.memory import QUESTION_ANSWERS_FIELD, SessionMemory -from hyperforge.models import MemoryConfig, NucliaDBMemoryConfig, Rules from nucliadb_models import CreateResourcePayload, InputConversationField from nucliadb_models.resource import KnowledgeBoxObj from nucliadb_models.text import TextField from nucliadb_sdk import NucliaDB +from hyperforge.api.models import INFO_FIELD_ID +from hyperforge.memory.memory import QUESTION_ANSWERS_FIELD, SessionMemory +from hyperforge.models import MemoryConfig, NucliaDBMemoryConfig, Rules + async def test_memory_save_load(sdk: NucliaDB, arag_kb: KnowledgeBoxObj): config = MemoryConfig( diff --git a/hyperforge/tests/models/test_utils.py b/hyperforge/tests/models/test_utils.py index 0163016..ca44618 100644 --- a/hyperforge/tests/models/test_utils.py +++ b/hyperforge/tests/models/test_utils.py @@ -3,12 +3,12 @@ from enum import Enum from typing import List, Optional, Union -import pytest -from hyperforge.api.utils import to_strict_json_schema -from hyperforge.models import Context, JSONObject from inline_snapshot import snapshot from pydantic import BaseModel, Field +from hyperforge.api.utils import to_strict_json_schema +from hyperforge.models import Context, JSONObject + class Table(str, Enum): orders = "orders" diff --git a/hyperforge/tests/server/test_session_manager.py b/hyperforge/tests/server/test_session_manager.py index 0027dad..8ab6e26 100644 --- a/hyperforge/tests/server/test_session_manager.py +++ b/hyperforge/tests/server/test_session_manager.py @@ -2,6 +2,7 @@ from unittest.mock import AsyncMock, MagicMock, patch import pytest + from hyperforge.pubsub import StartInteraction from hyperforge.server.session import SessionManager diff --git a/hyperforge/tests/standalone/test_in_memory_cache.py b/hyperforge/tests/standalone/test_in_memory_cache.py index 2fd172f..77fab48 100644 --- a/hyperforge/tests/standalone/test_in_memory_cache.py +++ b/hyperforge/tests/standalone/test_in_memory_cache.py @@ -4,6 +4,7 @@ import pytest from httpx import AsyncClient from httpx._transports.asgi import ASGITransport + from hyperforge.api.models import InteractionRequest from hyperforge.context.agent import ContextAgent from hyperforge.context.config import ContextAgentConfig diff --git a/hyperforge/tests/standalone/test_standalone.py b/hyperforge/tests/standalone/test_standalone.py index beeca9b..f758e99 100644 --- a/hyperforge/tests/standalone/test_standalone.py +++ b/hyperforge/tests/standalone/test_standalone.py @@ -9,6 +9,11 @@ import uvicorn from httpx import AsyncClient from httpx._transports.asgi import ASGITransport +from mcp import ClientSession +from mcp.client.streamable_http import streamable_http_client +from mcp.types import TextContent +from websockets.asyncio.client import connect + from hyperforge.api.models import ( InteractionOperation, InteractionRequest, @@ -27,10 +32,6 @@ from hyperforge.standalone.config import StandaloneConfig from hyperforge.standalone.settings import StandaloneSettings from hyperforge.trace import trace_agent -from mcp import ClientSession -from mcp.client.streamable_http import streamable_http_client -from mcp.types import TextContent -from websockets.asyncio.client import connect pytestmark = pytest.mark.asyncio diff --git a/hyperforge/tests/test_convert_arag_answer.py b/hyperforge/tests/test_convert_arag_answer.py index b31a59e..111a063 100644 --- a/hyperforge/tests/test_convert_arag_answer.py +++ b/hyperforge/tests/test_convert_arag_answer.py @@ -3,6 +3,9 @@ import json import pytest +from mcp.types import EmbeddedResource, ImageContent, TextContent, TextResourceContents +from nuclia.lib.nua_responses import Image + from hyperforge.api.v1.mcp_content import convert_arag_answer_to_content from hyperforge.interaction import AnswerOperation, AragAnswer, ARAGException from hyperforge.models import ( @@ -14,8 +17,6 @@ Step, VegaLiteVisualization, ) -from mcp.types import EmbeddedResource, ImageContent, TextContent, TextResourceContents -from nuclia.lib.nua_responses import Image # ============================================================================ # Fixtures & Helpers diff --git a/hyperforge/tests/test_mcp_interaction.py b/hyperforge/tests/test_mcp_interaction.py index ae6aa01..ad64e49 100644 --- a/hyperforge/tests/test_mcp_interaction.py +++ b/hyperforge/tests/test_mcp_interaction.py @@ -2,6 +2,10 @@ from typing import Any from httpx import AsyncClient +from hyperforge_mcp.agent import MCPAgent +from hyperforge_mcp.config import MCPAgentConfig, Transport +from nucliadb_models.resource import KnowledgeBoxObj + from hyperforge.configure import get_driver_config_instance from hyperforge.llm import NUAConnection from hyperforge.manager import Manager @@ -14,9 +18,6 @@ from hyperforge.prompts import PromptConfig from hyperforge.server.session import SessionManager from hyperforge.workflows import WorkflowData -from hyperforge_mcp.agent import MCPAgent -from hyperforge_mcp.config import MCPAgentConfig, Transport -from nucliadb_models.resource import KnowledgeBoxObj NUA_KEY = os.environ.get( "NUA_KEY", diff --git a/hyperforge/tests/test_next.py b/hyperforge/tests/test_next.py index 1f5328c..d765716 100644 --- a/hyperforge/tests/test_next.py +++ b/hyperforge/tests/test_next.py @@ -1,6 +1,7 @@ import os import pytest + from hyperforge.engine import main as arag_main from hyperforge.interaction import AragAnswer from hyperforge.minimal_fixtures import cassette_nua_key diff --git a/hyperforge/tests/unit/arag/agents/test_step_title.py b/hyperforge/tests/unit/arag/agents/test_step_title.py index 91f5b8a..f546efa 100644 --- a/hyperforge/tests/unit/arag/agents/test_step_title.py +++ b/hyperforge/tests/unit/arag/agents/test_step_title.py @@ -1,5 +1,4 @@ import pytest -from hyperforge.agent import Agent, AgentConfig from hyperforge_generate.config import GenerateAgentConfig from hyperforge_mcp.config import MCPAgentConfig from hyperforge_nucliadb.ask.config import AskAgentConfig @@ -8,6 +7,8 @@ from hyperforge_smart.config import SmartAgentConfig from hyperforge_summarize.config import SummarizeAgentConfig +from hyperforge.agent import Agent, AgentConfig + class StubAgent(Agent): def __init__(self, config: AgentConfig): diff --git a/hyperforge/tests/unit/arag/test_mcp_oauth_callback.py b/hyperforge/tests/unit/arag/test_mcp_oauth_callback.py index 568e0fb..cfbe875 100644 --- a/hyperforge/tests/unit/arag/test_mcp_oauth_callback.py +++ b/hyperforge/tests/unit/arag/test_mcp_oauth_callback.py @@ -46,6 +46,7 @@ def _inject_key(monkeypatch): def _make_app(): """Return a minimal FastAPI app that mounts only the oauth router.""" from fastapi import FastAPI + from hyperforge.api.settings import Settings from hyperforge.api.v1.router import router diff --git a/hyperforge/tests/unit/arag/test_memory_chat_history.py b/hyperforge/tests/unit/arag/test_memory_chat_history.py index 9f3f329..05dc111 100644 --- a/hyperforge/tests/unit/arag/test_memory_chat_history.py +++ b/hyperforge/tests/unit/arag/test_memory_chat_history.py @@ -3,10 +3,11 @@ """ import pytest +from nuclia.lib.nua_responses import Author + from hyperforge.memory.memory import EphemeralSessionMemory from hyperforge.models import HistoryQuestionAnswer, MemoryConfig, Rules from hyperforge.server.cache import NoCache -from nuclia.lib.nua_responses import Author def _make_ephemeral_session() -> EphemeralSessionMemory: From 6d6b48ad21209125e32e8b5b9286dde32444c282 Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Mon, 13 Jul 2026 16:37:44 +0200 Subject: [PATCH 05/10] better config and py.typed --- Makefile | 2 +- agents/conditional/pyproject.toml | 2 +- .../src/hyperforge_conditional/py.typed | 0 agents/external/pyproject.toml | 2 +- .../external/src/hyperforge_external/py.typed | 0 agents/generate/pyproject.toml | 2 +- .../generate/src/hyperforge_generate/py.typed | 0 agents/google/pyproject.toml | 2 +- agents/historical/pyproject.toml | 2 +- .../src/hyperforge_historical/py.typed | 0 agents/http/pyproject.toml | 2 +- agents/http/src/hyperforge_http/py.typed | 0 agents/mcp/pyproject.toml | 2 +- agents/nucliadb/pyproject.toml | 2 +- agents/passthrough/pyproject.toml | 2 +- .../src/hyperforge_passthrough/py.typed | 0 agents/perplexity/pyproject.toml | 2 +- agents/perplexity_search/pyproject.toml | 2 +- agents/related/pyproject.toml | 2 +- .../related/src/hyperforge_related/py.typed | 0 agents/remi/pyproject.toml | 2 +- agents/remi/src/hyperforge_remi/py.typed | 0 agents/rephrase/pyproject.toml | 2 +- agents/restart/pyproject.toml | 2 +- .../restart/src/hyperforge_restart/py.typed | 0 agents/restricted/pyproject.toml | 2 +- .../src/hyperforge_restricted/py.typed | 0 agents/smart/pyproject.toml | 2 +- agents/smart/src/hyperforge_smart/py.typed | 0 agents/static/pyproject.toml | 2 +- agents/static/src/hyperforge_static/py.typed | 0 agents/static_string/pyproject.toml | 2 +- agents/summarize/pyproject.toml | 2 +- .../src/hyperforge_summarize/py.typed | 0 hyperforge/pyproject.toml | 2 +- mypy.ini | 23 + pyproject.toml | 2 +- uv.lock | 893 +----------------- 38 files changed, 51 insertions(+), 911 deletions(-) create mode 100644 agents/conditional/src/hyperforge_conditional/py.typed create mode 100644 agents/external/src/hyperforge_external/py.typed create mode 100644 agents/generate/src/hyperforge_generate/py.typed create mode 100644 agents/historical/src/hyperforge_historical/py.typed create mode 100644 agents/http/src/hyperforge_http/py.typed create mode 100644 agents/passthrough/src/hyperforge_passthrough/py.typed create mode 100644 agents/related/src/hyperforge_related/py.typed create mode 100644 agents/remi/src/hyperforge_remi/py.typed create mode 100644 agents/restart/src/hyperforge_restart/py.typed create mode 100644 agents/restricted/src/hyperforge_restricted/py.typed create mode 100644 agents/smart/src/hyperforge_smart/py.typed create mode 100644 agents/static/src/hyperforge_static/py.typed create mode 100644 agents/summarize/src/hyperforge_summarize/py.typed diff --git a/Makefile b/Makefile index e5f2509..525647e 100644 --- a/Makefile +++ b/Makefile @@ -27,7 +27,7 @@ install: install-test: uv sync --group dev -fmt: +format fmt: @for dir in $(COMPONENTS); do $(MAKE) -C $$dir format || exit 1; done extract-openai: diff --git a/agents/conditional/pyproject.toml b/agents/conditional/pyproject.toml index 9a95a1d..da5a82e 100644 --- a/agents/conditional/pyproject.toml +++ b/agents/conditional/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/conditional/src/hyperforge_conditional/py.typed b/agents/conditional/src/hyperforge_conditional/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/external/pyproject.toml b/agents/external/pyproject.toml index f0a0e83..d2f6bcc 100644 --- a/agents/external/pyproject.toml +++ b/agents/external/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/external/src/hyperforge_external/py.typed b/agents/external/src/hyperforge_external/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/generate/pyproject.toml b/agents/generate/pyproject.toml index 42ff9dd..6b5f493 100644 --- a/agents/generate/pyproject.toml +++ b/agents/generate/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/generate/src/hyperforge_generate/py.typed b/agents/generate/src/hyperforge_generate/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/google/pyproject.toml b/agents/google/pyproject.toml index 09e9c16..ccdaabd 100644 --- a/agents/google/pyproject.toml +++ b/agents/google/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge", "google-genai"] [dependency-groups] diff --git a/agents/historical/pyproject.toml b/agents/historical/pyproject.toml index 4629b73..47daf0a 100644 --- a/agents/historical/pyproject.toml +++ b/agents/historical/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/historical/src/hyperforge_historical/py.typed b/agents/historical/src/hyperforge_historical/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/http/pyproject.toml b/agents/http/pyproject.toml index 1f0f607..8d8965c 100644 --- a/agents/http/pyproject.toml +++ b/agents/http/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/http/src/hyperforge_http/py.typed b/agents/http/src/hyperforge_http/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/mcp/pyproject.toml b/agents/mcp/pyproject.toml index a61a154..a056660 100644 --- a/agents/mcp/pyproject.toml +++ b/agents/mcp/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/nucliadb/pyproject.toml b/agents/nucliadb/pyproject.toml index 566bf27..b1c8b56 100644 --- a/agents/nucliadb/pyproject.toml +++ b/agents/nucliadb/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge", "lingua-language-detector", "nucliadb_sdk"] [dependency-groups] diff --git a/agents/passthrough/pyproject.toml b/agents/passthrough/pyproject.toml index eee2809..6396137 100644 --- a/agents/passthrough/pyproject.toml +++ b/agents/passthrough/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/passthrough/src/hyperforge_passthrough/py.typed b/agents/passthrough/src/hyperforge_passthrough/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/perplexity/pyproject.toml b/agents/perplexity/pyproject.toml index 4564b3c..032df4e 100644 --- a/agents/perplexity/pyproject.toml +++ b/agents/perplexity/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = [ "hyperforge", "httpx", diff --git a/agents/perplexity_search/pyproject.toml b/agents/perplexity_search/pyproject.toml index b76fb17..546d52f 100644 --- a/agents/perplexity_search/pyproject.toml +++ b/agents/perplexity_search/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = [ "hyperforge", "httpx", diff --git a/agents/related/pyproject.toml b/agents/related/pyproject.toml index f850e58..7461180 100644 --- a/agents/related/pyproject.toml +++ b/agents/related/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/related/src/hyperforge_related/py.typed b/agents/related/src/hyperforge_related/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/remi/pyproject.toml b/agents/remi/pyproject.toml index 3e63aed..e2333f7 100644 --- a/agents/remi/pyproject.toml +++ b/agents/remi/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/remi/src/hyperforge_remi/py.typed b/agents/remi/src/hyperforge_remi/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/rephrase/pyproject.toml b/agents/rephrase/pyproject.toml index ea92252..a7577cb 100644 --- a/agents/rephrase/pyproject.toml +++ b/agents/rephrase/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/restart/pyproject.toml b/agents/restart/pyproject.toml index 4cc1f75..8f72263 100644 --- a/agents/restart/pyproject.toml +++ b/agents/restart/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/restart/src/hyperforge_restart/py.typed b/agents/restart/src/hyperforge_restart/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/restricted/pyproject.toml b/agents/restricted/pyproject.toml index 124ad5d..cff54fe 100644 --- a/agents/restricted/pyproject.toml +++ b/agents/restricted/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge", "RestrictedPython"] [dependency-groups] diff --git a/agents/restricted/src/hyperforge_restricted/py.typed b/agents/restricted/src/hyperforge_restricted/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/smart/pyproject.toml b/agents/smart/pyproject.toml index b8a4f98..66d5c52 100644 --- a/agents/smart/pyproject.toml +++ b/agents/smart/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/smart/src/hyperforge_smart/py.typed b/agents/smart/src/hyperforge_smart/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/static/pyproject.toml b/agents/static/pyproject.toml index 1e98caf..d33196e 100644 --- a/agents/static/pyproject.toml +++ b/agents/static/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/static/src/hyperforge_static/py.typed b/agents/static/src/hyperforge_static/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/agents/static_string/pyproject.toml b/agents/static_string/pyproject.toml index 8965097..f974720 100644 --- a/agents/static_string/pyproject.toml +++ b/agents/static_string/pyproject.toml @@ -5,7 +5,7 @@ name = "hyperforge_static_string" version = "0.0.1" authors = [] description = "A Hyperforge agent that always returns a fixed string" -requires-python = ">=3.10" +requires-python = ">=3.12" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", diff --git a/agents/summarize/pyproject.toml b/agents/summarize/pyproject.toml index 837fec4..e9d4366 100644 --- a/agents/summarize/pyproject.toml +++ b/agents/summarize/pyproject.toml @@ -17,7 +17,7 @@ classifiers = [ "Programming Language :: Python :: 3 :: Only", "Topic :: Software Development :: Libraries :: Python Modules", ] -requires-python = ">=3.10, <4" +requires-python = ">=3.12, <4" dependencies = ["hyperforge"] [dependency-groups] diff --git a/agents/summarize/src/hyperforge_summarize/py.typed b/agents/summarize/src/hyperforge_summarize/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/hyperforge/pyproject.toml b/hyperforge/pyproject.toml index b7a26f9..3e8bbca 100644 --- a/hyperforge/pyproject.toml +++ b/hyperforge/pyproject.toml @@ -8,7 +8,7 @@ version = "1.0.0" authors = [{ name = "AI Data Team", email = "learning@nuclia.com" }] description = "Agentic Framework for Orchestrated Runtime, Governance, and Execution" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.12" classifiers = [ "Programming Language :: Python :: 3", "Operating System :: OS Independent", diff --git a/mypy.ini b/mypy.ini index faaa129..9d8533f 100644 --- a/mypy.ini +++ b/mypy.ini @@ -2,6 +2,8 @@ python_version = 3.12 warn_return_any = False warn_unused_configs = True +disable_error_code = import-untyped +plugins = pydantic.mypy [mypy-grpc.*] ignore_missing_imports = True @@ -11,3 +13,24 @@ ignore_missing_imports = True [mypy-prometheus_client.*] ignore_missing_imports = True + +[mypy-stashify_protos.*] +ignore_missing_imports = True + +[mypy-deprecated.*] +ignore_missing_imports = True + +[mypy-mrflagly.*] +ignore_missing_imports = True + +[mypy-fire.*] +ignore_missing_imports = True + +[mypy-pytest_docker_fixtures.*] +ignore_missing_imports = True + +[mypy-sqlalchemy_utils.*] +ignore_missing_imports = True + +[mypy-requests.*] +ignore_missing_imports = True diff --git a/pyproject.toml b/pyproject.toml index f773b6c..34c38f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "hyperforge_workspace" -requires-python = ">=3.10" +requires-python = ">=3.12" version = "1.0.0" dependencies = [ "hyperforge", diff --git a/uv.lock b/uv.lock index 4452bd0..e4c5883 100644 --- a/uv.lock +++ b/uv.lock @@ -1,11 +1,9 @@ version = 1 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agents/mcp/src/hyperforge_mcp/multi.py | 6 +++--- .../hyperforge_nucliadb/advanced_ask_agent.py | 2 +- .../src/hyperforge_nucliadb/ask/ask.py | 6 +++--- .../hyperforge_nucliadb/basic_ask_agent.py | 8 ++++---- .../src/hyperforge_nucliadb/sync/agent.py | 2 +- .../hyperforge_nucliadb/sync/config_driver.py | 2 +- .../src/hyperforge_nucliadb/sync/driver.py | 2 +- .../src/hyperforge_restricted/agent.py | 6 +++--- .../src/hyperforge_restricted/model.py | 6 +++--- .../src/hyperforge_restricted/worker.py | 6 +++--- agents/smart/src/hyperforge_smart/config.py | 4 ++-- agents/static/src/hyperforge_static/agent.py | 2 +- hyperforge/src/hyperforge/__init__.py | 2 +- .../versions/2847934e2d59_unique_driver.py | 2 +- hyperforge/src/hyperforge/api/app.py | 4 ++-- .../src/hyperforge/api/authentication.py | 3 ++- hyperforge/src/hyperforge/api/utils.py | 8 ++++---- .../src/hyperforge/api/v1/mcp_interaction.py | 3 ++- hyperforge/src/hyperforge/broker/redis.py | 2 +- hyperforge/src/hyperforge/configure.py | 4 ++-- hyperforge/src/hyperforge/context/agent.py | 2 +- hyperforge/src/hyperforge/db/agents.py | 20 +++++++++---------- hyperforge/src/hyperforge/db/encryption.py | 8 ++++---- hyperforge/src/hyperforge/dummy.py | 2 +- hyperforge/src/hyperforge/feature_flag.py | 6 +++--- hyperforge/src/hyperforge/fixtures.py | 14 ++++++------- hyperforge/src/hyperforge/openapi.py | 2 +- hyperforge/src/hyperforge/retrieval/agent.py | 2 +- hyperforge/src/hyperforge/server/session.py | 4 ++-- hyperforge/src/hyperforge/server/web.py | 2 +- hyperforge/src/hyperforge/standalone/app.py | 4 ++-- 35 files changed, 91 insertions(+), 86 deletions(-) diff --git a/agents/conditional/src/hyperforge_conditional/conditional.py b/agents/conditional/src/hyperforge_conditional/conditional.py index 28f98e8..386a513 100644 --- a/agents/conditional/src/hyperforge_conditional/conditional.py +++ b/agents/conditional/src/hyperforge_conditional/conditional.py @@ -112,7 +112,7 @@ def serialize_conditional_agent( def is_conditional_agent(cls, value: list[Dict[str, Any]]) -> list[BaseModel]: if value is None: return value - result = [] + result: list[BaseModel] = [] for agent_cfg in value: module = agent_cfg.get("module") if module is None: @@ -120,8 +120,8 @@ def is_conditional_agent(cls, value: list[Dict[str, Any]]) -> list[BaseModel]: agent_config_klass = get_agent_config_klass(module) agent_config_instance = agent_config_klass.model_validate(agent_cfg) - result.append(agent_config_instance) - return result # type: ignore + result.append(cast(BaseModel, agent_config_instance)) + return result class Conditional: @@ -228,7 +228,7 @@ async def __call__( if condition and self.then is not None: for then_agent in self.then: - await then_agent(memory, manager) # type: ignore + await then_agent(memory, manager) elif condition is False and self.else_ is not None: for else_agent in self.else_: - await else_agent(memory, manager) # type: ignore + await else_agent(memory, manager) diff --git a/agents/conditional/src/hyperforge_conditional/context_agent.py b/agents/conditional/src/hyperforge_conditional/context_agent.py index 65a28b3..665593f 100644 --- a/agents/conditional/src/hyperforge_conditional/context_agent.py +++ b/agents/conditional/src/hyperforge_conditional/context_agent.py @@ -19,7 +19,7 @@ class ContextConditionalAgentConfig(ConditionalAgentConfig, ContextAgentConfig): model_config = ConfigDict(title="Condition") module: Literal["context_conditional"] = "context_conditional" - on: Literal["QUESTION", "CONTEXT"] = Field( # type: ignore + on: Literal["QUESTION", "CONTEXT"] = Field( default="QUESTION", title="Evaluate condition on", description="Source to evaluate the condition on. CONTEXT is only valid when this agent is used as a next agent in a chain.", @@ -58,7 +58,7 @@ async def get_question_context( flow_id: str, extra_context: Optional[Dict[str, Any]] = None, ): - self.config: ContextConditionalAgentConfig # type: ignore + self.config: ContextConditionalAgentConfig if extra_context is not None: question, question_uuid = await self.rephrase( memory=memory, @@ -137,17 +137,17 @@ async def get_question_context( if self.next_agent is not None: extra_context = extra_context or {} - for agent in ( # type: ignore + for nagent in ( selected_agents + [self.fallback] if self.fallback is not None else selected_agents ): - if agent: + if nagent: answer_summaries = memory.get_agent_answer_summaries( - flow_id=flow_id, agent_id=agent.agent_id + flow_id=flow_id, agent_id=nagent.agent_id ) if answer_summaries: - extra_context[agent.agent_id] = "\n".join(answer_summaries) + extra_context[nagent.agent_id] = "\n".join(answer_summaries) await self.next_agent.get_question_context( memory, diff --git a/agents/generate/src/hyperforge_generate/agent.py b/agents/generate/src/hyperforge_generate/agent.py index 8c34feb..452692d 100644 --- a/agents/generate/src/hyperforge_generate/agent.py +++ b/agents/generate/src/hyperforge_generate/agent.py @@ -71,12 +71,15 @@ async def __call__( format_prompt=False, generative_model=self.config.model, max_tokens=2000, - tracking=memory.get_tracking_info(), ) agent_path = f"/generation/{self.config.id if self.config.id else 'default'}" resp, input_tokens, output_tokens = await manager.execute_raw( - chat_model, memory=memory, module="generate", agent_path=agent_path + chat_model, + memory=memory, + module="generate", + agent_path=agent_path, + tracking=memory.get_tracking_info(), ) generated_text = resp.answer or "" diff --git a/agents/mcp/src/hyperforge_mcp/agent.py b/agents/mcp/src/hyperforge_mcp/agent.py index ec743dc..f3d92f7 100644 --- a/agents/mcp/src/hyperforge_mcp/agent.py +++ b/agents/mcp/src/hyperforge_mcp/agent.py @@ -65,7 +65,7 @@ config_schema=MCPAgentConfig, ) class MCPAgent(ContextAgent, Agent[MCPAgentConfig]): - __published_functions__: Dict[str, FunctionDefinition] # type: ignore[misc] # instance-level, not ClassVar + __published_functions__: Dict[str, FunctionDefinition] # type: ignore[misc] # ty: ignore[invalid-attribute-override] # instance-level, not ClassVar driver: Union[MCPStdioDriver, MCPHTTPDriver, None] resources: List[types.Resource] prompts: List[types.Prompt] diff --git a/agents/mcp/src/hyperforge_mcp/multi.py b/agents/mcp/src/hyperforge_mcp/multi.py index 872a275..6fbc984 100644 --- a/agents/mcp/src/hyperforge_mcp/multi.py +++ b/agents/mcp/src/hyperforge_mcp/multi.py @@ -24,7 +24,7 @@ class MultiMCPAgent(Agent[MultiMCPAgentConfig], ContextAgent): - config: MultiMCPAgentConfig # type: ignore + config: MultiMCPAgentConfig agents: list[MCPAgent] main_agent: MCPAgent @@ -294,7 +294,7 @@ async def get_question_context( global_output_tokens += output_tokens self.main_agent.tools.append( types.Tool(name=agent.config.id, description=response, inputSchema={}) - ) # type: ignore + ) response, input_tokens, output_tokens = await self.summarize_prompts( manager, @@ -305,7 +305,7 @@ async def get_question_context( global_output_tokens += output_tokens self.main_agent.prompts.append( types.Prompt(name=agent.config.id, description=response) - ) # type: ignore + ) context = Context( agent_id=self.agent_id, diff --git a/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py b/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py index 351d05c..1cc7217 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/advanced_ask_agent.py @@ -85,7 +85,7 @@ async def rag( nucliadb_driver = get_ndb_driver(manager, source) context = Context( - agent_id=self.config.id, + agent_id=self.config.id, # ty: ignore[invalid-argument-type] original_question_uuid=memory.original_question_uuid, actual_question_uuid=question_uuid, question=question, diff --git a/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py b/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py index f5b1668..b29e131 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/ask/ask.py @@ -96,7 +96,7 @@ async def ask_analysis_query( config=self.config, question=question, step_title=self.step_title("Choose parameters"), - ) # type: ignore + ) result.append(analysis) return result @@ -242,7 +242,7 @@ async def rag( raise Exception("No NDB available") context = Context( - agent_id=self.config.id, + agent_id=self.config.id, # ty: ignore[invalid-argument-type] original_question_uuid=memory.original_question_uuid, actual_question_uuid=question_uuid, question=question, @@ -284,7 +284,7 @@ async def rag( # for now I'm only adding it if we have no fallback agent configured if self.config.fallback is None: context = Context( - agent_id=self.config.id, + agent_id=self.config.id, # ty: ignore[invalid-argument-type] original_question_uuid=memory.original_question_uuid, actual_question_uuid=missing_uuid, question=missing, diff --git a/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py b/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py index abc30dd..e8c1d77 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/basic_ask_agent.py @@ -584,7 +584,7 @@ async def search_images( if len(images_urls) > 0: context = Context( - agent_id=self.config.id, + agent_id=self.config.id, # ty: ignore[invalid-argument-type] original_question_uuid=memory.original_question_uuid, actual_question_uuid=None, question="Search images by title. Title: " @@ -626,8 +626,8 @@ async def all_images_by_title( for resource_id in resource_ids: resource = await nucliadb_driver.get_resource_by_id( query_params={ - "show": ["basic", "extracted"], # type: ignore - "extracted": ["metadata", "file"], # type: ignore + "show": ["basic", "extracted"], + "extracted": ["metadata", "file"], }, rid=resource_id, ) @@ -637,7 +637,7 @@ async def all_images_by_title( images_urls = await self.get_all_images(resource=resource) if len(images_urls) > 0: context = Context( - agent_id=self.config.id, + agent_id=self.config.id, # ty: ignore[invalid-argument-type] original_question_uuid=memory.original_question_uuid, actual_question_uuid=None, question="All images by title. Title: " + title, diff --git a/agents/nucliadb/src/hyperforge_nucliadb/sync/agent.py b/agents/nucliadb/src/hyperforge_nucliadb/sync/agent.py index 5491070..deff5a0 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/sync/agent.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/sync/agent.py @@ -41,7 +41,7 @@ description="Provide answer questions from synched resources. This agent is responsible for handling the authentication flow and resource validation for synched resources, and then providing the valid resources to the Basic Ask Agent to answer questions.", config_schema=SyncAskAgentConfig, ) -class SyncAskAgent(BasicAskAgent): # type: ignore +class SyncAskAgent(BasicAskAgent): sources: Dict[str, SyncDriver] settings: OAuthSettings = OAuthSettings() diff --git a/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py b/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py index b797578..123f711 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py @@ -9,7 +9,7 @@ class SyncConnection(NucliaDBConnection): - connection_ids: list[str] # type: ignore + connection_ids: list[str] @property def kb_url(self) -> str: diff --git a/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py b/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py index d8e04ff..50aa6a3 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py @@ -58,7 +58,7 @@ class ExternalConnectionOutput(BaseModel): ) class SyncDriver(NucliaDBDriver): async_driver: AsyncClient - config: SyncConnection # type: ignore + config: SyncConnection information: Dict[str, ExternalConnectionOutput] sync_configs: Dict[str, list[str]] diff --git a/agents/restricted/src/hyperforge_restricted/agent.py b/agents/restricted/src/hyperforge_restricted/agent.py index c79dab5..af9545d 100644 --- a/agents/restricted/src/hyperforge_restricted/agent.py +++ b/agents/restricted/src/hyperforge_restricted/agent.py @@ -26,7 +26,7 @@ from nuclia_models.predict.remi import RemiResponse from nucliadb_telemetry.utils import get_telemetry from opentelemetry import trace -from RestrictedPython.Guards import safe_builtins # type: ignore +from RestrictedPython.Guards import safe_builtins from hyperforge_restricted.config import PythonAgentConfig from hyperforge_restricted.decision import ( @@ -616,7 +616,7 @@ async def handle_queue_item( schema = CHOOSE_SCHEMA.copy() schema["parameters"]["properties"]["selected"]["enum"].extend( # type: ignore options.keys() - ) # type: ignore + ) prompt = CHOOSE_AGENT_TEMPLATE.render( options=options, question=question, extra_info=extra_info ) @@ -690,7 +690,7 @@ async def handle_queue_item( ) ) decision = await conditional_agent.make_decision( - *item.args, # type: ignore + *item.args, **item.keyword_args, ) return decision diff --git a/agents/restricted/src/hyperforge_restricted/model.py b/agents/restricted/src/hyperforge_restricted/model.py index c80f3ed..5573dc5 100644 --- a/agents/restricted/src/hyperforge_restricted/model.py +++ b/agents/restricted/src/hyperforge_restricted/model.py @@ -1,5 +1,5 @@ from dataclasses import dataclass -from typing import Any, Sequence, assert_never +from typing import Any, Sequence, assert_never, cast from hyperforge.definition import FunctionDefinition from hyperforge.memory import Context @@ -34,7 +34,7 @@ class RestrictedPythonTask(BaseModel): | RemiResponse | RestrictedPythonTask ) -WorkerModels = { +WorkerModels: dict[str, type[BaseModel]] = { "Context": Context, "RemiResponse": RemiResponse, } @@ -55,7 +55,7 @@ def deserialize(msg: Any) -> WorkerTypes: }, ) else: - return WorkerModels[model_name].model_validate(msg) # type: ignore + return cast(WorkerTypes, WorkerModels[model_name].model_validate(msg)) elif isinstance(msg, list): return [deserialize(m) for m in msg] else: diff --git a/agents/restricted/src/hyperforge_restricted/worker.py b/agents/restricted/src/hyperforge_restricted/worker.py index b6eb909..669454f 100644 --- a/agents/restricted/src/hyperforge_restricted/worker.py +++ b/agents/restricted/src/hyperforge_restricted/worker.py @@ -6,11 +6,11 @@ from hyperforge.definition import FunctionDefinition from hyperforge.memory import Chunk from hyperforge.memory.memory import Context -from RestrictedPython import compile_restricted # type: ignore -from RestrictedPython.Guards import ( # type: ignore +from RestrictedPython import compile_restricted +from RestrictedPython.Guards import ( guarded_iter_unpack_sequence, guarded_unpack_sequence, - safe_builtins, # type: ignore + safe_builtins, ) from hyperforge_restricted.model import RestrictedPythonTask diff --git a/agents/smart/src/hyperforge_smart/config.py b/agents/smart/src/hyperforge_smart/config.py index 3592919..cf8adb2 100644 --- a/agents/smart/src/hyperforge_smart/config.py +++ b/agents/smart/src/hyperforge_smart/config.py @@ -121,7 +121,7 @@ def serialize_smart_agent( def is_smart_agent(cls, value: list[Dict[str, Any]]) -> list[BaseModel]: if value is None: return value - result = [] + result: list[BaseModel] = [] for agent_cfg in value: module = agent_cfg.get("module") if module is None: @@ -130,4 +130,4 @@ def is_smart_agent(cls, value: list[Dict[str, Any]]) -> list[BaseModel]: agent_config_klass = get_agent_config_klass(module) agent_config_instance = agent_config_klass.model_validate(agent_cfg) result.append(agent_config_instance) - return result # type: ignore + return result diff --git a/agents/static/src/hyperforge_static/agent.py b/agents/static/src/hyperforge_static/agent.py index f561945..d34748a 100644 --- a/agents/static/src/hyperforge_static/agent.py +++ b/agents/static/src/hyperforge_static/agent.py @@ -32,7 +32,7 @@ async def static_context( self, memory: QuestionMemory, manager: Manager, - question: Optional[str] = "", + question: str = "", question_uuid: Optional[str] = None, ) -> Context: if question_uuid is None: diff --git a/hyperforge/src/hyperforge/__init__.py b/hyperforge/src/hyperforge/__init__.py index ce5e345..cff947c 100644 --- a/hyperforge/src/hyperforge/__init__.py +++ b/hyperforge/src/hyperforge/__init__.py @@ -1,6 +1,6 @@ import logging -import fire # type: ignore +import fire import jinja2 # Jinja vulnerability is not a concern since this is used to format a string prompt, not to render HTML diff --git a/hyperforge/src/hyperforge/alembic/versions/2847934e2d59_unique_driver.py b/hyperforge/src/hyperforge/alembic/versions/2847934e2d59_unique_driver.py index 2d2be90..1b69fd3 100644 --- a/hyperforge/src/hyperforge/alembic/versions/2847934e2d59_unique_driver.py +++ b/hyperforge/src/hyperforge/alembic/versions/2847934e2d59_unique_driver.py @@ -55,7 +55,7 @@ def upgrade() -> None: def downgrade() -> None: """Downgrade schema.""" # ### commands auto generated by Alembic - please adjust! ### - op.drop_constraint(None, "retrieval_agents_drivers", type_="unique") + op.drop_constraint(None, "retrieval_agents_drivers", type_="unique") # type: ignore[arg-type] # ty: ignore[invalid-argument-type] op.create_index( op.f("ix_retrieval_agents_drivers_provider"), "retrieval_agents_drivers", diff --git a/hyperforge/src/hyperforge/api/app.py b/hyperforge/src/hyperforge/api/app.py index 2aab29c..90c3e45 100644 --- a/hyperforge/src/hyperforge/api/app.py +++ b/hyperforge/src/hyperforge/api/app.py @@ -1,7 +1,7 @@ from contextlib import asynccontextmanager from typing import Any, Optional, Tuple -import prometheus_client # type: ignore +import prometheus_client from fastapi import APIRouter, FastAPI from lru import LRU from mcp.server.lowlevel.server import Server as MCPServer @@ -12,7 +12,7 @@ from nucliadb_telemetry.logs import setup_logging from nucliadb_telemetry.settings import LogLevel, LogSettings from nucliadb_telemetry.utils import clean_telemetry, setup_telemetry -from prometheus_client import CONTENT_TYPE_LATEST # type: ignore +from prometheus_client import CONTENT_TYPE_LATEST from starlette.middleware.authentication import AuthenticationMiddleware from starlette.responses import PlainTextResponse diff --git a/hyperforge/src/hyperforge/api/authentication.py b/hyperforge/src/hyperforge/api/authentication.py index 8606a0c..8e101e3 100644 --- a/hyperforge/src/hyperforge/api/authentication.py +++ b/hyperforge/src/hyperforge/api/authentication.py @@ -51,13 +51,14 @@ def __init__(self) -> None: ] self.security_groups_headers = ["X-NUCLIADB-SECURITY-GROUPS"] - async def authenticate(self, request) -> tuple[AuthCredentials, BaseUser] | None: + async def authenticate(self, conn) -> tuple[AuthCredentials, BaseUser] | None: # There are two groups of headers to authenticate: X-STF-* and # X-NUCLIADB-*. As authorizer should only resolve to one set of headers, # we scan and try to find any of both. While endpoint roles are properly # synchronized with authorizer rules, we don't really care which one # there is, nor we will mix them + request = conn auth_creds = None for roles_header in self.roles_headers: if roles_header in request.headers: diff --git a/hyperforge/src/hyperforge/api/utils.py b/hyperforge/src/hyperforge/api/utils.py index fc0122e..6d6512d 100644 --- a/hyperforge/src/hyperforge/api/utils.py +++ b/hyperforge/src/hyperforge/api/utils.py @@ -323,10 +323,10 @@ def requires_nucliadb_memory(func: Callable) -> Callable: @wraps(func) async def wrapper(*args, **kwargs): # Extract required parameters from kwargs - request: Request = kwargs.get("request") - agent_id: str = kwargs.get("agent_id") - workflow_id: str = kwargs.get("workflow_id", "default") - x_stf_account: str = kwargs.get("x_stf_account") + request: Request = kwargs.get("request") # type: ignore[assignment] # ty: ignore[invalid-assignment] + agent_id: str = kwargs.get("agent_id") # type: ignore[assignment] # ty: ignore[invalid-assignment] + workflow_id: str = kwargs.get("workflow_id", "default") # type: ignore[assignment] + x_stf_account: str = kwargs.get("x_stf_account") # type: ignore[assignment] # ty: ignore[invalid-assignment] if not request or not agent_id or not x_stf_account: raise HTTPException( diff --git a/hyperforge/src/hyperforge/api/v1/mcp_interaction.py b/hyperforge/src/hyperforge/api/v1/mcp_interaction.py index 44ead98..415bd2f 100644 --- a/hyperforge/src/hyperforge/api/v1/mcp_interaction.py +++ b/hyperforge/src/hyperforge/api/v1/mcp_interaction.py @@ -121,7 +121,8 @@ async def call_tool( ) websocket.queue.put_nowait( UserToAgentInteraction( - request_id=msg.feedback.request_id, response=result.content + request_id=msg.feedback.request_id, + response=result.content, # type: ignore[arg-type] # ty: ignore[invalid-argument-type] ) ) elif msg.operation == AnswerOperation.ANSWER: diff --git a/hyperforge/src/hyperforge/broker/redis.py b/hyperforge/src/hyperforge/broker/redis.py index 14984b7..f566b40 100644 --- a/hyperforge/src/hyperforge/broker/redis.py +++ b/hyperforge/src/hyperforge/broker/redis.py @@ -231,4 +231,4 @@ async def initialize(self) -> None: pass async def finalize(self) -> None: - await self._client.aclose() # type: ignore[attr-defined] + await self._client.aclose() # type: ignore[attr-defined] # ty: ignore[unresolved-attribute] diff --git a/hyperforge/src/hyperforge/configure.py b/hyperforge/src/hyperforge/configure.py index ea5e530..fdad842 100644 --- a/hyperforge/src/hyperforge/configure.py +++ b/hyperforge/src/hyperforge/configure.py @@ -123,9 +123,9 @@ def get_caller_module( """ Pulled out of pyramid """ - module_globals = sys._getframe(level).f_globals # type: ignore + module_globals = sys._getframe(level).f_globals module_name = module_globals.get("__name__") or "__main__" - module = sys.modules[module_name] # type: ignore + module = sys.modules[module_name] return module diff --git a/hyperforge/src/hyperforge/context/agent.py b/hyperforge/src/hyperforge/context/agent.py index 6e4bf2d..a2cb574 100644 --- a/hyperforge/src/hyperforge/context/agent.py +++ b/hyperforge/src/hyperforge/context/agent.py @@ -42,7 +42,7 @@ async def build_context_agent(config: ContextAgentConfig) -> "ContextAgent": assert issubclass(agent_class, ContextAgent), ( f"Agent {config.module} is not a ContextAgent" ) - return await agent_class.from_config(config) + return await agent_class.from_config(config) # type: ignore[return-type] # ty: ignore[invalid-return-type] class ContextAgent: diff --git a/hyperforge/src/hyperforge/db/agents.py b/hyperforge/src/hyperforge/db/agents.py index 76f04db..0621f58 100644 --- a/hyperforge/src/hyperforge/db/agents.py +++ b/hyperforge/src/hyperforge/db/agents.py @@ -677,9 +677,9 @@ async def get_agent_config( # Queries for each agent type queries = [ sa.select( - sa.literal_column(f"'{table.name}'").label("kind"), # type:ignore + sa.literal_column(f"'{table.name}'").label("kind"), table.c.id, - sa.null().label("identifier"), # type:ignore + sa.null().label("identifier"), sa.null().label("name"), sa.null().label("provider"), column.label("config"), @@ -700,9 +700,9 @@ async def get_agent_config( # Query for drivers queries.append( sa.select( - sa.literal_column("'driver'").label("kind"), # type:ignore + sa.literal_column("'driver'").label("kind"), retrieval_agents_drivers.c.id, - retrieval_agents_drivers.c.identifier, # type:ignore + retrieval_agents_drivers.c.identifier, retrieval_agents_drivers.c.driver.label("name"), retrieval_agents_drivers.c.provider, retrieval_agents_drivers.c.config, @@ -713,9 +713,9 @@ async def get_agent_config( # Query for rules queries.append( sa.select( - sa.literal_column("'rules'").label("kind"), # type:ignore + sa.literal_column("'rules'").label("kind"), sa.null().label("id"), - sa.null().label("identifier"), # type:ignore + sa.null().label("identifier"), sa.null().label("name"), sa.null().label("provider"), retrieval_agent_config.c.rules.label("config"), @@ -725,9 +725,9 @@ async def get_agent_config( ) queries.append( sa.select( - sa.literal_column("'memory'").label("kind"), # type:ignore + sa.literal_column("'memory'").label("kind"), sa.null().label("id"), - sa.null().label("identifier"), # type:ignore + sa.null().label("identifier"), sa.null().label("name"), sa.null().label("provider"), retrieval_agent_config.c.memory.label("config"), @@ -737,9 +737,9 @@ async def get_agent_config( ) workflow_query = ( sa.select( - retrieval_agent_workflow.c.name, # type: ignore + retrieval_agent_workflow.c.name, retrieval_agent_workflow.c.description, - retrieval_agent_workflow.c.parameters, # type: ignore + retrieval_agent_workflow.c.parameters, retrieval_agent_workflow.c.rules, ) .where(retrieval_agent_workflow.c.account == account) diff --git a/hyperforge/src/hyperforge/db/encryption.py b/hyperforge/src/hyperforge/db/encryption.py index b89c731..6397ba2 100644 --- a/hyperforge/src/hyperforge/db/encryption.py +++ b/hyperforge/src/hyperforge/db/encryption.py @@ -51,16 +51,16 @@ def fernet_key_from_passphrase( def dump_without_encrypted_fields( model: DriverConfig[EncryptedPayload], ) -> dict[str, Any]: - data = model.model_dump() # type: ignore - for field in model.config.encrypted_fields: # type: ignore + data = model.model_dump() + for field in model.config.encrypted_fields: if field in data["config"]: del data["config"][field] return {"config": data} def encrypt_fields(model: EncryptedPayload) -> dict[str, Any]: - data = model.model_dump() # type: ignore - for field in model.encrypted_fields: # type: ignore + data = model.model_dump() + for field in model.encrypted_fields: if field in data: if isinstance(data[field], str): data[field] = encrypt_data(data=data[field]) diff --git a/hyperforge/src/hyperforge/dummy.py b/hyperforge/src/hyperforge/dummy.py index 8e3888e..9b03b2e 100644 --- a/hyperforge/src/hyperforge/dummy.py +++ b/hyperforge/src/hyperforge/dummy.py @@ -1,6 +1,6 @@ from typing import Any -from stashify_protos.protos import idp_pb2 +from stashify_protos.protos import idp_pb2 # ty: ignore[unresolved-import] class DummyIDPRegionalGRPCUtility: diff --git a/hyperforge/src/hyperforge/feature_flag.py b/hyperforge/src/hyperforge/feature_flag.py index d6eb03e..1da75e0 100644 --- a/hyperforge/src/hyperforge/feature_flag.py +++ b/hyperforge/src/hyperforge/feature_flag.py @@ -4,7 +4,7 @@ from functools import cached_property from typing import Any, Optional -import mrflagly # type: ignore +import mrflagly import pydantic from pydantic_settings import BaseSettings, SettingsConfigDict @@ -40,9 +40,9 @@ class FlagService: def __init__(self): self.settings = Settings() if self.settings.flag_settings_url is None: - self.flag_service = mrflagly.FlagService(data=json.dumps(DEFAULT_FLAG_DATA)) + self.flag_service = mrflagly.FlagService(data=json.dumps(DEFAULT_FLAG_DATA)) # ty: ignore[unresolved-attribute] else: - self.flag_service = mrflagly.FlagService( + self.flag_service = mrflagly.FlagService( # ty: ignore[unresolved-attribute] url=self.settings.flag_settings_url ) diff --git a/hyperforge/src/hyperforge/fixtures.py b/hyperforge/src/hyperforge/fixtures.py index 635abeb..a092c75 100644 --- a/hyperforge/src/hyperforge/fixtures.py +++ b/hyperforge/src/hyperforge/fixtures.py @@ -22,12 +22,12 @@ from nucliadb_models.resource import KnowledgeBoxObj from nucliadb_sdk import NucliaDB, NucliaDBAsync from nucliadb_sdk.tests.fixtures import NucliaFixture -from pytest_docker_fixtures import images # type: ignore # type: ignore -from pytest_docker_fixtures.containers.pg import pg_image # type: ignore -from pytest_docker_fixtures.containers.valkey import valkey_image # type: ignore +from pytest_docker_fixtures import images +from pytest_docker_fixtures.containers.pg import pg_image +from pytest_docker_fixtures.containers.valkey import valkey_image from redis.asyncio import Redis from sqlalchemy import create_engine -from sqlalchemy_utils import ( # type: ignore +from sqlalchemy_utils import ( create_database, database_exists, drop_database, @@ -49,8 +49,8 @@ NUA = os.environ.get("NUA_KEY", "DUMMY") -images.settings["nucliadb"]["env"]["NUA_API_KEY"] = NUA -images.settings["nucliadb"]["env"]["DUMMY_PREDICT"] = "False" +images.settings["nucliadb"]["env"]["NUA_API_KEY"] = NUA # type: ignore +images.settings["nucliadb"]["env"]["DUMMY_PREDICT"] = "False" # type: ignore NUCLIA_Make_article = "https://storage.googleapis.com/ncl-testbed-gcp-stage-1/test_nucliadb/articles.export" @@ -162,7 +162,7 @@ async def arag_settings(sdk_async: NucliaDBAsync, valkey_url: str): ) -images.settings["postgresql"].update( +images.settings["postgresql"].update( # type: ignore { "version": "16.1", "env": { diff --git a/hyperforge/src/hyperforge/openapi.py b/hyperforge/src/hyperforge/openapi.py index bbcf517..06299b2 100644 --- a/hyperforge/src/hyperforge/openapi.py +++ b/hyperforge/src/hyperforge/openapi.py @@ -56,7 +56,7 @@ def extract_openapi_command(component_id: str, title: str, router: APIRouter): document["x-metadata"] = { component_id: { "commit": commit_id, - "last_updated": datetime.datetime.utcnow().isoformat(), + "last_updated": datetime.datetime.now(datetime.timezone.utc).isoformat(), } } diff --git a/hyperforge/src/hyperforge/retrieval/agent.py b/hyperforge/src/hyperforge/retrieval/agent.py index c327d53..a1802bf 100644 --- a/hyperforge/src/hyperforge/retrieval/agent.py +++ b/hyperforge/src/hyperforge/retrieval/agent.py @@ -70,7 +70,7 @@ async def from_config_class(cls, config: RetrievalAgentConfig): context: list[ContextAgent] = [] for context_agent_obj in config.context: agent_class = get_agent_klass(context_agent_obj.module) - context.append(await agent_class.from_config(context_agent_obj)) # type: ignore[arg-type] + context.append(await agent_class.from_config(context_agent_obj)) # type: ignore[arg-type] # ty: ignore[invalid-argument-type] generation = [] for generation_agent_obj in config.generation: diff --git a/hyperforge/src/hyperforge/server/session.py b/hyperforge/src/hyperforge/server/session.py index 5151597..5cb8528 100644 --- a/hyperforge/src/hyperforge/server/session.py +++ b/hyperforge/src/hyperforge/server/session.py @@ -96,9 +96,9 @@ async def activation_listener(self): except (asyncio.CancelledError, KeyboardInterrupt): logger.info("Activation listener cancelled, exiting...") break - except Exception: + except Exception as e: logger.exception("Error processing activation message") - errors.capture_exception() + errors.capture_exception(e) async def initialize(self, health_check: bool = True) -> None: diff --git a/hyperforge/src/hyperforge/server/web.py b/hyperforge/src/hyperforge/server/web.py index 0c9fbac..7ea7823 100644 --- a/hyperforge/src/hyperforge/server/web.py +++ b/hyperforge/src/hyperforge/server/web.py @@ -1,7 +1,7 @@ import asyncio import os -import prometheus_client # type: ignore +import prometheus_client import uvicorn from starlette.applications import Starlette from starlette.requests import Request diff --git a/hyperforge/src/hyperforge/standalone/app.py b/hyperforge/src/hyperforge/standalone/app.py index be303fe..17c4de3 100644 --- a/hyperforge/src/hyperforge/standalone/app.py +++ b/hyperforge/src/hyperforge/standalone/app.py @@ -10,7 +10,7 @@ from pathlib import Path from typing import Any, Tuple -import prometheus_client # type: ignore +import prometheus_client from fastapi import APIRouter, FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.staticfiles import StaticFiles @@ -19,7 +19,7 @@ from mcp.server.streamable_http import StreamableHTTPServerTransport from nucliadb_telemetry.logs import setup_logging from nucliadb_telemetry.settings import LogLevel, LogSettings -from prometheus_client import CONTENT_TYPE_LATEST # type: ignore +from prometheus_client import CONTENT_TYPE_LATEST from redis.asyncio import Redis from starlette.authentication import AuthCredentials, AuthenticationBackend, BaseUser from starlette.middleware.authentication import AuthenticationMiddleware From 72f98996a72dd71fead6b5b67b1df03744ed4e30 Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Wed, 15 Jul 2026 08:44:27 +0200 Subject: [PATCH 07/10] fix tests --- agents/nucliadb/tests/test_nucliadb.py | 6 +++--- agents/nucliadb/tests/test_sync.py | 2 +- agents/remi/tests/test_remi.py | 2 +- agents/rephrase/tests/test_rephrase.py | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/agents/nucliadb/tests/test_nucliadb.py b/agents/nucliadb/tests/test_nucliadb.py index 4e1e951..eb9d4f9 100644 --- a/agents/nucliadb/tests/test_nucliadb.py +++ b/agents/nucliadb/tests/test_nucliadb.py @@ -250,7 +250,7 @@ async def callback(obj: AragAnswer): @pytest.mark.asyncio -@pytest.mark.vcr(ignore_localhost=True) +@pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]) async def test_nucliadb_agent_simple(): answers = [] @@ -276,7 +276,7 @@ async def callback(obj: AragAnswer): @pytest.mark.asyncio -@pytest.mark.vcr(ignore_localhost=True) +@pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]) async def test_nucliadb_agent_simple_disable_ai_parameter_search(): question_memory = await arag_main( agent_id="default", @@ -294,7 +294,7 @@ async def test_nucliadb_agent_simple_disable_ai_parameter_search(): @pytest.mark.asyncio -@pytest.mark.vcr(ignore_localhost=True) +@pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]) async def test_nucliadb_agent_basic_ask(): config = deepcopy(CONFIG_SIMPLE) diff --git a/agents/nucliadb/tests/test_sync.py b/agents/nucliadb/tests/test_sync.py index 904c2ea..0f29f42 100644 --- a/agents/nucliadb/tests/test_sync.py +++ b/agents/nucliadb/tests/test_sync.py @@ -22,7 +22,7 @@ "KB_E103CAF3_F8CB_4161_A57C_AAD1192D0666" ) or cassette_nua_key("https://europe-1.nuclia.cloud/") -pytestmark = [pytest.mark.vcr(ignore_localhost=True), pytest.mark.asyncio] +pytestmark = [pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]), pytest.mark.asyncio] CONFIG = { "drivers": [ diff --git a/agents/remi/tests/test_remi.py b/agents/remi/tests/test_remi.py index bf10c4d..90247e4 100644 --- a/agents/remi/tests/test_remi.py +++ b/agents/remi/tests/test_remi.py @@ -16,7 +16,7 @@ ) pytestmark = [ - pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.nuclia.cloud"]), + pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.nuclia.cloud", "europe-1.dp.progress.cloud"]), pytest.mark.asyncio, ] diff --git a/agents/rephrase/tests/test_rephrase.py b/agents/rephrase/tests/test_rephrase.py index 38795c2..76f5960 100644 --- a/agents/rephrase/tests/test_rephrase.py +++ b/agents/rephrase/tests/test_rephrase.py @@ -19,7 +19,7 @@ ) or cassette_nua_key("https://europe-1.nuclia.cloud/") pytestmark = [ pytest.mark.vcr( - ignore_localhost=True, ignore_hosts=["test", "europe-1.nuclia.cloud"] + ignore_localhost=True, ignore_hosts=["test", "europe-1.nuclia.cloud", "europe-1.dp.progress.cloud"] ), pytest.mark.asyncio, ] From 03cac106dd1c044ad8b4664a9b94db0edb6c1afe Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Wed, 15 Jul 2026 10:02:00 +0200 Subject: [PATCH 08/10] fixes --- agents/google/src/hyperforge_google/driver.py | 3 +- .../test_nucliadb_agent_basic_ask.yaml | 1230 +-- .../test_nucliadb_agent_simple.yaml | 4083 ++++++--- ...nt_simple_disable_ai_parameter_search.yaml | 5079 +++-------- .../cassettes/test_sync/test_sync_agent.yaml | 2062 ++--- .../cassettes/test_remi/test_remi[full].yaml | 1176 +++ .../test_remi/test_remi[partial_answers].yaml | 1125 +++ .../test_remi_not_enough_data[full].yaml | 991 +++ ...remi_not_enough_data[partial_answers].yaml | 991 +++ agents/remi/tests/test_remi.py | 2 +- .../test_rephrase/test_rephrase_agent.yaml | 7788 +++++++++++++++++ .../test_rephrase_agent_only_rephrase.yaml | 4686 ++++++++++ agents/rephrase/tests/test_rephrase.py | 4 +- 13 files changed, 22108 insertions(+), 7112 deletions(-) create mode 100644 agents/remi/tests/cassettes/test_remi/test_remi[full].yaml create mode 100644 agents/remi/tests/cassettes/test_remi/test_remi[partial_answers].yaml create mode 100644 agents/remi/tests/cassettes/test_remi/test_remi_not_enough_data[full].yaml create mode 100644 agents/remi/tests/cassettes/test_remi/test_remi_not_enough_data[partial_answers].yaml create mode 100644 agents/rephrase/tests/cassettes/test_rephrase/test_rephrase_agent.yaml create mode 100644 agents/rephrase/tests/cassettes/test_rephrase/test_rephrase_agent_only_rephrase.yaml diff --git a/agents/google/src/hyperforge_google/driver.py b/agents/google/src/hyperforge_google/driver.py index 587dd59..e7a3847 100644 --- a/agents/google/src/hyperforge_google/driver.py +++ b/agents/google/src/hyperforge_google/driver.py @@ -1,6 +1,6 @@ import json -from google.auth import load_credentials_from_dict # type: ignore +from google.auth import load_credentials_from_dict from google.genai import Client from hyperforge.configure import driver from hyperforge.driver import Driver @@ -20,7 +20,6 @@ class GoogleDriver(Driver): @classmethod async def init(cls, driver: GoogleDriverConfig) -> Self: - credentials = None location = None if driver.config.vertexai: diff --git a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml index c41dda8..edfbfe6 100644 --- a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml +++ b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml @@ -1,6 +1,6 @@ interactions: - request: - body: '' + body: '{"prefixes": [{"prefix": "/n/i"}]}' headers: Accept: - '*/*' @@ -8,125 +8,82 @@ interactions: - gzip, deflate Connection: - keep-alive + Content-Length: + - '32' Host: - europe-1.nuclia.cloud User-Agent: - - nuclia.py/4.9.25 - x-nuclia-nuakey: + - nucliadb-sdk/6.13.1.post6414 + content-type: + - application/json + x-stf-serviceaccount: - DUMMY - method: GET - uri: https://europe-1.nuclia.cloud/api/authorizer/info + method: POST + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/catalog/facets response: body: - string: '{"auth":"nua_key","user":{"identity_type":"nua_key","user_id":"03d47998-f672-4a9b-a57b-bde3a9c3ee8c","account_id":"4f9285c7-7151-4431-94e6-3f1fb0d66aca","account_type":"v3enterprise","allow_kb_management":false},"ip_info":null}' + string: '{"facets":{"/n/i":986,"/n/i/application":1,"/n/i/application/json":1,"/n/i/text":985,"/n/i/text/markdown":985}}' headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Content-Length: - - '227' + - '111' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 03 Jun 2026 07:41:14 GMT + - Wed, 15 Jul 2026 07:57:29 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '5' + - '13' + x-nuclia-trace-id: + - 89f06a16cd94f93220bc3a5b132be6aa status: code: 200 message: OK - request: - body: '{"question": "", "retrieval": true, "user_id": "rephrase", "system": null, - "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nYou are an expert at rephrasing - complex questions for an agentic RAG system.\n\nYour task is to review the main - question and any provided context, then rephrase the question to maximize clarity - and focus. Follow these steps:\n\n1. Carefully analyze the main question and - all context provided, including sources, previous questions and answers, and - any other relevant information.\n2. I present, assess whether previous questions - and answers (history) are necessary for rephrasing. Only use history if it is - relevant and improves the clarity or specificity of the main question; otherwise, - ignore it.\n3. If the question can be made clearer or more specific, rephrase - it accordingly. If it is already clear and focused, return it unchanged.\n4. - Only use information present in the provided context. Do not introduce external - knowledge or assumptions.\n5. Return a JSON object with the following fields:\n - - \"rephrased_question\": The rephrased version of the main question, keep the - same question if no rephrasing is needed or possible.\n - \"rules\": Any - rules or guidelines that should be followed when generating the answer.\n - - \"reason\": Explain why the rephrasing was necessary or beneficial.\nReturn - only the JSON object as your response.\n\n\n\n\n# MAIN QUESTION:\nComo usar - max_tokens.answer. En espa\u00f1ol y dame link a la doucmentaci\u00f3n"}, "citations": - false, "citation_threshold": null, "generative_model": "gemini-2.5-flash-lite", - "max_tokens": 2000, "query_context_images": {}, "prefer_markdown": null, "json_schema": - {"title": "rephrase", "description": "", "type": "object", "properties": {"rephrased_question": - {"type": "string", "description": "Rephrased version of the main question."}, - "rules": {"type": "array", "items": {"type": "string"}, "description": "Rules - or guidelines to follow when generating the answer."}, "reason": {"type": "string", - "description": "Reason for rephrasing explaining why it is necessary."}}, "required": - ["rephrased_question"]}, "format_prompt": false, "rerank_context": false, "tools": - [], "tool_choice": {"type": "required"}, "reasoning": false, "seed": null}' + body: '{"prefixes": [{"prefix": "/s/p"}]}' headers: Accept: - - application/x-ndjson + - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - - '2251' - Content-Type: - - application/json + - '32' Host: - europe-1.nuclia.cloud User-Agent: - - nuclia.py/4.9.25 - x-client-ident: - - default - x-message: - - 3665ef769eae4304b588cd4d5b659d55 - x-origin: - - RAO - x-session: - - default_default_session - x-show-consumption: - - 'true' - x-stf-nuakey: + - nucliadb-sdk/6.13.1.post6414 + content-type: + - application/json + x-stf-serviceaccount: - DUMMY method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/chat + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/catalog/facets response: body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"rephrased_question\":\"\xBFC\xF3mo - se utiliza el par\xE1metro `max_tokens` para controlar la longitud de las - respuestas en espa\xF1ol, y d\xF3nde puedo encontrar la documentaci\xF3n oficial?\",\"rules\":[\"Proporcionar - una explicaci\xF3n clara sobre el uso de `max_tokens`.\",\"Incluir un enlace - directo a la documentaci\xF3n relevante.\",\"La respuesta debe estar en espa\xF1ol.\"],\"reason\":\"La - pregunta original es un poco ambigua al preguntar 'c\xF3mo usar max_tokens.answer'. - La reformulaci\xF3n especifica que se refiere al uso del par\xE1metro `max_tokens` - para controlar la longitud de las respuestas, que es su funci\xF3n principal. - Adem\xE1s, solicita expl\xEDcitamente la documentaci\xF3n oficial, lo que - hace la petici\xF3n m\xE1s precisa.\"}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":7,\"output_tokens\":13,\"timings\":{\"generative\":1.010955389996525},\"input_nuclia_tokens\":0.007,\"output_nuclia_tokens\":0.013}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.0071,\"output\":0.01344,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" + string: '{"facets":{"/s/p":986,"/s/p/ca":3,"/s/p/cy":1,"/s/p/da":1,"/s/p/en":967,"/s/p/eo":4,"/s/p/la":7,"/s/p/nb":1,"/s/p/tl":1,"/s/p/yo":1}}' headers: Alt-Svc: - - h3=":443"; ma=2592000 - Transfer-Encoding: - - chunked + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '133' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - - application/x-ndjson + - application/json date: - - Wed, 03 Jun 2026 07:41:15 GMT - nuclia-learning-id: - - 798a95c9e4824f8485787cba6794f48d - nuclia-learning-model: - - gemini-2.5-flash-lite + - Wed, 15 Jul 2026 07:57:29 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '1017' + - '12' x-nuclia-trace-id: - - ec1ad51f024d71a9a99c30a0d434690e + - 4031c7b360f8b69d15c3fafc5ef91c75 status: code: 200 message: OK @@ -158,7 +115,7 @@ interactions: Materials 2026","related":"","text":"","uri":""}]}}}' headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Content-Length: - '575' access-control-expose-headers: @@ -166,13 +123,13 @@ interactions: content-type: - application/json date: - - Wed, 03 Jun 2026 07:41:15 GMT + - Wed, 15 Jul 2026 07:57:30 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '10' + - '15' x-nuclia-trace-id: - - c2aebb4f4102750d0a99000708fab199 + - 7e940b2f60aba5c8b4b9300c73d4fe5e status: code: 200 message: OK @@ -201,7 +158,7 @@ interactions: Docs","description":"","learning_configuration":null,"external_index_provider":null,"configured_external_index_provider":{"type":"unset"},"similarity":null,"hidden_resources_enabled":false,"hidden_resources_hide_on_creation":false,"enforce_security":false},"model":null}' headers: Alt-Svc: - - h3=":443"; 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If you do not find an answer + in the context, please say \"Right now I don''t have enough context to answer + your question. Please contact us at support@nuclia.com\". 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"]}" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Length: - - '1814' + - '12993' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 03 Jun 2026 07:41:15 GMT,Wed, 03 Jun 2026 07:41:15 GMT + - Wed, 15 Jul 2026 07:57:29 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '34' + - '54' x-nuclia-trace-id: - - f83bd826095ee394cd44b8404b397d0f + - abd17c2fc125f60f4da5070f1256e313 status: code: 200 message: OK @@ -424,7 +390,7 @@ interactions: Materials 2026","related":"","text":"","uri":""}]}' headers: Alt-Svc: - 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> develop > js sdk > type aliases > WorkflowType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-04-22T20:03:49.324172\",\"modified\":\"2026-06-02T15:38:22.886483\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"8798b24b72a74c63a79ec0e2f10a1423\":{\"id\":\"8798b24b72a74c63a79ec0e2f10a1423\",\"slug\":\"docs-ingestion-cloud-storage-sync-md\",\"title\":\"docs - > ingestion > cloud storage sync\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-04-13T08:05:37.518460\",\"modified\":\"2026-05-05T14:56:07.367333\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false}},\"paragraphs\":{\"results\":[{\"score\":0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - Europe (AWS) \\n \\n 63.179.23.139/32 \\n 3.78.13.149/32 \\n 3.66.123.24/32 - \\n \\n Europe (GCP) \\n \\n 35.204.65.155/32 \\n 34.91.38.151/32 \\n 35.204.139.129/32 - \\n 35.204.108.221/32 \\n \\n Israel \\n \\n 51.17.150.97/32 \\n \\n United - States \\n \\n 3.137.28.168/32 \\n \",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":341,\"end\":578},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - \\n id: outbound-ips \\n title: Outbound IP Addresses \\n \\n If your firewall - blocks inbound requests from the internet, you may need to allowlist the following - IP addresses. These are the source IPs that Progress Agentic RAG uses when - making outbound connections (e.g., webhooks, sync agents, or other integrations - calling your infrastructure). \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":341},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > management > security > 5 outbound ips\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":45},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - \\n optional security: object \\n \\n groups \\n \\n groups: string[] \\n - \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:166 \\n - \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Defined in - \\n libs/sdk-core/src/lib/db/search/search.models.ts:165\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":225,\"end\":477},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / SuggestOptions \\n Interface: SuggestOptions - \\n Properties \\n highlight? \\n \\n optional highlight: boolean \\n \\n - Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:164 \\n \\n - security? \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":225},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > SuggestOptions\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":53},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"af442e9be6484c44a4a1dbc396e38dca\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KBKVSchemas\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":50},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"af442e9be6484c44a4a1dbc396e38dca\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KBKVSchemas \\n Interface: KBKVSchemas - \\n Properties \\n schemas \\n \\n schemas: object \\n \\n Index Signature - \\n [name: string]: KVSchema \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:602\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":238},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KeyValueFieldData\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":56},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - \\n optional errors: IError[] \\n \\n Inherited from \\n IFieldDataBase.errors - \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:150 - \\n \\n extracted? \\n \\n optional extracted: ExtractedData \\n \\n Inherited - from \\n IFieldDataBase.extracted \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:148 - \\n \\n status? \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":295,\"end\":623},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - \\n optional status: null \\\\| string \\n \\n Inherited from \\n IFieldDataBase.status - \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:151 - \\n \\n value? \\n \\n optional value: object \\n \\n Index Signature \\n - [key: string]: KVValue \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:606\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":623,\"end\":928},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KeyValueFieldData \\n Interface: KeyValueFieldData - \\n Extends \\n \\n IFieldDataBase \\n \\n Properties \\n error? \\n \\n optional - error: IError \\n \\n Inherited from \\n IFieldDataBase.error \\n Defined - in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:149 \\n \\n errors? - \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":295},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KVSchema\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":47},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KVSchema \\n Interface: KVSchema \\n Properties - \\n description? \\n \\n optional description: string \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/resource/resource.models.ts:597 \\n \\n fields \\n - \\n fields: KVSchemaField[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:598 - \\n \\n name \\n \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":330},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:596\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":330,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KVSchemaField\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":52},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - key: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:589 - \\n \\n required? \\n \\n optional required: boolean \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/resource/resource.models.ts:592 \\n \\n type \\n - \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":226,\"end\":444},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - type: KVFieldType \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:590\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":444,\"end\":537},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KVSchemaField \\n Interface: KVSchemaField - \\n Properties \\n description? \\n \\n optional description: string \\n \\n - Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:591 \\n - \\n key \\n 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+ string: "{\"resources\":{\"b28e388104644fc5bba68c0cd44e01d4\":{\"id\":\"b28e388104644fc5bba68c0cd44e01d4\",\"slug\":\"docs-agentic-how-to-agentic-retrieval-md\",\"title\":\"docs + > agentic > how to > agentic retrieval\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-29T13:39:03.379512\",\"modified\":\"2026-06-29T13:39:03.379525\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"f13642b2862f4a1ea2144c9eb2abb2a3\":{\"id\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"slug\":\"docs-develop-js-sdk-functions-normalizeGlobalBackendUrl-md\",\"title\":\"docs + > develop > js sdk > functions > normalizeGlobalBackendUrl\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-26T08:38:33.060378\",\"modified\":\"2026-07-14T12:47:15.556468\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"ed6fda7434aa43a3bd5138b95e7fe081\":{\"id\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"slug\":\"docs-develop-python-sdk-15-memory-md\",\"title\":\"docs + > develop > python sdk > 15 memory\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-26T08:31:21.372271\",\"modified\":\"2026-06-26T08:59:38.187065\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"a572ce0c7eb949e4a17babae21a03a4a\":{\"id\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"slug\":\"navigation-pages\",\"title\":\"navigation-pages\",\"summary\":\"\",\"icon\":\"application/json\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"fr\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-23T10:58:22.812220\",\"modified\":\"2026-06-23T10:58:22.812242\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"2a0d73029ad24c599d6dc1e117a41897\":{\"id\":\"2a0d73029ad24c599d6dc1e117a41897\",\"slug\":\"docs-agentic-deploy-md\",\"title\":\"docs + > agentic > deploy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-29T13:39:02.557258\",\"modified\":\"2026-06-29T13:39:02.557276\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"007b445866574d7ca69891d388663cc6\":{\"id\":\"007b445866574d7ca69891d388663cc6\",\"slug\":\"docs-management-security-5-public-ips-md\",\"title\":\"docs + > management > security > 5 public ips\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-07-03T09:28:57.937064\",\"modified\":\"2026-07-03T09:28:57.937076\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false}},\"paragraphs\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Outbound \u2014 the source IP addresses that Progress Agentic RAG uses when + making connections to your systems (e.g., webhooks, sync agents, or other + integrations). Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\":5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-resources\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-search\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-rag-lab\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"account-arag\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-kv-schemas\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"user-profile\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-users\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-widgets\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-remi-analytics\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"agent-users\",\"labels\":[]}],\"facets\":{},\"query\":\"\",\"total\":2013,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"]}" 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"chatgpt-azure-4o-mini"}' headers: Accept: - '*/*' @@ -672,7 +563,7 @@ interactions: Connection: - keep-alive Content-Length: - - '240' + - '195' Host: - europe-1.nuclia.cloud User-Agent: @@ -693,216 +584,163 @@ interactions: endpoint\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" `/\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"ask\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"`\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" para\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" establecer\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" l\xEDmites\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" en\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" + la\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" cantidad\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" + de\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" tokens\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" + que\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" se\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" + 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m\xE1s\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" - informaci\xF3n\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\",\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" - no\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" dudes\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" - en\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" preguntar\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\".\"}}\n{\"item\":{\"type\":\"retrieval\",\"results\":{\"resources\":{\"1ceff32ecafc4f559c9636e9d1e8cd89\":{\"id\":\"1ceff32ecafc4f559c9636e9d1e8cd89\",\"slug\":\"docs-develop-js-sdk-interfaces-QAOperation-md\",\"title\":\"docs - > develop > js sdk > interfaces > QAOperation\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:10.613432\",\"modified\":\"2026-06-02T15:39:44.624338\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/0-277\":{\"score\":0.003403005190193653,\"score_type\":\"RERANKER\",\"order\":17,\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / QAOperation \\n Interface: QAOperation - \\n Extends \\n \\n BaseOperation \\n \\n Properties \\n generate_answers_prompt? - \\n \\n optional generate_answers_prompt: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:102 - \\n \\n max_questions? 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[\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"nu\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"cl\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"ia\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"/core\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" + Docs\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"](\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"https\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"://\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"nu\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"cl\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"ia\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\".com\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"/docs\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\").\"}}\n{\"item\":{\"type\":\"retrieval\",\"results\":{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.0018969200318679214,\"score_type\":\"RERANKER\",\"order\":15,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\",\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\",\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\":{\"score\":0.0018895386019721627,\"score_type\":\"RERANKER\",\"order\":16,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\":{\"score\":0.0031115086749196053,\"score_type\":\"RERANKER\",\"order\":14,\"text\":\" + Key components of a split strategy include: \\n \\n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \\n Custom split: + Determines the method used for splitting: \\n Manual splitting: Splits content + based on a specified delimiter (default is \\\\n ). \\n\",\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":963,\"end\":1239,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.0016875095898285508,\"score_type\":\"RERANKER\",\"order\":17,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.03588069602847099,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\" \\n \\n Maximum supported input tokens: \\n Description: The maximum number of tokens that the model can accept as input. Be mindful that this takes into account the tokens used in the prompt, query and context. Also take note that some models may provide their context window as the total between input and - output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.003839395008981228,\"score_type\":\"RERANKER\",\"order\":16,\"text\":\" + output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.025565214455127716,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\" Description: The maximum number of tokens that the model can generate as output. Again, we should keep in mind that this value summed to the Maximum supported input tokens should not exceed the total context size supported by the model. - \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\":{\"score\":0.0023322338238358498,\"score_type\":\"RERANKER\",\"order\":18,\"text\":\" - Example: For Phi 4 Reasoning Plus, the total context size is 32768 tokens, - as we want to leave room for the output, we will set the maximum supported - input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output - tokens: \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2575,\"end\":2804,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs - > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-06-02T15:40:39.731007\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.022201228886842728,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.0555151142179966,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.023689471185207367,\"score_type\":\"RERANKER\",\"order\":12,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.07477004081010818,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.015306354500353336,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.043042849749326706,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens? - \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs - > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-02T22:04:59.702465\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.8172829747200012,\"score_type\":\"RERANKER\",\"order\":0,\"text\":\" - Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n - - Context size: Limits the amount of retrieved information sent to the LLM - \\n - Answer length: Limits the length of the generated response \\n Important - Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs - > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2025-10-27T14:35:08.325400\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.19269122183322906,\"score_type\":\"RERANKER\",\"order\":1,\"text\":\" - max_tokens: the maximum number of input tokens to put in the final context - (including the prompt, the retrieved results and the user question). \\n max_output_tokens: - the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs - > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2025-08-27T15:32:35.381741\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.013955709524452686,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" + \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.02806073985993862,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle rate limits effectively, ensuring that your application respects the limits - and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\":{\"score\":0.003976228181272745,\"score_type\":\"RERANKER\",\"order\":15,\"text\":\" - retries = 0 \\n while retries < max_retries: \\n response = requests.get(url, - headers=headers) \\n if response.status_code == 200: \\n return response.json() - \\n elif response.status_code == 429: \\n wait_time = 2 retries # Exponential - backoff: 2^retries \\n print(f Rate limit exceeded. Retrying in {wait_time} - seconds... ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() - \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint - \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, - headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits - \\n Here's an example of how to use the try_after key from the response to - manage rate limits: \\n\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":1900,\"end\":2653,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs - > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2025-04-15T14:51:41.582119\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.007204769179224968,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\"@nuclia/core - / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n - Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED - \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n - PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED - \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED - \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 - AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 - \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n - \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 - \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed - \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED - \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in - \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 - NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 - \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED - \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n - \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 - \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed - \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED - \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 - \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time - \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED - \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 - TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"666b3a9d01f74323b6ca6c9939834bec\":{\"id\":\"666b3a9d01f74323b6ca6c9939834bec\",\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\",\"title\":\"docs - > develop > js sdk > enums > StatsType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"sv\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.005040\",\"modified\":\"2025-04-15T14:51:41.005050\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\":{\"score\":0.005752544850111008,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\"@nuclia/core - / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated \\n Table - of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES \\n CHARS - \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME \\n RESOURCES - \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration Members - \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\n \u2022 - BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 - \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198 - \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n - \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 - \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 - \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 - RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 - \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 - \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 - TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs - > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-06-02T15:43:13.632521\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.004556113854050636,\"score_type\":\"RERANKER\",\"order\":14,\"text\":\" - \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589 - \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: - number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:586 - \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.001995538594201207,\"score_type\":\"RERANKER\",\"order\":19,\"text\":\" - \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 - \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n - \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? - \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"4be29234ea764ce7889f78e60fe8ec78\":{\"id\":\"4be29234ea764ce7889f78e60fe8ec78\",\"slug\":\"docs-rag-advanced-models-md\",\"title\":\"docs - > rag > advanced > models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-08-19T16:35:49.576447\",\"modified\":\"2025-08-27T15:33:08.935390\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\":{\"score\":0.007907291874289513,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" - \\n id: models \\n title: Semantic models \\n \\n | Model Name | Size | Threshold - | Max Tokens | Matryoshka | Multilingual | External | \\n | --------------------------- - | -------- | ------------- | -------------- | -------------- | ---------------- - | ------------ | \\n | en-2024-04-24 | 768 | 0.47 | 2048 | No | No | No | - \\n | multilingual-2023-08-16 | 1024 | 0.7 | 512 | No | Yes | No | \\n | multilingual-2024-05-06 - | 1024 | 0.4 | 2048 | No | Yes | No | \\n | Open AI small | 1536 | 0.5 | 8192 - | Yes | No | No | \\n | Open AI large | 3072 | 0.5 | 8192 | Yes | No | Yes - | \\n | Google multilingual Gecko | 768 | 0.55 | 3072 | Yes | Yes | Yes | - \\n | Hugging Face | N/A | N/A | N/A | N/A | N/A | Yes | \\n Embeddings models - \\n Embeddings are like fingerprints for words or data. They help computers - understand the similarities and differences between them, making it easier - for machines to perform tasks like understanding language or recognizing patterns. - \\n\",\"id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":935,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs - > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.03448852524161339,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" - ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which - may increase your usage costs. \\n You may need to increase max_tokens to - give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.02779562398791313,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\",\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\":{\"score\":0.0014214043039828539,\"score_type\":\"RERANKER\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.07094483077526093,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000, \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) - \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs - > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-06-02T15:39:28.012340\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.03775027394294739,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.07055959850549698,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.4437209367752075,\"score_type\":\"RERANKER\",\"order\":0,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.10338596254587173,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? - \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.014785407111048698,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\" + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.05510690063238144,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? - \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs - > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-06-02T15:38:04.014988\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\":{\"score\":0.00821969285607338,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n - Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED - \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 - \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED - \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED - \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in - \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n - NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 - \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED - \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n - \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 - \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED - \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 - \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED - \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: - train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}}},\"query\":\"\xBFC\xF3mo - se utiliza el par\xE1metro `max_tokens` para controlar la longitud de las - respuestas en espa\xF1ol, y d\xF3nde puedo encontrar la documentaci\xF3n 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QueryInfo\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:36.036743\",\"modified\":\"2026-07-14T12:52:03.497025\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4e12158442054714a4caff0af00da2f5/t/page/0-396\":{\"score\":0.007785655092447996,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / QueryInfo \\n Interface: QueryInfo \\n + Properties \\n entities? \\n \\n optional entities: TokenSearch \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:423 \\n \\n language \\n \\n + language: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:418 + \\n \\n max_context \\n \\n max_context: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:422 + \\n \\n query \\n \\n\",\"id\":\"4e12158442054714a4caff0af00da2f5/t/page/0-396\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":396,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"ecabb0862fa24c8fb8e01d4930a3f7a2\":{\"id\":\"ecabb0862fa24c8fb8e01d4930a3f7a2\",\"slug\":\"docs-develop-js-sdk-variables-MAX_FACETS_PER_REQUEST-md\",\"title\":\"docs + > develop > js sdk > variables > 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headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Transfer-Encoding: - chunked access-control-expose-headers: @@ -910,293 +748,15 @@ interactions: content-type: - application/x-ndjson date: - - Wed, 03 Jun 2026 07:41:17 GMT - nuclia-learning-id: - - 2a829a254cf74ed08f380a3234a5f768 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '1320' - x-nuclia-trace-id: - - 713209dff1a8573f8eb4a9979bba3e31 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-basic_ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfC\u00f3mo - se utiliza el par\u00e1metro `max_tokens` para controlar la longitud de las - respuestas en espa\u00f1ol, y d\u00f3nde puedo encontrar la documentaci\u00f3n - oficial?\n\n\n\nContext:\n\n**block-AA**\n\n## Chunk: docs - > rag > advanced > consumption.\n``` Use the max_tokens parameter on the /ask - endpoint to set hard limits on: \n - Context size: Limits the amount of retrieved - information sent to the LLM \n - Answer length: Limits the length of the generated - response \n Important Considerations \n Context Limitations: \n ```\n\n\n---\"\n\n\n**block-AB**\n\n## - Chunk: docs > develop > js sdk > interfaces > PredictAnswerOptions\n``` \n - optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: docs > rag > advanced > openai - api compatible models\n``` \n \n Maximum supported input tokens: \n Description: - The maximum number of tokens that the model can accept as input. Be mindful - that this takes into account the tokens used in the prompt, query and context. - Also take note that some models may provide their context window as the total - between input and output tokens, while others may provide it as the input tokens - only. \n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: docs > develop > python - sdk > 05 search\n``` \n SDK: \n \n ```python \n from nuclia import sdk \n from - nucliadb_models.search import AskRequest, Reasoning \n search = sdk.NucliaSearch() - \n query = AskRequest( \n query= My question with extra reasoning effort , \n - max_tokens=5000, \n reasoning=Reasoning( \n display=True, # Show reasoning in - the response \n effort= low , # Can be low , medium , or high \n budget_tokens=1024 - # How many tokens reasoning can use \n ), \n ) \n search.ask(query=query) \n - ``` \n Model Support for Reasoning Options: \n ```\n\n\n---\"\n\n\n**block-AE**\n\n## - Chunk: docs > rag > advanced > widget > features\n``` max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: - docs > develop > js sdk > interfaces > ChatOptions\n``` \n optional highlight: - boolean \n \n Inherited from \n BaseSearchOptions.highlight \n Defined in \n - libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? \n - \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n BaseSearchOptions.keyword_filters - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 \n \n max_tokens? - \n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: docs > develop > js sdk > interfaces - > ChatOptions\n``` \n optional max_tokens: number \\| object \n \n Defines - the maximum number of tokens that the model will take as context. \n Defined - in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 \n \n min_score? - \n ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: docs > develop > js sdk > interfaces - > PredictAnswerOptions\n``` \n optional generative_model: string \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \n \n json_schema? \n - \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: docs > ingestion - > how to > rate limiting\n``` ) \n time.sleep(wait_time) \n retries += 1 \n - else: \n response.raise_for_status() \n raise Exception( Max retries exceeded - ) \n Example usage \n url = https://your-endpoint \n headers = { Authorization - : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, - headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate - limits effectively, ensuring that your application respects the limits and retries - appropriately. ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: docs > rag > advanced - > openai api compatible models\n``` Description: The maximum number of tokens - that the model can generate as output. Again, we should keep in mind that this - value summed to the Maximum supported input tokens should not exceed the total - context size supported by the model. \n ```\n\n\n---\"\n\n\n\n\n\n\n"}, - "citations": false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 2000, "query_context_images": {}, "prefer_markdown": null, "json_schema": - {"title": "validate_or_answer", "description": "Validate or answer", "parameters": - {"type": "object", "properties": {"reason": {"type": "string", "description": - "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": - "Partial or complete answer to the user query from the information in the context."}, - "missing_info_query": {"type": "string", "description": "Query needed to retrieve - the missing information in case the context is not enough to answer the question. - If the context does not answer the question at all, just return the original - question."}, "useful": {"type": "string", "description": "Is the context useful - to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", - "items": {"type": "string", "description": "Block ID cited in the answer, e.g. - block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, - "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, - "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "required"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '8294' - Content-Type: - - application/json - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-origin: - - RAO - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context - provides information on how to use the `max_tokens` parameter to control the - length of responses. It specifies that `max_tokens` can limit the length of - the generated response and the amount of input tokens. However, the context - does not provide a direct link or reference to the official documentation.\",\"answer\":\"El - par\xE1metro `max_tokens` se utiliza en el endpoint /ask para establecer l\xEDmites - en la longitud de las respuestas generadas. Este par\xE1metro puede limitar - la longitud de la respuesta generada y tambi\xE9n el tama\xF1o del contexto. - Para encontrar la documentaci\xF3n oficial, se necesita informaci\xF3n adicional.\",\"missing_info_query\":\"\xBFD\xF3nde - puedo encontrar la documentaci\xF3n oficial sobre el uso del par\xE1metro - `max_tokens`?\",\"useful\":\"yes\",\"citations\":[\"block-AA\",\"block-AE\"]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":50,\"output_tokens\":19,\"timings\":{\"generative\":2.20798238000134},\"input_nuclia_tokens\":0.05,\"output_nuclia_tokens\":0.019}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.04956,\"output\":0.01932,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 03 Jun 2026 07:41:19 GMT - nuclia-learning-id: - - 8c27804426164f02bf178a8d671e911a - nuclia-learning-model: - - chatgpt-azure-4o-mini - via: - - 1.1 google - x-envoy-upstream-service-time: - - '2213' - x-nuclia-trace-id: - - 82e7eca5c06374a195e3471467b3631b - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "summarize", "system": "You - are a helpful AI assistant. Your role is to provide accurate, clear, and well-structured - answers based strictly on the information provided to you.\nKey principles:\n- - Answer only using the information in the provided context\n- Do not use external - knowledge, assumptions, or prior experience\n- Maintain a professional and informative - tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge - this clearly\n\nAlways follow any additional instructions provided about format, - style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": - {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## - Question\n\u00bfC\u00f3mo se utiliza el par\u00e1metro `max_tokens` para controlar - la longitud de las respuestas en espa\u00f1ol, y d\u00f3nde puedo encontrar - la documentaci\u00f3n oficial?\n\n## Provided Context\n[START OF CONTEXT]\n## - Retrieval on nuclia-docs Knowledge Box\n\n# \u00bfC\u00f3mo se utiliza el par\u00e1metro - `max_tokens` para controlar la longitud de las respuestas en espa\u00f1ol, y - d\u00f3nde puedo encontrar la documentaci\u00f3n oficial?\n\n El par\u00e1metro - `max_tokens` se utiliza en el endpoint /ask para establecer l\u00edmites en - la longitud de las respuestas generadas. Este par\u00e1metro puede limitar la - longitud de la respuesta generada y tambi\u00e9n el tama\u00f1o del contexto. - Para encontrar la documentaci\u00f3n oficial, se necesita informaci\u00f3n adicional.\n[END - OF CONTEXT]\n\n## Answering Guidelines\n- Carefully read all context; it may - be lengthy or detailed\n- Do not omit or overlook any relevant information\n- - If the context is incomplete or insufficient, state: \"Not enough data to answer - this.\"\n- Read carefully any extra instructions below if provided and use them - to answer\n\nNow provide your answer to the question: \u00bfC\u00f3mo se utiliza - el par\u00e1metro `max_tokens` para controlar la longitud de las respuestas - en espa\u00f1ol, y d\u00f3nde puedo encontrar la documentaci\u00f3n oficial?"}, - "citations": null, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 5000, "query_context_images": {}, "prefer_markdown": null, "json_schema": - null, "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "auto"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '2269' - Content-Type: - - application/json - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-client-ident: - - default - x-message: - - 3665ef769eae4304b588cd4d5b659d55 - x-origin: - - RAO - x-session: - - default_default_session - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"text\",\"text\":\"El\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" - par\xE1\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"metro\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" - `\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"max\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"_tokens\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"`\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" - 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headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 03 Jun 2026 07:41:21 GMT + - Wed, 15 Jul 2026 07:57:30 GMT nuclia-learning-id: - - 8d704ba193f9468ea55587e70f8a3571 - nuclia-learning-model: - - chatgpt-azure-4o-mini + - b998902b69a64991b0562d98066936ae via: - 1.1 google x-envoy-upstream-service-time: - - '485' + - '1131' x-nuclia-trace-id: - - 9e3ec38e60aff5d8458c021406e20bec + - 1f58c9a2a2b0c06fe59b3a0ee1738eaa status: code: 200 message: OK diff --git a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_simple.yaml b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_simple.yaml index b5fe3e3..c2e0a4e 100644 --- a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_simple.yaml +++ b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_simple.yaml @@ -1,6 +1,6 @@ interactions: - request: - body: '' + body: '{"prefixes": [{"prefix": "/s/p"}]}' headers: Accept: - '*/*' @@ -8,137 +8,8 @@ interactions: - gzip, deflate Connection: - keep-alive - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-nuclia-nuakey: - - DUMMY - method: GET - uri: https://europe-1.nuclia.cloud/api/authorizer/info - response: - body: - string: '{"auth":"nua_key","user":{"identity_type":"nua_key","user_id":"03d47998-f672-4a9b-a57b-bde3a9c3ee8c","account_id":"4f9285c7-7151-4431-94e6-3f1fb0d66aca","account_type":"v3enterprise","allow_kb_management":false},"ip_info":null}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Content-Length: - - '227' - content-type: - - application/json - date: - - Wed, 03 Jun 2026 07:40:54 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '12' - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rephrase", "system": null, - "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nYou are an expert at rephrasing - complex questions for an agentic RAG system.\n\nYour task is to review the main - question and any provided context, then rephrase the question to maximize clarity - and focus. Follow these steps:\n\n1. Carefully analyze the main question and - all context provided, including sources, previous questions and answers, and - any other relevant information.\n2. I present, assess whether previous questions - and answers (history) are necessary for rephrasing. Only use history if it is - relevant and improves the clarity or specificity of the main question; otherwise, - ignore it.\n3. If the question can be made clearer or more specific, rephrase - it accordingly. If it is already clear and focused, return it unchanged.\n4. - Only use information present in the provided context. Do not introduce external - knowledge or assumptions.\n5. 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\\n \\n optional - error: IError \\n \\n Inherited from \\n IFieldDataBase.error \\n Defined - in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:149 \\n \\n errors? - \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":295},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KVSchema\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":47},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KVSchema \\n Interface: KVSchema \\n Properties - \\n description? \\n \\n optional description: string \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/resource/resource.models.ts:597 \\n \\n fields \\n - \\n fields: KVSchemaField[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:598 - \\n \\n name \\n \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":330},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:596\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":330,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > interfaces > KVSchemaField\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":52},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - key: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:589 - \\n \\n required? \\n \\n optional required: boolean \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/resource/resource.models.ts:592 \\n \\n type \\n - \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":226,\"end\":444},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" - type: KVFieldType \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:590\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":444,\"end\":537},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"7d409453a7584981beaeb2944e4560f5\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KVSchemaField \\n Interface: KVSchemaField - \\n Properties \\n description? \\n \\n optional description: string \\n \\n - Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:591 \\n - \\n key \\n \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":226},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"97fa1944fdbe4e99a9e482179dc344ed\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs - > develop > js sdk > type aliases > KVValue\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":48},\"fuzzy_result\":false}],\"facets\":{},\"query\":\"\",\"total\":4965,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"0492d4f0-3034-4392-869f-f204c3865fa4\"]}" + string: "{\"resources\":{\"2a0d73029ad24c599d6dc1e117a41897\":{\"id\":\"2a0d73029ad24c599d6dc1e117a41897\",\"slug\":\"docs-agentic-deploy-md\",\"title\":\"docs + > agentic > deploy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-29T13:39:02.557258\",\"modified\":\"2026-06-29T13:39:02.557276\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"007b445866574d7ca69891d388663cc6\":{\"id\":\"007b445866574d7ca69891d388663cc6\",\"slug\":\"docs-management-security-5-public-ips-md\",\"title\":\"docs + > management > security > 5 public ips\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-07-03T09:28:57.937064\",\"modified\":\"2026-07-03T09:28:57.937076\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false},\"b28e388104644fc5bba68c0cd44e01d4\":{\"id\":\"b28e388104644fc5bba68c0cd44e01d4\",\"slug\":\"docs-agentic-how-to-agentic-retrieval-md\",\"title\":\"docs + > agentic > how to > agentic retrieval\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-29T13:39:03.379512\",\"modified\":\"2026-06-29T13:39:03.379525\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false}},\"paragraphs\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Outbound \u2014 the source IP addresses that Progress Agentic RAG uses when + making connections to your systems (e.g., webhooks, sync agents, or other + integrations). Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. 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retrieval\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-29T13:39:03.379512\",\"modified\":\"2026-06-29T13:39:03.379525\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false}},\"paragraphs\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Outbound \u2014 the source IP addresses that Progress Agentic RAG uses when + making connections to your systems (e.g., webhooks, sync agents, or other + integrations). Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\":5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-resources\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-search\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-rag-lab\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"account-arag\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-kv-schemas\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"user-profile\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-users\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-widgets\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-remi-analytics\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"agent-users\",\"labels\":[]}],\"facets\":{},\"query\":\"\",\"total\":2013,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"]}" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Length: - - '115' + - '16898' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 03 Jun 2026 07:40:56 GMT - nuclia-learning-model: - - multilingual + - Wed, 15 Jul 2026 07:57:01 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '18' + - '61' x-nuclia-trace-id: - - 38dba171afa52b879dd362a03660e679 + - fd0133a4dc6f4164c2934bd79e84dadf status: code: 200 message: OK - request: body: '{"user_id": "arag-ask", "texts": ["Explica c\u00f3mo usar el par\u00e1metro - `max_tokens` para controlar la longitud de las respuestas en espa\u00f1ol, y - proporciona un enlace a la documentaci\u00f3n oficial."]}' + `max_tokens` en espa\u00f1ol y proporciona un enlace a la documentaci\u00f3n + oficial."]}' headers: Accept: - '*/*' @@ -713,7 +563,7 @@ interactions: Connection: - keep-alive Content-Length: - - '191' + - '145' Host: - europe-1.nuclia.cloud User-Agent: @@ -726,141 +576,33 @@ interactions: uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/predict/run-agents-text response: body: - string: '{"results":[{"input_nuclia_tokens":0.0,"output_nuclia_tokens":0.0,"time":0.00011992454528808594,"payloads":[]}]}' + string: '{"results":[{"input_nuclia_tokens":0.0,"output_nuclia_tokens":0.0,"time":0.0001354217529296875,"payloads":[]}]}' headers: Alt-Svc: - h3=":443"; ma=2592000 Content-Length: - - '112' + - '111' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 03 Jun 2026 07:40:56 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '119' - x-nuclia-trace-id: - - 0a3bde14541781bfd96ace7dd76c4253 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "arag-ask", "system": null, - "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\n\nInformation about the KB:\n\n# - nuclia-docs\n\ndescription: Documentation of the Nuclia API, recipies, reference - \n\nAnd given the question: Explica c\u00f3mo usar el par\u00e1metro `max_tokens` - para controlar la longitud de las respuestas en espa\u00f1ol, y proporciona - un enlace a la documentaci\u00f3n oficial.\n\n## labels: {''pmm'': [''Videos'', - ''Partner Content'', ''Softcat'', ''Sales Enablement Assets'', ''KO 26'', ''Data - Sheets'', ''Progress Agentic RAG Training Materials 2026'']}\n\n## Facets:\n\n## - Content Types\nThe following content types are available in the KB:\n\n- /n/i: - 974\n- text/markdown: 974\nThe following languages are available in the KB:\n\n- - ca: 3\n- cy: 1\n- da: 1\n- en: 955\n- eo: 4\n- la: 7\n- nb: 1\n- tl: 1\n- yo: - 1\n\n\n# Important rules to follow\n\n\nprompt=''Be polite''\n"}, "citations": - false, "citation_threshold": null, "generative_model": "gemini-2.5-flash", "max_tokens": - 2000, "query_context_images": {}, "prefer_markdown": null, "json_schema": {"title": - "ask_configuration", "description": "Configuration extracted from reasoning - engine", "parameters": {"type": "object", "properties": {"link": {"type": "boolean", - "description": "The user wants link reference to the answer?"}, "knowledge_scan": - {"type": "string", "description": "If the query requires a knowledge aggregation - or scan search to answer define the entities, labels and relations to query - in the KB. Example queries: How many ..."}, "semantic_query": {"type": "string", - "description": "Rephrase this question so its better for semantic retrieval, - and keep the rephrased question in the same language as the original. Please - define ONLY the question without any explanation. JUST A SENTENCE. DO NOT ADD - ANY EXTRA NOTES AT THE END, JUST ONE SENTENCE"}, "lexical_query": {"type": "string", - "description": "Rephrase this question so its better for lexical retrieval, - translate the lexical rephrased question to cy. Please define ONLY the question - without any explanation. JUST A SENTENCE. DO NOT ADD ANY EXTRA NOTES AT THE - END, JUST ONE SENTENCE"}, "visual": {"type": "boolean", "description": "Is required - an analysis of an image to answer this question, answer with false or true"}, - "keywords_filter": {"type": "array", "items": {"type": "string"}, "description": - "Extract if any the keywords that should appear on the retrieved results and - its a must match, make sure that are keywords that are not common words, and - that are not the same as the question or answer. Please define ONLY the keywords - without any explanation. Only one or two words maximum. JUST A LIST OF KEYWORDS. - DO NOT ADD ANY EXTRA NOTES AT THE END, JUST A LIST OF KEYWORDS"}, "reason": - {"type": "string"}, "entities": {"type": "array", "description": "Entities related - to the user question to query in the KB", "items": {"type": "string"}}, "relations": - {"type": "array", "description": "Relations related to the user question to - query in the KB", "items": {"type": "string"}}, "pre_queries": {"type": "array", - "items": {"type": "string"}, "description": "Pre queries to run before the main - query to gather more information"}}, "required": ["semantic_query", "lexical_query", - "visual", "keywords_filter", "reason", "pre_queries"]}}, "format_prompt": false, - "rerank_context": false, "tools": [], "tool_choice": {"type": "required"}, "reasoning": - false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '3398' - Content-Type: - - application/json - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-client-ident: - - default - x-message: - - 87d67c9d09f34ce39179c9d292aa9e0a - x-origin: - - RAO - x-session: - - default_default_session - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"link\":true,\"semantic_query\":\"Explica - c\xF3mo utilizar el par\xE1metro `max_tokens` para controlar la longitud de - las respuestas y proporciona un enlace a la documentaci\xF3n oficial en espa\xF1ol.\",\"lexical_query\":\"Sut - i ddefnyddio'r paramedr `max_tokens` i reoli hyd ymatebion a darparu dolen - i'r ddogfennaeth swyddogol?\",\"visual\":false,\"keywords_filter\":[\"max_tokens\",\"longitud\",\"respuestas\"],\"reason\":\"The - user wants to know how to use the `max_tokens` parameter, needs the explanation - in Spanish, and requests a link to the official documentation.\",\"entities\":[],\"relations\":[],\"pre_queries\":[]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":18,\"output_tokens\":136,\"timings\":{\"generative\":1.6629762089942233},\"input_nuclia_tokens\":0.018,\"output_nuclia_tokens\":0.136}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.01761,\"output\":0.1358,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" - headers: - Alt-Svc: - - h3=":443"; 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ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Content-Length: - - '351' + - '327' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 03 Jun 2026 07:40:58 GMT + - Wed, 15 Jul 2026 07:57:04 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '32' + - '31' x-nuclia-trace-id: - - 19f4c825182b2a9042d10a3d9f791547 + - 78087710ad3c2e987f1a8256015dcaff status: code: 200 message: OK - request: - body: '{"query": "Explica c\u00f3mo utilizar el par\u00e1metro `max_tokens` para - controlar la longitud de las respuestas y proporciona un enlace a la documentaci\u00f3n - oficial en espa\u00f1ol.", "filters": [], "min_score": {"semantic": 0.4}, "show": - ["basic", "origin", "extra", "extracted", "values", "relations"], "extracted": - ["text", "metadata", "file", "link"], "vectorset": "multilingual-2024-05-06", - "security": {"groups": []}, "features": ["semantic"], "reranker": "noop"}' + body: '{"query": "Expl\u00edcame c\u00f3mo utilizar el par\u00e1metro `max_tokens` + y d\u00f3nde encontrar su documentaci\u00f3n oficial.", "filters": [], "min_score": + {"semantic": 0.4}, "show": ["basic", "origin", "extra", "extracted", "values", + "relations"], "extracted": ["text", "metadata", "file", "link"], "vectorset": + "multilingual-2024-05-06", "security": {"groups": []}, "features": ["semantic"], + "reranker": "noop"}' headers: Accept: - '*/*' @@ -920,7 +661,7 @@ interactions: Connection: - keep-alive Content-Length: - - '431' + - '370' Host: - europe-1.nuclia.cloud User-Agent: @@ -933,145 +674,206 @@ interactions: uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find response: body: - string: "{\"resources\":{\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs - > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2025-08-27T15:32:35.381741\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: - rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate - limits are an essential aspect of the Agentic RAG platform, ensuring fair - usage and optimal performance for all users interacting with Agentic RAG APIs. - This document outlines the rate limits enforced by Agentic RAG and provides - guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic - RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: - By default, the sum of all authenticated requests in a Agentic RAG account - cannot exceed 2400 requests per minute. Note that this limit can be customized - on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) - if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic - RAG implements a back-pressure mechanism to manage ingestion pipeline overload. - This mainly affects endpoints for uploading data and creating or updating - resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres - to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) - and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe - official Agentic RAG API clients already have built-in mechanisms for retrying - requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- - [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are - interacting directly with the API, we recommend using an [exponential backoff - retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits - are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response - will include a `try_after` key with an estimated UTC time for retrying the - request. You can use this value for retry logic as an alternative to the exponential - backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an - example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport - time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, - headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n - \ response = requests.get(url, headers=headers)\\n if response.status_code - == 200:\\n return response.json()\\n elif response.status_code - == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n - \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n - \ time.sleep(wait_time)\\n retries += 1\\n else:\\n - \ response.raise_for_status()\\n raise Exception(\\\"Max retries - exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders - = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, - headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's - an example of how to use the try_after key from the response to manage rate - limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport - requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n - \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, - headers=headers)\\n response_body = response.json()\\n if response.status_code - == 200:\\n return response.json()\\n elif response.status_code - == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n - \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n - \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n - \ print(\\n f\\\"Rate limit exceeded. Retrying at - {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n - \ retries += 1\\n else:\\n response.raise_for_status()\\n - \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl - = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer - YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese - examples demonstrate how to handle rate limits effectively, ensuring that - your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" - \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate - limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, - ensuring fair usage and optimal performance for all users interacting with - Agentic RAG APIs. This document outlines the rate limits enforced by Agentic - RAG and provides guidelines for handling rate-limited responses effectively. - \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: - \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated - requests in a Agentic RAG account cannot exceed 2400 requests per minute. - Note that this limit can be customized on a per-account basis. Please contact - Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion - back pressure limits: Agentic RAG implements a back-pressure mechanism to - manage ingestion pipeline overload. This mainly affects endpoints for uploading - data and creating or updating resources. \\n \\n \\n Handling Rate-Limited - Responses \\n Agentic RAG adheres to the HTTP standard and will return a response - with a 429 status codes when the limits are exceeded. \\n The official Agentic - RAG API clients already have built-in mechanisms for retrying requests when - rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript - client \\n \\n However, if you are interacting directly with the API, we recommend - using an exponential backoff retry strategy when limits are reached. \\n When - ingestion back pressure rate limits are hit, the response will include a try_after - key with an estimated UTC time for retrying the request. You can use this - value for retry logic as an alternative to the exponential backoff strategy. - \\n Example 1: Regular API rate limits \\n Here's an example of how to implement - an exponential backoff retry strategy in Python: \\n ```python \\n import - time \\n import requests \\n def make_request_with_exponential_backoff(url, - headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: - \\n response = requests.get(url, headers=headers) \\n if response.status_code - == 200: \\n return response.json() \\n elif response.status_code == 429: \\n - wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit - exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n - retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( - Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n - headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, - headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits - \\n Here's an example of how to use the try_after key from the response to - manage rate limits: \\n ```python \\n import time \\n from datetime import - datetime \\n import requests \\n def make_request_with_try_after_info(url, - headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: - \\n response = requests.get(url, headers=headers) \\n response_body = response.json() - \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code - == 429 and try_after in response_body: \\n try_after = response_body[ try_after - ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n - wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n - f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... - \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() - \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint - \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, - headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle - rate limits effectively, ensuring that your application respects the limits - and retries appropriately.\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":373,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":236,\"key\":\"\"},{\"start\":236,\"end\":373,\"key\":\"\"}]},{\"start\":373,\"end\":721,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":374,\"end\":590,\"key\":\"\"},{\"start\":590,\"end\":653,\"key\":\"\"},{\"start\":653,\"end\":721,\"key\":\"\"}]},{\"start\":721,\"end\":932,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":726,\"end\":846,\"key\":\"\"},{\"start\":846,\"end\":932,\"key\":\"\"}]},{\"start\":932,\"end\":1222,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":937,\"end\":1095,\"key\":\"\"},{\"start\":1095,\"end\":1222,\"key\":\"\"}]},{\"start\":1222,\"end\":1656,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1225,\"end\":1416,\"key\":\"\"},{\"start\":1416,\"end\":1561,\"key\":\"\"},{\"start\":1561,\"end\":1656,\"key\":\"\"}]},{\"start\":1656,\"end\":1900,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1657,\"end\":1900,\"key\":\"\"}]},{\"start\":1900,\"end\":2653,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1901,\"end\":2653,\"key\":\"\"}]},{\"start\":2653,\"end\":2959,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2654,\"end\":2959,\"key\":\"\"}]},{\"start\":2959,\"end\":3310,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2960,\"end\":3206,\"key\":\"\"},{\"start\":3206,\"end\":3256,\"key\":\"\"},{\"start\":3256,\"end\":3310,\"key\":\"\"}]},{\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3311,\"end\":3757,\"key\":\"\"}]}],\"ner\":{\"Agentic - 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backoff: 2^retries \\n print(f Rate limit exceeded. Retrying in {wait_time} - seconds... ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() - \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint - \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, - headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits - \\n Here's an example of how to use the try_after key from the response to - manage rate limits: \\n\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1900,\"end\":2653,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.5950654149055481,\"score_type\":\"VECTOR\",\"order\":4,\"text\":\" - ) \\n 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Ask.AskTokens\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:53.076880\",\"modified\":\"2025-04-15T14:50:53.076888\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Ask.AskTokens\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + string: "{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.5957227945327759,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.6580718755722046,\"score_type\":\"VECTOR\",\"order\":2,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"328ed5b87692439a881303c7c1d4eefc\":{\"id\":\"328ed5b87692439a881303c7c1d4eefc\",\"slug\":\"docs-develop-js-sdk-interfaces-Ask-AskTokens-md\",\"title\":\"docs + > develop > js sdk > interfaces > Ask.AskTokens\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:53.076880\",\"modified\":\"2026-06-09T08:08:31.130765\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Ask.AskTokens\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) / [Exports](../modules.md) / [Ask](../modules/Ask.md) / AskTokens\\n\\n# Interface: AskTokens\\n\\n[Ask](../modules/Ask.md).AskTokens\\n\\n## Table of contents\\n\\n### Properties\\n\\n- [input](Ask.AskTokens.md#input)\\n- [output](Ask.AskTokens.md#output)\\n\\n## @@ -1081,39 +883,633 @@ interactions: / Exports / Ask / AskTokens \\n Interface: AskTokens \\n Ask.AskTokens \\n Table of contents \\n Properties \\n \\n input \\n output \\n \\n Properties \\n input \\n \u2022 input: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:107 - \\n \\n output \\n \u2022 output: number \\n Defined in \\n 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libs/sdk-core/src/lib/db/search/ask.models.ts:108\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":333,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":333,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:48.615466Z\",\"last_understanding\":\"2026-06-09T08:08:46.641235Z\",\"last_extract\":\"2026-06-09T08:08:46.412513Z\",\"last_processing_start\":\"2026-06-09T08:08:46.395307Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces > Ask.AskTokens\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > 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libs/sdk-core/src/lib/db/search/ask.models.ts:108\",\"id\":\"328ed5b87692439a881303c7c1d4eefc/t/page/0-333\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\",\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"docs - > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\",\"modified\":\"2026-06-02T15:43:14.212428\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\n\\n# - Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\n\\n> - `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\n\\n***\\n\\n### - llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\n\\n***\\n\\n### - manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**: `object`\\n\\n#### - splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\n\\n***\\n\\n### - max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**: `number`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\n\\n***\\n\\n### - name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:534](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L534)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"7074857dc3154de0e287bf1a4a31f240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\n Interface: SplitStrategy - \\n Properties \\n custom_split? \\n \\n optional custom_split: CustomSplitStrategy - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 \\n \\n - llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n manual_split? \\n \\n - optional manual_split: object \\n \\n splitter \\n \\n splitter: string \\n - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:538 \\n \\n max_paragraph? - \\n \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 - \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n 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Ask.AskTokens\"}}}}},\"security\":null,\"fields\":{\"/a/title\":{\"paragraphs\":{\"328ed5b87692439a881303c7c1d4eefc/a/title/0-52\":{\"score\":0.59093177318573,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\"docs + > develop > js sdk > interfaces > Ask.AskTokens\",\"id\":\"328ed5b87692439a881303c7c1d4eefc/a/title/0-52\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":52,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n 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+ > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.58331698179245,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1b2a9e67b9f14a0cb81efaa05b8793b8\":{\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8\",\"slug\":\"docs-develop-js-sdk-interfaces-AugmentedField-md\",\"title\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:00:05.937520\",\"modified\":\"2026-07-14T12:49:32.163110\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AugmentedField\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / AugmentedField\\n\\n# + Interface: AugmentedField\\n\\n## Properties\\n\\n### applied\\\\_data\\\\_augmentation\\n\\n> + **applied\\\\_data\\\\_augmentation**: [`AppliedDataAugmentation`](AppliedDataAugmentation.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:571](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L571)\\n\\n***\\n\\n### + input\\\\_nuclia\\\\_tokens\\n\\n> **input\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:572](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L572)\\n\\n***\\n\\n### + metadata\\n\\n> **metadata**: [`FieldMetadata`](FieldMetadata.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:570](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L570)\\n\\n***\\n\\n### + output\\\\_nuclia\\\\_tokens\\n\\n> **output\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:573](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L573)\\n\\n***\\n\\n### + time\\n\\n> **time**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:574](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L574)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"1e8e95b886439059ae4ec717c9bcf283\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n time: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:574\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":641,\"key\":\"\"}]},{\"start\":641,\"end\":729,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":642,\"end\":729,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:33.585675Z\",\"last_understanding\":\"2026-07-14T12:49:33.340252Z\",\"last_extract\":\"2026-07-14T12:49:32.941746Z\",\"last_processing_start\":\"2026-07-14T12:49:32.917812Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > AugmentedField\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\":{\"score\":0.5832796692848206,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n\",\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"7b41750917e349598c846526162ebb68\":{\"id\":\"7b41750917e349598c846526162ebb68\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensDetails-md\",\"title\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:51.860964\",\"modified\":\"2026-07-14T12:51:57.270898\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensDetails\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensDetails\\n\\n# + Interface: NucliaTokensDetails\\n\\n## Extends\\n\\n- [`CommonMetricDetails`](CommonMetricDetails.md)\\n\\n## + Properties\\n\\n### identifier\\n\\n> **identifier**: `object`\\n\\n#### model\\n\\n> + **model**: `null` \\\\| `string`\\n\\n#### service\\n\\n> **service**: `string`\\n\\n#### + source\\n\\n> **source**: `string`\\n\\n#### type\\n\\n> **type**: `string`\\n\\n#### + version\\n\\n> **version**: `string`\\n\\n#### Overrides\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`identifier`](CommonMetricDetails.md#identifier)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:150](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L150)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\n\\n> **nuclia\\\\_tokens**: `object`\\n\\n#### image\\n\\n> + **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` + \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:157](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L157)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\\\_billed\\n\\n> **nuclia\\\\_tokens\\\\_billed**: `object`\\n\\n#### + image\\n\\n> **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: + `null` \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:164](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L164)\\n\\n***\\n\\n### + raw\\\\_usage\\n\\n> **raw\\\\_usage**: `object`\\n\\n#### image\\n\\n> **image**: + `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` \\\\| `number`\\n\\n#### + output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### search\\n\\n> **search**: + `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: `null` \\\\| `number`\\n\\n#### + Inherited from\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`raw_usage`](CommonMetricDetails.md#raw_usage)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:134](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L134)\\n\\n***\\n\\n### + requests\\n\\n> **requests**: `object`\\n\\n#### api\\n\\n> **api**: `null` + \\\\| `number`\\n\\n#### chrome\\\\_extension\\n\\n> **chrome\\\\_extension**: + `null` \\\\| `number`\\n\\n#### dashboard\\n\\n> **dashboard**: `null` \\\\| + `number`\\n\\n#### desktop\\n\\n> **desktop**: `null` \\\\| `number`\\n\\n#### + internal\\n\\n> **internal**: `null` \\\\| `number`\\n\\n#### web\\n\\n> **web**: + `null` \\\\| `number`\\n\\n#### widget\\n\\n> **widget**: `null` \\\\| `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:171](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L171)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"cc5e5459b176800496de165483959c09\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensDetails \\n Interface: NucliaTokensDetails + \\n Extends \\n \\n CommonMetricDetails \\n \\n Properties \\n identifier + \\n \\n identifier: object \\n \\n model \\n \\n model: null \\\\| string + \\n \\n service \\n \\n service: string \\n \\n source \\n \\n source: string + \\n \\n type \\n \\n type: string \\n \\n version \\n \\n version: string + \\n \\n Overrides \\n CommonMetricDetails.identifier \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:150 + \\n \\n nuclia_tokens \\n \\n nuclia_tokens: object \\n \\n image \\n \\n + image: null \\\\| number \\n \\n input \\n \\n input: null \\\\| number \\n + \\n output \\n \\n output: null \\\\| number \\n \\n search \\n \\n search: + null \\\\| number \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:157 \\n \\n nuclia_tokens_billed + \\n \\n nuclia_tokens_billed: object \\n \\n image \\n \\n image: null \\\\| + number \\n \\n input \\n \\n input: null \\\\| number \\n \\n output \\n \\n + output: null \\\\| number \\n \\n search \\n \\n search: null \\\\| number + \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:164 \\n \\n raw_usage \\n \\n raw_usage: + object \\n \\n image \\n \\n image: null \\\\| number \\n \\n input \\n \\n + input: null \\\\| number \\n \\n output \\n \\n output: null \\\\| number + \\n \\n search \\n \\n search: null \\\\| number \\n \\n storage \\n \\n storage: + null \\\\| number \\n \\n Inherited from \\n CommonMetricDetails.raw_usage + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:134 \\n \\n requests + \\n \\n requests: object \\n \\n api \\n \\n api: null \\\\| number \\n \\n + chrome_extension \\n \\n chrome_extension: null \\\\| number \\n \\n dashboard + \\n \\n dashboard: null \\\\| number \\n \\n desktop \\n \\n desktop: null + \\\\| number \\n \\n internal \\n \\n internal: null \\\\| number \\n \\n + web \\n \\n web: null \\\\| number \\n \\n widget \\n \\n widget: null \\\\| + number \\n \\n Defined in \\n 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+ > develop > js sdk > interfaces > NucliaTokensDetails\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\"}}}}},\"security\":null,\"fields\":{\"/a/title\":{\"paragraphs\":{\"7b41750917e349598c846526162ebb68/a/title/0-58\":{\"score\":0.5879815220832825,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"id\":\"7b41750917e349598c846526162ebb68/a/title/0-58\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":58,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\",\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\n\\n# + Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\n\\n> + `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\n\\n***\\n\\n### + effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n \\n optional effort: NumericReasoningEffort \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\",\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\":\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\":{\"score\":0.59784996509552,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs + > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\n\\n# + Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n***\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\\_PROCESSED**: + `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n***\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\n\\n***\\n\\n### + PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"pages_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n***\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**: `\\\"paragraphs_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n***\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\n\\n***\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**: `\\\"resources_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n***\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**: `\\\"searches_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n***\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\n\\n***\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**: `\\\"suggestions_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\":{\"score\":0.6053361892700195,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.604081392288208,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.6232776641845703,\"score_type\":\"VECTOR\",\"order\":4,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.6080663204193115,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different providers\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":319,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":198,\"key\":\"\"},{\"start\":198,\"end\":267,\"key\":\"\"},{\"start\":267,\"end\":319,\"key\":\"\"}]},{\"start\":319,\"end\":747,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":320,\"end\":399,\"key\":\"\"},{\"start\":399,\"end\":517,\"key\":\"\"},{\"start\":517,\"end\":615,\"key\":\"\"},{\"start\":615,\"end\":661,\"key\":\"\"},{\"start\":661,\"end\":747,\"key\":\"\"}]},{\"start\":747,\"end\":1008,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":748,\"end\":872,\"key\":\"\"},{\"start\":872,\"end\":1008,\"key\":\"\"}]},{\"start\":1008,\"end\":1650,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1011,\"end\":1650,\"key\":\"\"}]},{\"start\":1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1653,\"end\":2326,\"key\":\"\"}]},{\"start\":2326,\"end\":3075,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2329,\"end\":3075,\"key\":\"\"}]},{\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3076,\"end\":3340,\"key\":\"\"}]},{\"start\":3340,\"end\":4136,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3341,\"end\":4136,\"key\":\"\"}]},{\"start\":4136,\"end\":4446,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4137,\"end\":4228,\"key\":\"\"},{\"start\":4228,\"end\":4446,\"key\":\"\"}]},{\"start\":4446,\"end\":4728,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4447,\"end\":4728,\"key\":\"\"}]},{\"start\":4728,\"end\":4932,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4729,\"end\":4932,\"key\":\"\"}]},{\"start\":4932,\"end\":5189,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4933,\"end\":5189,\"key\":\"\"}]},{\"start\":5189,\"end\":5396,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5190,\"end\":5396,\"key\":\"\"}]},{\"start\":5396,\"end\":5615,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5397,\"end\":5615,\"key\":\"\"}]},{\"start\":5615,\"end\":5849,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5616,\"end\":5849,\"key\":\"\"}]}],\"ner\":{\"Knowledge + Box\":\"PRODUCT\",\"Agentic RAG\":\"PRODUCT\",\"LLM\":\"ORG\",\"json\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge + Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\":4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\",\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic + RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic + RAG\"},\"PRODUCT/Knowledge Box\":{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge + Box\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.6182262897491455,\"score_type\":\"VECTOR\",\"order\":5,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"666b3a9d01f74323b6ca6c9939834bec\":{\"id\":\"666b3a9d01f74323b6ca6c9939834bec\",\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\",\"title\":\"docs + > develop > js sdk > enums > StatsType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"sv\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.005040\",\"modified\":\"2026-06-09T08:13:37.365717\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/StatsType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / StatsType\\n\\n# Enumeration: StatsType\\n\\n**`Deprecated`**\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](StatsType.md#ai_tokens_used)\\n- + [BYTES](StatsType.md#bytes)\\n- [CHARS](StatsType.md#chars)\\n- [DOCS\\\\_NO\\\\_MEDIA](StatsType.md#docs_no_media)\\n- + [MEDIA\\\\_SECONDS](StatsType.md#media_seconds)\\n- [PAGES](StatsType.md#pages)\\n- + [PROCESSING\\\\_TIME](StatsType.md#processing_time)\\n- [RESOURCES](StatsType.md#resources)\\n- + [SEARCHES](StatsType.md#searches)\\n- [SUGGESTIONS](StatsType.md#suggestions)\\n- + [TRAIN\\\\_SECONDS](StatsType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n___\\n\\n### + BYTES\\n\\n\u2022 **BYTES** = ``\\\"bytes\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n___\\n\\n### + CHARS\\n\\n\u2022 **CHARS** = ``\\\"chars\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n___\\n\\n### + DOCS\\\\_NO\\\\_MEDIA\\n\\n\u2022 **DOCS\\\\_NO\\\\_MEDIA** = ``\\\"docs_no_media\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\n\\n\u2022 **MEDIA\\\\_SECONDS** = ``\\\"media_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n___\\n\\n### + PAGES\\n\\n\u2022 **PAGES** = ``\\\"pages\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n___\\n\\n### + PROCESSING\\\\_TIME\\n\\n\u2022 **PROCESSING\\\\_TIME** = ``\\\"processing_time\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n___\\n\\n### + RESOURCES\\n\\n\u2022 **RESOURCES** = ``\\\"resources\\\"``\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n___\\n\\n### + SEARCHES\\n\\n\u2022 **SEARCHES** = ``\\\"searches\\\"``\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n___\\n\\n### + SUGGESTIONS\\n\\n\u2022 **SUGGESTIONS** = ``\\\"suggestions\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"13077c5570e4437fb58c1c5ab75d310b\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated \\n Table + of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES \\n CHARS + \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME \\n RESOURCES + \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration Members + \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\n \u2022 + BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 + \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198 + \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n + \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 + \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 + \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 + RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 + \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1386,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:40.544839Z\",\"last_understanding\":\"2026-06-09T08:13:38.735743Z\",\"last_extract\":\"2026-06-09T08:13:38.231630Z\",\"last_processing_start\":\"2026-06-09T08:13:38.196724Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > StatsType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > StatsType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\":{\"score\":0.5969122648239136,\"score_type\":\"VECTOR\",\"order\":10,\"text\":\"@nuclia/core + / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated \\n Table + of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES \\n CHARS + \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME \\n RESOURCES + \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration Members + \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\n \u2022 + BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 + \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198 + \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n + \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 + \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 + \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 + RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 + \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\",\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.5916427373886108,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"24ad6997e1fe4a109d67d7802a083678\":{\"id\":\"24ad6997e1fe4a109d67d7802a083678\",\"slug\":\"docs-develop-js-sdk-interfaces-TokenConsumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > TokenConsumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:48:34.202249\",\"modified\":\"2026-07-14T12:49:12.506050\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/TokenConsumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / TokenConsumption\\n\\n# + Interface: TokenConsumption\\n\\n## Properties\\n\\n### image\\n\\n> **image**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:236](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L236)\\n\\n***\\n\\n### + input\\n\\n> **input**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:234](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L234)\\n\\n***\\n\\n### + output\\n\\n> **output**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:235](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L235)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"7bb8b1c3910c7d06c2a257bc3f1cff47\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / TokenConsumption \\n Interface: TokenConsumption + \\n Properties \\n image \\n \\n image: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:236 + \\n \\n input \\n \\n input: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:234 + \\n \\n output \\n \\n output: number \\n \\n Defined in \\n 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Be mindful that this takes into - account the tokens used in the prompt, query and context. Also take note that - some models may provide their context window as the total between input and - output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.6280757188796997,\"score_type\":\"VECTOR\",\"order\":2,\"text\":\" + > rag > advanced > openai api compatible models\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.5845197439193726,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\" Description: The maximum number of tokens that the model can generate as output. Again, we should keep in mind that this value summed to the Maximum supported input tokens should not exceed the total context size supported by the model. - \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\":{\"score\":0.5632287263870239,\"score_type\":\"VECTOR\",\"order\":17,\"text\":\" - Example: For Phi 4 Reasoning Plus, the total context size is 32768 tokens, - as we want to leave room for the output, we will set the maximum supported - input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output - tokens: 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Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 - \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 - \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 - \\n \\n y \\n \\n y: number \\n \\n Defined in \\n 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- > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.5863822102546692,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n - Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs - > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.6590254306793213,\"score_type\":\"VECTOR\",\"order\":1,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.6283514499664307,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n 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`object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\n\\n> + **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\n\\n***\\n\\n### + tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"9a5a602361bcf49d7619fc3995646f40\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:21.098517Z\",\"last_understanding\":\"2026-07-14T12:49:20.847235Z\",\"last_extract\":\"2026-07-14T12:49:20.704679Z\",\"last_processing_start\":\"2026-07-14T12:49:20.686749Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageStructure\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\":{\"score\":0.594045877456665,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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+ Box\":\"PRODUCT\",\"Agentic RAG\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge + Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\":4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\",\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Knowledge + Box\":{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge Box\"},\"PRODUCT/Agentic + RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic + RAG\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.6513248682022095,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189\":{\"score\":0.5947358012199402,\"score_type\":\"VECTOR\",\"order\":17,\"text\":\" + json \\n { \\n consumption : { \\n normalized_tokens : { \\n input : 13, \\n + output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { \\n input + : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n \\n Understanding + Token Types \\n Normalized Tokens (normalized_tokens): \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":11,\"start\":4932,\"end\":5189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.6478315591812134,\"score_type\":\"VECTOR\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.6060314774513245,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"8b3e0ef630a346d1b591143309db87ec\":{\"id\":\"8b3e0ef630a346d1b591143309db87ec\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensMetric-md\",\"title\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"la\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:48.423622\",\"modified\":\"2026-07-14T12:51:37.835367\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensMetric\\n\\n# + Interface: NucliaTokensMetric\\n\\n## Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"UsageMetric.name\"],\"paragraphs\":[{\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":281,\"key\":\"\"}]},{\"start\":281,\"end\":511,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":282,\"end\":511,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:39.809701Z\",\"last_understanding\":\"2026-07-14T12:51:39.578340Z\",\"last_extract\":\"2026-07-14T12:51:39.320533Z\",\"last_processing_start\":\"2026-07-14T12:51:39.300652Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\":{\"score\":0.6047216653823853,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n\",\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`: returns several result sets according the different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns @@ -1424,72 +2650,78 @@ interactions: nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination : { value : Python } \\n } \\n } \\n ) \\n For more information about graph - querying, please refer to Nuclia's graph \\n doc or the API 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> develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.5834914445877075,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\" - ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which - may increase your usage costs. \\n You may need to increase max_tokens to - give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.5830544233322144,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\" + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.5931872725486755,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000, \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) - \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0c8acfaf951e48a6a31b52234305fa57\":{\"id\":\"0c8acfaf951e48a6a31b52234305fa57\",\"slug\":\"docs-develop-js-sdk-interfaces-Ask-MetadataAskResponseItem-md\",\"title\":\"docs - > develop > js sdk > interfaces > 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MetadataAskResponseItem\\n\\n[Ask](../modules/Ask.md).MetadataAskResponseItem\\n\\n## - Table of contents\\n\\n### Properties\\n\\n- [timings](Ask.MetadataAskResponseItem.md#timings)\\n- - [tokens](Ask.MetadataAskResponseItem.md#tokens)\\n- [type](Ask.MetadataAskResponseItem.md#type)\\n\\n## - Properties\\n\\n### timings\\n\\n\u2022 **timings**: [`AskTimings`](Ask.AskTimings.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:71](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L71)\\n\\n___\\n\\n### - tokens\\n\\n\u2022 **tokens**: [`AskTokens`](Ask.AskTokens.md)\\n\\n#### Defined - in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:70](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L70)\\n\\n___\\n\\n### - type\\n\\n\u2022 **type**: ``\\\"metadata\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:69](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L69)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"158d00991b6256aa585f95f97d90e8c6\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - / Exports / Ask / MetadataAskResponseItem \\n Interface: MetadataAskResponseItem - \\n Ask.MetadataAskResponseItem \\n Table of contents \\n Properties \\n \\n - timings \\n tokens \\n type \\n \\n Properties \\n timings \\n \u2022 timings: - AskTimings \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:71 - \\n \\n tokens \\n \u2022 tokens: AskTokens \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:70 - \\n \\n type \\n \u2022 type: metadata \\n Defined in \\n 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Ask.MetadataAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0c8acfaf951e48a6a31b52234305fa57/t/page/0-485\":{\"score\":0.5690771341323853,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core - / Exports / Ask / MetadataAskResponseItem \\n Interface: MetadataAskResponseItem - \\n Ask.MetadataAskResponseItem \\n Table of contents \\n Properties \\n \\n - timings \\n tokens \\n type \\n \\n Properties \\n timings \\n \u2022 timings: - AskTimings \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:71 - \\n \\n tokens \\n \u2022 tokens: AskTokens \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:70 - \\n \\n type \\n \u2022 type: metadata \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:69\",\"id\":\"0c8acfaf951e48a6a31b52234305fa57/t/page/0-485\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":485,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"828f2dc506434dad90774c5d9e055100\":{\"id\":\"828f2dc506434dad90774c5d9e055100\",\"slug\":\"docs-develop-js-sdk-interfaces-Search-Pagination-md\",\"title\":\"docs - > develop > js sdk > interfaces > Search.Pagination\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:13.065988\",\"modified\":\"2025-04-15T14:49:13.065998\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Search.Pagination\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) - / [Exports](../modules.md) / [Search](../modules/Search.md) / Pagination\\n\\n# - Interface: Pagination\\n\\n[Search](../modules/Search.md).Pagination\\n\\n## - Hierarchy\\n\\n- **`Pagination`**\\n\\n \u21B3 [`Sentences`](Search.Sentences.md)\\n\\n - \ \u21B3 [`Fulltext`](Search.Fulltext.md)\\n\\n \u21B3 [`Paragraphs`](Search.Paragraphs.md)\\n\\n## - Table of contents\\n\\n### Properties\\n\\n- [next\\\\_page](Search.Pagination.md#next_page)\\n- - [page\\\\_number](Search.Pagination.md#page_number)\\n- [page\\\\_size](Search.Pagination.md#page_size)\\n- - [total](Search.Pagination.md#total)\\n\\n## Properties\\n\\n### next\\\\_page\\n\\n\u2022 - **next\\\\_page**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:213](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L213)\\n\\n___\\n\\n### - page\\\\_number\\n\\n\u2022 **page\\\\_number**: `number`\\n\\n#### Defined - in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:211](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L211)\\n\\n___\\n\\n### - page\\\\_size\\n\\n\u2022 **page\\\\_size**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:212](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L212)\\n\\n___\\n\\n### - total\\n\\n\u2022 **total**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:210](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L210)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e9273e51e5f1a29264acd1644519affd\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - / Exports / Search / Pagination \\n Interface: Pagination \\n Search.Pagination - \\n Hierarchy \\n \\n Pagination \\n \\n \u21B3 Sentences \\n \u21B3 Fulltext - \\n \u21B3 Paragraphs \\n Table of contents \\n Properties \\n \\n next_page - \\n page_number \\n page_size \\n total \\n \\n Properties \\n next_page \\n - \u2022 next_page: boolean \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:213 - \\n \\n page_number \\n \u2022 page_number: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:211 - \\n \\n page_size \\n \u2022 page_size: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:212 - \\n \\n total \\n \u2022 total: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:210\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":244,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":244,\"key\":\"\"}]},{\"start\":244,\"end\":667,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":245,\"end\":667,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:49:31.578135Z\",\"last_understanding\":\"2025-04-15T14:49:16.415424Z\",\"last_extract\":\"2025-04-15T14:49:15.976719Z\",\"last_processing_start\":\"2025-04-15T14:49:15.943627Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > Search.Pagination\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > Search.Pagination\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"828f2dc506434dad90774c5d9e055100/t/page/244-667\":{\"score\":0.5596579313278198,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\" - Properties \\n next_page \\n \u2022 next_page: boolean \\n Defined in \\n - libs/sdk-core/src/lib/db/search/search.models.ts:213 \\n \\n page_number \\n - \u2022 page_number: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:211 - \\n \\n page_size \\n \u2022 page_size: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:212 - \\n \\n total \\n \u2022 total: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:210\",\"id\":\"828f2dc506434dad90774c5d9e055100/t/page/244-667\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":244,\"end\":667,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\",\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs - > develop > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2025-12-30T08:56:44.797830\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.608078122138977,\"score_type\":\"VECTOR\",\"order\":10,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.6294876337051392,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.6052892804145813,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\",\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs + > develop > python sdk > 14 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responses.\\n\\n## @@ -1787,57 +3019,82 @@ interactions: Operations: Check for different operation types (START, ANSWER, DONE, ERROR) when processing responses \\n Clean Up Sessions: Delete sessions when done to avoid clutter \\n Use Interactive CLI: For manual testing and exploration, - the interactive CLI provides the best experience \\n 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QAOperation\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:10.613432\",\"modified\":\"2026-06-02T15:39:44.624338\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/QAOperation\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / QAOperation\\n\\n# - Interface: QAOperation\\n\\n## Extends\\n\\n- `BaseOperation`\\n\\n## Properties\\n\\n### - generate\\\\_answers\\\\_prompt?\\n\\n> `optional` **generate\\\\_answers\\\\_prompt**: - `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:102](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L102)\\n\\n***\\n\\n### - max\\\\_questions?\\n\\n> `optional` **max\\\\_questions**: `number`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:106](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L106)\\n\\n***\\n\\n### - question\\\\_generator\\\\_prompt?\\n\\n> `optional` **question\\\\_generator\\\\_prompt**: - `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:103](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L103)\\n\\n***\\n\\n### - summary\\\\_prompt?\\n\\n> `optional` **summary\\\\_prompt**: `string`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:104](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L104)\\n\\n***\\n\\n### - system\\\\_question\\\\_generator\\\\_prompt?\\n\\n> `optional` **system\\\\_question\\\\_generator\\\\_prompt**: - `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:105](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L105)\\n\\n***\\n\\n### - triggers?\\n\\n> `optional` **triggers**: [`TaskTrigger`](TaskTrigger.md)[]\\n\\n#### - Inherited from\\n\\n`BaseOperation.triggers`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:59](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L59)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"b73bf2eeb6305feec55587493b42f69d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / QAOperation \\n Interface: QAOperation - \\n Extends \\n \\n BaseOperation \\n \\n Properties \\n generate_answers_prompt? - \\n \\n optional generate_answers_prompt: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:102 - \\n \\n max_questions? \\n \\n optional max_questions: number \\n \\n Defined - in \\n libs/sdk-core/src/lib/db/task/task.models.ts:106 \\n \\n question_generator_prompt? - \\n \\n optional question_generator_prompt: string \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/task/task.models.ts:103 \\n \\n summary_prompt? \\n - \\n optional summary_prompt: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:104 - \\n \\n system_question_generator_prompt? \\n \\n optional system_question_generator_prompt: - string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:105 - \\n \\n triggers? \\n \\n optional triggers: TaskTrigger[] \\n \\n Inherited - from \\n BaseOperation.triggers \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:59\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":277,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":150,\"key\":\"\"},{\"start\":150,\"end\":277,\"key\":\"\"}]},{\"start\":277,\"end\":542,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":280,\"end\":412,\"key\":\"\"},{\"start\":412,\"end\":542,\"key\":\"\"}]},{\"start\":542,\"end\":816,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":545,\"end\":685,\"key\":\"\"},{\"start\":685,\"end\":816,\"key\":\"\"}]},{\"start\":816,\"end\":959,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":819,\"end\":959,\"key\":\"\"}]}],\"ner\":{\"TaskTrigger\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"TaskTrigger\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":838,\"end\":849}]}]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:40:39.013422Z\",\"last_understanding\":\"2026-06-02T15:40:37.940589Z\",\"last_extract\":\"2026-06-02T15:40:37.441350Z\",\"last_processing_start\":\"2026-06-02T15:40:37.418586Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/TaskTrigger\":{\"position\":[{\"start\":838,\"end\":849}],\"entity\":\"TaskTrigger\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > QAOperation\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > QAOperation\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/277-542\":{\"score\":0.5726113319396973,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" - \\n optional max_questions: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:106 - \\n \\n question_generator_prompt? \\n \\n optional question_generator_prompt: - string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:103 - \\n \\n summary_prompt? \\n\",\"id\":\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/277-542\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":277,\"end\":542,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs - > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2025-10-27T14:35:08.325400\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + Token usage \\n \\n \\n\",\"id\":\"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":19,\"start\":6346,\"end\":6668,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\",\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\n\\n# + Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\n\\n> + `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\n\\n***\\n\\n### + effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n \\n optional effort: NumericReasoningEffort \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\",\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\":\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\":{\"score\":0.6087685823440552,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.6161205768585205,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n 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+ > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.6049302220344543,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n 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features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows you to embed the Agentic RAG search experience directly into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe - easiest way to explore the different features of the widgets is to use the - [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section - and to play with the different options.\\n\\nThe _Embed widget_ button will - generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different - types of widgets:\\n\\n- **Embedded in page**: the search input is embedded - in a page and the results are displayed under the input. Once the initial - answer is displayed, the user can click on _Ask more_ to access the full chat - interface. Note: For the correct reading of the results, the width of the - widget container should not be less than 384px.\\n\\n Web components:\\n\\n - \ ```html\\n \\n \\n - \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n - \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- - **Popup modal**: the search inout and the results are displayed in a popup - modal.\\n\\n Web component:\\n\\n ```html\\n \\n - \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows - you to customize the behavior of the widget. It is a comma-separated list - of features among the following:\\n\\n- `filter`: display a filter dropdown - in the search bar.\\n- `navigateToFile`: open the file in the browser when - clicking on the result (by default, the file is displayed in the viewer).\\n- - `navigateToLink`: open the link in the browser when clicking on the result - (by default, the link is displayed in the viewer).\\n- `permalink`: add the - search query and criteria to the URL, allowing the widget to re-render the - same results upon loading.\\n- `relations`: display an info card on the right - side of the widget listing all the relations of the entity mentioned in the - user query.\\n- `suggestions`: display a list of suggested resource titles - matching the user input.\\n- `suggestLabels`: display a list of suggestions - based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: - display a list of suggestions based on the NERs extracted from the user query - when the user starts typing in the search bar.\\n- `displayMetadata`: display - the metadata associated with the resource in the result rows.\\n- `answers`: - trigger the answer generation process when the user makes a search.\\n- `hideResults`: - hide the search results, only the generative answer will be displayed.\\n- - `hideThumbnails`: hide the thumbnails associated with the resource in the - result rows.\\n- `displayFieldList`: display a section listing all the fields - of the resource in the right sidebar of the viewer. This section is only visible - for resources containing multiple fields.\\n- `citations`: include citations - in the generative answer.\\n- `rephrase`: rephrase the user question in order - to optimize the quality of the search results.\\n- `debug`: display extra - buttons to download the last request full log of the debug metadata returned - by the API. It must not be used in production.\\n- `preferMarkdown`: require - the generative answer to be formatted in Markdown.\\n- `openNewTab`: open - the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use - the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: - the previous questions and answers in the chat mode will not be passed as - context when generating a new answer.\\n- `showHidden`: display hidden resources - in the search results.\\n- `showAttachedImages`: display images attached to - the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- - `backend`: the URL of the backend to use. Useful if you use your own proxy - to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: - the Knowledge Box id.\\n- `placeholder`: the text displayed in the search - bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: - `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It - is not recommended to use it in production (the API key is meant to be injected - by your proxy).\\n- `account`: the account id.\\n- `state`: the publication - state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone - NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access - the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark - mode.\\n- `filters`: define the filters offered to the user in the search - bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: - define filters that will be applied by default to any query.\\n- `csspath`: - the path to the CSS file to use to customize the widget style.\\n- `prompt`: - the prompt to use for the generative model. It must use `{context}` and `{question}` - variables.\\n- `system_prompt`: the system prompt to use for the generative - model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query - to get the best search results.\\n- `generativemodel`: the generative model - to use for the answer generation.\\n- `rag_strategies`: the RAG strategies - to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG - strategies to apply to the retrieved images.\\n- `not_enough_data_message`: - the message to display when there is not enough data to generate an answer.\\n- - `ask_to_resource`: the resource ID to use as context for the generative model.\\n- - `max_tokens`: the maximum number of input tokens to put in the final context - (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: - the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum - number of paragraphs to pass in the context to the generative model (default: - 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- - `json_schema`: the JSON schema to use to get a JSON answer from the generative - model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- - `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: - custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" - \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic - RAG widgets allows you to embed the Agentic RAG search experience directly - into your website or web application through a simple HTML snippet: \\n ```html - \\n \\n \\n \\n ``` \\n The easiest way to explore the different features - of the widgets is to use the Agentic RAG Dashboard in the Widgets section - and to play with the different options. \\n The Embed widget button will generate - the HTML snippet for you. \\n Widget types \\n There are 3 different types - of widgets: \\n \\n Embedded in page: the search input is embedded in a page - and the results are displayed under the input. Once the initial answer is - displayed, the user can click on Ask more to access the full chat interface. - Note: For the correct reading of the results, the width of the widget container - should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n - \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: - \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed - in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter - \\n The features parameter allows you to customize the behavior of the widget. - It is a comma-separated list of features among the following: \\n \\n filter: - display a filter dropdown in the search bar. \\n navigateToFile: open the - file in the browser when clicking on the result (by default, the file is displayed - in the viewer). \\n navigateToLink: open the link in the browser when clicking - on the result (by default, the link is displayed in the viewer). \\n permalink: - add the search query and criteria to the URL, allowing the widget to re-render - the same results upon loading. \\n relations: display an info card on the - right side of the widget listing all the relations of the entity mentioned - in the user query. \\n suggestions: display a list of suggested resource titles - matching the user input. \\n suggestLabels: display a list of suggestions - based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: - display a list of suggestions based on the NERs extracted from the user query - when the user starts typing in the search bar. \\n displayMetadata: display - the metadata associated with the resource in the result rows. \\n answers: - trigger the answer generation process when the user makes a search. \\n hideResults: - hide the search results, only the generative answer will be displayed. \\n - hideThumbnails: hide the thumbnails associated with the resource in the result - rows. \\n displayFieldList: display a section listing all the fields of the - resource in the right sidebar of the viewer. This section is only visible - for resources containing multiple fields. \\n citations: include citations - in the generative answer. \\n rephrase: rephrase the user question in order - to optimize the quality of the search results. \\n debug: display extra buttons - to download the last request full log of the debug metadata returned by the - API. It must not be used in production. \\n preferMarkdown: require the generative - answer to be formatted in Markdown. \\n openNewTab: open the link in a new - tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters - instead of the default AND logic. \\n noChatHistory: the previous questions - and answers in the chat mode will not be passed as context when generating - a new answer. \\n showHidden: display hidden resources in the search results. - \\n showAttachedImages: display images attached to the matching paragraphs - in the search results. \\n \\n Other parameters \\n \\n backend: the URL of - the backend to use. Useful if you use your own proxy to access the Agentic - RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. - \\n placeholder: the text displayed in the search bar when it is empty. \\n - lang: the language of the widget. Currently supported: ca, fr, en, es. Default: - en. \\n apikey: the API key to use. It is not recommended to use it in production - (the API key is meant to be injected by your proxy). \\n account: the account - id. \\n state: the publication state of the Knowledge Box. \\n standalone: - set to true when using a standalone NucliaDB instance. \\n proxy: set to true - when using a proxy to access the Agentic RAG API. \\n mode: set to dark to - display the widget in dark mode. \\n filters: define the filters offered to - the user in the search bar among labels, entities, created and labelFamilies. - \\n preselected_filters: define filters that will be applied by default to - any query. \\n csspath: the path to the CSS file to use to customize the widget - style. \\n prompt: the prompt to use for the generative model. It must use - {context} and {question} variables. \\n system_prompt: the system prompt to - use for the generative model. \\n rephrase_prompt: the prompt to use when - optimizing the user query to get the best search results. \\n generativemodel: - the generative model to use for the answer generation. \\n rag_strategies: - the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: - the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: - the message to display when there is not enough data to generate an answer. - \\n ask_to_resource: the resource ID to use as context for the generative - model. \\n max_tokens: the maximum number of input tokens to put in the final - context (including the prompt, the retrieved results and the user question). - \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: - the maximum number of paragraphs to pass in the context to the generative - model (default: 20). \\n query_prepend: the hard-coded text to prepend to - the user query. \\n json_schema: the JSON schema to use to get a JSON answer - from the generative model. \\n vectorset: the embedding model to use for the - semantic search. \\n chat_placeholder: the placeholder to display in the chat - input. \\n audit_metadata: custom metatada to add in API calls for auditing - purposes. \\n 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- RAG\":\"PRODUCT\",\"Markdown\":\"ORG\",\"the Knowledge Box\":\"ORG\",\"NucliaDB\":\"ORG\",\"CSS\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic - RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\":[{\"start\":3046,\"end\":3054}]},{\"text\":\"the - Knowledge 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Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the - Knowledge Box\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\":3046,\"end\":3054}],\"entity\":\"Markdown\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs - > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":9.94736385345459,\"score_type\":\"BM25\",\"order\":4,\"text\":\" - max_tokens: the maximum number of input tokens to put in the final context - (including the prompt, the retrieved results and the user question). \\n max_output_tokens: - the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207\":{\"score\":8.573065757751465,\"score_type\":\"BM25\",\"order\":8,\"text\":\" - preferMarkdown: require the generative answer to be formatted in Markdown. - \\n openNewTab: open the link in a new tab when clicking on the result. \\n - orFilterLogic: use the OR logic for filters instead of the default AND logic. - \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":11,\"start\":2980,\"end\":3207,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\":{\"score\":7.8066325187683105,\"score_type\":\"BM25\",\"order\":9,\"text\":\" - max_paragraphs: the maximum number of paragraphs to pass in the context to - the generative model (default: 20). \\n query_prepend: the hard-coded text - to prepend to the user query. \\n json_schema: the JSON schema to use to get - a JSON answer from the generative model. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":21,\"start\":5412,\"end\":5678,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551\":{\"score\":5.430427074432373,\"score_type\":\"BM25\",\"order\":15,\"text\":\" - navigateToFile: open the file in the browser when clicking on the result (by - default, the file is displayed in the viewer). \\n navigateToLink: open the - link in the browser when clicking on the result (by default, the link is displayed - in the viewer). \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":5,\"start\":1299,\"end\":1551,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299\":{\"score\":5.174862861633301,\"score_type\":\"BM25\",\"order\":19,\"text\":\" - \\n Web component: \\n html \\n \\n The features parameter \\n The features - parameter allows you to customize the behavior of the widget. It is a comma-separated - list of features among the following: \\n \\n filter: display a filter dropdown - in the search bar. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":1049,\"end\":1299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs - > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-06-02T15:39:28.012340\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# - Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> - `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### - citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### - citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### - context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### - format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### - generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### - json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### - max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined - in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### - prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### - query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| - `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### - query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: - `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> - **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### - query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: - `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### - reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### - rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### - retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### - system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### - truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### - user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### - prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3207e2a9db5e75240b8535b73d1e3324\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions - \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n - \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n - Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? - \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 - \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined - in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? - \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 - \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n - Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? - \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in - \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? - \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 - \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n - \\n query_context_images? \\n \\n optional query_context_images: object \\n - 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in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n - \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 - \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 - \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 - RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 - \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 - \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n - libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 - TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\",\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"docs - > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\",\"modified\":\"2026-06-02T15:43:14.212428\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\n\\n# - Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\n\\n> - `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\n\\n***\\n\\n### - llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\n\\n***\\n\\n### - manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**: `object`\\n\\n#### - splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\n\\n***\\n\\n### - max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**: `number`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\n\\n***\\n\\n### - name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:534](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L534)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"7074857dc3154de0e287bf1a4a31f240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\n Interface: SplitStrategy - \\n Properties \\n custom_split? \\n \\n optional custom_split: CustomSplitStrategy - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 \\n \\n - llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n manual_split? \\n \\n - optional manual_split: object \\n \\n splitter \\n \\n splitter: string \\n - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:538 \\n \\n max_paragraph? - \\n \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 - \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\":234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\":\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:44:43.409066Z\",\"last_understanding\":\"2026-06-02T15:44:17.926743Z\",\"last_extract\":\"2026-06-02T15:44:17.298950Z\",\"last_processing_start\":\"2026-06-02T15:44:17.276971Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\":{\"score\":0.5252838134765625,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\" - \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 - \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\",\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"03c141d8a4704db794df47287f7a9e64\":{\"id\":\"03c141d8a4704db794df47287f7a9e64\",\"slug\":\"docs-develop-js-sdk-classes-KnowledgeBox-md\",\"title\":\"docs - > develop > js sdk > classes > KnowledgeBox\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:27.481326\",\"modified\":\"2026-06-02T15:38:18.347887\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/classes/KnowledgeBox\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / KnowledgeBox\\n\\n# - Class: KnowledgeBox\\n\\nProvides access to all the Knowledge Box contents - and services in read mode.\\n\\nFor any Knowledge Box operation that requires - write access, you need to use `WritableKnowledgeBox` instead.\\n\\n## Extends\\n\\n- - [`IKnowledgeBox`](../interfaces/IKnowledgeBox.md)\\n\\n## Extended by\\n\\n- - [`WritableKnowledgeBox`](WritableKnowledgeBox.md)\\n\\n## Implements\\n\\n- - [`IKnowledgeBox`](../interfaces/IKnowledgeBox.md)\\n\\n## Constructors\\n\\n### - new KnowledgeBox()\\n\\n> **new KnowledgeBox**(`nuclia`, `account`, `data`): - [`KnowledgeBox`](KnowledgeBox.md)\\n\\n#### Parameters\\n\\n\u2022 **nuclia**: - [`INuclia`](../interfaces/INuclia.md)\\n\\n\u2022 **account**: `string`\\n\\n\u2022 - **data**: [`IKnowledgeBoxBase`](../interfaces/IKnowledgeBoxBase.md) \\\\| - [`IKnowledgeBoxStandalone`](../interfaces/IKnowledgeBoxStandalone.md)\\n\\n#### - Returns\\n\\n[`KnowledgeBox`](KnowledgeBox.md)\\n\\n#### Inherited from\\n\\n`IKnowledgeBox.constructor`\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:148](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L148)\\n\\n## - Properties\\n\\n### accountId\\n\\n> **accountId**: `string`\\n\\n#### Defined - in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:98](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L98)\\n\\n***\\n\\n### - allowed\\\\_ip\\\\_addresses?\\n\\n> `optional` **allowed\\\\_ip\\\\_addresses**: - `null` \\\\| `string`[]\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_ip_addresses`](../interfaces/IKnowledgeBox.md#allowed_ip_addresses)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_ip_addresses`](../interfaces/IKnowledgeBox.md#allowed_ip_addresses)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:38](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L38)\\n\\n***\\n\\n### - allowed\\\\_origins?\\n\\n> `optional` **allowed\\\\_origins**: `null` \\\\| - `string`[]\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_origins`](../interfaces/IKnowledgeBox.md#allowed_origins)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_origins`](../interfaces/IKnowledgeBox.md#allowed_origins)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:37](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L37)\\n\\n***\\n\\n### - created?\\n\\n> `optional` **created**: `string`\\n\\n#### Implementation - of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`created`](../interfaces/IKnowledgeBox.md#created)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`created`](../interfaces/IKnowledgeBox.md#created)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:36](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L36)\\n\\n***\\n\\n### - description?\\n\\n> `optional` **description**: `string`\\n\\n#### Implementation - of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`description`](../interfaces/IKnowledgeBox.md#description)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`description`](../interfaces/IKnowledgeBox.md#description)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:34](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L34)\\n\\n***\\n\\n### - enforce\\\\_security?\\n\\n> `optional` **enforce\\\\_security**: `boolean`\\n\\n#### - Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`enforce_security`](../interfaces/IKnowledgeBox.md#enforce_security)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`enforce_security`](../interfaces/IKnowledgeBox.md#enforce_security)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:42](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L42)\\n\\n***\\n\\n### - external\\\\_index\\\\_provider?\\n\\n> `optional` **external\\\\_index\\\\_provider**: - `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`external_index_provider`](../interfaces/IKnowledgeBox.md#external_index_provider)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`external_index_provider`](../interfaces/IKnowledgeBox.md#external_index_provider)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:43](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L43)\\n\\n***\\n\\n### - hidden\\\\_resources\\\\_enabled?\\n\\n> `optional` **hidden\\\\_resources\\\\_enabled**: - `boolean`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_enabled`](../interfaces/IKnowledgeBox.md#hidden_resources_enabled)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_enabled`](../interfaces/IKnowledgeBox.md#hidden_resources_enabled)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:40](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L40)\\n\\n***\\n\\n### - hidden\\\\_resources\\\\_hide\\\\_on\\\\_creation?\\n\\n> `optional` **hidden\\\\_resources\\\\_hide\\\\_on\\\\_creation**: - `boolean`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_hide_on_creation`](../interfaces/IKnowledgeBox.md#hidden_resources_hide_on_creation)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_hide_on_creation`](../interfaces/IKnowledgeBox.md#hidden_resources_hide_on_creation)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:41](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L41)\\n\\n***\\n\\n### - id\\n\\n> **id**: `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`id`](../interfaces/IKnowledgeBox.md#id)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`id`](../interfaces/IKnowledgeBox.md#id)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:30](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L30)\\n\\n***\\n\\n### - nuclia\\n\\n> `protected` **nuclia**: [`INuclia`](../interfaces/INuclia.md)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:99](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L99)\\n\\n***\\n\\n### - search\\\\_configs?\\n\\n> `optional` **search\\\\_configs**: `object`\\n\\n#### - Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `any`\\n\\n#### Implementation - of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search_configs`](../interfaces/IKnowledgeBox.md#search_configs)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search_configs`](../interfaces/IKnowledgeBox.md#search_configs)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:39](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L39)\\n\\n***\\n\\n### - slug\\n\\n> **slug**: `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`slug`](../interfaces/IKnowledgeBox.md#slug)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`slug`](../interfaces/IKnowledgeBox.md#slug)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:31](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L31)\\n\\n***\\n\\n### - state?\\n\\n> `optional` **state**: [`KBStates`](../type-aliases/KBStates.md)\\n\\n#### - Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`state`](../interfaces/IKnowledgeBox.md#state)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`state`](../interfaces/IKnowledgeBox.md#state)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:33](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L33)\\n\\n***\\n\\n### - title\\n\\n> **title**: `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`title`](../interfaces/IKnowledgeBox.md#title)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`title`](../interfaces/IKnowledgeBox.md#title)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:32](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L32)\\n\\n***\\n\\n### - zone\\n\\n> **zone**: `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`zone`](../interfaces/IKnowledgeBox.md#zone)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`zone`](../interfaces/IKnowledgeBox.md#zone)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:35](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L35)\\n\\n## - Accessors\\n\\n### fullpath\\n\\n> `get` **fullpath**(): `string`\\n\\nThe - Knowledge Box fullpath on the regional API.\\n\\nExample: `https://europe-1.rag.progress.cloud/api/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0`\\n\\n#### - Returns\\n\\n`string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`fullpath`](../interfaces/IKnowledgeBox.md#fullpath)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`fullpath`](../interfaces/IKnowledgeBox.md#fullpath)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:144](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L144)\\n\\n***\\n\\n### - path\\n\\n> `get` **path**(): `string`\\n\\nThe Knowledge Box path on the - regional API.\\n\\nExample: `/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0`\\n\\n#### - Returns\\n\\n`string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`path`](../interfaces/IKnowledgeBox.md#path)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`path`](../interfaces/IKnowledgeBox.md#path)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:135](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L135)\\n\\n## - Methods\\n\\n### ask()\\n\\n#### ask(query, context, features, options)\\n\\n> - **ask**(`query`, `context`?, `features`?, `options`?): `Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nRetrieves - a generative answer for the given query.\\n\\nThe generative answer is a text - that is generated chunk by chunk by the language model.\\nIt is retrieved - through a readable HTTP stream, so the `chat()` method returns an `Observable`\\nemitting - a value each time a new chunk is available.\\nThe `incomplete` attribute of - the emitted value indicates if the asnwer is complete or not.\\n\\nExample:\\n```ts\\n - nuclia.knowledgeBox\\n .ask('where does the Little Prince live')\\n .pipe(filter((answer) - => !answer.incomplete))\\n .subscribe((answer) => {\\n console.log(answer.text);\\n - \ });\\n ```\\n\\n##### Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022 - **context?**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n\u2022 - **features?**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n\u2022 - **options?**: [`ChatOptions`](../interfaces/ChatOptions.md)\\n\\n##### Returns\\n\\n`Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n##### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`ask`](../interfaces/IKnowledgeBox.md#ask)\\n\\n##### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:368](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L368)\\n\\n#### - ask(query, context, features, options, callback)\\n\\n> **ask**(`query`, `context`?, - `features`?, `options`?, `callback`?): `Observable`\\\\<`null`\\\\>\\n\\n##### - Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022 **context?**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n\u2022 - **features?**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n\u2022 - **options?**: [`ChatOptions`](../interfaces/ChatOptions.md)\\n\\n\u2022 **callback?**\\n\\n##### - Returns\\n\\n`Observable`\\\\<`null`\\\\>\\n\\n##### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`ask`](../interfaces/IKnowledgeBox.md#ask)\\n\\n##### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:374](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L374)\\n\\n***\\n\\n### - catalog()\\n\\n> **catalog**(`query`, `options`?): `Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Parameters\\n\\n\u2022 - **query**: `string` \\\\| [`CatalogQuery`](../interfaces/CatalogQuery.md)\\n\\n\u2022 - **options?**: [`CatalogOptions`](../interfaces/CatalogOptions.md)\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`catalog`](../interfaces/IKnowledgeBox.md#catalog)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:646](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L646)\\n\\n***\\n\\n### - counters()\\n\\n> **counters**(): `Observable`\\\\<[`Counters`](../interfaces/Counters.md)\\\\>\\n\\nReturns - totals for each kind of contents stored in the Knowledge Box (resources, fields, - paragraphs, vectors)\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Counters`](../interfaces/Counters.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`counters`](../interfaces/IKnowledgeBox.md#counters)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:671](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L671)\\n\\n***\\n\\n### - createAgenticRAGPipeline()\\n\\n> **createAgenticRAGPipeline**(`steps`): [`Pipeline`](../namespaces/Agentic/classes/Pipeline.md)\\n\\n#### - Parameters\\n\\n\u2022 **steps**: [`Steps`](../namespaces/Agentic/type-aliases/Steps.md)\\n\\n#### - Returns\\n\\n[`Pipeline`](../namespaces/Agentic/classes/Pipeline.md)\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`createAgenticRAGPipeline`](../interfaces/IKnowledgeBox.md#createagenticragpipeline)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:346](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L346)\\n\\n***\\n\\n### - feedback()\\n\\n> **feedback**(`answerId`, `good`, `feedback`, `text_block_id`?): - `Observable`\\\\<`void`\\\\>\\n\\n#### Parameters\\n\\n\u2022 **answerId**: - `string`\\n\\n\u2022 **good**: `boolean`\\n\\n\u2022 **feedback**: `string` - = `''`\\n\\n\u2022 **text\\\\_block\\\\_id?**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<`void`\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`feedback`](../interfaces/IKnowledgeBox.md#feedback)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:660](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L660)\\n\\n***\\n\\n### - find()\\n\\n> **find**(`query`, `features`, `options`?): `Observable`\\\\<[`FindResults`](../namespaces/Search/interfaces/FindResults.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms - a find operation in the Knowledge Box, which is the recommended way to search - for results.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .find('where - does the Little Prince live')\\n .subscribe((searchResult) => {\\n console.log('search - result', searchResult);\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **query**: - `string`\\n\\n\u2022 **features**: [`Features`](../namespaces/Search/enumerations/Features.md)[] - = `[]`\\n\\n\u2022 **options?**: [`SearchOptions`](../interfaces/SearchOptions.md)\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`FindResults`](../namespaces/Search/interfaces/FindResults.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`find`](../interfaces/IKnowledgeBox.md#find)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:408](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L408)\\n\\n***\\n\\n### - generate()\\n\\n> **generate**(`question`, `context`): `Observable`\\\\<`object`\\\\>\\n\\nPerforms - a question answering operation based on a given context.\\n\\nExample:\\n - ```ts\\n nuclia.knowledgeBox\\n .generate('Who is Eric from Toronto?', [\\n - \ 'Eric is a taxi driver',\\n 'Eric was born in France',\\n 'Eric - lives in Toronto',\\n ]))\\n .subscribe(({ answer }) => {\\n console.log('answer', - answer);\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **question**: `string`\\n\\n\u2022 - **context**: `string`[] = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\n##### - answer\\n\\n> **answer**: `string`\\n\\n##### cannotAnswer\\n\\n> **cannotAnswer**: - `boolean`\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generate`](../interfaces/IKnowledgeBox.md#generate)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:530](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L530)\\n\\n***\\n\\n### - generateJSON()\\n\\n> **generateJSON**(`question`, `json_schema`, `context`): - `Observable`\\\\<`object`\\\\>\\n\\nPerforms a question answering operation - using a JSON schema.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .generateJSON(\\n - \ 'Who is Eric from Toronto?',\\n {\\n name: 'info',\\n parameters: - {\\n properties: {\\n location: {\\n title: - 'Location',\\n description: 'The location of the person',\\n type: - 'string',\\n },\\n name: {\\n title: 'Name',\\n - \ description: 'The name of the person',\\n type: 'string',\\n - \ },\\n },\\n required: ['name', 'location'],\\n - \ },\\n },\\n [\\n 'Eric is a taxi driver',\\n 'Eric - was born in France',\\n 'Eric lives in Toronto',\\n ],\\n )).subscribe((answer) - => {\\n console.log('location', answer.answer.location);\\n });\\n ```\\n\\n#### - Parameters\\n\\n\u2022 **question**: `string`\\n\\n\u2022 **json\\\\_schema**: - `object`\\n\\n\u2022 **context**: `string`[] = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\n##### - answer\\n\\n> **answer**: `object`\\n\\n##### success\\n\\n> **success**: - `boolean`\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generateJSON`](../interfaces/IKnowledgeBox.md#generatejson)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:574](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L574)\\n\\n***\\n\\n### - generateRandomQuestionAboutResource()\\n\\n> **generateRandomQuestionAboutResource**(`resource`): - `Observable`\\\\<`string`\\\\>\\n\\nGenerates a random question about the - given resource.\\nIt picks an entities relation from the extracted metadata - and generates a question about it.\\nIt returns an empty string if no question - can be generated.\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .getResource('09a94719a6444c5a9689394f6ed9baf6', - [ResourceProperties.EXTRACTED], [ExtractedDataTypes.METADATA])\\n .pipe(\\n - \ switchMap((resource) => knowledgeBox.generateRandomQuestionAboutResource(resource)),\\n - \ )\\n .subscribe((question) => {\\n console.log('question', question);\\n - \ });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **resource**: [`Resource`](Resource.md)\\n\\n#### - Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generateRandomQuestionAboutResource`](../interfaces/IKnowledgeBox.md#generaterandomquestionaboutresource)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:620](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L620)\\n\\n***\\n\\n### - getConfiguration()\\n\\n> **getConfiguration**(): `Observable`\\\\<`object`\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getConfiguration`](../interfaces/IKnowledgeBox.md#getconfiguration)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:738](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L738)\\n\\n***\\n\\n### - getEntities()\\n\\n> **getEntities**(): `Observable`\\\\<[`Entities`](../interfaces/Entities.md)\\\\>\\n\\nReturns - all the NER families defined in the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Entities`](../interfaces/Entities.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getEntities`](../interfaces/IKnowledgeBox.md#getentities)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:167](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L167)\\n\\n***\\n\\n### - getEntitiesGroup()\\n\\n> **getEntitiesGroup**(`groupId`): `Observable`\\\\<[`EntitiesGroup`](../interfaces/EntitiesGroup.md)\\\\>\\n\\nReturns - the NER family with the given id.\\n\\n#### Parameters\\n\\n\u2022 **groupId**: - `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`EntitiesGroup`](../interfaces/EntitiesGroup.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getEntitiesGroup`](../interfaces/IKnowledgeBox.md#getentitiesgroup)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:172](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L172)\\n\\n***\\n\\n### - getExtractStrategies()\\n\\n> **getExtractStrategies**(): `Observable`\\\\<[`ExtractStrategies`](../type-aliases/ExtractStrategies.md)\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`ExtractStrategies`](../type-aliases/ExtractStrategies.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getExtractStrategies`](../interfaces/IKnowledgeBox.md#getextractstrategies)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:914](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L914)\\n\\n***\\n\\n### - getFacets()\\n\\n> **getFacets**(`facets`): `Observable`\\\\<[`FacetsResult`](../namespaces/Search/interfaces/FacetsResult.md)\\\\>\\n\\nGet - the total amount of matches in the Knowledge Box for specific criteria (facets) - passed in argument\\n\\n#### Parameters\\n\\n\u2022 **facets**: `string`[]\\n\\nList - of facets to request\\n\\n#### Returns\\n\\n`Observable`\\\\<[`FacetsResult`](../namespaces/Search/interfaces/FacetsResult.md)\\\\>\\n\\nAn - observable containing an object where each key is a string and maps to an - object containing values and their corresponding counts.\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFacets`](../interfaces/IKnowledgeBox.md#getfacets)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:199](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L199)\\n\\n***\\n\\n### - getFullResource()\\n\\n> **getFullResource**(`uuid`): `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\nRetrieves - a resource from the Knowledge Box with all its attached metadata and content.\\n\\n#### - Parameters\\n\\n\u2022 **uuid**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFullResource`](../interfaces/IKnowledgeBox.md#getfullresource)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:258](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L258)\\n\\n***\\n\\n### - getFullResourceBySlug()\\n\\n> **getFullResourceBySlug**(`slug`): `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### - Parameters\\n\\n\u2022 **slug**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFullResourceBySlug`](../interfaces/IKnowledgeBox.md#getfullresourcebyslug)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:290](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L290)\\n\\n***\\n\\n### - getGenerativeProviders()\\n\\n> **getGenerativeProviders**(): `Observable`\\\\<[`GenerativeProviders`](../interfaces/GenerativeProviders.md)\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`GenerativeProviders`](../interfaces/GenerativeProviders.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getGenerativeProviders`](../interfaces/IKnowledgeBox.md#getgenerativeproviders)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:748](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L748)\\n\\n***\\n\\n### - getInvites()\\n\\n> **getInvites**(): `Observable`\\\\<[`KbInvite`](../interfaces/KbInvite.md)[]\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`KbInvite`](../interfaces/KbInvite.md)[]\\\\>\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:763](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L763)\\n\\n***\\n\\n### - getKVSchema()\\n\\n> **getKVSchema**(`name`): `Observable`\\\\<[`KVSchema`](../interfaces/KVSchema.md)\\\\>\\n\\nReturns - the KV schema with the given name.\\n\\n#### Parameters\\n\\n\u2022 **name**: - `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`KVSchema`](../interfaces/KVSchema.md)\\\\>\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:190](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L190)\\n\\n***\\n\\n### - getKVSchemas()\\n\\n> **getKVSchemas**(): `Observable`\\\\<[`KBKVSchemas`](../interfaces/KBKVSchemas.md)\\\\>\\n\\nReturns - all the KV schemas defined in the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`KBKVSchemas`](../interfaces/KBKVSchemas.md)\\\\>\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:185](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L185)\\n\\n***\\n\\n### - getLabels()\\n\\n> **getLabels**(): `Observable`\\\\<[`LabelSets`](../interfaces/LabelSets.md)\\\\>\\n\\nReturns - all the labels defined in the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`LabelSets`](../interfaces/LabelSets.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getLabels`](../interfaces/IKnowledgeBox.md#getlabels)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:177](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L177)\\n\\n***\\n\\n### - getLearningSchema()\\n\\n> **getLearningSchema**(): `Observable`\\\\<[`LearningConfigurations`](../interfaces/LearningConfigurations.md)\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`LearningConfigurations`](../interfaces/LearningConfigurations.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getLearningSchema`](../interfaces/IKnowledgeBox.md#getlearningschema)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:742](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L742)\\n\\n***\\n\\n### - getResource()\\n\\n> **getResource**(`uuid`, `show`, `extracted`): `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\nRetrieves - a resource from the Knowledge Box.\\n\\n- `show` defines which properties - are returned. Default retrieves only the basic metadata.\\n- `extracted` defines - which extracted data are returned.\\n It is ignored if `ResourceProperties.EXTRACTED` - is not in the returned properties.\\n Default is an empty array.\\n\\nExample:\\n - ```ts\\n nuclia.db\\n .getKnowledgeBox()\\n .pipe(switchMap((knowledgeBox) - => knowledgeBox.getResource('09a94719a6444c5a9689394f6ed9baf6')))\\n .subscribe((resource) - => {\\n console.log('resource', resource);\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 - **uuid**: `string`\\n\\n\u2022 **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[] - = `...`\\n\\n\u2022 **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[] - = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getResource`](../interfaces/IKnowledgeBox.md#getresource)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L247)\\n\\n***\\n\\n### - getResourceBySlug()\\n\\n> **getResourceBySlug**(`slug`, `show`, `extracted`): - `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### Parameters\\n\\n\u2022 - **slug**: `string`\\n\\n\u2022 **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[] - = `...`\\n\\n\u2022 **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[] - = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getResourceBySlug`](../interfaces/IKnowledgeBox.md#getresourcebyslug)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:282](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L282)\\n\\n***\\n\\n### - getResourceFromData()\\n\\n> **getResourceFromData**(`data`): [`Resource`](Resource.md)\\n\\n#### - Parameters\\n\\n\u2022 **data**: [`IResource`](../interfaces/IResource.md)\\n\\n#### - Returns\\n\\n[`Resource`](Resource.md)\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:342](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L342)\\n\\n***\\n\\n### - getSearchConfig()\\n\\n> **getSearchConfig**(`id`): `Observable`\\\\<[`SearchConfig`](../type-aliases/SearchConfig.md)\\\\>\\n\\n#### - Parameters\\n\\n\u2022 **id**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`SearchConfig`](../type-aliases/SearchConfig.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSearchConfig`](../interfaces/IKnowledgeBox.md#getsearchconfig)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:922](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L922)\\n\\n***\\n\\n### - getSearchConfigs()\\n\\n> **getSearchConfigs**(): `Observable`\\\\<[`SearchConfigs`](../type-aliases/SearchConfigs.md)\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`SearchConfigs`](../type-aliases/SearchConfigs.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSearchConfigs`](../interfaces/IKnowledgeBox.md#getsearchconfigs)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:926](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L926)\\n\\n***\\n\\n### - getSplitStrategies()\\n\\n> **getSplitStrategies**(): `Observable`\\\\<[`SplitStrategies`](../type-aliases/SplitStrategies.md)\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`SplitStrategies`](../type-aliases/SplitStrategies.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSplitStrategies`](../interfaces/IKnowledgeBox.md#getsplitstrategies)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:918](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L918)\\n\\n***\\n\\n### - getTempToken()\\n\\n> **getTempToken**(`payload`?, `ignoreExpiration`?): `Observable`\\\\<`string`\\\\>\\n\\nReturns - an ephemeral token.\\nRequires account id and zone to be set in the Nuclia - options (except when working with a local NucliaDB instance).\\n\\nThis is - useful when displaying a clickable link to a file in a private Knowledge Box\\n(the - token will authorize the request even though there are no authentication headers).\\n\\nExample:\\n - ```ts\\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`;\\n - ```\\n\\n#### Parameters\\n\\n\u2022 **payload?**: `any`\\n\\nOptional payload - to provide extra data for the token generation\\n\\n\u2022 **ignoreExpiration?**: - `boolean` = `false`\\n\\nOptional By default, a temp token is valid for 5min - and the same token returned if this method is called several times during - this time.\\nPassing ignoreExpiration flag to true will ignore this expiration - delay and will always return a new token.\\n\\n#### Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getTempToken`](../interfaces/IKnowledgeBox.md#gettemptoken)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:707](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L707)\\n\\n***\\n\\n### - getUsers()\\n\\n> **getUsers**(): `Observable`\\\\<[`FullKbUser`](../interfaces/FullKbUser.md)[]\\\\>\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`FullKbUser`](../interfaces/FullKbUser.md)[]\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getUsers`](../interfaces/IKnowledgeBox.md#getusers)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:754](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L754)\\n\\n***\\n\\n### - listenToAllNotifications()\\n\\n> **listenToAllNotifications**(): `Observable`\\\\<[`NotificationMessage`](../interfaces/NotificationMessage.md)[]\\\\>\\n\\nStart - listening to all the notifications sent by the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`NotificationMessage`](../interfaces/NotificationMessage.md)[]\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToAllNotifications`](../interfaces/IKnowledgeBox.md#listentoallnotifications)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:775](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L775)\\n\\n***\\n\\n### - listenToProcessingNotifications()\\n\\n> **listenToProcessingNotifications**(): - `Observable`\\\\<[`ResourceBaseNotification`](../interfaces/ResourceBaseNotification.md)[]\\\\>\\n\\nStart - listening to the Knowledge Box notifications, and returns the list of notifications - for resources which have processing completed (either successfully or not).\\nNotifications - are sent anytime processing is completed, and processing is done anytime the - resource is created or modified (like when a summary is added to the resource - for example).\\n\\n#### Returns\\n\\n`Observable`\\\\<[`ResourceBaseNotification`](../interfaces/ResourceBaseNotification.md)[]\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToProcessingNotifications`](../interfaces/IKnowledgeBox.md#listentoprocessingnotifications)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:854](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L854)\\n\\n***\\n\\n### - listenToResourceOperationNotifications()\\n\\n> **listenToResourceOperationNotifications**(): - `Observable`\\\\<[`ResourceOperationNotification`](../interfaces/ResourceOperationNotification.md)[]\\\\>\\n\\nStart - listening to the Knowledge Box notifications, and returns the list of notifications - corresponding to the different operations affecting the resources:\\ncreated, - modified, deleted.\\nThe notification contains the resource id, title, a timestamp - and a flag indicating if the operation was successful or not.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`ResourceOperationNotification`](../interfaces/ResourceOperationNotification.md)[]\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToResourceOperationNotifications`](../interfaces/IKnowledgeBox.md#listentoresourceoperationnotifications)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:799](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L799)\\n\\n***\\n\\n### - listResources()\\n\\n> **listResources**(`page`?, `size`?): `Observable`\\\\<[`ResourceList`](../interfaces/ResourceList.md)\\\\>\\n\\nLists - all the resources stored in the Knowledge Box.\\n\\n#### Parameters\\n\\n\u2022 - **page?**: `number`\\n\\n\u2022 **size?**: `number`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`ResourceList`](../interfaces/ResourceList.md)\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listResources`](../interfaces/IKnowledgeBox.md#listresources)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:676](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L676)\\n\\n***\\n\\n### - predictAnswer()\\n\\n> **predictAnswer**(`question`, `options`?, `synchronous`?): - `Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) \\\\| - [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms a question - answering operation\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .predictAnswer('Who - is Eric from Toronto?'))\\n .subscribe((answer) => {\\n if (answer.type - !== 'error') {\\n console.log('answer', answer.text);\\n }\\n });\\n - ```\\n\\n#### Parameters\\n\\n\u2022 **question**: `string`\\n\\n\u2022 **options?**: - [`PredictAnswerOptions`](../interfaces/PredictAnswerOptions.md)\\n\\n\u2022 - **synchronous?**: `boolean` = `true`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`predictAnswer`](../interfaces/IKnowledgeBox.md#predictanswer)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:506](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L506)\\n\\n***\\n\\n### - processingStatus()\\n\\n> **processingStatus**(`cursor`?, `scheduled`?, `limit`?): - `Observable`\\\\<`object`\\\\>\\n\\n#### Parameters\\n\\n\u2022 **cursor?**: - `string`\\n\\n\u2022 **scheduled?**: `boolean`\\n\\n\u2022 **limit?**: `number`\\n\\n#### - Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\n##### cursor\\n\\n> **cursor**: - `string`\\n\\n##### results\\n\\n> **results**: [`ProcessingStatus`](../interfaces/ProcessingStatus.md)[]\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`processingStatus`](../interfaces/IKnowledgeBox.md#processingstatus)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:896](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L896)\\n\\n***\\n\\n### - rephrase()\\n\\n> **rephrase**(`question`, `user_context`?, `rephrase_prompt`?): - `Observable`\\\\<`string`\\\\>\\n\\nPerforms a question rephrasing operation.\\nIt - returns a rephrased question that can be used as input for the `generate()` - method.\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .rephrase('Eric lives - Toronto')\\n .subscribe((rephrased) => {\\n console.log('rephrased', rephrased); - // Where does Eric live?\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **question**: - `string`\\n\\n\u2022 **user\\\\_context?**: `string`[]\\n\\n\u2022 **rephrase\\\\_prompt?**: - `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`rephrase`](../interfaces/IKnowledgeBox.md#rephrase)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:598](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L598)\\n\\n***\\n\\n### - search()\\n\\n> **search**(`query`, `features`, `options`?): `Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms - a search operation in the knowledge box.\\n\\nIt is similar to `find()` but - the results are not nested.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n - \ .search('where does the Little Prince live', [Search.Features.KEYWORD])\\n - \ .subscribe((searchResult) => {\\n console.log('search result', searchResult);\\n - \ });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022 - **features**: [`Features`](../namespaces/Search/enumerations/Features.md)[] - = `[]`\\n\\n\u2022 **options?**: [`SearchOptions`](../interfaces/SearchOptions.md)\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search`](../interfaces/IKnowledgeBox.md#search)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:430](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L430)\\n\\n***\\n\\n### - stopListeningToNotifications()\\n\\n> **stopListeningToNotifications**(): - `void`\\n\\nStop listening the notifications sent by the Knowledge Box.\\n\\n#### - Returns\\n\\n`void`\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`stopListeningToNotifications`](../interfaces/IKnowledgeBox.md#stoplisteningtonotifications)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:786](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L786)\\n\\n***\\n\\n### - suggest()\\n\\n> **suggest**(`query`, `inTitleOnly`, `features`, `options`?): - `Observable`\\\\<[`Suggestions`](../namespaces/Search/interfaces/Suggestions.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nSuggests - paragraphs based on the given query.\\n\\n#### Parameters\\n\\n\u2022 **query**: - `string`\\n\\n\u2022 **inTitleOnly**: `boolean` = `false`\\n\\n\u2022 **features**: - [`SuggestionFeatures`](../namespaces/Search/enumerations/SuggestionFeatures.md)[] - = `[]`\\n\\n\u2022 **options?**: [`SuggestOptions`](../interfaces/SuggestOptions.md)\\n\\n#### - Returns\\n\\n`Observable`\\\\<[`Suggestions`](../namespaces/Search/interfaces/Suggestions.md) - \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#### Inherited - from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`suggest`](../interfaces/IKnowledgeBox.md#suggest)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:651](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L651)\\n\\n***\\n\\n### - summarize()\\n\\n> **summarize**(`ressourceIds`, `user_prompt`?, `generative_model`?): - `Observable`\\\\<`string`\\\\>\\n\\nSummarize resources.\\n\\nIt reads the - resources text content and return a global summary about them and one summery - per resource.\\n\\nThe optional `user_prompt` parameter allows you to specify - a text that will be used to generate the summary,\\nand must use the `{text}` - placeholder to indicate where the resource text should be inserted\\n(example: - 'Make a one-line summary of the following text: {text}').\\n\\nExample:\\n - ```ts\\n nuclia.knowledgeBox\\n .summarize(['09a94719a6444c5a9689394f6ed9baf6'])\\n - \ .subscribe((summary) => {\\n console.log('Summary', summary);\\n });\\n - ```\\n\\n#### Parameters\\n\\n\u2022 **ressourceIds**: `string`[]\\n\\n\u2022 - **user\\\\_prompt?**: `string`\\n\\n\u2022 **generative\\\\_model?**: `string`\\n\\n#### - Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`summarize`](../interfaces/IKnowledgeBox.md#summarize)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:456](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L456)\\n\\n***\\n\\n### - tokens()\\n\\n> **tokens**(`text`): `Observable`\\\\<[`SentenceToken`](../interfaces/SentenceToken.md)[]\\\\>\\n\\nPerforms - a tokenization of the given text.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n - \ .tokens('Does James Joyce live in Dublin?')\\n .subscribe((tokens) => - {\\n console.log('tokens', tokens);\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 - **text**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`SentenceToken`](../interfaces/SentenceToken.md)[]\\\\>\\n\\n#### - Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`tokens`](../interfaces/IKnowledgeBox.md#tokens)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:486](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L486)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"61889effe1181f7a4b4bac909424adb7\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / KnowledgeBox \\n Class: KnowledgeBox \\n - Provides access to all the Knowledge Box contents and services in read mode. - \\n For any Knowledge Box operation that requires write access, you need to - use WritableKnowledgeBox instead. \\n Extends \\n \\n IKnowledgeBox \\n \\n - Extended by \\n \\n WritableKnowledgeBox \\n \\n Implements \\n \\n IKnowledgeBox - \\n \\n Constructors \\n new KnowledgeBox() \\n \\n new KnowledgeBox(nuclia, - account, data): KnowledgeBox \\n \\n Parameters \\n \u2022 nuclia: INuclia - \\n \u2022 account: string \\n \u2022 data: IKnowledgeBoxBase \\\\| IKnowledgeBoxStandalone - \\n Returns \\n KnowledgeBox \\n Inherited from \\n IKnowledgeBox.constructor - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:148 \\n Properties \\n - accountId \\n \\n accountId: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:98 - \\n \\n allowed_ip_addresses? \\n \\n optional allowed_ip_addresses: null - \\\\| string[] \\n \\n Implementation of \\n IKnowledgeBox.allowed_ip_addresses - \\n Inherited from \\n IKnowledgeBox.allowed_ip_addresses \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.models.ts:38 \\n \\n allowed_origins? \\n \\n - optional allowed_origins: null \\\\| string[] \\n \\n Implementation of \\n - IKnowledgeBox.allowed_origins \\n Inherited from \\n IKnowledgeBox.allowed_origins - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:37 \\n \\n created? - \\n \\n optional created: string \\n \\n Implementation of \\n IKnowledgeBox.created - \\n Inherited from \\n IKnowledgeBox.created \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:36 - \\n \\n description? \\n \\n optional description: string \\n \\n Implementation - of \\n IKnowledgeBox.description \\n Inherited from \\n IKnowledgeBox.description - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:34 \\n \\n enforce_security? - \\n \\n optional enforce_security: boolean \\n \\n Implementation of \\n IKnowledgeBox.enforce_security - \\n Inherited from \\n IKnowledgeBox.enforce_security \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:42 - \\n \\n external_index_provider? \\n \\n optional external_index_provider: - string \\n \\n Implementation of \\n IKnowledgeBox.external_index_provider - \\n Inherited from \\n IKnowledgeBox.external_index_provider \\n Defined in - \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:43 \\n \\n hidden_resources_enabled? - \\n \\n optional hidden_resources_enabled: boolean \\n \\n Implementation - of \\n IKnowledgeBox.hidden_resources_enabled \\n Inherited from \\n IKnowledgeBox.hidden_resources_enabled - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:40 \\n \\n hidden_resources_hide_on_creation? - \\n \\n optional hidden_resources_hide_on_creation: boolean \\n \\n Implementation - of \\n IKnowledgeBox.hidden_resources_hide_on_creation \\n Inherited from - \\n IKnowledgeBox.hidden_resources_hide_on_creation \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:41 - \\n \\n id \\n \\n id: string \\n \\n Implementation of \\n IKnowledgeBox.id - \\n Inherited from \\n IKnowledgeBox.id \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:30 - \\n \\n nuclia \\n \\n protected nuclia: INuclia \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:99 - \\n \\n search_configs? \\n \\n optional search_configs: object \\n \\n Index - Signature \\n [key: string]: any \\n Implementation of \\n IKnowledgeBox.search_configs - \\n Inherited from \\n IKnowledgeBox.search_configs \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:39 - \\n \\n slug \\n \\n slug: string \\n \\n Implementation of \\n IKnowledgeBox.slug - \\n Inherited from \\n IKnowledgeBox.slug \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:31 - \\n \\n state? \\n \\n optional state: KBStates \\n \\n Implementation of - \\n IKnowledgeBox.state \\n Inherited from \\n IKnowledgeBox.state \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:33 \\n \\n title \\n \\n title: - string \\n \\n Implementation of \\n IKnowledgeBox.title \\n Inherited from - \\n IKnowledgeBox.title \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:32 - \\n \\n zone \\n \\n zone: string \\n \\n Implementation of \\n IKnowledgeBox.zone - \\n Inherited from \\n IKnowledgeBox.zone \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:35 - \\n Accessors \\n fullpath \\n \\n get fullpath(): string \\n \\n The Knowledge - Box fullpath on the regional API. \\n Example: https://europe-1.rag.progress.cloud/api/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0 - \\n Returns \\n string \\n Implementation of \\n IKnowledgeBox.fullpath \\n - Inherited from \\n IKnowledgeBox.fullpath \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:144 - \\n \\n path \\n \\n get path(): string \\n \\n The Knowledge Box path on - the regional API. \\n Example: /v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0 - \\n Returns \\n string \\n Implementation of \\n IKnowledgeBox.path \\n Inherited - from \\n IKnowledgeBox.path \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:135 - \\n Methods \\n ask() \\n ask(query, context, features, options) \\n \\n ask(query, - context?, features?, options?): Observable\\\\ \\n \\n Retrieves a generative - answer for the given query. \\n The generative answer is a text that is generated - chunk by chunk by the language model. \\n It is retrieved through a readable - HTTP stream, so the chat() method returns an Observable \\n emitting a value - each time a new chunk is available. \\n The incomplete attribute of the emitted - value indicates if the asnwer is complete or not. \\n Example: \\n ts \\n - nuclia.knowledgeBox \\n .ask('where does the Little Prince live') \\n .pipe(filter((answer) - => !answer.incomplete)) \\n .subscribe((answer) => { \\n console.log(answer.text); - \\n }); \\n Parameters \\n \u2022 query: string \\n \u2022 context?: ContextEntry[] - \\n \u2022 features?: Features[] \\n \u2022 options?: ChatOptions \\n Returns - \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.ask \\n Defined in - \\n libs/sdk-core/src/lib/db/kb/kb.ts:368 \\n ask(query, context, features, - options, callback) \\n \\n ask(query, context?, features?, options?, callback?): - Observable\\\\ \\n \\n Parameters \\n \u2022 query: string \\n \u2022 context?: - ContextEntry[] \\n \u2022 features?: Features[] \\n \u2022 options?: ChatOptions - \\n \u2022 callback? \\n Returns \\n Observable\\\\ \\n Inherited from \\n - IKnowledgeBox.ask \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:374 - \\n \\n catalog() \\n \\n catalog(query, options?): Observable\\\\ \\n \\n - Parameters \\n \u2022 query: string \\\\| CatalogQuery \\n \u2022 options?: - CatalogOptions \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.catalog - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:646 \\n \\n counters() - \\n \\n counters(): Observable\\\\ \\n \\n Returns totals for each kind of - contents stored in the Knowledge Box (resources, fields, paragraphs, vectors) - \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.counters - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:671 \\n \\n createAgenticRAGPipeline() - \\n \\n createAgenticRAGPipeline(steps): Pipeline \\n \\n Parameters \\n \u2022 - steps: Steps \\n Returns \\n Pipeline \\n Inherited from \\n IKnowledgeBox.createAgenticRAGPipeline - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:346 \\n \\n feedback() - \\n \\n feedback(answerId, good, feedback, text_block_id?): Observable\\\\ - \\n \\n Parameters \\n \u2022 answerId: string \\n \u2022 good: boolean \\n - \u2022 feedback: string = '' \\n \u2022 text_block_id?: string \\n Returns - \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.feedback \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.ts:660 \\n \\n find() \\n \\n find(query, - features, options?): Observable\\\\ \\n \\n Performs a find operation in the - Knowledge Box, which is the recommended way to search for results. \\n Example: - \\n ts \\n nuclia.knowledgeBox \\n .find('where does the Little Prince live') - \\n .subscribe((searchResult) => { \\n console.log('search result', searchResult); - \\n }); \\n Parameters \\n \u2022 query: string \\n \u2022 features: Features[] - = [] \\n \u2022 options?: SearchOptions \\n Returns \\n Observable\\\\ \\n - Inherited from \\n IKnowledgeBox.find \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:408 - \\n \\n generate() \\n \\n generate(question, context): Observable\\\\ \\n - \\n Performs a question answering operation based on a given context. \\n - Example: \\n ts \\n nuclia.knowledgeBox \\n .generate('Who is Eric from Toronto?', - [ \\n 'Eric is a taxi driver', \\n 'Eric was born in France', \\n 'Eric lives - in Toronto', \\n ])) \\n .subscribe(({ answer }) => { \\n console.log('answer', - answer); \\n }); \\n Parameters \\n \u2022 question: string \\n \u2022 context: - string[] = [] \\n Returns \\n Observable\\\\ \\n answer \\n \\n answer: string - \\n \\n cannotAnswer \\n \\n cannotAnswer: boolean \\n \\n Inherited from - \\n IKnowledgeBox.generate \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:530 - \\n \\n generateJSON() \\n \\n generateJSON(question, json_schema, context): - Observable\\\\ \\n \\n Performs a question answering operation using a JSON - schema. \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .generateJSON( \\n - 'Who is Eric from Toronto?', \\n { \\n name: 'info', \\n parameters: { \\n - properties: { \\n location: { \\n title: 'Location', \\n description: 'The - location of the person', \\n type: 'string', \\n }, \\n name: { \\n title: - 'Name', \\n description: 'The name of the person', \\n type: 'string', \\n - }, \\n }, \\n required: ['name', 'location'], \\n }, \\n }, \\n [ \\n 'Eric - is a taxi driver', \\n 'Eric was born in France', \\n 'Eric lives in Toronto', - \\n ], \\n )).subscribe((answer) => { \\n console.log('location', answer.answer.location); - \\n }); \\n Parameters \\n \u2022 question: string \\n \u2022 json_schema: - object \\n \u2022 context: string[] = [] \\n Returns \\n Observable\\\\ \\n - answer \\n \\n answer: object \\n \\n success \\n \\n success: boolean \\n - \\n Inherited from \\n IKnowledgeBox.generateJSON \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:574 - \\n \\n generateRandomQuestionAboutResource() \\n \\n generateRandomQuestionAboutResource(resource): - Observable\\\\ \\n \\n Generates a random question about the given resource. - \\n It picks an entities relation from the extracted metadata and generates - a question about it. \\n It returns an empty string if no question can be - generated. \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .getResource('09a94719a6444c5a9689394f6ed9baf6', - [ResourceProperties.EXTRACTED], [ExtractedDataTypes.METADATA]) \\n .pipe( - \\n switchMap((resource) => knowledgeBox.generateRandomQuestionAboutResource(resource)), - \\n ) \\n .subscribe((question) => { \\n console.log('question', question); - \\n }); \\n Parameters \\n \u2022 resource: Resource \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.generateRandomQuestionAboutResource \\n - Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:620 \\n \\n getConfiguration() - \\n \\n getConfiguration(): Observable\\\\ \\n \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getConfiguration \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:738 - \\n \\n getEntities() \\n \\n getEntities(): Observable\\\\ \\n \\n Returns - all the NER families defined in the Knowledge Box. \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getEntities \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:167 - \\n \\n getEntitiesGroup() \\n \\n getEntitiesGroup(groupId): Observable\\\\ - \\n \\n Returns the NER family with the given id. \\n Parameters \\n \u2022 - groupId: string \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getEntitiesGroup - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:172 \\n \\n getExtractStrategies() - \\n \\n getExtractStrategies(): Observable\\\\ \\n \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getExtractStrategies \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.ts:914 \\n \\n getFacets() \\n \\n getFacets(facets): - Observable\\\\ \\n \\n Get the total amount of matches in the Knowledge Box - for specific criteria (facets) passed in argument \\n Parameters \\n \u2022 - facets: string[] \\n List of facets to request \\n Returns \\n Observable\\\\ - \\n An observable containing an object where each key is a string and maps - to an object containing values and their corresponding counts. \\n Inherited - from \\n IKnowledgeBox.getFacets \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:199 - \\n \\n getFullResource() \\n \\n getFullResource(uuid): Observable\\\\ \\n - \\n Retrieves a resource from the Knowledge Box with all its attached metadata - and content. \\n Parameters \\n \u2022 uuid: string \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getFullResource \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:258 - \\n \\n getFullResourceBySlug() \\n \\n getFullResourceBySlug(slug): Observable\\\\ - \\n \\n Parameters \\n \u2022 slug: string \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getFullResourceBySlug \\n Defined in - \\n libs/sdk-core/src/lib/db/kb/kb.ts:290 \\n \\n getGenerativeProviders() - \\n \\n getGenerativeProviders(): Observable\\\\ \\n \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getGenerativeProviders \\n Defined in - \\n libs/sdk-core/src/lib/db/kb/kb.ts:748 \\n \\n getInvites() \\n \\n getInvites(): - Observable\\\\ \\n \\n Returns \\n Observable\\\\ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:763 - \\n \\n getKVSchema() \\n \\n getKVSchema(name): Observable\\\\ \\n \\n Returns - the KV schema with the given name. \\n Parameters \\n \u2022 name: string - \\n Returns \\n Observable\\\\ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:190 - \\n \\n getKVSchemas() \\n \\n getKVSchemas(): Observable\\\\ \\n \\n Returns - all the KV schemas defined in the Knowledge Box. \\n Returns \\n Observable\\\\ - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:185 \\n \\n getLabels() - \\n \\n getLabels(): Observable\\\\ \\n \\n Returns all the labels defined - in the Knowledge Box. \\n Returns \\n Observable\\\\ \\n Inherited from \\n - IKnowledgeBox.getLabels \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:177 - \\n \\n getLearningSchema() \\n \\n getLearningSchema(): Observable\\\\ \\n - \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getLearningSchema - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:742 \\n \\n getResource() - \\n \\n getResource(uuid, show, extracted): Observable\\\\ \\n \\n Retrieves - a resource from the Knowledge Box. \\n \\n show defines which properties are - returned. Default retrieves only the basic metadata. \\n extracted defines - which extracted data are returned. \\n It is ignored if ResourceProperties.EXTRACTED - is not in the returned properties. \\n Default is an empty array. \\n \\n - Example: \\n ts \\n nuclia.db \\n .getKnowledgeBox() \\n .pipe(switchMap((knowledgeBox) - => knowledgeBox.getResource('09a94719a6444c5a9689394f6ed9baf6'))) \\n .subscribe((resource) - => { \\n console.log('resource', resource); \\n }); \\n Parameters \\n \u2022 - uuid: string \\n \u2022 show: ResourceProperties[] = ... \\n \u2022 extracted: - ExtractedDataTypes[] = [] \\n Returns \\n Observable\\\\ \\n Inherited from - \\n IKnowledgeBox.getResource \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:247 - \\n \\n getResourceBySlug() \\n \\n getResourceBySlug(slug, show, extracted): - Observable\\\\ \\n \\n Parameters \\n \u2022 slug: string \\n \u2022 show: - ResourceProperties[] = ... \\n \u2022 extracted: ExtractedDataTypes[] = [] - \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getResourceBySlug - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:282 \\n \\n getResourceFromData() - \\n \\n getResourceFromData(data): Resource \\n \\n Parameters \\n \u2022 - data: IResource \\n Returns \\n Resource \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:342 - \\n \\n getSearchConfig() \\n \\n getSearchConfig(id): Observable\\\\ \\n - \\n Parameters \\n \u2022 id: string \\n Returns \\n Observable\\\\ \\n Inherited - from \\n IKnowledgeBox.getSearchConfig \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:922 - \\n \\n getSearchConfigs() \\n \\n getSearchConfigs(): Observable\\\\ \\n - \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getSearchConfigs - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:926 \\n \\n getSplitStrategies() - \\n \\n getSplitStrategies(): Observable\\\\ \\n \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.getSplitStrategies \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.ts:918 \\n \\n getTempToken() \\n \\n getTempToken(payload?, - ignoreExpiration?): Observable\\\\ \\n \\n Returns an ephemeral token. \\n - Requires account id and zone to be set in the Nuclia options (except when - working with a local NucliaDB instance). \\n This is useful when displaying - a clickable link to a file in a private Knowledge Box \\n (the token will - authorize the request even though there are no authentication headers). \\n - Example: \\n ts \\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`; - \\n Parameters \\n \u2022 payload?: any \\n Optional payload to provide extra - data for the token generation \\n \u2022 ignoreExpiration?: boolean = false - \\n Optional By default, a temp token is valid for 5min and the same token - returned if this method is called several times during this time. \\n Passing - ignoreExpiration flag to true will ignore this expiration delay and will always - return a new token. \\n Returns \\n Observable\\\\ \\n Inherited from \\n - IKnowledgeBox.getTempToken \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:707 - \\n \\n getUsers() \\n \\n getUsers(): Observable\\\\ \\n \\n Returns \\n - Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getUsers \\n Defined in - \\n libs/sdk-core/src/lib/db/kb/kb.ts:754 \\n \\n listenToAllNotifications() - \\n \\n listenToAllNotifications(): Observable\\\\ \\n \\n Start listening - to all the notifications sent by the Knowledge Box. \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.listenToAllNotifications \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.ts:775 \\n \\n listenToProcessingNotifications() - \\n \\n listenToProcessingNotifications(): Observable\\\\ \\n \\n Start listening - to the Knowledge Box notifications, and returns the list of notifications - for resources which have processing completed (either successfully or not). - \\n Notifications are sent anytime processing is completed, and processing - is done anytime the resource is created or modified (like when a summary is - added to the resource for example). \\n Returns \\n Observable\\\\ \\n Inherited - from \\n IKnowledgeBox.listenToProcessingNotifications \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.ts:854 \\n \\n listenToResourceOperationNotifications() - \\n \\n listenToResourceOperationNotifications(): Observable\\\\ \\n \\n Start - listening to the Knowledge Box notifications, and returns the list of notifications - corresponding to the different operations affecting the resources: \\n created, - modified, deleted. \\n The notification contains the resource id, title, a - timestamp and a flag indicating if the operation was successful or not. \\n - Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.listenToResourceOperationNotifications - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:799 \\n \\n listResources() - \\n \\n listResources(page?, size?): Observable\\\\ \\n \\n Lists all the - resources stored in the Knowledge Box. \\n Parameters \\n \u2022 page?: number - \\n \u2022 size?: number \\n Returns \\n Observable\\\\ \\n Inherited from - \\n IKnowledgeBox.listResources \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:676 - \\n \\n predictAnswer() \\n \\n predictAnswer(question, options?, synchronous?): - Observable\\\\ \\n \\n Performs a question answering operation \\n Example: - \\n ts \\n nuclia.knowledgeBox \\n .predictAnswer('Who is Eric from Toronto?')) - \\n .subscribe((answer) => { \\n if (answer.type !== 'error') { \\n console.log('answer', - answer.text); \\n } \\n }); \\n Parameters \\n \u2022 question: string \\n - \u2022 options?: PredictAnswerOptions \\n \u2022 synchronous?: boolean = true - \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.predictAnswer - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:506 \\n \\n processingStatus() - \\n \\n processingStatus(cursor?, scheduled?, limit?): Observable\\\\ \\n - \\n Parameters \\n \u2022 cursor?: string \\n \u2022 scheduled?: boolean \\n - \u2022 limit?: number \\n Returns \\n Observable\\\\ \\n cursor \\n \\n cursor: - string \\n \\n results \\n \\n results: ProcessingStatus[] \\n \\n Inherited - from \\n IKnowledgeBox.processingStatus \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:896 - \\n \\n rephrase() \\n \\n rephrase(question, user_context?, rephrase_prompt?): - Observable\\\\ \\n \\n Performs a question rephrasing operation. \\n It returns - a rephrased question that can be used as input for the generate() method. - \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .rephrase('Eric lives Toronto') - \\n .subscribe((rephrased) => { \\n console.log('rephrased', rephrased); // - Where does Eric live? \\n }); \\n Parameters \\n \u2022 question: string \\n - \u2022 user_context?: string[] \\n \u2022 rephrase_prompt?: string \\n Returns - \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.rephrase \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.ts:598 \\n \\n search() \\n \\n search(query, - features, options?): Observable\\\\ \\n \\n Performs a search operation in - the knowledge box. \\n It is similar to find() but the results are not nested. - \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .search('where does the Little - Prince live', [Search.Features.KEYWORD]) \\n .subscribe((searchResult) => - { \\n console.log('search result', searchResult); \\n }); \\n Parameters \\n - \u2022 query: string \\n \u2022 features: Features[] = [] \\n \u2022 options?: - SearchOptions \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.search - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:430 \\n \\n stopListeningToNotifications() - \\n \\n stopListeningToNotifications(): void \\n \\n Stop listening the notifications - sent by the Knowledge Box. \\n Returns \\n void \\n Inherited from \\n IKnowledgeBox.stopListeningToNotifications - \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:786 \\n \\n suggest() - \\n \\n suggest(query, inTitleOnly, features, options?): Observable\\\\ \\n - \\n Suggests paragraphs based on the given query. \\n Parameters \\n \u2022 - query: string \\n \u2022 inTitleOnly: boolean = false \\n \u2022 features: - SuggestionFeatures[] = [] \\n \u2022 options?: SuggestOptions \\n Returns - \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.suggest \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.ts:651 \\n \\n summarize() \\n \\n summarize(ressourceIds, - user_prompt?, generative_model?): Observable\\\\ \\n \\n Summarize resources. - \\n It reads the resources text content and return a global summary about - them and one summery per resource. \\n The optional user_prompt parameter - allows you to specify a text that will be used to generate the summary, \\n - and must use the {text} placeholder to indicate where the resource text should - be inserted \\n (example: 'Make a one-line summary of the following text: - {text}'). \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .summarize(['09a94719a6444c5a9689394f6ed9baf6']) - \\n .subscribe((summary) => { \\n console.log('Summary', summary); \\n }); - \\n Parameters \\n \u2022 ressourceIds: string[] \\n \u2022 user_prompt?: - string \\n \u2022 generative_model?: string \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.summarize \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:456 - \\n \\n tokens() \\n \\n tokens(text): Observable\\\\ \\n \\n Performs a tokenization - of the given text. \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .tokens('Does - James Joyce live in Dublin?') \\n .subscribe((tokens) => { \\n console.log('tokens', - tokens); \\n }); \\n Parameters \\n \u2022 text: string \\n Returns \\n Observable\\\\ - \\n Inherited from \\n IKnowledgeBox.tokens \\n Defined in \\n 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- Joyce\",\"type\":\"entity\",\"group\":\"PERSON\"},\"to\":{\"value\":\"Dublin\",\"type\":\"entity\",\"group\":\"GPE\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > classes > KnowledgeBox\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > classes > KnowledgeBox\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"03c141d8a4704db794df47287f7a9e64/t/page/15230-15632\":{\"score\":0.5163909792900085,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" - Example: \\n ts \\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`; - \\n Parameters \\n \u2022 payload?: any \\n Optional payload to provide extra - data for the token generation \\n \u2022 ignoreExpiration?: boolean = false - \\n Optional By default, a temp token is valid for 5min and the same token - returned if this method is called several times 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models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2025-08-27T15:33:06.862963\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: - openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible - LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG - allows you to connect to any OpenAI API compatible LLM. This means that you - can use any LLM that has an API compatible with the OpenAI API which has become - a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, - open-source LLMs hosted by cloud providers or commercial LLMs are compatible - with the OpenAI API. This means that you can use them with Agentic RAG without - any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box - configuration you can do so in three manners, through the API, the Nuclia - CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers - the most user-friendly way to modify the configuration of your knowledge box - and we will use it in this example.\\n\\nWe will be setting up a connection - to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers - a wide range of open-source and commercial models compatible with the OpenAI - API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), - the API parameters are located under the **API** tab.\\n\\n1. **Open the AI - Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. - **Select \u201COpenAI API Compatible Model\u201D** \\n From the models - list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n - \ Toggle the option for using you own `OpenAI API Compatible Key` if it is - not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - - **API Key**:\\n - Description: The API key for your LLM. This is the key - that you would use as an authorization header in the API. You may leave this - blank if the endpoint you are connecting to does not require an API key.\\n - \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n - \ - Description: The URL of the API endpoint for your LLM. This may be - shared between multiple models.\\n - Example: For OpenRouter, it is the - same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - - Description: The name of the model you want to use, it needs to exactly match - the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus - in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - - **Maximum supported input tokens**:\\n - Description: The maximum number - of tokens that the model can accept as input. Be mindful that this takes into - account the tokens used in the prompt, query and context. Also take note that - some models may provide their context window as the total between input and - output tokens, while others may provide it as the input tokens only.\\n - - Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, - as we want to leave room for the output, we will set the maximum supported - input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output - tokens**:\\n - Description: The maximum number of tokens that the model - can generate as output. Again, we should keep in mind that this value summed - to the **Maximum supported input tokens** should not exceed the total context - size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the - maximum output tokens is specified at `32768`, but we already reserved `31744` - for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - - **Model Features**:\\n - Description: Under this section you will find - multiple toggles related to features supported by the model, these vary from - model to model, but most often the default values are well suited to most - use cases. The most relevant toggle is for `Image Support` which allows you - to use images as input for the model.\\n - Example: Image input is not - supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** - \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample - query in Agentic RAG or via API/CLI. Adjust your prompt templates and token - settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" - \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API - compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic - RAG allows you to connect to any OpenAI API compatible LLM. This means that - you can use any LLM that has an API compatible with the OpenAI API which has - become a standard in the industry. \\n Many of the options for self-hosted - LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible - with the OpenAI API. This means that you can use them with Agentic RAG without - any modifications. \\n Configuration \\n To modify your knowledge box configuration - you can do so in three manners, through the API, the Nuclia CLI / SDK or the - Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly - way to modify the configuration of your knowledge box and we will use it in - this example. \\n We will be setting up a connection to the Phi 4 Reasoning - Plus model, hosted by OpenRouter which offers a wide range of open-source - and commercial models compatible with the OpenAI API. We can see more information - about this specific model here, the API parameters are located under the API - tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, - click AI Models. \\n Select OpenAI API Compatible Model \\n From the models - list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle - the option for using you own OpenAI API Compatible Key if it is not already - enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: - \\n \\n Description: The API key for your LLM. This is the key that you would - use as an authorization header in the API. You may leave this blank if the - endpoint you are connecting to does not require an API key. \\n Example: We - will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: - The URL of the API endpoint for your LLM. This may be shared between multiple - models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 - \\n \\n \\n Model: \\n Description: The name of the model you want to use, - it needs to exactly match the name of the model in the API. \\n Example: For - Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. - \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number - of tokens that the model can accept as input. Be mindful that this takes into - account the tokens used in the prompt, query and context. Also take note that - some models may provide their context window as the total between input and - output tokens, while others may provide it as the input tokens only. \\n Example: - For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want - to leave room for the output, we will set the maximum supported input tokens - as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n - Description: The maximum number of tokens that the model can generate as output. - Again, we should keep in mind that this value summed to the Maximum supported - input tokens should not exceed the total context size supported by the model. - \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified - at 32768, but we already reserved 31744 for the input tokens, so we will set - this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: - Under this section you will find multiple toggles related to features supported - by the model, these vary from model to model, but most often the default values - are well suited to most use cases. The most relevant toggle is for Image Support - which allows you to use images as input for the model. \\n Example: Image - input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. - \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n - Run a sample query in Agentic RAG or via API/CLI. 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\u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# - Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: - `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### - line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### - text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### - width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### - x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### - y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"b174ec97138cd1778c102918672d3f07\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n - Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 - \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 - \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 - \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:48.900917Z\",\"last_understanding\":\"2026-06-02T15:43:43.037157Z\",\"last_extract\":\"2026-06-02T15:43:42.655587Z\",\"last_processing_start\":\"2026-06-02T15:43:42.629857Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.531770646572113,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n - Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs - > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# - Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different - search endpoints:\\n\\n- `search`: returns several result sets according the - different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns - a single result set where all different results are merged into a hierarchical - structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n - \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia - kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = - sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', - '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n - \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My - search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search - search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased - on a `find` request, Nuclia uses a generative AI to answer the question based - on the context without hallucinations and with the find result and relations.\\n\\n- - CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = - sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n - \ You can also use the `AskRequest` item to configure the request with all - the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from - nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n - \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n - \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support - reasoning. In some models, reasoning is enabled by default, while in others - it must be explicitly requested. You can control this behavior using the reasoning - parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from - nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n - \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n - \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # - Show reasoning in the response\\n effort=\\\"low\\\", # Can be - \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 - # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n - \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 - support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` - only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), - Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* - Enabling reasoning can use additional tokens, which may increase your usage - costs.\\n* You may need to increase `max_tokens` to give the LLM enough room - to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint - that involves search (`search`, `find` and `ask`) also support more advanced - filtering expressions. Expressions can have one of the following operators:\\n\\n- - `all`: this is the default. Will make search return results containing all - specified filter labels.\\n- `any`: returns results containing at least one - of the labels.\\n- `none`: returns results that do not contain any of the - labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote - that multiple expressions can be chained in the `filters` parameter and the - conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- - CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search - import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My - question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n - \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to - improve the quality of the answers by extending the search results passed - to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask - --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = - sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": - \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) - for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to - use all the options supported by the `/find` and `/ask` endpoints,\\nbut not - all of the options can be passed as specific parameter.\\nIn these cases, - you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- - CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My - search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n - \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": - 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search - = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", - \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n - \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n - \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported - by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph - can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb - search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": - {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = - sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": - {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": - {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": - \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information - about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) - or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search - and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: - \\n \\n search: returns several result sets according the different search - techniques (full-text, fuzzy, semantic). \\n find: returns a single result - set where all different results are merged into a hierarchical structure. - \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n - \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search - find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] - \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() - \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) - \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia - kb search find --query= My search --json \\n Get YAML output: \\n bash \\n - nuclia kb search search --query= My search --yaml \\n Generative answer \\n - Based on a find request, Nuclia uses a generative AI to answer the question - based on the context without hallucinations and with the find result and relations. - \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n - \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() - \\n search.ask(query= My question ) \\n You can also use the AskRequest item - to configure the request with all the parameters supported: \\n ```python - \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest - \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question - , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` - \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is - enabled by default, while in others it must be explicitly requested. You can - control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n - ```python \\n from nuclia import sdk \\n from nucliadb_models.search import - AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( - \\n query= My question with extra reasoning effort , \\n max_tokens=5000, - \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response - \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # - How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) - \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 - support effort only. \\n Google & Anthropic models \u2192 support budget_tokens - only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), - Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning - \\n Enabling reasoning can use additional tokens, which may increase your - usage costs. \\n You may need to increase max_tokens to give the LLM enough - room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint - that involves search (search, find and ask) also support more advanced filtering - expressions. Expressions can have one of the following operators: \\n \\n - all: this is the default. Will make search return results containing all specified - filter labels. \\n any: returns results containing at least one of the labels. - \\n none: returns results that do not contain any of the labels. \\n not_all: - returns results that do not contain all specified labels. \\n \\n Note that - multiple expressions can be chained in the filters parameter and the conjunction - of all of them will be computed. \\n Here are some examples: \\n \\n CLI: - \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] - \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search - import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My - question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], - \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve - the quality of the answers by extending the search results passed to the LLM - as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My - question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python - \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= - My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies - documentation for more information. \\n Complex queries \\n The Python SDK - allows to use all the options supported by the /find and /ask endpoints, \\n - but not all of the options can be passed as specific parameter. \\n In these - cases, you can just pass your query as a dictionnary in the query parameter. - \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My - search , filters : [ /icon/application/pdf , /classification.labels/region/Asia - ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n - \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() - \\n search.find(query={ query : My search , filters : [ /icon/application/pdf - , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My - search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries - supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge - graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia - kb search graph --query='{ query : { prop : path , source : { value : Rust - }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from - nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ - \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination - : { value : Python } \\n } \\n } \\n ) \\n For more information about graph - querying, please refer to Nuclia's graph \\n doc or the API 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- > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.5417318344116211,\"score_type\":\"VECTOR\",\"order\":5,\"text\":\" - \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search - import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( - \\n query= My question with extra reasoning effort , \\n max_tokens=5000, - \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response - \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # - How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) - \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.5198746919631958,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\" - ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which - may increase your usage costs. \\n You may need to increase max_tokens to - give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"d73154bff6b84efb8fd2f780cb325820\":{\"id\":\"d73154bff6b84efb8fd2f780cb325820\",\"slug\":\"docs-develop-js-sdk-interfaces-GraphStrategy-md\",\"title\":\"docs - > develop > js sdk > interfaces > GraphStrategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:42.740271\",\"modified\":\"2026-06-02T15:40:24.653084\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/GraphStrategy\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / GraphStrategy\\n\\n# - Interface: GraphStrategy\\n\\n## Properties\\n\\n### ~~agentic\\\\_graph\\\\_only?~~\\n\\n> - `optional` **agentic\\\\_graph\\\\_only**: `boolean`\\n\\n#### Deprecated\\n\\nUse - exclude_processor_relations\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:396](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L396)\\n\\n***\\n\\n### - exclude\\\\_processor\\\\_relations?\\n\\n> `optional` **exclude\\\\_processor\\\\_relations**: - `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:397](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L397)\\n\\n***\\n\\n### - hops\\n\\n> **hops**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:391](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L391)\\n\\n***\\n\\n### - name\\n\\n> **name**: [`GRAPH`](../enumerations/RagStrategyName.md#graph)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:390](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L390)\\n\\n***\\n\\n### - query\\\\_entity\\\\_detection?\\n\\n> `optional` **query\\\\_entity\\\\_detection**: - `\\\"predict\\\"` \\\\| `\\\"suggest\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:400](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L400)\\n\\n***\\n\\n### - relation\\\\_ranking?\\n\\n> `optional` **relation\\\\_ranking**: `\\\"generative\\\"` - \\\\| `\\\"reranker\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:398](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L398)\\n\\n***\\n\\n### - relation\\\\_text\\\\_as\\\\_paragraphs?\\n\\n> `optional` **relation\\\\_text\\\\_as\\\\_paragraphs**: - `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:399](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L399)\\n\\n***\\n\\n### - top\\\\_k\\n\\n> **top\\\\_k**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:392](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L392)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"27a5993f7e7cbea2db8dfa40e8e080a9\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / GraphStrategy \\n Interface: GraphStrategy - \\n Properties \\n ~~agentic_graph_only?~~ \\n \\n optional agentic_graph_only: - boolean \\n \\n Deprecated \\n Use exclude_processor_relations \\n Defined - in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:396 \\n \\n exclude_processor_relations? - \\n \\n optional exclude_processor_relations: boolean \\n \\n Defined in \\n - libs/sdk-core/src/lib/db/kb/kb.models.ts:397 \\n \\n hops \\n \\n hops: number - \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:391 \\n \\n - name \\n \\n name: GRAPH \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:390 - \\n \\n query_entity_detection? \\n \\n optional query_entity_detection: predict - \\\\| suggest \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:400 - \\n \\n relation_ranking? \\n \\n optional relation_ranking: generative \\\\| - reranker \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:398 - \\n \\n relation_text_as_paragraphs? \\n \\n optional relation_text_as_paragraphs: - boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:399 - \\n \\n top_k \\n \\n top_k: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:392\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":303,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":116,\"key\":\"\"},{\"start\":116,\"end\":303,\"key\":\"\"}]},{\"start\":303,\"end\":514,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":306,\"end\":514,\"key\":\"\"}]},{\"start\":514,\"end\":758,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":515,\"end\":621,\"key\":\"\"},{\"start\":621,\"end\":758,\"key\":\"\"}]},{\"start\":758,\"end\":1031,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":761,\"end\":910,\"key\":\"\"},{\"start\":910,\"end\":1031,\"key\":\"\"}]},{\"start\":1031,\"end\":1108,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1032,\"end\":1108,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:41:56.445021Z\",\"last_understanding\":\"2026-06-02T15:41:55.943903Z\",\"last_extract\":\"2026-06-02T15:41:55.317924Z\",\"last_processing_start\":\"2026-06-02T15:41:55.292975Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > GraphStrategy\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > GraphStrategy\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108\":{\"score\":0.5245689153671265,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" - top_k: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:392\",\"id\":\"d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":1031,\"end\":1108,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"8b3e0ef630a346d1b591143309db87ec\":{\"id\":\"8b3e0ef630a346d1b591143309db87ec\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensMetric-md\",\"title\":\"docs - > develop > js sdk > interfaces > NucliaTokensMetric\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"la\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:48.423622\",\"modified\":\"2026-06-02T15:41:27.179307\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) - \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensMetric\\n\\n# - Interface: NucliaTokensMetric\\n\\n## Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## - Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### - Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### - name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### - value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### - Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"364d53482d534a8d35dafdae78bb6482\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core - \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric - \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: - NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined - in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: - nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 - \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value - \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"UsageMetric.name\"],\"paragraphs\":[{\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":281,\"key\":\"\"}]},{\"start\":281,\"end\":511,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":282,\"end\":511,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:38.829539Z\",\"last_understanding\":\"2026-06-02T15:43:35.837370Z\",\"last_extract\":\"2026-06-02T15:43:35.557254Z\",\"last_processing_start\":\"2026-06-02T15:43:35.535837Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs - > develop > js sdk > interfaces > NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs - > develop > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\":{\"score\":0.5204078555107117,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\" - name: nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in - \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n value \\n \\n value: - number \\n \\n Overrides \\n UsageMetric.value \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":281,\"end\":511,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs - > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2025-10-27T14:35:08.325400\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + string: "{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.6116746664047241,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows you to embed the Agentic RAG search experience directly into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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+ RAG\":\"PRODUCT\",\"the Knowledge Box\":\"ORG\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\":[{\"start\":3046,\"end\":3054}]},{\"text\":\"the + Knowledge Box\",\"label\":\"ORG\",\"positions\":[{\"start\":4031,\"end\":4048}]},{\"text\":\"NucliaDB\",\"label\":\"ORG\",\"positions\":[{\"start\":4100,\"end\":4108}]},{\"text\":\"CSS\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":4476,\"end\":4479}]},{\"text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:04.788053Z\",\"last_understanding\":\"2026-06-09T08:08:04.303970Z\",\"last_extract\":\"2026-06-09T08:08:01.001532Z\",\"last_processing_start\":\"2026-06-09T08:08:00.904137Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/the + Knowledge Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"PRODUCT/Agentic RAG\":{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"entity\":\"Agentic + RAG\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\":3046,\"end\":3054}],\"entity\":\"Markdown\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.03200204813108039,\"score_type\":\"BOTH\",\"order\":1,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\",\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\",\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\n\\n# + Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**: + `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\n\\n> + **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\n\\n***\\n\\n### + tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"9a5a602361bcf49d7619fc3995646f40\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:21.098517Z\",\"last_understanding\":\"2026-07-14T12:49:20.847235Z\",\"last_extract\":\"2026-07-14T12:49:20.704679Z\",\"last_processing_start\":\"2026-07-14T12:49:20.686749Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageStructure\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\":{\"score\":0.014084507042253521,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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+ > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.029877369007803793,\"score_type\":\"BOTH\",\"order\":2,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.016666666666666666,\"score_type\":\"BM25\",\"order\":6,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/split-strategies\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + split-strategies\\ntitle: Use split strategies\\n---\\n\\n# Split strategies\\n\\nIn + some cases, default chunking methods aren't enough to handle the complexity + of your data.\\nImagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved.\\n\\nTo address this, you can define a split strategy that + controls how content is broken into chunks during ingestion.\\nSplit strategies + are reusable and configurable, allowing you to tailor the chunking process + to your specific needs.\\n\\nOnce defined, these strategies can be applied + across multiple ingestion jobs, giving you flexibility and consistency in + how your data is processed.\\n\\n## What is a split strategy?\\n\\nA split + strategy is a reusable configuration that defines how your data is divided + into chunks during ingestion. By customizing a split strategy, you can optimize + chunking for different types of content and use cases.\\n\\nKey components + of a split strategy include:\\n\\n- **Max paragraph size**: Sets the maximum + size (in characters or tokens) for each chunk.\\n- **Custom split**: Determines + the method used for splitting:\\n - **Manual splitting**: Splits content + based on a specified delimiter (default is `\\\"\\\\n\\\"`).\\n - **LLM splitting**: + Uses a language model to segment text intelligently, with optional rules to + guide the process.\\n\\nNote that you can only choose one splitting method + per strategy, and that you will have to set custom_split to 1 for manual splitting + or 2 for LLM splitting.\\n\\n## LLM Splitting\\n\\nLLM splitting leverages + a language model to intelligently segment text into meaningful chunks, going + beyond simple character or line-based splitting. This approach is ideal when + you need context-aware chunking, such as dividing content by sections, topics, + or other logical boundaries.\\n\\nYou can enable LLM splitting with default + settings, or customize it for your specific needs using these parameters:\\n\\n- + **LLM**: Specify the language model in the **generative_model** field. Optionally, + provide credentials for your chosen LLM provider in **user_keys**.\\n- **Rules**: + Define natural language instructions to guide how the text should be split. + For example, you might use rules like \\\"Split at each section heading\\\" + or \\\"Separate lists into individual items\\\".\\n\\nExample configuration:\\n\\n```json\\n{\\n + \ \\\"name\\\": \\\"llm\\\",\\n \\\"max_paragraph\\\": 1000,\\n + \ \\\"custom_split\\\": 2, \\n \\\"llm_split\\\": {\\n \\\"llm\\\": + {\\n \\\"generative_model\\\": \\\"chatgpt-azure-4o-mini\\\",\\n + \ },\\n \\\"rules\\\": [\\n \\\"Split + at each section heading\\\"\\n ]\\n },\\n }\\n```\\n\\nExperiment + with different rules and models to optimize chunking for your use case. Note + that using an LLM may increase processing time and cost, and could slightly + alter the extracted text.\\n\\n## Manual Splitting\\nManual splitting divides + text based on a specified delimiter, making it straightforward and efficient + for simpler chunking needs. This method is particularly useful when your content + has clear, consistent separators, such as paragraphs or bullet points.\\nYou + can customize manual splitting using the following parameters:\\n- **Splitter**: + Choose the character or string that indicates where to split the text. The + default is a double newline character (`\\\"\\\\n\\\\n\\\"`), but you can + set it to any delimiter that suits your content structure.\\nExample configuration:\\n\\n```json\\n + {\\n \\\"name\\\": \\\"manual\\\",\\n \\\"custom_split\\\": + 1,\\n \\\"manual_split\\\": {\\n \\\"splitter\\\": \\\"\\\\n\\\"\\n + \ }\\n }\\n``` \\n\\n## Strategy creation and management\\n\\nBefore + we can use a split strategy, we need to create it. We can create as many strategies + as we want, and just use the right one for each processing job. Once created + we can not modify our strategies, but we can delete them and inspect the ones + we have created for a given kb.\\n\\n### Dashboard\\n\\nTo create a strategy, + just go to the section **AI Models** and then to **Extract & split**. Then + you can click on the button **Create configuration** , next to **Split configurations** + to create a new strategy. Once there, you can fill in the necessary fields + with the desired configuration.\\nIn the same section, you can also see the + list of strategies you have created, and you can delete them once you no longer + need them.\\n\\n### CLI\\n\\nTo create a strategy using the CLI, you can use + the command `nuclia kb split_strategies add` with the desired configuration + in JSON format. You can also list the strategies you have created with `nuclia + kb split_strategies list`, and delete them with `nuclia kb split_strategies + delete`.\\n\\n```bash\\nnuclia kb split_strategies add --config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}'\\nnuclia kb split_strategies + list\\nnuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo create a strategy using the SDK, you can use the `add` method + of the `split_strategies` object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the `list` + method, and delete them with the `delete` method.\\n\\n```python\\nfrom nuclia + import sdk\\nsplit_strategies = sdk.NucliaKB().split_strategies\\nprint(split_strategies.list())\\nid + = split_strategies.add(config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}')\\nsplit_strategies.delete(id=id)\\n```\\n\\n## + Use split strategies for processing\\n\\n### Dashboard\\n\\nTo use a split + strategy for processing documents, just upload the document normally, enable + **Customize data extraction** and select the strategy you want to use in the + dropdown menu. Once the document is uploaded, it will be processed using the + selected strategy.\\n\\n### CLI\\n\\nTo use a split strategy for processing + documents using the CLI, you can use the command `nuclia kb upload file` with + the `--split_strategy` option, passing the ID of the strategy you want to + use.\\n\\n```bash\\nnuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n```bash\\nnuclia + kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo use a split strategy for processing documents using the SDK, you + can use the `file` method of the `NucliaUpload` object, passing the path to + the file and the ID of the strategy you want to use.\\n\\n```python\\nfrom + nuclia import sdk\\nupload = sdk.NucliaUpload()\\nupload.file(path=FILE_PATH, + split_strategy=\\\"1361c0c7-918a-4a7f-b44b-ba37437619fb\\\")\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"834cf2c7e30daced272772d79b257cf0\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: split-strategies \\n title: Use split strategies \\n \\n Split strategies + \\n In some cases, default chunking methods aren't enough to handle the complexity + of your data. \\n Imagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved. \\n To address this, you can define a split strategy that controls + how content is broken into chunks during ingestion. \\n Split strategies are + reusable and configurable, allowing you to tailor the chunking process to + your specific needs. \\n Once defined, these strategies can be applied across + multiple ingestion jobs, giving you flexibility and consistency in how your + data is processed. \\n What is a split strategy? \\n A split strategy is a + reusable configuration that defines how your data is divided into chunks during + ingestion. By customizing a split strategy, you can optimize chunking for + different types of content and use cases. \\n Key components of a split strategy + include: \\n \\n Max paragraph size: Sets the maximum size (in characters + or tokens) for each chunk. \\n Custom split: Determines the method used for + splitting: \\n Manual splitting: Splits content based on a specified delimiter + (default is \\\\n ). \\n LLM splitting: Uses a language model to segment text + intelligently, with optional rules to guide the process. \\n \\n Note that + you can only choose one splitting method per strategy, and that you will have + to set custom_split to 1 for manual splitting or 2 for LLM splitting. \\n + LLM Splitting \\n LLM splitting leverages a language model to intelligently + segment text into meaningful chunks, going beyond simple character or line-based + splitting. This approach is ideal when you need context-aware chunking, such + as dividing content by sections, topics, or other logical boundaries. \\n + You can enable LLM splitting with default settings, or customize it for your + specific needs using these parameters: \\n \\n LLM: Specify the language model + in the generative_model field. Optionally, provide credentials for your chosen + LLM provider in user_keys. \\n Rules: Define natural language instructions + to guide how the text should be split. For example, you might use rules like + Split at each section heading or Separate lists into individual items . \\n + \\n Example configuration: \\n json \\n { \\n name : llm , \\n max_paragraph + : 1000, \\n custom_split : 2, \\n llm_split : { \\n llm : { \\n generative_model + : chatgpt-azure-4o-mini , \\n }, \\n rules : [ \\n Split at each section heading + \\n ] \\n }, \\n } \\n Experiment with different rules and models to optimize + chunking for your use case. Note that using an LLM may increase processing + time and cost, and could slightly alter the extracted text. \\n Manual Splitting + \\n Manual splitting divides text based on a specified delimiter, making it + straightforward and efficient for simpler chunking needs. This method is particularly + useful when your content has clear, consistent separators, such as paragraphs + or bullet points. \\n You can customize manual splitting using the following + parameters: \\n - Splitter: Choose the character or string that indicates + where to split the text. The default is a double newline character ( \\\\n\\\\n + ), but you can set it to any delimiter that suits your content structure. + \\n Example configuration: \\n json \\n { \\n name : manual , \\n custom_split + : 1, \\n manual_split : { \\n splitter : \\\\n \\n } \\n } \\n Strategy creation + and management \\n Before we can use a split strategy, we need to create it. + We can create as many strategies as we want, and just use the right one for + each processing job. Once created we can not modify our strategies, but we + can delete them and inspect the ones we have created for a given kb. \\n Dashboard + \\n To create a strategy, just go to the section AI Models and then to Extract + & split. Then you can click on the button Create configuration , next to Split + configurations to create a new strategy. Once there, you can fill in the necessary + fields with the desired configuration. \\n In the same section, you can also + see the list of strategies you have created, and you can delete them once + you no longer need them. \\n CLI \\n To create a strategy using the CLI, you + can use the command nuclia kb split_strategies add with the desired configuration + in JSON format. You can also list the strategies you have created with nuclia + kb split_strategies list, and delete them with nuclia kb split_strategies + delete. \\n bash \\n nuclia kb split_strategies add --config='{ name : strategy1 + , custom_split : 1, manual_split : { splitter : \\\\n }}' \\n nuclia kb split_strategies + list \\n nuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To create a strategy using the SDK, you can use the add method + of the split_strategies object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the list method, + and delete them with the delete method. \\n python \\n from nuclia import + sdk \\n split_strategies = sdk.NucliaKB().split_strategies \\n print(split_strategies.list()) + \\n id = split_strategies.add(config='{ name : strategy1 , custom_split : + 1, manual_split : { splitter : \\\\n }}') \\n split_strategies.delete(id=id) + \\n Use split strategies for processing \\n Dashboard \\n To use a split strategy + for processing documents, just upload the document normally, enable Customize + data extraction and select the strategy you want to use in the dropdown menu. + Once the document is uploaded, it will be processed using the selected strategy. + \\n CLI \\n To use a split strategy for processing documents using the CLI, + you can use the command nuclia kb upload file with the --split_strategy option, + passing the ID of the strategy you want to use. \\n bash \\n nuclia kb upload + file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n bash \\n nuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To use a split strategy for processing documents using the SDK, + you can use the file method of the NucliaUpload object, passing the path to + the file and the ID of the strategy you want to use. \\n python \\n from nuclia + import sdk \\n upload = sdk.NucliaUpload() \\n upload.file(path=FILE_PATH, + split_strategy= 1361c0c7-918a-4a7f-b44b-ba37437619fb 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Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n 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interfaces > NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\":{\"score\":0.015151515151515152,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\" + name: nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n value \\n \\n value: + number \\n \\n Overrides \\n UsageMetric.value \\n Defined in \\n 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UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\n\\n# + Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n***\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\\_PROCESSED**: + `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n***\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\n\\n***\\n\\n### + PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"pages_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n***\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**: `\\\"paragraphs_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n***\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\n\\n***\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**: `\\\"resources_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n***\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**: `\\\"searches_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n***\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\n\\n***\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**: `\\\"suggestions_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\":{\"score\":0.01639344262295082,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"666b3a9d01f74323b6ca6c9939834bec\":{\"id\":\"666b3a9d01f74323b6ca6c9939834bec\",\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\",\"title\":\"docs + > develop > js sdk > enums > StatsType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"sv\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.005040\",\"modified\":\"2026-06-09T08:13:37.365717\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/StatsType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / StatsType\\n\\n# Enumeration: StatsType\\n\\n**`Deprecated`**\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](StatsType.md#ai_tokens_used)\\n- + [BYTES](StatsType.md#bytes)\\n- [CHARS](StatsType.md#chars)\\n- [DOCS\\\\_NO\\\\_MEDIA](StatsType.md#docs_no_media)\\n- + [MEDIA\\\\_SECONDS](StatsType.md#media_seconds)\\n- [PAGES](StatsType.md#pages)\\n- + [PROCESSING\\\\_TIME](StatsType.md#processing_time)\\n- [RESOURCES](StatsType.md#resources)\\n- + [SEARCHES](StatsType.md#searches)\\n- [SUGGESTIONS](StatsType.md#suggestions)\\n- + [TRAIN\\\\_SECONDS](StatsType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n___\\n\\n### + BYTES\\n\\n\u2022 **BYTES** = ``\\\"bytes\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n___\\n\\n### + CHARS\\n\\n\u2022 **CHARS** = ``\\\"chars\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n___\\n\\n### + DOCS\\\\_NO\\\\_MEDIA\\n\\n\u2022 **DOCS\\\\_NO\\\\_MEDIA** = ``\\\"docs_no_media\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\n\\n\u2022 **MEDIA\\\\_SECONDS** = ``\\\"media_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n___\\n\\n### + PAGES\\n\\n\u2022 **PAGES** = ``\\\"pages\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n___\\n\\n### + PROCESSING\\\\_TIME\\n\\n\u2022 **PROCESSING\\\\_TIME** = ``\\\"processing_time\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n___\\n\\n### + RESOURCES\\n\\n\u2022 **RESOURCES** = ``\\\"resources\\\"``\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n___\\n\\n### + SEARCHES\\n\\n\u2022 **SEARCHES** = ``\\\"searches\\\"``\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n___\\n\\n### + SUGGESTIONS\\n\\n\u2022 **SUGGESTIONS** = ``\\\"suggestions\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"13077c5570e4437fb58c1c5ab75d310b\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated \\n Table + of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES \\n CHARS + \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME \\n RESOURCES + \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration Members + \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\n \u2022 + BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 + \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198 + \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n + \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 + \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 + \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 + RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 + \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1386,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:40.544839Z\",\"last_understanding\":\"2026-06-09T08:13:38.735743Z\",\"last_extract\":\"2026-06-09T08:13:38.231630Z\",\"last_processing_start\":\"2026-06-09T08:13:38.196724Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > StatsType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > StatsType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\":{\"score\":0.014925373134328358,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\"@nuclia/core + / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated \\n Table + of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES \\n CHARS + \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME \\n RESOURCES + \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration Members + \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\n \u2022 + BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 + \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198 + \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\n + \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 + \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 + \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES \\n \u2022 + RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201 + \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.01639344262295082,\"score_type\":\"BM25\",\"order\":8,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.014705882352941176,\"score_type\":\"BM25\",\"order\":14,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\",\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.016666666666666666,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"7d8e7683f33b4b0b8ee731110bf45edb\":{\"id\":\"7d8e7683f33b4b0b8ee731110bf45edb\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-AskTokens-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > AskTokens\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:47.782882\",\"modified\":\"2026-07-14T12:48:06.281745\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/AskTokens\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / AskTokens\\n\\n# Interface: AskTokens\\n\\n## Properties\\n\\n### input\\n\\n> + **input**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:141](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L141)\\n\\n***\\n\\n### + input\\\\_nuclia\\n\\n> **input\\\\_nuclia**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:143](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L143)\\n\\n***\\n\\n### + output\\n\\n> **output**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:142](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L142)\\n\\n***\\n\\n### + output\\\\_nuclia\\n\\n> **output\\\\_nuclia**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:144](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L144)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"5e477e1d8dad0481f7f21bc261ad2078\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / AskTokens \\n Interface: AskTokens + \\n Properties \\n input \\n \\n input: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:141 + \\n \\n input_nuclia \\n \\n input_nuclia: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:143 + \\n \\n output \\n \\n output: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:142 + \\n \\n output_nuclia \\n \\n output_nuclia: number \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:144\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":501,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":501,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:22.066021Z\",\"last_understanding\":\"2026-07-14T12:48:21.072972Z\",\"last_extract\":\"2026-07-14T12:48:20.730655Z\",\"last_processing_start\":\"2026-07-14T12:48:20.711935Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > AskTokens\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > AskTokens\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"7d8e7683f33b4b0b8ee731110bf45edb/t/page/0-501\":{\"score\":0.014705882352941176,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / AskTokens \\n Interface: AskTokens + \\n Properties \\n input \\n \\n input: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:141 + \\n \\n input_nuclia \\n \\n input_nuclia: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:143 + \\n \\n output \\n \\n output: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:142 + \\n \\n output_nuclia \\n \\n output_nuclia: number \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:144\",\"id\":\"7d8e7683f33b4b0b8ee731110bf45edb/t/page/0-501\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":501,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.014492753623188406,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\":{\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\",\"slug\":\"docs-zone-api-mdx\",\"title\":\"docs + > zone api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:43.137585\",\"modified\":\"2026-06-09T08:18:13.619843\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/zone-api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + knowledgebox-zone-api\\nhide_title: true\\ntitle: Knowledge Box & Zone API\\n---\\n\\nimport + ApiDocMdx from '@theme/ApiDocMdx';\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"4060ad5ed8c024c99b505a02edfce76f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: knowledgebox-zone-api \\n hide_title: true \\n title: Knowledge Box + & Zone API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` \\n + @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] > .container + { \\n max-width: 100% !important; \\n } \\n [class ='docItemCol'] { \\n max-width: + 100% !important; \\n } \\n } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":252,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":209,\"key\":\"\"},{\"start\":209,\"end\":240,\"key\":\"\"},{\"start\":240,\"end\":252,\"key\":\"\"}]},{\"start\":252,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":253,\"end\":300,\"key\":\"\"},{\"start\":300,\"end\":329,\"key\":\"\"}]}],\"ner\":{\"ApiDocMdx\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"ApiDocMdx\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":93,\"end\":102}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:20.481514Z\",\"last_understanding\":\"2026-06-09T08:18:19.956554Z\",\"last_extract\":\"2026-06-09T08:18:18.741090Z\",\"last_processing_start\":\"2026-06-09T08:18:18.713420Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/ApiDocMdx\":{\"position\":[{\"start\":93,\"end\":102}],\"entity\":\"ApiDocMdx\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > zone api.\",\"extracted\":{\"text\":{\"text\":\"docs > zone api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/0-252\":{\"score\":0.014285714285714285,\"score_type\":\"BM25\",\"order\":17,\"text\":\" + \\n id: knowledgebox-zone-api \\n hide_title: true \\n title: Knowledge Box + & Zone API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` \\n + @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] > .container + { \\n max-width: 100% !important; \\n\",\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/0-252\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":252,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4c90aa2f4b77410e9d26d981d9852823\":{\"id\":\"4c90aa2f4b77410e9d26d981d9852823\",\"slug\":\"docs-develop-dotnet-sdk-ISearchService-md\",\"title\":\"docs + > develop > dotnet sdk > ISearchService\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T16:03:50.526195\",\"modified\":\"2026-06-09T08:17:37.193513\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/dotnet-sdk/ISearchService\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + ISearchService Documentation\\n\\nThe `ISearchService` provides AI-powered + search, question-answering, and retrieval capabilities across Knowledge Boxes, + including both synchronous and streaming responses.\\n\\n## Methods\\n\\n### + AskAsync\\n\\nAsk a question to the Knowledge Box and get a synchronous response + with RAG (Retrieval-Augmented Generation).\\n\\n**Signature:**\\n```csharp\\nTask> + AskAsync(AskRequest request)\\n```\\n\\n**Parameters:**\\n- `request` (AskRequest, + required): The ask request containing query and configuration\\n\\n**Returns:** + Complete response with answer, citations, and metadata\\n\\n**Example:**\\n```csharp\\nvar + askRequest = new AskRequest\\n{\\n Query = \\\"What are the main benefits + of using this system?\\\"\\n};\\n\\nvar response = await searchService.AskAsync(askRequest);\\nif + (response.IsSuccessful)\\n{\\n var result = response.Data;\\n Console.WriteLine($\\\"Answer: + {result.Answer}\\\");\\n Console.WriteLine($\\\"Citations: {result.Citations?.Count + ?? 0}\\\");\\n}\\n```\\n\\n---\\n\\n### AskStreamAsync (with AskRequest)\\n\\nAsk + a question to the Knowledge Box and get a streaming response.\\n\\n**Signature:**\\n```csharp\\nIAsyncEnumerable + AskStreamAsync(AskRequest request)\\n```\\n\\n**Parameters:**\\n- `request` + (AskRequest, required): The ask request containing query and configuration\\n\\n**Returns:** + Streaming updates as they become available\\n\\n**Example:**\\n```csharp\\nvar + askRequest = new AskRequest(query) { Citations = new Citations(true) };\\n\\nvar + result = client.Search.AskStreamAsync(askRequest);\\nstring streamingResponse + = string.Empty;\\nDictionary? allResources;\\nIEnumerable<(int + Number, KeyValuePair Item)>? citations;\\nawait foreach (var + response in result)\\n \\t{\\n \\t\\tswitch (response.Item)\\n \\t\\t{\\n + \\t\\t\\tcase AnswerContent answer:\\n \\t\\t\\t\\tstreamingResponse += answer.Text;\\n + \\t\\t\\t\\tbreak;\\n \\t\\t\\tcase RetrievalContent retrieval:\\n \\t\\t\\t\\tallResources + = retrieval.Results?.Resources;\\n \\t\\t\\t\\tbreak;\\n \\t\\t\\tcase CitationsContent + c:\\n \\t\\t\\t\\tcitations = c.Citations.Index(); // Index is optional, this + adds an numeric index to the citations.\\n \\t\\t\\t\\tbreak;\\n \\t\\t}\\n + \\t}\\n```\\n\\n---\\n\\n### AskStreamAsync (with query string)\\n\\nAsk a + question using default options and get a streaming response.\\n\\n**Signature:**\\n```csharp\\nIAsyncEnumerable + AskStreamAsync(string query)\\n```\\n\\n**Parameters:**\\n- `query` (string, + required): The question to ask\\n\\n**Returns:** Streaming response updates\\n\\n**Example:**\\n```csharp\\nstring + streamingResponse = string.Empty;\\nawait foreach (var update in searchService.AskStreamAsync(\\\"How + do I configure authentication?\\\"))\\n{\\n if (update.Item is AnswerContent + answerContent)\\n {\\n streamingResponse += answerContent.Text;\\n + \ }\\n}\\n```\\n\\n---\\n\\n### FindAsync\\n\\nFind resources in the Knowledge + Box with detailed paragraph-level search results.\\n\\n**Signature:**\\n```csharp\\nTask> + FindAsync(FindRequest request)\\n```\\n\\n**Parameters:**\\n- `request` (FindRequest, + required): The find request with search parameters\\n\\n**Returns:** Detailed + search results with paragraph-level information\\n\\n**Example:**\\n```csharp\\nvar + findRequest = new FindRequest\\n{\\n Query = \\\"security implementation\\\"\\n};\\n\\nvar + response = await searchService.FindAsync(findRequest);\\nif (response.IsSuccessful)\\n{\\n + \ var results = response.Data;\\n Console.WriteLine($\\\"Found {results.Total} + results\\\");\\n foreach (var resource in results.Resources)\\n {\\n + \ Console.WriteLine($\\\"Resource: {resource.Title}\\\");\\n }\\n}\\n```\\n\\n---\\n\\n### + CatalogAsync\\n\\nList resources in the Knowledge Box using POST with JSON + body.\\n\\n**Signature:**\\n```csharp\\nTask> + CatalogAsync(CatalogRequest request)\\n```\\n\\n**Parameters:**\\n- `request` + (CatalogRequest, required): The catalog request parameters\\n\\n**Returns:** + Resource catalog with filtering and pagination\\n\\n**Example:**\\n```csharp\\nvar + catalogRequest = new CatalogRequest\\n{\\n // Configure catalog parameters\\n};\\n\\nvar + response = await searchService.CatalogAsync(catalogRequest);\\nif (response.IsSuccessful)\\n{\\n + \ var catalog = response.Data;\\n Console.WriteLine($\\\"Catalog contains + {catalog.Total} resources\\\");\\n}\\n```\\n\\n---\\n\\n### SuggestAsync\\n\\nRetrieve + paragraph and entity suggestions for a query.\\n\\n**Signature:**\\n```csharp\\nTask> + SuggestAsync(string query)\\n```\\n\\n**Parameters:**\\n- `query` (string, + required): The query to get suggestions for\\n\\n**Returns:** Suggestions + including paragraphs and entities\\n\\n**Example:**\\n```csharp\\nvar response + = await searchService.SuggestAsync(\\\"authentication\\\");\\nif (response.IsSuccessful)\\n{\\n + \ var suggestions = response.Data;\\n Console.WriteLine($\\\"Found {suggestions.Paragraphs?.Count + ?? 0} paragraph suggestions\\\");\\n Console.WriteLine($\\\"Found {suggestions.Entities?.Count + ?? 0} entity suggestions\\\");\\n}\\n```\\n\\n---\\n\\n### SummarizeAsync\\n\\nSummarize + a set of resources using AI.\\n\\n**Signature:**\\n```csharp\\nTask> + SummarizeAsync(SummarizeRequest request)\\n```\\n\\n**Parameters:**\\n- `request` + (SummarizeRequest, required): The summarization request with resources and + options\\n\\n**Returns:** AI-generated summary of the specified resources\\n\\n**Example:**\\n```csharp\\nvar + summarizeRequest = new SummarizeRequest\\n{\\n Resources = new[] { \\\"resource-1\\\", + \\\"resource-2\\\" }\\n};\\n\\nvar response = await searchService.SummarizeAsync(summarizeRequest);\\nif + (response.IsSuccessful)\\n{\\n var summary = response.Data;\\n Console.WriteLine($\\\"Summary: + {summary.Summary}\\\");\\n}\\n```\\n\\n---\\n\\n### GraphSearchAsync\\n\\nSearch + Knowledge Box graph using triplets (vertex-edge-vertex).\\n\\n**Signature:**\\n```csharp\\nTask> + GraphSearchAsync(GraphSearchRequest request)\\n```\\n\\n**Parameters:**\\n- + `request` (GraphSearchRequest, required): Graph search parameters\\n\\n**Returns:** + Graph search results with relationships\\n\\n**Example:**\\n```csharp\\nvar + graphRequest = new GraphSearchRequest\\n{\\n Query = \\\"security protocols\\\"\\n};\\n\\nvar + response = await searchService.GraphSearchAsync(graphRequest);\\nif (response.IsSuccessful)\\n{\\n + \ var results = response.Data;\\n Console.WriteLine($\\\"Found {results.Paths?.Count + ?? 0} graph paths\\\");\\n}\\n```\\n\\n---\\n\\n### GraphNodesSearchAsync\\n\\nSearch + Knowledge Box graph nodes (vertices).\\n\\n**Signature:**\\n```csharp\\nTask> + GraphNodesSearchAsync(GraphNodesSearchRequest request)\\n```\\n\\n**Parameters:**\\n- + `request` (GraphNodesSearchRequest, required): Graph nodes search parameters\\n\\n**Returns:** + Graph nodes matching the search criteria\\n\\n**Example:**\\n```csharp\\nvar + nodesRequest = new GraphNodesSearchRequest\\n{\\n Query = \\\"user authentication\\\"\\n};\\n\\nvar + response = await searchService.GraphNodesSearchAsync(nodesRequest);\\nif (response.IsSuccessful)\\n{\\n + \ var nodes = response.Data;\\n Console.WriteLine($\\\"Found {nodes.Nodes?.Count + ?? 0} matching nodes\\\");\\n}\\n```\\n\\n---\\n\\n### GraphRelationsSearchAsync\\n\\nSearch + Knowledge Box graph relations (edges).\\n\\n**Signature:**\\n```csharp\\nTask> + GraphRelationsSearchAsync(GraphRelationsSearchRequest request)\\n```\\n\\n**Parameters:**\\n- + `request` (GraphRelationsSearchRequest, required): Graph relations search + parameters\\n\\n**Returns:** Graph relations matching the search criteria\\n\\n**Example:**\\n```csharp\\nvar + relationsRequest = new GraphRelationsSearchRequest\\n{\\n Query = \\\"implements\\\"\\n};\\n\\nvar + response = await searchService.GraphRelationsSearchAsync(relationsRequest);\\nif + (response.IsSuccessful)\\n{\\n var relations = response.Data;\\n Console.WriteLine($\\\"Found + {relations.Relations?.Count ?? 0} matching relations\\\");\\n}\\n```\\n\\n---\\n\\n### + SendFeedbackAsync\\n\\nSend feedback for a search operation to improve future + results.\\n\\n**Signature:**\\n```csharp\\nTask> SendFeedbackAsync(FeedbackRequest + request)\\n```\\n\\n**Parameters:**\\n- `request` (FeedbackRequest, required): + Feedback data including operation ID and rating\\n\\n**Returns:** True if + feedback was submitted successfully\\n\\n**Example:**\\n```csharp\\nvar feedbackRequest + = new FeedbackRequest\\n{\\n Ident = \\\"operation-123\\\",\\n Good + = true,\\n Task = TaskType.Question\\n};\\n\\nvar response = await searchService.SendFeedbackAsync(feedbackRequest);\\nif + (response.IsSuccessful)\\n{\\n Console.WriteLine(\\\"Feedback submitted + successfully\\\");\\n}\\n```\\n\\n---\\n\\n### SearchAsync\\n\\nSearch Knowledge + Box and retrieve separate results for documents, paragraphs, and sentences.\\n\\n**Signature:**\\n```csharp\\nTask> + SearchAsync(SearchRequest request)\\n```\\n\\n**Parameters:**\\n- `request` + (SearchRequest, required): Comprehensive search request parameters\\n\\n**Returns:** + Structured search results with multiple result types\\n\\n**Example:**\\n```csharp\\nvar + searchRequest = new SearchRequest\\n{\\n Query = \\\"API documentation\\\"\\n};\\n\\nvar + response = await searchService.SearchAsync(searchRequest);\\nif (response.IsSuccessful)\\n{\\n + \ var results = response.Data;\\n Console.WriteLine($\\\"Documents: {results.Documents?.Count + ?? 0}\\\");\\n Console.WriteLine($\\\"Paragraphs: {results.Paragraphs?.Count + ?? 0}\\\");\\n Console.WriteLine($\\\"Sentences: {results.Sentences?.Count + ?? 0}\\\");\\n}\\n```\\n\\n---\\n\\n### AskResourceAsync\\n\\nAsk a question + to a specific resource and get a synchronous response.\\n\\n**Signature:**\\n```csharp\\nTask> + AskResourceAsync(string resourceId, AskRequest request)\\n```\\n\\n**Parameters:**\\n- + `resourceId` (string, required): The unique identifier of the resource\\n- + `request` (AskRequest, required): The ask request parameters\\n\\n**Returns:** + Answer specific to the resource\\n\\n**Example:**\\n```csharp\\nvar askRequest + = new AskRequest\\n{\\n Query = \\\"What does this document explain?\\\"\\n};\\n\\nvar + response = await searchService.AskResourceAsync(\\\"my-resource-id\\\", askRequest);\\nif + (response.IsSuccessful)\\n{\\n var result = response.Data;\\n Console.WriteLine($\\\"Resource-specific + answer: {result.Answer}\\\");\\n}\\n```\\n\\n---\\n\\n### AskResourceStreamAsync + (with AskRequest)\\n\\nAsk a question to a specific resource and get a streaming + response.\\n\\n**Signature:**\\n```csharp\\nIAsyncEnumerable + AskResourceStreamAsync(string resourceId, AskRequest request)\\n```\\n\\n**Parameters:**\\n- + `resourceId` (string, required): The unique identifier of the resource\\n- + `request` (AskRequest, required): The ask request parameters\\n\\n**Returns:** + Streaming response specific to the resource\\n\\n**Example:**\\n```csharp\\nvar + askRequest = new AskRequest\\n{\\n Query = \\\"Summarize this document's + main points\\\"\\n};\\n\\nawait foreach (var update in searchService.AskResourceStreamAsync(\\\"my-resource-id\\\", + askRequest))\\n{\\n if (update.Item is AnswerContent answerContent && !string.IsNullOrEmpty(answerContent.Text))\\n + \ {\\n Console.Write(answerContent.Text);\\n }\\n}\\n```\\n\\n---\\n\\n### + AskResourceStreamAsync (with query string)\\n\\nAsk a question to a specific + resource with simplified query.\\n\\n**Signature:**\\n```csharp\\nIAsyncEnumerable + AskResourceStreamAsync(string resourceId, string query)\\n```\\n\\n**Parameters:**\\n- + `resourceId` (string, required): The unique identifier of the resource\\n- + `query` (string, required): The question to ask\\n\\n**Returns:** Streaming + response for the resource\\n\\n**Example:**\\n```csharp\\nawait foreach (var + update in searchService.AskResourceStreamAsync(\\\"my-resource-id\\\", \\\"What + are the key features?\\\"))\\n{\\n if (update.Item is AnswerContent answerContent + && !string.IsNullOrEmpty(answerContent.Text))\\n {\\n Console.Write(answerContent.Text);\\n + \ }\\n}\\n```\\n\\n---\\n\\n### AskResourceBySlugAsync\\n\\nAsk a question + about a specific resource by slug synchronously.\\n\\n**Signature:**\\n```csharp\\nTask> + AskResourceBySlugAsync(string slug, AskRequest request)\\n```\\n\\n**Parameters:**\\n- + `slug` (string, required): The slug identifier of the resource\\n- `request` + (AskRequest, required): The ask request parameters\\n\\n**Returns:** Answer + specific to the resource identified by slug\\n\\n**Example:**\\n```csharp\\nvar + askRequest = new AskRequest\\n{\\n Query = \\\"What is the purpose of this + document?\\\"\\n};\\n\\nvar response = await searchService.AskResourceBySlugAsync(\\\"my-document-slug\\\", + askRequest);\\nif (response.IsSuccessful)\\n{\\n var result = response.Data;\\n + \ Console.WriteLine($\\\"Answer: {result.Answer}\\\");\\n}\\n```\\n\\n---\\n\\n### + PredictProxyAsync\\n\\nProxy endpoint that forwards requests to the Predict + API with Knowledge Box configuration.\\n\\n**Signature:**\\n```csharp\\nTask> + PredictProxyAsync(PredictProxiedEndpoints endpoint)\\n```\\n\\n**Parameters:**\\n- + `endpoint` (PredictProxiedEndpoints, required): The predict endpoint to call\\n\\n**Returns:** + Response from the predict API\\n\\n**Example:**\\n```csharp\\nvar response + = await searchService.PredictProxyAsync(PredictProxiedEndpoints.Tokens);\\nif + (response.IsSuccessful)\\n{\\n var result = response.Data;\\n Console.WriteLine($\\\"Predict + API response: {result}\\\");\\n}\\n```\\n\\n---\\n\\n### SearchResourceAsync\\n\\nSearch + within a specific resource by ID.\\n\\n**Signature:**\\n```csharp\\nTask> + SearchResourceAsync(string resourceId, string query)\\n```\\n\\n**Parameters:**\\n- + `resourceId` (string, required): The unique identifier of the resource\\n- + `query` (string, required): The search query\\n\\n**Returns:** Search results + within the specified resource\\n\\n**Example:**\\n```csharp\\nvar response + = await searchService.SearchResourceAsync(\\\"my-resource-id\\\", \\\"configuration + settings\\\");\\nif (response.IsSuccessful)\\n{\\n var results = response.Data;\\n + \ Console.WriteLine($\\\"Found {results.Total} matches in resource\\\");\\n + \ foreach (var paragraph in results.Paragraphs ?? [])\\n {\\n Console.WriteLine($\\\"Match: + {paragraph.Text}\\\");\\n }\\n}\\n```\\n\\n## Usage Notes\\n\\n- All methods + are asynchronous and return `ApiResponse` wrappers\\n- Check `response.IsSuccessful` + before accessing `response.Data`\\n- Streaming methods return `IAsyncEnumerable` + for real-time response processing\\n- For streaming examples, add `using Progress.Nuclia.Model.Streaming;` + to access `AnswerContent`\\n- Ask methods provide RAG capabilities with citations + and source information\\n- Graph search methods enable relationship-based + queries\\n- Resource-specific methods limit scope to individual documents\\n- + Feedback helps improve AI model performance over time\",\"format\":\"MARKDOWN\",\"md5\":\"2f83b67777d41ea27cd89e74129405eb\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"ISearchService + Documentation \\n The ISearchService provides AI-powered search, question-answering, + and retrieval capabilities across Knowledge Boxes, including both synchronous + and streaming responses. \\n Methods \\n AskAsync \\n Ask a question to the + Knowledge Box and get a synchronous response with RAG (Retrieval-Augmented + Generation). \\n Signature: \\n csharp \\n Task> AskAsync(AskRequest request) + \\n Parameters: \\n - request (AskRequest, required): The ask request containing + query and configuration \\n Returns: Complete response with answer, citations, + and metadata \\n Example: \\n ```csharp \\n var askRequest = new AskRequest + \\n { \\n Query = What are the main benefits of using this system? \\n }; + \\n var response = await searchService.AskAsync(askRequest); \\n if (response.IsSuccessful) + \\n { \\n var result = response.Data; \\n Console.WriteLine($ Answer: {result.Answer} + ); \\n Console.WriteLine($ Citations: {result.Citations?.Count ?? 0} ); \\n + } \\n ``` \\n \\n AskStreamAsync (with AskRequest) \\n Ask a question to the + Knowledge Box and get a streaming response. \\n Signature: \\n csharp \\n + IAsyncEnumerable AskStreamAsync(AskRequest request) \\n Parameters: \\n - + request (AskRequest, required): The ask request containing query and configuration + \\n Returns: Streaming updates as they become available \\n Example: \\n ```csharp + \\n var askRequest = new AskRequest(query) { Citations = new Citations(true) + }; \\n var result = client.Search.AskStreamAsync(askRequest); \\n string streamingResponse + = string.Empty; \\n Dictionary? allResources; \\n IEnumerable? citations; + \\n await foreach (var response in result) \\n { \\n switch (response.Item) + \\n { \\n case AnswerContent answer: \\n streamingResponse += answer.Text; + \\n break; \\n case RetrievalContent retrieval: \\n allResources = retrieval.Results?.Resources; + \\n break; \\n case CitationsContent c: \\n citations = c.Citations.Index(); + // Index is optional, this adds an numeric index to the citations. \\n break; + \\n } \\n } \\n ``` \\n \\n AskStreamAsync (with query string) \\n Ask a question + using default options and get a streaming response. \\n Signature: \\n csharp + \\n IAsyncEnumerable AskStreamAsync(string query) \\n Parameters: \\n - query + (string, required): The question to ask \\n Returns: Streaming response updates + \\n Example: \\n csharp \\n string streamingResponse = string.Empty; \\n await + foreach (var update in searchService.AskStreamAsync( How do I configure authentication? + )) \\n { \\n if (update.Item is AnswerContent answerContent) \\n { \\n streamingResponse + += answerContent.Text; \\n } \\n } \\n \\n FindAsync \\n Find resources in + the Knowledge Box with detailed paragraph-level search results. \\n Signature: + \\n csharp \\n Task> FindAsync(FindRequest request) \\n Parameters: \\n - + request (FindRequest, required): The find request with search parameters \\n + Returns: Detailed search results with paragraph-level information \\n Example: + \\n ```csharp \\n var findRequest = new FindRequest \\n { \\n Query = security + implementation \\n }; \\n var response = await searchService.FindAsync(findRequest); + \\n if (response.IsSuccessful) \\n { \\n var results = response.Data; \\n + Console.WriteLine($ Found {results.Total} results ); \\n foreach (var resource + in results.Resources) \\n { \\n Console.WriteLine($ Resource: {resource.Title} + ); \\n } \\n } \\n ``` \\n \\n CatalogAsync \\n List resources in the Knowledge + Box using POST with JSON body. \\n Signature: \\n csharp \\n Task> CatalogAsync(CatalogRequest + request) \\n Parameters: \\n - request (CatalogRequest, required): The catalog + request parameters \\n Returns: Resource catalog with filtering and pagination + \\n Example: \\n ```csharp \\n var catalogRequest = new CatalogRequest \\n + { \\n // Configure catalog parameters \\n }; \\n var response = await searchService.CatalogAsync(catalogRequest); + \\n if (response.IsSuccessful) \\n { \\n var catalog = response.Data; \\n + Console.WriteLine($ Catalog contains {catalog.Total} resources ); \\n } \\n + ``` \\n \\n SuggestAsync \\n Retrieve paragraph and entity suggestions for + a query. \\n Signature: \\n csharp \\n Task> SuggestAsync(string query) \\n + Parameters: \\n - query (string, required): The query to get suggestions for + \\n Returns: Suggestions including paragraphs and entities \\n Example: \\n + csharp \\n var response = await searchService.SuggestAsync( authentication + ); \\n if (response.IsSuccessful) \\n { \\n var suggestions = response.Data; + \\n Console.WriteLine($ Found {suggestions.Paragraphs?.Count ?? 0} paragraph + suggestions ); \\n Console.WriteLine($ Found {suggestions.Entities?.Count + ?? 0} entity suggestions ); \\n } \\n \\n SummarizeAsync \\n Summarize a set + of resources using AI. \\n Signature: \\n csharp \\n Task> SummarizeAsync(SummarizeRequest + request) \\n Parameters: \\n - request (SummarizeRequest, required): The summarization + request with resources and options \\n Returns: AI-generated summary of the + specified resources \\n Example: \\n ```csharp \\n var summarizeRequest = + new SummarizeRequest \\n { \\n Resources = new[] { resource-1 , resource-2 + } \\n }; \\n var response = await searchService.SummarizeAsync(summarizeRequest); + \\n if (response.IsSuccessful) \\n { \\n var summary = response.Data; \\n + Console.WriteLine($ Summary: {summary.Summary} ); \\n } \\n ``` \\n \\n GraphSearchAsync + \\n Search Knowledge Box graph using triplets (vertex-edge-vertex). \\n Signature: + \\n csharp \\n Task> GraphSearchAsync(GraphSearchRequest request) \\n Parameters: + \\n - request (GraphSearchRequest, required): Graph search parameters \\n + Returns: Graph search results with relationships \\n Example: \\n ```csharp + \\n var graphRequest = new GraphSearchRequest \\n { \\n Query = security protocols + \\n }; \\n var response = await searchService.GraphSearchAsync(graphRequest); + \\n if (response.IsSuccessful) \\n { \\n var results = response.Data; \\n + Console.WriteLine($ Found {results.Paths?.Count ?? 0} graph paths ); \\n } + \\n ``` \\n \\n GraphNodesSearchAsync \\n Search Knowledge Box graph nodes + (vertices). \\n Signature: \\n csharp \\n Task> GraphNodesSearchAsync(GraphNodesSearchRequest + request) \\n Parameters: \\n - request (GraphNodesSearchRequest, required): + Graph nodes search parameters \\n Returns: Graph nodes matching the search + criteria \\n Example: \\n ```csharp \\n var nodesRequest = new GraphNodesSearchRequest + \\n { \\n Query = user authentication \\n }; \\n var response = await searchService.GraphNodesSearchAsync(nodesRequest); + \\n if (response.IsSuccessful) \\n { \\n var nodes = response.Data; \\n Console.WriteLine($ + Found {nodes.Nodes?.Count ?? 0} matching nodes ); \\n } \\n ``` \\n \\n GraphRelationsSearchAsync + \\n Search Knowledge Box graph relations (edges). \\n Signature: \\n csharp + \\n Task> GraphRelationsSearchAsync(GraphRelationsSearchRequest request) \\n + Parameters: \\n - request (GraphRelationsSearchRequest, required): Graph relations + search parameters \\n Returns: Graph relations matching the search criteria + \\n Example: \\n ```csharp \\n var relationsRequest = new GraphRelationsSearchRequest + \\n { \\n Query = implements \\n }; \\n var response = await searchService.GraphRelationsSearchAsync(relationsRequest); + \\n if (response.IsSuccessful) \\n { \\n var relations = response.Data; \\n + Console.WriteLine($ Found {relations.Relations?.Count ?? 0} matching relations + ); \\n } \\n ``` \\n \\n SendFeedbackAsync \\n Send feedback for a search + operation to improve future results. \\n Signature: \\n csharp \\n Task> SendFeedbackAsync(FeedbackRequest + request) \\n Parameters: \\n - request (FeedbackRequest, required): Feedback + data including operation ID and rating \\n Returns: True if feedback was submitted + successfully \\n Example: \\n ```csharp \\n var feedbackRequest = new FeedbackRequest + \\n { \\n Ident = operation-123 , \\n Good = true, \\n Task = TaskType.Question + \\n }; \\n var response = await searchService.SendFeedbackAsync(feedbackRequest); + \\n if (response.IsSuccessful) \\n { \\n Console.WriteLine( Feedback submitted + successfully ); \\n } \\n ``` \\n \\n SearchAsync \\n Search Knowledge Box + and retrieve separate results for documents, paragraphs, and sentences. \\n + Signature: \\n csharp \\n Task> SearchAsync(SearchRequest request) \\n Parameters: + \\n - request (SearchRequest, required): Comprehensive search request parameters + \\n Returns: Structured search results with multiple result types \\n Example: + \\n ```csharp \\n var searchRequest = new SearchRequest \\n { \\n Query = + API documentation \\n }; \\n var response = await searchService.SearchAsync(searchRequest); + \\n if (response.IsSuccessful) \\n { \\n var results = response.Data; \\n + Console.WriteLine($ Documents: {results.Documents?.Count ?? 0} ); \\n Console.WriteLine($ + Paragraphs: {results.Paragraphs?.Count ?? 0} ); \\n Console.WriteLine($ Sentences: + {results.Sentences?.Count ?? 0} ); \\n } \\n ``` \\n \\n AskResourceAsync + \\n Ask a question to a specific resource and get a synchronous response. + \\n Signature: \\n csharp \\n Task> AskResourceAsync(string resourceId, AskRequest + request) \\n Parameters: \\n - resourceId (string, required): The unique identifier + of the resource \\n - request (AskRequest, required): The ask request parameters + \\n Returns: Answer specific to the resource \\n Example: \\n ```csharp \\n + var askRequest = new AskRequest \\n { \\n Query = What does this document + explain? \\n }; \\n var response = await searchService.AskResourceAsync( my-resource-id + , askRequest); \\n if (response.IsSuccessful) \\n { \\n var result = response.Data; + \\n Console.WriteLine($ Resource-specific answer: {result.Answer} ); \\n } + \\n ``` \\n \\n AskResourceStreamAsync (with AskRequest) \\n Ask a question + to a specific resource and get a streaming response. \\n Signature: \\n csharp + \\n IAsyncEnumerable AskResourceStreamAsync(string resourceId, AskRequest + request) \\n Parameters: \\n - resourceId (string, required): The unique identifier + of the resource \\n - request (AskRequest, required): The ask request parameters + \\n Returns: Streaming response specific to the resource \\n Example: \\n + ```csharp \\n var askRequest = new AskRequest \\n { \\n Query = Summarize + this document's main points \\n }; \\n await foreach (var update in searchService.AskResourceStreamAsync( + my-resource-id , askRequest)) \\n { \\n if (update.Item is AnswerContent answerContent + && !string.IsNullOrEmpty(answerContent.Text)) \\n { \\n Console.Write(answerContent.Text); + \\n } \\n } \\n ``` \\n \\n AskResourceStreamAsync (with query string) \\n + Ask a question to a specific resource with simplified query. \\n Signature: + \\n csharp \\n IAsyncEnumerable AskResourceStreamAsync(string resourceId, + string query) \\n Parameters: \\n - resourceId (string, required): The unique + identifier of the resource \\n - query (string, required): The question to + ask \\n Returns: Streaming response for the resource \\n Example: \\n csharp + \\n await foreach (var update in searchService.AskResourceStreamAsync( my-resource-id + , What are the key features? )) \\n { \\n if (update.Item is AnswerContent + answerContent && !string.IsNullOrEmpty(answerContent.Text)) \\n { \\n Console.Write(answerContent.Text); + \\n } \\n } \\n \\n AskResourceBySlugAsync \\n Ask a question about a specific + resource by slug synchronously. \\n Signature: \\n csharp \\n Task> AskResourceBySlugAsync(string + slug, AskRequest request) \\n Parameters: \\n - slug (string, required): The + slug identifier of the resource \\n - request (AskRequest, required): The + ask request parameters \\n Returns: Answer specific to the resource identified + by slug \\n Example: \\n ```csharp \\n var askRequest = new AskRequest \\n + { \\n Query = What is the purpose of this document? \\n }; \\n var response + = await searchService.AskResourceBySlugAsync( my-document-slug , askRequest); + \\n if (response.IsSuccessful) \\n { \\n var result = response.Data; \\n Console.WriteLine($ + Answer: {result.Answer} ); \\n } \\n ``` \\n \\n PredictProxyAsync \\n Proxy + endpoint that forwards requests to the Predict API with Knowledge Box configuration. + \\n Signature: \\n csharp \\n Task> PredictProxyAsync(PredictProxiedEndpoints + endpoint) \\n Parameters: \\n - endpoint (PredictProxiedEndpoints, required): + The predict endpoint to call \\n Returns: Response from the predict API \\n + Example: \\n csharp \\n var response = await searchService.PredictProxyAsync(PredictProxiedEndpoints.Tokens); + \\n if (response.IsSuccessful) \\n { \\n var result = response.Data; \\n Console.WriteLine($ + Predict API response: {result} ); \\n } \\n \\n SearchResourceAsync \\n Search + within a specific resource by ID. \\n Signature: \\n csharp \\n Task> SearchResourceAsync(string + resourceId, string query) \\n Parameters: \\n - resourceId (string, required): + The unique identifier of the resource \\n - query (string, required): The + search query \\n Returns: Search results within the specified resource \\n + Example: \\n csharp \\n var response = await searchService.SearchResourceAsync( + my-resource-id , configuration settings ); \\n if (response.IsSuccessful) + \\n { \\n var results = response.Data; \\n Console.WriteLine($ Found {results.Total} + matches in resource ); \\n foreach (var paragraph in results.Paragraphs ?? + []) \\n { \\n Console.WriteLine($ Match: {paragraph.Text} ); \\n } \\n } \\n + Usage Notes \\n \\n All methods are asynchronous and return ApiResponse wrappers + \\n Check response.IsSuccessful before accessing response.Data \\n Streaming + methods return IAsyncEnumerable for real-time response processing \\n For + streaming examples, add using Progress.Nuclia.Model.Streaming; to access AnswerContent + \\n Ask methods provide RAG capabilities with citations and source information + \\n Graph search methods enable relationship-based queries \\n Resource-specific + methods limit scope to individual documents \\n Feedback helps improve AI + model performance over time \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"results.Total\",\"response.Data\",\"catalog.Total\"],\"paragraphs\":[{\"start\":0,\"end\":202,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":202,\"key\":\"\"}]},{\"start\":202,\"end\":408,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":203,\"end\":336,\"key\":\"\"},{\"start\":336,\"end\":408,\"key\":\"\"}]},{\"start\":408,\"end\":681,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":409,\"end\":681,\"key\":\"\"}]},{\"start\":681,\"end\":924,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":682,\"end\":924,\"key\":\"\"}]},{\"start\":924,\"end\":1129,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":925,\"end\":1040,\"key\":\"\"},{\"start\":1040,\"end\":1129,\"key\":\"\"}]},{\"start\":1129,\"end\":1371,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1130,\"end\":1371,\"key\":\"\"}]},{\"start\":1371,\"end\":1628,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1372,\"end\":1484,\"key\":\"\"},{\"start\":1484,\"end\":1513,\"key\":\"\"},{\"start\":1513,\"end\":1628,\"key\":\"\"}]},{\"start\":1628,\"end\":1893,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1629,\"end\":1742,\"key\":\"\"},{\"start\":1742,\"end\":1893,\"key\":\"\"}]},{\"start\":1893,\"end\":2108,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1894,\"end\":2025,\"key\":\"\"},{\"start\":2025,\"end\":2108,\"key\":\"\"}]},{\"start\":2108,\"end\":2459,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2109,\"end\":2459,\"key\":\"\"}]},{\"start\":2459,\"end\":2796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2460,\"end\":2566,\"key\":\"\"},{\"start\":2566,\"end\":2796,\"key\":\"\"}]},{\"start\":2796,\"end\":3012,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2797,\"end\":3012,\"key\":\"\"}]},{\"start\":3012,\"end\":3265,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3013,\"end\":3265,\"key\":\"\"}]},{\"start\":3265,\"end\":3486,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3266,\"end\":3486,\"key\":\"\"}]},{\"start\":3486,\"end\":3714,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3487,\"end\":3714,\"key\":\"\"}]},{\"start\":3714,\"end\":3937,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3715,\"end\":3867,\"key\":\"\"},{\"start\":3867,\"end\":3937,\"key\":\"\"}]},{\"start\":3937,\"end\":4146,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3938,\"end\":4146,\"key\":\"\"}]},{\"start\":4146,\"end\":4389,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4147,\"end\":4389,\"key\":\"\"}]},{\"start\":4389,\"end\":4703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4390,\"end\":4454,\"key\":\"\"},{\"start\":4454,\"end\":4703,\"key\":\"\"}]},{\"start\":4703,\"end\":4953,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4704,\"end\":4953,\"key\":\"\"}]},{\"start\":4953,\"end\":5191,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4954,\"end\":5103,\"key\":\"\"},{\"start\":5103,\"end\":5191,\"key\":\"\"}]},{\"start\":5191,\"end\":5484,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5192,\"end\":5484,\"key\":\"\"}]},{\"start\":5484,\"end\":5702,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5485,\"end\":5702,\"key\":\"\"}]},{\"start\":5702,\"end\":5943,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5703,\"end\":5943,\"key\":\"\"}]},{\"start\":5943,\"end\":6179,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5944,\"end\":6179,\"key\":\"\"}]},{\"start\":6179,\"end\":6446,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6180,\"end\":6340,\"key\":\"\"},{\"start\":6340,\"end\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+ Boxes\":\"PRODUCT\",\"AskRequest\":\"ORG\",\"Citations\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Knowledge + Boxes\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":132,\"end\":147}]},{\"text\":\"AskRequest\",\"label\":\"ORG\",\"positions\":[{\"start\":958,\"end\":968}]},{\"text\":\"AskStreamAsync\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":1079,\"end\":1093}]},{\"text\":\"Citations\",\"label\":\"PERSON\",\"positions\":[{\"start\":1351,\"end\":1360}]},{\"text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":3253,\"end\":3257}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:17:57.228342Z\",\"last_understanding\":\"2026-06-09T08:17:56.698164Z\",\"last_extract\":\"2026-06-09T08:17:39.254767Z\",\"last_processing_start\":\"2026-06-09T08:17:39.144685Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/JSON\":{\"position\":[{\"start\":3253,\"end\":3257}],\"entity\":\"JSON\"},\"PRODUCT/Knowledge + Boxes\":{\"position\":[{\"start\":132,\"end\":147}],\"entity\":\"Knowledge + Boxes\"},\"PERSON/Citations\":{\"position\":[{\"start\":1351,\"end\":1360}],\"entity\":\"Citations\"},\"ORG/AskRequest\":{\"position\":[{\"start\":958,\"end\":968}],\"entity\":\"AskRequest\"},\"PRODUCT/AskStreamAsync\":{\"position\":[{\"start\":1079,\"end\":1093}],\"entity\":\"AskStreamAsync\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > dotnet sdk > ISearchService\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > dotnet sdk > ISearchService\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4c90aa2f4b77410e9d26d981d9852823/t/page/11448-11676\":{\"score\":0.014084507042253521,\"score_type\":\"BM25\",\"order\":18,\"text\":\" + - endpoint (PredictProxiedEndpoints, required): The predict endpoint to call + \\n Returns: Response from the predict API \\n Example: \\n csharp \\n var + response = await searchService.PredictProxyAsync(PredictProxiedEndpoints.Tokens); + \\n\",\"id\":\"4c90aa2f4b77410e9d26d981d9852823/t/page/11448-11676\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":45,\"start\":11448,\"end\":11676,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.02967032967032967,\"score_type\":\"BOTH\",\"order\":3,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.028782894736842105,\"score_type\":\"BOTH\",\"order\":4,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n 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ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined 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simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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+ RAG\":\"PRODUCT\",\"the Knowledge Box\":\"ORG\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\":[{\"start\":3046,\"end\":3054}]},{\"text\":\"the + Knowledge Box\",\"label\":\"ORG\",\"positions\":[{\"start\":4031,\"end\":4048}]},{\"text\":\"NucliaDB\",\"label\":\"ORG\",\"positions\":[{\"start\":4100,\"end\":4108}]},{\"text\":\"CSS\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":4476,\"end\":4479}]},{\"text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:04.788053Z\",\"last_understanding\":\"2026-06-09T08:08:04.303970Z\",\"last_extract\":\"2026-06-09T08:08:01.001532Z\",\"last_processing_start\":\"2026-06-09T08:08:00.904137Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/the + Knowledge Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"PRODUCT/Agentic RAG\":{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"entity\":\"Agentic + RAG\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\":3046,\"end\":3054}],\"entity\":\"Markdown\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.015873015873015872,\"score_type\":\"BM25\",\"order\":7,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\",\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.015151515151515152,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\",\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\",\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\n\\n# + Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**: + `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\n\\n> + **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\n\\n***\\n\\n### + tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"9a5a602361bcf49d7619fc3995646f40\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:21.098517Z\",\"last_understanding\":\"2026-07-14T12:49:20.847235Z\",\"last_extract\":\"2026-07-14T12:49:20.704679Z\",\"last_processing_start\":\"2026-07-14T12:49:20.686749Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageStructure\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\":{\"score\":0.014492753623188406,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"7b41750917e349598c846526162ebb68\":{\"id\":\"7b41750917e349598c846526162ebb68\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensDetails-md\",\"title\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:51.860964\",\"modified\":\"2026-07-14T12:51:57.270898\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensDetails\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensDetails\\n\\n# + Interface: NucliaTokensDetails\\n\\n## Extends\\n\\n- [`CommonMetricDetails`](CommonMetricDetails.md)\\n\\n## + Properties\\n\\n### identifier\\n\\n> **identifier**: `object`\\n\\n#### model\\n\\n> + **model**: `null` \\\\| `string`\\n\\n#### service\\n\\n> **service**: `string`\\n\\n#### + source\\n\\n> **source**: `string`\\n\\n#### type\\n\\n> **type**: `string`\\n\\n#### + version\\n\\n> **version**: `string`\\n\\n#### Overrides\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`identifier`](CommonMetricDetails.md#identifier)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:150](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L150)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\n\\n> **nuclia\\\\_tokens**: `object`\\n\\n#### image\\n\\n> + **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` + \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:157](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L157)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\\\_billed\\n\\n> **nuclia\\\\_tokens\\\\_billed**: `object`\\n\\n#### + image\\n\\n> **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: + `null` \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:164](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L164)\\n\\n***\\n\\n### + raw\\\\_usage\\n\\n> **raw\\\\_usage**: `object`\\n\\n#### image\\n\\n> **image**: + `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` \\\\| `number`\\n\\n#### + output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### search\\n\\n> **search**: + `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: `null` \\\\| `number`\\n\\n#### + Inherited from\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`raw_usage`](CommonMetricDetails.md#raw_usage)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:134](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L134)\\n\\n***\\n\\n### + requests\\n\\n> **requests**: `object`\\n\\n#### api\\n\\n> **api**: `null` + \\\\| `number`\\n\\n#### chrome\\\\_extension\\n\\n> **chrome\\\\_extension**: + `null` \\\\| `number`\\n\\n#### dashboard\\n\\n> **dashboard**: `null` \\\\| + `number`\\n\\n#### desktop\\n\\n> **desktop**: `null` \\\\| `number`\\n\\n#### + internal\\n\\n> **internal**: `null` \\\\| `number`\\n\\n#### web\\n\\n> **web**: + `null` \\\\| `number`\\n\\n#### widget\\n\\n> **widget**: `null` \\\\| `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:171](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L171)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"cc5e5459b176800496de165483959c09\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensDetails \\n Interface: NucliaTokensDetails + \\n Extends \\n \\n CommonMetricDetails \\n \\n Properties \\n identifier + \\n \\n identifier: object \\n \\n model \\n \\n model: null \\\\| string + \\n \\n service \\n \\n service: string \\n \\n source \\n \\n source: string + \\n \\n type \\n \\n type: string \\n \\n version \\n \\n version: string + \\n \\n Overrides \\n CommonMetricDetails.identifier \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:150 + \\n \\n nuclia_tokens \\n \\n nuclia_tokens: object \\n \\n image \\n \\n + image: null \\\\| number \\n \\n input \\n \\n input: null \\\\| number \\n + \\n output \\n \\n output: null \\\\| number \\n \\n search \\n \\n search: + null \\\\| number \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:157 \\n \\n nuclia_tokens_billed + \\n \\n nuclia_tokens_billed: object \\n \\n image \\n \\n image: null \\\\| + number \\n \\n input \\n \\n input: null \\\\| number \\n \\n output \\n \\n + output: null \\\\| number \\n \\n search \\n \\n search: null \\\\| number + \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:164 \\n \\n raw_usage \\n \\n raw_usage: + object \\n \\n image \\n \\n image: null \\\\| number \\n \\n input \\n \\n + input: null \\\\| number \\n \\n output \\n \\n output: null \\\\| number + \\n \\n search \\n \\n search: null \\\\| number \\n \\n storage \\n \\n storage: + null \\\\| number \\n \\n Inherited from \\n CommonMetricDetails.raw_usage + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:134 \\n \\n requests + \\n \\n requests: object \\n \\n api \\n \\n api: null \\\\| number \\n \\n + chrome_extension \\n \\n chrome_extension: null \\\\| number \\n \\n dashboard + \\n \\n dashboard: null \\\\| number \\n \\n desktop \\n \\n desktop: null + \\\\| number \\n \\n internal \\n \\n internal: null \\\\| number \\n \\n + web \\n \\n web: null \\\\| number \\n \\n widget \\n \\n widget: null \\\\| + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:171\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":287,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":287,\"key\":\"\"}]},{\"start\":287,\"end\":494,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":288,\"end\":494,\"key\":\"\"}]},{\"start\":494,\"end\":799,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":495,\"end\":799,\"key\":\"\"}]},{\"start\":799,\"end\":1082,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":800,\"end\":1082,\"key\":\"\"}]},{\"start\":1082,\"end\":1410,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1083,\"end\":1410,\"key\":\"\"}]},{\"start\":1410,\"end\":1626,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1411,\"end\":1626,\"key\":\"\"}]},{\"start\":1626,\"end\":1744,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1627,\"end\":1744,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:59.450996Z\",\"last_understanding\":\"2026-07-14T12:51:59.220573Z\",\"last_extract\":\"2026-07-14T12:51:58.606094Z\",\"last_processing_start\":\"2026-07-14T12:51:58.573992Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\"}}}}},\"security\":null,\"fields\":{\"/a/title\":{\"paragraphs\":{\"7b41750917e349598c846526162ebb68/a/title/0-58\":{\"score\":0.016666666666666666,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"id\":\"7b41750917e349598c846526162ebb68/a/title/0-58\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":58,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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+ > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.016666666666666666,\"score_type\":\"BM25\",\"order\":2,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.014492753623188406,\"score_type\":\"BM25\",\"order\":18,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/split-strategies\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + split-strategies\\ntitle: Use split strategies\\n---\\n\\n# Split strategies\\n\\nIn + some cases, default chunking methods aren't enough to handle the complexity + of your data.\\nImagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved.\\n\\nTo address this, you can define a split strategy that + controls how content is broken into chunks during ingestion.\\nSplit strategies + are reusable and configurable, allowing you to tailor the chunking process + to your specific needs.\\n\\nOnce defined, these strategies can be applied + across multiple ingestion jobs, giving you flexibility and consistency in + how your data is processed.\\n\\n## What is a split strategy?\\n\\nA split + strategy is a reusable configuration that defines how your data is divided + into chunks during ingestion. By customizing a split strategy, you can optimize + chunking for different types of content and use cases.\\n\\nKey components + of a split strategy include:\\n\\n- **Max paragraph size**: Sets the maximum + size (in characters or tokens) for each chunk.\\n- **Custom split**: Determines + the method used for splitting:\\n - **Manual splitting**: Splits content + based on a specified delimiter (default is `\\\"\\\\n\\\"`).\\n - **LLM splitting**: + Uses a language model to segment text intelligently, with optional rules to + guide the process.\\n\\nNote that you can only choose one splitting method + per strategy, and that you will have to set custom_split to 1 for manual splitting + or 2 for LLM splitting.\\n\\n## LLM Splitting\\n\\nLLM splitting leverages + a language model to intelligently segment text into meaningful chunks, going + beyond simple character or line-based splitting. This approach is ideal when + you need context-aware chunking, such as dividing content by sections, topics, + or other logical boundaries.\\n\\nYou can enable LLM splitting with default + settings, or customize it for your specific needs using these parameters:\\n\\n- + **LLM**: Specify the language model in the **generative_model** field. Optionally, + provide credentials for your chosen LLM provider in **user_keys**.\\n- **Rules**: + Define natural language instructions to guide how the text should be split. + For example, you might use rules like \\\"Split at each section heading\\\" + or \\\"Separate lists into individual items\\\".\\n\\nExample configuration:\\n\\n```json\\n{\\n + \ \\\"name\\\": \\\"llm\\\",\\n \\\"max_paragraph\\\": 1000,\\n + \ \\\"custom_split\\\": 2, \\n \\\"llm_split\\\": {\\n \\\"llm\\\": + {\\n \\\"generative_model\\\": \\\"chatgpt-azure-4o-mini\\\",\\n + \ },\\n \\\"rules\\\": [\\n \\\"Split + at each section heading\\\"\\n ]\\n },\\n }\\n```\\n\\nExperiment + with different rules and models to optimize chunking for your use case. Note + that using an LLM may increase processing time and cost, and could slightly + alter the extracted text.\\n\\n## Manual Splitting\\nManual splitting divides + text based on a specified delimiter, making it straightforward and efficient + for simpler chunking needs. This method is particularly useful when your content + has clear, consistent separators, such as paragraphs or bullet points.\\nYou + can customize manual splitting using the following parameters:\\n- **Splitter**: + Choose the character or string that indicates where to split the text. The + default is a double newline character (`\\\"\\\\n\\\\n\\\"`), but you can + set it to any delimiter that suits your content structure.\\nExample configuration:\\n\\n```json\\n + {\\n \\\"name\\\": \\\"manual\\\",\\n \\\"custom_split\\\": + 1,\\n \\\"manual_split\\\": {\\n \\\"splitter\\\": \\\"\\\\n\\\"\\n + \ }\\n }\\n``` \\n\\n## Strategy creation and management\\n\\nBefore + we can use a split strategy, we need to create it. We can create as many strategies + as we want, and just use the right one for each processing job. Once created + we can not modify our strategies, but we can delete them and inspect the ones + we have created for a given kb.\\n\\n### Dashboard\\n\\nTo create a strategy, + just go to the section **AI Models** and then to **Extract & split**. Then + you can click on the button **Create configuration** , next to **Split configurations** + to create a new strategy. Once there, you can fill in the necessary fields + with the desired configuration.\\nIn the same section, you can also see the + list of strategies you have created, and you can delete them once you no longer + need them.\\n\\n### CLI\\n\\nTo create a strategy using the CLI, you can use + the command `nuclia kb split_strategies add` with the desired configuration + in JSON format. You can also list the strategies you have created with `nuclia + kb split_strategies list`, and delete them with `nuclia kb split_strategies + delete`.\\n\\n```bash\\nnuclia kb split_strategies add --config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}'\\nnuclia kb split_strategies + list\\nnuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo create a strategy using the SDK, you can use the `add` method + of the `split_strategies` object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the `list` + method, and delete them with the `delete` method.\\n\\n```python\\nfrom nuclia + import sdk\\nsplit_strategies = sdk.NucliaKB().split_strategies\\nprint(split_strategies.list())\\nid + = split_strategies.add(config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}')\\nsplit_strategies.delete(id=id)\\n```\\n\\n## + Use split strategies for processing\\n\\n### Dashboard\\n\\nTo use a split + strategy for processing documents, just upload the document normally, enable + **Customize data extraction** and select the strategy you want to use in the + dropdown menu. Once the document is uploaded, it will be processed using the + selected strategy.\\n\\n### CLI\\n\\nTo use a split strategy for processing + documents using the CLI, you can use the command `nuclia kb upload file` with + the `--split_strategy` option, passing the ID of the strategy you want to + use.\\n\\n```bash\\nnuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n```bash\\nnuclia + kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo use a split strategy for processing documents using the SDK, you + can use the `file` method of the `NucliaUpload` object, passing the path to + the file and the ID of the strategy you want to use.\\n\\n```python\\nfrom + nuclia import sdk\\nupload = sdk.NucliaUpload()\\nupload.file(path=FILE_PATH, + split_strategy=\\\"1361c0c7-918a-4a7f-b44b-ba37437619fb\\\")\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"834cf2c7e30daced272772d79b257cf0\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: split-strategies \\n title: Use split strategies \\n \\n Split strategies + \\n In some cases, default chunking methods aren't enough to handle the complexity + of your data. \\n Imagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved. \\n To address this, you can define a split strategy that controls + how content is broken into chunks during ingestion. \\n Split strategies are + reusable and configurable, allowing you to tailor the chunking process to + your specific needs. \\n Once defined, these strategies can be applied across + multiple ingestion jobs, giving you flexibility and consistency in how your + data is processed. \\n What is a split strategy? \\n A split strategy is a + reusable configuration that defines how your data is divided into chunks during + ingestion. By customizing a split strategy, you can optimize chunking for + different types of content and use cases. \\n Key components of a split strategy + include: \\n \\n Max paragraph size: Sets the maximum size (in characters + or tokens) for each chunk. \\n Custom split: Determines the method used for + splitting: \\n Manual splitting: Splits content based on a specified delimiter + (default is \\\\n ). \\n LLM splitting: Uses a language model to segment text + intelligently, with optional rules to guide the process. \\n \\n Note that + you can only choose one splitting method per strategy, and that you will have + to set custom_split to 1 for manual splitting or 2 for LLM splitting. \\n + LLM Splitting \\n LLM splitting leverages a language model to intelligently + segment text into meaningful chunks, going beyond simple character or line-based + splitting. This approach is ideal when you need context-aware chunking, such + as dividing content by sections, topics, or other logical boundaries. \\n + You can enable LLM splitting with default settings, or customize it for your + specific needs using these parameters: \\n \\n LLM: Specify the language model + in the generative_model field. Optionally, provide credentials for your chosen + LLM provider in user_keys. \\n Rules: Define natural language instructions + to guide how the text should be split. For example, you might use rules like + Split at each section heading or Separate lists into individual items . \\n + \\n Example configuration: \\n json \\n { \\n name : llm , \\n max_paragraph + : 1000, \\n custom_split : 2, \\n llm_split : { \\n llm : { \\n generative_model + : chatgpt-azure-4o-mini , \\n }, \\n rules : [ \\n Split at each section heading + \\n ] \\n }, \\n } \\n Experiment with different rules and models to optimize + chunking for your use case. Note that using an LLM may increase processing + time and cost, and could slightly alter the extracted text. \\n Manual Splitting + \\n Manual splitting divides text based on a specified delimiter, making it + straightforward and efficient for simpler chunking needs. This method is particularly + useful when your content has clear, consistent separators, such as paragraphs + or bullet points. \\n You can customize manual splitting using the following + parameters: \\n - Splitter: Choose the character or string that indicates + where to split the text. The default is a double newline character ( \\\\n\\\\n + ), but you can set it to any delimiter that suits your content structure. + \\n Example configuration: \\n json \\n { \\n name : manual , \\n custom_split + : 1, \\n manual_split : { \\n splitter : \\\\n \\n } \\n } \\n Strategy creation + and management \\n Before we can use a split strategy, we need to create it. + We can create as many strategies as we want, and just use the right one for + each processing job. Once created we can not modify our strategies, but we + can delete them and inspect the ones we have created for a given kb. \\n Dashboard + \\n To create a strategy, just go to the section AI Models and then to Extract + & split. Then you can click on the button Create configuration , next to Split + configurations to create a new strategy. Once there, you can fill in the necessary + fields with the desired configuration. \\n In the same section, you can also + see the list of strategies you have created, and you can delete them once + you no longer need them. \\n CLI \\n To create a strategy using the CLI, you + can use the command nuclia kb split_strategies add with the desired configuration + in JSON format. You can also list the strategies you have created with nuclia + kb split_strategies list, and delete them with nuclia kb split_strategies + delete. \\n bash \\n nuclia kb split_strategies add --config='{ name : strategy1 + , custom_split : 1, manual_split : { splitter : \\\\n }}' \\n nuclia kb split_strategies + list \\n nuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To create a strategy using the SDK, you can use the add method + of the split_strategies object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the list method, + and delete them with the delete method. \\n python \\n from nuclia import + sdk \\n split_strategies = sdk.NucliaKB().split_strategies \\n print(split_strategies.list()) + \\n id = split_strategies.add(config='{ name : strategy1 , custom_split : + 1, manual_split : { splitter : \\\\n }}') \\n split_strategies.delete(id=id) + \\n Use split strategies for processing \\n Dashboard \\n To use a split strategy + for processing documents, just upload the document normally, enable Customize + data extraction and select the strategy you want to use in the dropdown menu. + Once the document is uploaded, it will be processed using the selected strategy. + \\n CLI \\n To use a split strategy for processing documents using the CLI, + you can use the command nuclia kb upload file with the --split_strategy option, + passing the ID of the strategy you want to use. \\n bash \\n nuclia kb upload + file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n bash \\n nuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To use a split strategy for processing documents using the SDK, + you can use the file method of the NucliaUpload object, passing the path to + the file and the ID of the strategy you want to use. \\n python \\n from nuclia + import sdk \\n upload = sdk.NucliaUpload() \\n upload.file(path=FILE_PATH, + split_strategy= 1361c0c7-918a-4a7f-b44b-ba37437619fb 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Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n 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UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\n\\n# + Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n***\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\\_PROCESSED**: + `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n***\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\n\\n***\\n\\n### + PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"pages_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n***\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**: `\\\"paragraphs_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n***\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\n\\n***\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**: `\\\"resources_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n***\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**: `\\\"searches_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n***\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\n\\n***\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**: `\\\"suggestions_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > 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libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.015625,\"score_type\":\"VECTOR\",\"order\":10,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.01639344262295082,\"score_type\":\"BM25\",\"order\":4,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.014705882352941176,\"score_type\":\"BM25\",\"order\":17,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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+ Box\":\"PRODUCT\",\"Agentic RAG\":\"PRODUCT\",\"LLM\":\"ORG\",\"json\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge + Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\":4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\",\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic + 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Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\",\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.015873015873015872,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.015625,\"score_type\":\"BM25\",\"order\":9,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.015384615384615385,\"score_type\":\"BM25\",\"order\":11,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n 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+ min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"end\":654}]},{\"text\":\"112 + min\",\"label\":\"TIME\",\"positions\":[{\"start\":2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\",\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PERSON/boolean\":{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"},\"TIME/112 + min\":{\"position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.030834914611005692,\"score_type\":\"BOTH\",\"order\":0,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9a6df7db9c6d4036b949d3f8a134ca1c\":{\"id\":\"9a6df7db9c6d4036b949d3f8a134ca1c\",\"slug\":\"docs-develop-python-sdk-02-auth-md\",\"title\":\"docs + > develop > python sdk > 02 auth\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:39.987690\",\"modified\":\"2026-06-09T08:08:06.772770\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/auth\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Authentication\\n\\nIn order to start working you can login either using a + user acount, or using an API key.\\n\\nNote: you can also login with a NUA + key. With the right permissions/scopes, a NUA key can be used not only for + processing features (see [NUA](07-nua.md)), but also for Knowledge Box management + operations (list/get/create/delete) in your authorized account.\\n\\n- CLI: + `nuclia auth nua [NUA_KEY]`\\n- SDK:\\n\\n ```python\\n from nuclia import + sdk\\n sdk.NucliaAuth().nua(NUA_KEY)\\n ```\\n\\nFor private or on-premise + deployments where the API endpoint differs from the token issuer, see [Custom + API endpoint](07-nua.md#custom-api-endpoint-private-or-on-premise-deployments-only).\\n\\n## + User authentication\\n\\nWith user authentication, there is no limitation + to the API endpoints you can use. You manage all the accounts and Knowledge + Boxes you have access to. It also allows to manage local NucliaDBs connected + to your accounts.\\n\\nBut it does not last very long (30 minutes), and it + requires a browser to complete the authentication process.\\nIt is a good + fit for interactive use, but not for long running scripts.\\n\\n- CLI: `nuclia + auth login`, and it will trigger a browser authentication flow, so you can + copy/paste the token in your terminal.\\n\\n- SDK: (even if it is not recommended + for long running scripts, user authentication can be done in the SDK)\\n\\n + \ ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().set_user_token(USER_TOKEN)\\n + \ ```\\n\\n## Personal access token\\n\\nA personal access token is a long-lived + user token that can be used to authenticate to the API. It is a good fit for + long running scripts. It grants the same access as user authentication.\\n\\nYou + define its expiration date (default is 90 days), and you can revoke it at + any time.\\n\\n- CLI:\\n\\n To generate a token, run:\\n\\n ```sh\\n nuclia + auth create_personal_token --description=\\\"My token\\\" --days=30\\n ```\\n\\n + \ To generate a token and use it as authentication in the CLI, run:\\n\\n + \ ```sh\\n nuclia auth create_personal_token --description=\\\"My token\\\" + --days=30 --login\\n ```\\n\\n To list the tokens, run:\\n\\n ```sh\\n + \ nuclia auth list_personal_tokens\\n ```\\n\\n To delete a token, run:\\n\\n + \ ```sh\\n nuclia auth delete_personal_token [TOKEN_ID]\\n ```\\n\\n- SDK:\\n\\n + \ ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().create_personal_token(description=\\\"My + token\\\", days=30)\\n sdk.NucliaAuth().list_personal_tokens()\\n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID)\\n + \ ```\\n\\n## API key\\n\\nAn API key can be generated from the Nuclia Dashboard, + see [Get an API key](https://docs.rag.progress.cloud/docs/guides/getting-started/quick-start/push#get-an-api-key).\\n\\nWhen + authenticating with an API key, you can only access the Knowledge Box that + is associated with this API key.\\nThe authentication will last as long as + the key is valid (potentially forever, but an API key can be revoked from + the Nuclia Dashboard).\\n\\nIt is the recommended way to authenticate for + long running scripts.\\n\\n- CLI: `nuclia auth kb [KB_URL] [API_KEY]`\\n- + SDK:\\n\\n ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().kb(url=KB_URL, + token=API_KEY)\\n ```\\n\\n- Provides access to NucliaDB API. For the managed + service you will need a service token.\\n\\n## Logout\\n\\n```sh\\nnuclia + auth logout\\n```\\n\\n## List configured user accounts, knowledge boxes and + NUA keys\\n\\nYou can list the configured authentication mechanisms using:\\n\\n```bash\\nnuclia + auth show\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"7adfd5cd16971a512ca4db72bb4047d5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Authentication + \\n In order to start working you can login either using a user acount, or + using an API key. \\n Note: you can also login with a NUA key. With the right + permissions/scopes, a NUA key can be used not only for processing features + (see NUA), but also for Knowledge Box management operations (list/get/create/delete) + in your authorized account. \\n \\n CLI: nuclia auth nua [NUA_KEY] \\n SDK: + \\n \\n python \\n from nuclia import sdk \\n sdk.NucliaAuth().nua(NUA_KEY) + \\n For private or on-premise deployments where the API endpoint differs from + the token issuer, see Custom API endpoint. \\n User authentication \\n With + user authentication, there is no limitation to the API endpoints you can use. + You manage all the accounts and Knowledge Boxes you have access to. It also + allows to manage local NucliaDBs connected to your accounts. \\n But it does + not last very long (30 minutes), and it requires a browser to complete the + authentication process. \\n It is a good fit for interactive use, but not + for long running scripts. \\n \\n \\n CLI: nuclia auth login, and it will + trigger a browser authentication flow, so you can copy/paste the token in + your terminal. \\n \\n \\n SDK: (even if it is not recommended for long running + scripts, user authentication can be done in the SDK) \\n \\n \\n python \\n + from nuclia import sdk \\n sdk.NucliaAuth().set_user_token(USER_TOKEN) \\n + Personal access token \\n A personal access token is a long-lived user token + that can be used to authenticate to the API. It is a good fit for long running + scripts. It grants the same access as user authentication. \\n You define + its expiration date (default is 90 days), and you can revoke it at any time. + \\n \\n CLI: \\n \\n To generate a token, run: \\n sh \\n nuclia auth create_personal_token + --description= My token --days=30 \\n To generate a token and use it as authentication + in the CLI, run: \\n sh \\n nuclia auth create_personal_token --description= + My token --days=30 --login \\n To list the tokens, run: \\n sh \\n nuclia + auth list_personal_tokens \\n To delete a token, run: \\n sh \\n nuclia auth + delete_personal_token [TOKEN_ID] \\n \\n SDK: \\n \\n python \\n from nuclia + import sdk \\n sdk.NucliaAuth().create_personal_token(description= My token + , days=30) \\n sdk.NucliaAuth().list_personal_tokens() \\n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID) + \\n API key \\n An API key can be generated from the Nuclia Dashboard, see + Get an API key. \\n When authenticating with an API key, you can only access + the Knowledge Box that is associated with this API key. \\n The authentication + will last as long as the key is valid (potentially forever, but an API key + can be revoked from the Nuclia Dashboard). \\n It is the recommended way to + authenticate for long running scripts. \\n \\n CLI: nuclia auth kb [KB_URL] + [API_KEY] \\n SDK: \\n \\n python \\n from nuclia import sdk \\n sdk.NucliaAuth().kb(url=KB_URL, + token=API_KEY) \\n \\n Provides access to NucliaDB API. For the managed service + you will need a service token. \\n \\n Logout \\n sh \\n nuclia auth logout + \\n List configured user accounts, knowledge boxes and NUA keys \\n You can + list the configured authentication mechanisms using: \\n bash \\n nuclia auth + 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+ days\":\"DATE\",\"30 minutes\":\"TIME\",\"30\":\"DATE\",\"days\":\"DATE\",\"days=30\":\"DATE\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"NucliaAuth\",\"label\":\"ORG\",\"positions\":[{\"start\":436,\"end\":446},{\"start\":1299,\"end\":1309},{\"start\":2179,\"end\":2189},{\"start\":2778,\"end\":2788}]},{\"text\":\"30 + minutes\",\"label\":\"TIME\",\"positions\":[{\"start\":859,\"end\":869}]},{\"text\":\"90 + days\",\"label\":\"DATE\",\"positions\":[{\"start\":1599,\"end\":1606}]},{\"text\":\"days\",\"label\":\"DATE\",\"positions\":[{\"start\":1750,\"end\":1754}]},{\"text\":\"30\",\"label\":\"DATE\",\"positions\":[{\"start\":1755,\"end\":1757},{\"start\":1898,\"end\":1900}]},{\"text\":\"days=30\",\"label\":\"DATE\",\"positions\":[{\"start\":2164,\"end\":2171}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:28.338373Z\",\"last_understanding\":\"2026-06-09T08:08:27.939363Z\",\"last_extract\":\"2026-06-09T08:08:26.245668Z\",\"last_processing_start\":\"2026-06-09T08:08:26.205740Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"DATE/days\":{\"position\":[{\"start\":1750,\"end\":1754}],\"entity\":\"days\"},\"TIME/30 + minutes\":{\"position\":[{\"start\":859,\"end\":869}],\"entity\":\"30 minutes\"},\"DATE/30\":{\"position\":[{\"start\":1755,\"end\":1757},{\"start\":1898,\"end\":1900}],\"entity\":\"30\"},\"ORG/NucliaAuth\":{\"position\":[{\"start\":436,\"end\":446},{\"start\":1299,\"end\":1309},{\"start\":2179,\"end\":2189},{\"start\":2778,\"end\":2788}],\"entity\":\"NucliaAuth\"},\"DATE/90 + days\":{\"position\":[{\"start\":1599,\"end\":1606}],\"entity\":\"90 days\"},\"DATE/days=30\":{\"position\":[{\"start\":2164,\"end\":2171}],\"entity\":\"days=30\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"developer\",\"metadata\":{\"paragraph_id\":\"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363\",\"source_start\":2164,\"source_end\":2171,\"to_start\":2179,\"to_end\":2189},\"from\":{\"value\":\"days=30\",\"type\":\"entity\",\"group\":\"DATE\"},\"to\":{\"value\":\"NucliaAuth\",\"type\":\"entity\",\"group\":\"ORG\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > python sdk > 02 auth\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 02 auth\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363\":{\"score\":0.026875901875901876,\"score_type\":\"BOTH\",\"order\":1,\"text\":\" + python \\n from nuclia import sdk \\n sdk.NucliaAuth().create_personal_token(description= + My token , days=30) \\n sdk.NucliaAuth().list_personal_tokens() \\n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID) + \\n API key \\n An API key can be generated from the Nuclia Dashboard, see + Get an API key. \\n\",\"id\":\"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":7,\"start\":2066,\"end\":2363,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}}},\"relations\":{\"entities\":{}},\"query\":\"\xBFD\xF3nde + puedo encontrar la documentaci\xF3n oficial de la API de Nuclia relacionada + con el par\xE1metro `max_tokens`?\",\"rephrased_query\":null,\"total\":1609,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"],\"min_score\":{\"semantic\":0.4,\"bm25\":0.0},\"best_matches\":[\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363\",\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"7b41750917e349598c846526162ebb68/a/title/0-58\",\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\",\"8b3e0ef630a346d1b591143309db87ec/a/title/0-57\",\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\"]}" + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Content-Length: + - '193751' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:00 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '473' + x-nuclia-trace-id: + - e9e39424f17cde100e336f3382ff1140 + status: + code: 200 + message: OK +- request: + body: '{"question": "\u00bfCu\u00e1l es la funci\u00f3n del par\u00e1metro `max_tokens` + en el contexto de las respuestas generadas por la API de Nuclia y c\u00f3mo + se utiliza?", "user_id": "arag-ask-rerank", "context": {"6e8250e6b5264156988657a221fd5e94/t/page/0-397": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / Ask / ConsumptionAskResponseItem + \n Interface: ConsumptionAskResponseItem \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n", + "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum + number of input tokens to put in the final context (including the prompt, the + retrieved results and the user question). \n max_output_tokens: the maximum + number of tokens to generate. \n\n", "4194605cdfbd414f8fa762630d555bc1/t/page/0-358": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / PageStructure \n Interface: PageStructure + \n Properties \n page \n \n page: object \n \n height \n \n height: number \n + \n width \n \n width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n", + "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n ```python + \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, + Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My + question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": " ::: \n + :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n", "1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239": + " Key components of a split strategy include: \n \n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \n Custom split: + Determines the method used for splitting: \n Manual splitting: Splits content + based on a specified delimiter (default is \\n ). \n\n", "8b3e0ef630a346d1b591143309db87ec/t/page/281-511": + " name: nuclia_tokens \n \n Overrides \n UsageMetric.name \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 + \n \n value \n \n value: number \n \n Overrides \n UsageMetric.value \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:184\n", "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType + \n Enumeration Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED + \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n", + "666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386": "@nuclia/core / Exports / + StatsType \n Enumeration: StatsType \n Deprecated \n Table of contents \n Enumeration + Members \n \n AI_TOKENS_USED \n BYTES \n CHARS \n DOCS_NO_MEDIA \n MEDIA_SECONDS + \n PAGES \n PROCESSING_TIME \n RESOURCES \n SEARCHES \n SUGGESTIONS \n TRAIN_SECONDS + \n \n Enumeration Members \n AI_TOKENS_USED \n \u2022 AI_TOKENS_USED = ai_tokens_used + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES \n \u2022 + BYTES = bytes \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n + \n CHARS \n \u2022 CHARS = chars \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 + \n \n DOCS_NO_MEDIA \n \u2022 DOCS_NO_MEDIA = docs_no_media \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:205 \n \n MEDIA_SECONDS \n \u2022 MEDIA_SECONDS + = media_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n + \n PAGES \n \u2022 PAGES = pages \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PROCESSING_TIME \n \u2022 PROCESSING_TIME = processing_time \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n RESOURCES \n \u2022 RESOURCES + = resources \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n + SEARCHES \n \u2022 SEARCHES = searches \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n SUGGESTIONS \n \u2022 SUGGESTIONS = suggestions \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 + \n \n TRAIN_SECONDS \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:203\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": + " Use the max_tokens parameter on the /ask endpoint to set hard limits on: \n + - Context size: Limits the amount of retrieved information sent to the LLM \n + - Answer length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": + " \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": + " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n", "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139": "@nuclia/core + / Exports / UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration + Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED + \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED + \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME + \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED + \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 + \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 + CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 + \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED + \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS + = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n", + "7d8e7683f33b4b0b8ee731110bf45edb/t/page/0-501": "@nuclia/core \u2022 Docs \n + \n @nuclia/core / Ask / AskTokens \n Interface: AskTokens \n Properties \n input + \n \n input: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:141 + \n \n input_nuclia \n \n input_nuclia: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:143 + \n \n output \n \n output: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:142 + \n \n output_nuclia \n \n output_nuclia: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:144\n", + "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": "@nuclia/core \u2022 Docs \n + \n @nuclia/core / Consumption \n Interface: Consumption \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n", "91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/0-252": + " \n id: knowledgebox-zone-api \n hide_title: true \n title: Knowledge Box & + Zone API \n \n import ApiDocMdx from ''@theme/ApiDocMdx''; \n \n {` \n @media + screen and (min-width: 998px) { \n [class =''docMainContainer''] > .container + { \n max-width: 100% !important; \n\n", "4c90aa2f4b77410e9d26d981d9852823/t/page/11448-11676": + " - endpoint (PredictProxiedEndpoints, required): The predict endpoint to call + \n Returns: Response from the predict API \n Example: \n csharp \n var response + = await searchService.PredictProxyAsync(PredictProxiedEndpoints.Tokens); \n\n", + "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": " \n optional max_tokens: + number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": + " \n optional max_tokens: number \\| object \n \n Defines the maximum number + of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n"}}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '12135' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank + response: + body: + string: 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '11230' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:00 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '155' + x-nuclia-trace-id: + - ad42401d94a71c07b1c5d3527d839997 + status: + code: 200 + message: OK +- request: + body: '{"question": "\u00bfD\u00f3nde puedo encontrar la documentaci\u00f3n oficial + de la API de Nuclia relacionada con el par\u00e1metro `max_tokens`?", "user_id": + "arag-ask-rerank", "context": {"6e8250e6b5264156988657a221fd5e94/t/page/0-397": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / Ask / ConsumptionAskResponseItem + \n Interface: ConsumptionAskResponseItem \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n", + "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum + number of input tokens to put in the final context (including the prompt, the + retrieved results and the user question). \n max_output_tokens: the maximum + number of tokens to generate. \n\n", "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / PageToken \n Interface: PageToken + \n Properties \n height \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \n \n text \n \n\n", "4194605cdfbd414f8fa762630d555bc1/t/page/0-358": "@nuclia/core + \u2022 Docs \n \n @nuclia/core / PageStructure \n Interface: PageStructure \n + Properties \n page \n \n page: object \n \n height \n \n height: number \n \n + width \n \n width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n", + "7b41750917e349598c846526162ebb68/a/title/0-58": "docs > develop > js sdk > + interfaces > NucliaTokensDetails\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": + " ::: \n :::warning \n Enabling reasoning can use additional tokens, which may + increase your usage costs. \n You may need to increase max_tokens to give the + LLM enough room to reason and generate an answer. \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": + " \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n", "1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239": " Key components + of a split strategy include: \n \n Max paragraph size: Sets the maximum size + (in characters or tokens) for each chunk. \n Custom split: Determines the method + used for splitting: \n Manual splitting: Splits content based on a specified + delimiter (default is \\n ). \n\n", "8b3e0ef630a346d1b591143309db87ec/t/page/281-511": + " name: nuclia_tokens \n \n Overrides \n UsageMetric.name \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 + \n \n value \n \n value: number \n \n Overrides \n UsageMetric.value \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:184\n", "8b3e0ef630a346d1b591143309db87ec/a/title/0-57": + "docs > develop > js sdk > interfaces > NucliaTokensMetric\n", "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType + \n Enumeration Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED + \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n", + "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": "@nuclia/core \u2022 Docs \n + \n @nuclia/core / Consumption \n Interface: Consumption \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n", "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": + " \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": + " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": + " Use the max_tokens parameter on the /ask endpoint to set hard limits on: \n + - Context size: Limits the amount of retrieved information sent to the LLM \n + - Answer length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n", "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139": + "@nuclia/core / Exports / UsageType \n Enumeration: UsageType \n Table of contents + \n Enumeration Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED + \n MEDIA_FILES_PROCESSED \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED + \n PARAGRAPHS_PROCESSED \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED + \n SLOW_PROCESSING_TIME \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration + Members \n AI_TOKENS_USED \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:190 \n \n BYTES_PROCESSED \n \u2022 + BYTES_PROCESSED = bytes_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:181 + \n \n CHARS_PROCESSED \n \u2022 CHARS_PROCESSED = chars_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:182 \n \n MEDIA_FILES_PROCESSED + \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 + \n \n MEDIA_SECONDS_PROCESSED \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS + \n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n", + "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": + " \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": " \n optional max_tokens: + number \\| object \n \n Defines the maximum number of tokens that the model + will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n", "9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363": + " python \n from nuclia import sdk \n sdk.NucliaAuth().create_personal_token(description= + My token , days=30) \n sdk.NucliaAuth().list_personal_tokens() \n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID) + \n API key \n An API key can be generated from the Nuclia Dashboard, see Get + an API key. \n\n"}}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '10345' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank + response: + body: + string: '{"context_scores":{"8b3e0ef630a346d1b591143309db87ec/a/title/0-57":0.509566068649292,"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833":0.49412184953689575,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.2976740598678589,"7b41750917e349598c846526162ebb68/a/title/0-58":0.2929997146129608,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.2317880541086197,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.2221934050321579,"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139":0.1937568634748459,"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.14596547186374664,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.1114344522356987,"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349":0.06477170437574387,"4194605cdfbd414f8fa762630d555bc1/t/page/0-358":0.06233103200793266,"6e8250e6b5264156988657a221fd5e94/t/page/0-397":0.05165378004312515,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.04543721675872803,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.03547750413417816,"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299":0.03155902028083801,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.028490042313933372,"8b3e0ef630a346d1b591143309db87ec/t/page/281-511":0.02822098881006241,"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363":0.011597361415624619,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709":0.00570803415030241,"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239":0.00020027377468068153}}' + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '1417' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:01 GMT + nuclia-learning-model: + - bge-reranker-v2-m3 + via: + - 1.1 google + x-envoy-upstream-service-time: + - '377' + x-nuclia-trace-id: + - 4235572f0e901435757b585312a6dba1 + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", + "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context + and user question, perform the following tasks:\n\n1. Select only information + directly relevant to the question.\n2. Break down compound sentences into simple, + single-idea statements. Preserve original phrasing when possible.\n3. For any + named entity with descriptive details, separate those details into distinct + propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", + \"she\", \"they\", \"this\", \"that\") with the full names of the entities they + reference, and add necessary modifiers to clarify meaning.\n5. The context may + be delimited by tags such as and . Treat + everything between these tags as context.\n6. Assess whether the context sufficiently + answers the question. If it answers it partially, provide the answer; if it + does not answer it fully, specify what information is missing to answer the + question.\n7. If the context does not answer the question at all, just return + the original question as the missing information.\n8. The `citations` field + consists ONLY in a list of block IDs that are relevant to the answer, following + these rules:\n - Use the format: block-AB\n - Just mention the block IDs, + do NOT include any other text.\n - Just mention the blocks actually relevant + and that contain information used in the answer, do NOT include blocks that + are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only + use those provided in the context.\n10. Your output must be a JSON object with + the following fields:\n - \"reason\": Explain your reasoning for the answer + or validation.\n - \"answer\": Provide a partial or complete answer to the + user query strictly from the information in the context. If there isn''t enough + information to even provide a partial answer, leave ''answer'' empty.\n - + \"missing_info_query\": If the context is insufficient, specify what information + is missing in a query shape; otherwise, leave it empty. Just return the query + needed to retrieve the missing information.\n - \"useful\": Indicate if the + context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": + List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", + \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you + MUST follow them carefully when generating the answer field. These instructions + may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfCu\u00e1l + es la funci\u00f3n del par\u00e1metro `max_tokens` en el contexto de las respuestas + generadas por la API de Nuclia y c\u00f3mo se utiliza?\n\n\n\nContext:\n\n**block-AA**\n\n## + Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use the max_tokens + parameter on the /ask endpoint to set hard limits on: \n - Context size: Limits + the amount of retrieved information sent to the LLM \n - Answer length: Limits + the length of the generated response \n Important Considerations \n Context + Limitations: \n\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## + Chunk: b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\nTags: /k/text\n``` @nuclia/core + / Exports / UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration + Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED + \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED + \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME + \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED + \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 + \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 + CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 + \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED + \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS + = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n + ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n + SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\nTags: + /k/text\n``` @nuclia/core \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: + UsageType \n Enumeration Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED + \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n + ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n + optional max_tokens: number \\| object \n \n Defines the maximum number of tokens + that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\n``` ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n ```\n\n\n---\"\n\n\n**block-AH**\n\n## + Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n optional max_tokens: + number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: 666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\nTags: + /k/text\n``` @nuclia/core / Exports / StatsType \n Enumeration: StatsType \n + Deprecated \n Table of contents \n Enumeration Members \n \n AI_TOKENS_USED + \n BYTES \n CHARS \n DOCS_NO_MEDIA \n MEDIA_SECONDS \n PAGES \n PROCESSING_TIME + \n RESOURCES \n SEARCHES \n SUGGESTIONS \n TRAIN_SECONDS \n \n Enumeration Members + \n AI_TOKENS_USED \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES \n \u2022 BYTES = bytes + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n \n CHARS \n \u2022 + CHARS = chars \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 \n + \n DOCS_NO_MEDIA \n \u2022 DOCS_NO_MEDIA = docs_no_media \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 + \n \n MEDIA_SECONDS \n \u2022 MEDIA_SECONDS = media_seconds \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:199 \n \n PAGES \n \u2022 PAGES = pages + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 \n \n PROCESSING_TIME + \n \u2022 PROCESSING_TIME = processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n RESOURCES \n \u2022 RESOURCES = resources \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 + \n \n SEARCHES \n \u2022 SEARCHES = searches \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n SUGGESTIONS \n \u2022 SUGGESTIONS = suggestions \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 + \n \n TRAIN_SECONDS \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:203\n ```\n\n\n---\"\n\n\n**block-AJ**\n\n## + Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\n``` \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": + false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", + "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": + {"title": "validate_or_answer", "description": "Validate or answer", "parameters": + {"type": "object", "properties": {"reason": {"type": "string", "description": + "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": + "Partial or complete answer to the user query from the information in the context."}, + "missing_info_query": {"type": "string", "description": "Query needed to retrieve + the missing information in case the context is not enough to answer the question. + If the context does not answer the question at all, just return the original + question."}, "useful": {"type": "string", "description": "Is the context useful + to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", + "items": {"type": "string", "description": "Block ID cited in the answer, e.g. + block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, + "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, + "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": + {"type": "required"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '12756' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-origin: + - RAO + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: '{"chunk":{"type":"object","object":{"reason":"The context provides + specific details about the `max_tokens` parameter in the Nuclia API. It explains + that `max_tokens` sets limits on the context size and the answer length. Additionally, + it clarifies that `max_tokens` refers to the maximum number of input tokens + in the final context, which includes the prompt, retrieved results, and user + question. This information directly addresses the user''s question about the + function and usage of the `max_tokens` parameter.","answer":"The `max_tokens` + parameter in the Nuclia API is used to set hard limits on two aspects: the + context size and the answer length. It limits the amount of retrieved information + sent to the language model (LLM) and also limits the length of the generated + response. Specifically, `max_tokens` refers to the maximum number of input + tokens to include in the final context, which encompasses the prompt, the + retrieved results, and the user question.","missing_info_query":"","useful":"yes","citations":["block-AA","block-AB"]}}} + + {"chunk":{"type":"status","code":"0"}} + + {"chunk":{"type":"meta","input_tokens":90,"output_tokens":25,"timings":{"generative":3.4407716739951866},"input_nuclia_tokens":0.09,"output_nuclia_tokens":0.025}} + + {"chunk":{"normalized_tokens":{"input":0.08952,"output":0.02508,"image":0.0},"customer_key_tokens":{"input":0.0,"output":0.0,"image":0.0},"type":"consumption"}} + + ' + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:01 GMT + nuclia-learning-id: + - f21a76ddd1b147a2abe7bdd0ef86c9a2 + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '3447' + x-nuclia-trace-id: + - a1e0dfd6daf0d8a64c9aef328f7ab424 + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", + "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context + and user question, perform the following tasks:\n\n1. Select only information + directly relevant to the question.\n2. Break down compound sentences into simple, + single-idea statements. Preserve original phrasing when possible.\n3. For any + named entity with descriptive details, separate those details into distinct + propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", + \"she\", \"they\", \"this\", \"that\") with the full names of the entities they + reference, and add necessary modifiers to clarify meaning.\n5. The context may + be delimited by tags such as and . Treat + everything between these tags as context.\n6. Assess whether the context sufficiently + answers the question. If it answers it partially, provide the answer; if it + does not answer it fully, specify what information is missing to answer the + question.\n7. If the context does not answer the question at all, just return + the original question as the missing information.\n8. The `citations` field + consists ONLY in a list of block IDs that are relevant to the answer, following + these rules:\n - Use the format: block-AB\n - Just mention the block IDs, + do NOT include any other text.\n - Just mention the blocks actually relevant + and that contain information used in the answer, do NOT include blocks that + are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only + use those provided in the context.\n10. Your output must be a JSON object with + the following fields:\n - \"reason\": Explain your reasoning for the answer + or validation.\n - \"answer\": Provide a partial or complete answer to the + user query strictly from the information in the context. If there isn''t enough + information to even provide a partial answer, leave ''answer'' empty.\n - + \"missing_info_query\": If the context is insufficient, specify what information + is missing in a query shape; otherwise, leave it empty. Just return the query + needed to retrieve the missing information.\n - \"useful\": Indicate if the + context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": + List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", + \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you + MUST follow them carefully when generating the answer field. These instructions + may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfD\u00f3nde + puedo encontrar la documentaci\u00f3n oficial de la API de Nuclia relacionada + con el par\u00e1metro `max_tokens`?\n\n\n\nContext:\n\n**block-AA**\n\n## + Chunk: 8b3e0ef630a346d1b591143309db87ec/a/title/0-57\nTags: /k/text\n``` docs + > develop > js sdk > interfaces > NucliaTokensMetric\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## + Chunk: 66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\nTags: /k/text\n``` @nuclia/core + \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration + Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED \n \n + BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n + ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n + optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: 7b41750917e349598c846526162ebb68/a/title/0-58\nTags: + /k/text\n``` docs > develop > js sdk > interfaces > NucliaTokensDetails\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## + Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n SDK: \n \n + ```python \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, + Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My + question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## + Chunk: b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\nTags: /k/text\n``` @nuclia/core + / Exports / UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration + Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED + \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED + \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME + \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED + \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 + \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 + CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 + \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED + \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS + = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n + ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use + the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context + size: Limits the amount of retrieved information sent to the LLM \n - Answer + length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n + optional max_tokens: number \\| object \n \n Defines the maximum number of tokens + that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\nTags: + /k/text\n``` @nuclia/core \u2022 Docs \n \n @nuclia/core / Consumption \n Interface: + Consumption \n Properties \n customer_key_tokens \n \n customer_key_tokens: + TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n ```\n\n\n---\"\n\n\n**block-AK**\n\n## + Chunk: 4194605cdfbd414f8fa762630d555bc1/t/page/0-358\nTags: /k/text\n``` @nuclia/core + \u2022 Docs \n \n @nuclia/core / PageStructure \n Interface: PageStructure \n + Properties \n page \n \n page: object \n \n height \n \n height: number \n \n + width \n \n width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n + ```\n\n\n---\"\n\n\n**block-AL**\n\n## Chunk: 6e8250e6b5264156988657a221fd5e94/t/page/0-397\nTags: + /k/text\n``` @nuclia/core \u2022 Docs \n \n @nuclia/core / Ask / ConsumptionAskResponseItem + \n Interface: ConsumptionAskResponseItem \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": false, "citation_threshold": null, "generative_model": + "chatgpt-azure-4o-mini", "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": + null, "json_schema": {"title": "validate_or_answer", "description": "Validate + or answer", "parameters": {"type": "object", "properties": {"reason": {"type": + "string", "description": "Reasoning for the answer or validation"}, "answer": + {"type": "string", "description": "Partial or complete answer to the user query + from the information in the context."}, "missing_info_query": {"type": "string", + "description": "Query needed to retrieve the missing information in case the + context is not enough to answer the question. If the context does not answer + the question at all, just return the original question."}, "useful": {"type": + "string", "description": "Is the context useful to answer the question?", "enum": + ["yes", "no"]}, "citations": {"type": "array", "items": {"type": "string", "description": + "Block ID cited in the answer, e.g. block-AB"}, "description": "List of block + IDs cited in the answer, if any"}}, "required": ["reason", "answer", "missing_info_query", + "useful", "citations"]}}, "format_prompt": false, "rerank_context": false, "tools": + [], "tool_choice": {"type": "required"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '12336' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-origin: + - RAO + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context + provides information about the `max_tokens` parameter, including its definition + and usage in the Nuclia API. However, it does not specify a direct link or + location for the official documentation. The relevant blocks mention the parameter + and its context but do not provide a specific URL or documentation page.\",\"answer\":\"The + `max_tokens` parameter is defined in the Nuclia API documentation. It is mentioned + in the context of the `/ask` endpoint, where it sets limits on context size + and answer length. However, the exact location of the official documentation + is not provided in the context.\",\"missing_info_query\":\"\xBFCu\xE1l es + la URL de la documentaci\xF3n oficial de la API de Nuclia relacionada con + el par\xE1metro `max_tokens`?\",\"useful\":\"yes\",\"citations\":[\"block-AC\",\"block-AH\",\"block-AI\"]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":86,\"output_tokens\":21,\"timings\":{\"generative\":3.092281664998154},\"input_nuclia_tokens\":0.086,\"output_nuclia_tokens\":0.021}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.08643,\"output\":0.02088,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:01 GMT + nuclia-learning-id: + - 7d8cbfbbdc81448e9a9cc2da88d03850 + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '3098' + x-nuclia-trace-id: + - 0a2dfad1529efc4a05919406f754cf50 + status: + code: 200 + message: OK +- request: + body: '{"query": "\u00bfD\u00f3nde puedo encontrar la documentaci\u00f3n oficial + de la API de Nuclia relacionada con el par\u00e1metro `max_tokens`?", "filters": + [], "show": ["basic", "origin", "extra", "extracted", "values", "relations"], + "extracted": ["text", "metadata", "file", "link"], "security": {"groups": []}, + "features": ["semantic", "keyword"], "reranker": "noop"}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '329' + Host: + - europe-1.nuclia.cloud + User-Agent: + - nucliadb-sdk/6.13.1.post6414 + content-type: + - application/json + x-stf-serviceaccount: + - DUMMY + method: POST + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find + response: + body: + string: "{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.014925373134328358,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"PRODUCT/Agentic RAG\":{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"entity\":\"Agentic + RAG\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\":3046,\"end\":3054}],\"entity\":\"Markdown\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.015873015873015872,\"score_type\":\"BM25\",\"order\":7,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\",\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.015151515151515152,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\",\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\",\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\n\\n# + Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**: + `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\n\\n> + **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\n\\n***\\n\\n### + tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"9a5a602361bcf49d7619fc3995646f40\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:21.098517Z\",\"last_understanding\":\"2026-07-14T12:49:20.847235Z\",\"last_extract\":\"2026-07-14T12:49:20.704679Z\",\"last_processing_start\":\"2026-07-14T12:49:20.686749Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageStructure\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageStructure\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\":{\"score\":0.014492753623188406,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface: PageStructure + \\n Properties \\n page \\n \\n page: object \\n \\n height \\n \\n height: + number \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\",\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"7b41750917e349598c846526162ebb68\":{\"id\":\"7b41750917e349598c846526162ebb68\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensDetails-md\",\"title\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:51.860964\",\"modified\":\"2026-07-14T12:51:57.270898\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensDetails\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensDetails\\n\\n# + Interface: NucliaTokensDetails\\n\\n## Extends\\n\\n- [`CommonMetricDetails`](CommonMetricDetails.md)\\n\\n## + Properties\\n\\n### identifier\\n\\n> **identifier**: `object`\\n\\n#### model\\n\\n> + **model**: `null` \\\\| `string`\\n\\n#### service\\n\\n> **service**: `string`\\n\\n#### + source\\n\\n> **source**: `string`\\n\\n#### type\\n\\n> **type**: `string`\\n\\n#### + version\\n\\n> **version**: `string`\\n\\n#### Overrides\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`identifier`](CommonMetricDetails.md#identifier)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:150](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L150)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\n\\n> **nuclia\\\\_tokens**: `object`\\n\\n#### image\\n\\n> + **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` + \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:157](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L157)\\n\\n***\\n\\n### + nuclia\\\\_tokens\\\\_billed\\n\\n> **nuclia\\\\_tokens\\\\_billed**: `object`\\n\\n#### + image\\n\\n> **image**: `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: + `null` \\\\| `number`\\n\\n#### output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### + search\\n\\n> **search**: `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: + `null` \\\\| `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:164](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L164)\\n\\n***\\n\\n### + raw\\\\_usage\\n\\n> **raw\\\\_usage**: `object`\\n\\n#### image\\n\\n> **image**: + `null` \\\\| `number`\\n\\n#### input\\n\\n> **input**: `null` \\\\| `number`\\n\\n#### + output\\n\\n> **output**: `null` \\\\| `number`\\n\\n#### search\\n\\n> **search**: + `null` \\\\| `number`\\n\\n#### storage\\n\\n> **storage**: `null` \\\\| `number`\\n\\n#### + Inherited from\\n\\n[`CommonMetricDetails`](CommonMetricDetails.md).[`raw_usage`](CommonMetricDetails.md#raw_usage)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:134](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L134)\\n\\n***\\n\\n### + requests\\n\\n> **requests**: `object`\\n\\n#### api\\n\\n> **api**: `null` + \\\\| `number`\\n\\n#### chrome\\\\_extension\\n\\n> **chrome\\\\_extension**: + `null` \\\\| `number`\\n\\n#### dashboard\\n\\n> **dashboard**: `null` \\\\| + `number`\\n\\n#### desktop\\n\\n> **desktop**: `null` \\\\| `number`\\n\\n#### + internal\\n\\n> **internal**: `null` \\\\| `number`\\n\\n#### web\\n\\n> **web**: + `null` \\\\| `number`\\n\\n#### widget\\n\\n> **widget**: `null` \\\\| `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:171](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L171)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"cc5e5459b176800496de165483959c09\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensDetails \\n Interface: NucliaTokensDetails + \\n Extends \\n \\n CommonMetricDetails \\n \\n Properties \\n identifier + \\n \\n identifier: object \\n \\n model \\n \\n model: null \\\\| string + \\n \\n service \\n \\n service: string \\n \\n source \\n \\n source: string + \\n \\n type \\n \\n type: string \\n \\n version \\n \\n version: string + \\n \\n Overrides \\n CommonMetricDetails.identifier \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:150 + \\n \\n nuclia_tokens \\n \\n nuclia_tokens: object \\n \\n image \\n \\n + image: null \\\\| number \\n \\n input \\n \\n input: null \\\\| number \\n + \\n output \\n \\n output: null \\\\| number \\n \\n search \\n \\n search: + null \\\\| number \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:157 \\n \\n nuclia_tokens_billed + \\n \\n nuclia_tokens_billed: object \\n \\n image \\n \\n image: null \\\\| + number \\n \\n input \\n \\n input: null \\\\| number \\n \\n output \\n \\n + output: null \\\\| number \\n \\n search \\n \\n search: null \\\\| number + \\n \\n storage \\n \\n storage: null \\\\| number \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:164 \\n \\n raw_usage \\n \\n raw_usage: + object \\n \\n image \\n \\n image: null \\\\| number \\n \\n input \\n \\n + input: null \\\\| number \\n \\n output \\n \\n output: null \\\\| number + \\n \\n search \\n \\n search: null \\\\| number \\n \\n storage \\n \\n storage: + null \\\\| number \\n \\n Inherited from \\n CommonMetricDetails.raw_usage + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:134 \\n \\n requests + \\n \\n requests: object \\n \\n api \\n \\n api: null \\\\| number \\n \\n + chrome_extension \\n \\n chrome_extension: null \\\\| number \\n \\n dashboard + \\n \\n dashboard: null \\\\| number \\n \\n desktop \\n \\n desktop: null + \\\\| number \\n \\n internal \\n \\n internal: null \\\\| number \\n \\n + web \\n \\n web: null \\\\| number \\n \\n widget \\n \\n widget: null \\\\| + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:171\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":287,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":287,\"key\":\"\"}]},{\"start\":287,\"end\":494,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":288,\"end\":494,\"key\":\"\"}]},{\"start\":494,\"end\":799,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":495,\"end\":799,\"key\":\"\"}]},{\"start\":799,\"end\":1082,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":800,\"end\":1082,\"key\":\"\"}]},{\"start\":1082,\"end\":1410,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1083,\"end\":1410,\"key\":\"\"}]},{\"start\":1410,\"end\":1626,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1411,\"end\":1626,\"key\":\"\"}]},{\"start\":1626,\"end\":1744,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1627,\"end\":1744,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:59.450996Z\",\"last_understanding\":\"2026-07-14T12:51:59.220573Z\",\"last_extract\":\"2026-07-14T12:51:58.606094Z\",\"last_processing_start\":\"2026-07-14T12:51:58.573992Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\"}}}}},\"security\":null,\"fields\":{\"/a/title\":{\"paragraphs\":{\"7b41750917e349598c846526162ebb68/a/title/0-58\":{\"score\":0.016666666666666666,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensDetails\",\"id\":\"7b41750917e349598c846526162ebb68/a/title/0-58\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":58,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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+ > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.016666666666666666,\"score_type\":\"BM25\",\"order\":2,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.014492753623188406,\"score_type\":\"BM25\",\"order\":18,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/split-strategies\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + split-strategies\\ntitle: Use split strategies\\n---\\n\\n# Split strategies\\n\\nIn + some cases, default chunking methods aren't enough to handle the complexity + of your data.\\nImagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved.\\n\\nTo address this, you can define a split strategy that + controls how content is broken into chunks during ingestion.\\nSplit strategies + are reusable and configurable, allowing you to tailor the chunking process + to your specific needs.\\n\\nOnce defined, these strategies can be applied + across multiple ingestion jobs, giving you flexibility and consistency in + how your data is processed.\\n\\n## What is a split strategy?\\n\\nA split + strategy is a reusable configuration that defines how your data is divided + into chunks during ingestion. By customizing a split strategy, you can optimize + chunking for different types of content and use cases.\\n\\nKey components + of a split strategy include:\\n\\n- **Max paragraph size**: Sets the maximum + size (in characters or tokens) for each chunk.\\n- **Custom split**: Determines + the method used for splitting:\\n - **Manual splitting**: Splits content + based on a specified delimiter (default is `\\\"\\\\n\\\"`).\\n - **LLM splitting**: + Uses a language model to segment text intelligently, with optional rules to + guide the process.\\n\\nNote that you can only choose one splitting method + per strategy, and that you will have to set custom_split to 1 for manual splitting + or 2 for LLM splitting.\\n\\n## LLM Splitting\\n\\nLLM splitting leverages + a language model to intelligently segment text into meaningful chunks, going + beyond simple character or line-based splitting. This approach is ideal when + you need context-aware chunking, such as dividing content by sections, topics, + or other logical boundaries.\\n\\nYou can enable LLM splitting with default + settings, or customize it for your specific needs using these parameters:\\n\\n- + **LLM**: Specify the language model in the **generative_model** field. Optionally, + provide credentials for your chosen LLM provider in **user_keys**.\\n- **Rules**: + Define natural language instructions to guide how the text should be split. + For example, you might use rules like \\\"Split at each section heading\\\" + or \\\"Separate lists into individual items\\\".\\n\\nExample configuration:\\n\\n```json\\n{\\n + \ \\\"name\\\": \\\"llm\\\",\\n \\\"max_paragraph\\\": 1000,\\n + \ \\\"custom_split\\\": 2, \\n \\\"llm_split\\\": {\\n \\\"llm\\\": + {\\n \\\"generative_model\\\": \\\"chatgpt-azure-4o-mini\\\",\\n + \ },\\n \\\"rules\\\": [\\n \\\"Split + at each section heading\\\"\\n ]\\n },\\n }\\n```\\n\\nExperiment + with different rules and models to optimize chunking for your use case. Note + that using an LLM may increase processing time and cost, and could slightly + alter the extracted text.\\n\\n## Manual Splitting\\nManual splitting divides + text based on a specified delimiter, making it straightforward and efficient + for simpler chunking needs. This method is particularly useful when your content + has clear, consistent separators, such as paragraphs or bullet points.\\nYou + can customize manual splitting using the following parameters:\\n- **Splitter**: + Choose the character or string that indicates where to split the text. The + default is a double newline character (`\\\"\\\\n\\\\n\\\"`), but you can + set it to any delimiter that suits your content structure.\\nExample configuration:\\n\\n```json\\n + {\\n \\\"name\\\": \\\"manual\\\",\\n \\\"custom_split\\\": + 1,\\n \\\"manual_split\\\": {\\n \\\"splitter\\\": \\\"\\\\n\\\"\\n + \ }\\n }\\n``` \\n\\n## Strategy creation and management\\n\\nBefore + we can use a split strategy, we need to create it. We can create as many strategies + as we want, and just use the right one for each processing job. Once created + we can not modify our strategies, but we can delete them and inspect the ones + we have created for a given kb.\\n\\n### Dashboard\\n\\nTo create a strategy, + just go to the section **AI Models** and then to **Extract & split**. Then + you can click on the button **Create configuration** , next to **Split configurations** + to create a new strategy. Once there, you can fill in the necessary fields + with the desired configuration.\\nIn the same section, you can also see the + list of strategies you have created, and you can delete them once you no longer + need them.\\n\\n### CLI\\n\\nTo create a strategy using the CLI, you can use + the command `nuclia kb split_strategies add` with the desired configuration + in JSON format. You can also list the strategies you have created with `nuclia + kb split_strategies list`, and delete them with `nuclia kb split_strategies + delete`.\\n\\n```bash\\nnuclia kb split_strategies add --config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}'\\nnuclia kb split_strategies + list\\nnuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo create a strategy using the SDK, you can use the `add` method + of the `split_strategies` object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the `list` + method, and delete them with the `delete` method.\\n\\n```python\\nfrom nuclia + import sdk\\nsplit_strategies = sdk.NucliaKB().split_strategies\\nprint(split_strategies.list())\\nid + = split_strategies.add(config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}')\\nsplit_strategies.delete(id=id)\\n```\\n\\n## + Use split strategies for processing\\n\\n### Dashboard\\n\\nTo use a split + strategy for processing documents, just upload the document normally, enable + **Customize data extraction** and select the strategy you want to use in the + dropdown menu. Once the document is uploaded, it will be processed using the + selected strategy.\\n\\n### CLI\\n\\nTo use a split strategy for processing + documents using the CLI, you can use the command `nuclia kb upload file` with + the `--split_strategy` option, passing the ID of the strategy you want to + use.\\n\\n```bash\\nnuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n```bash\\nnuclia + kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo use a split strategy for processing documents using the SDK, you + can use the `file` method of the `NucliaUpload` object, passing the path to + the file and the ID of the strategy you want to use.\\n\\n```python\\nfrom + nuclia import sdk\\nupload = sdk.NucliaUpload()\\nupload.file(path=FILE_PATH, + split_strategy=\\\"1361c0c7-918a-4a7f-b44b-ba37437619fb\\\")\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"834cf2c7e30daced272772d79b257cf0\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: split-strategies \\n title: Use split strategies \\n \\n Split strategies + \\n In some cases, default chunking methods aren't enough to handle the complexity + of your data. \\n Imagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved. \\n To address this, you can define a split strategy that controls + how content is broken into chunks during ingestion. \\n Split strategies are + reusable and configurable, allowing you to tailor the chunking process to + your specific needs. \\n Once defined, these strategies can be applied across + multiple ingestion jobs, giving you flexibility and consistency in how your + data is processed. \\n What is a split strategy? \\n A split strategy is a + reusable configuration that defines how your data is divided into chunks during + ingestion. By customizing a split strategy, you can optimize chunking for + different types of content and use cases. \\n Key components of a split strategy + include: \\n \\n Max paragraph size: Sets the maximum size (in characters + or tokens) for each chunk. \\n Custom split: Determines the method used for + splitting: \\n Manual splitting: Splits content based on a specified delimiter + (default is \\\\n ). \\n LLM splitting: Uses a language model to segment text + intelligently, with optional rules to guide the process. \\n \\n Note that + you can only choose one splitting method per strategy, and that you will have + to set custom_split to 1 for manual splitting or 2 for LLM splitting. \\n + LLM Splitting \\n LLM splitting leverages a language model to intelligently + segment text into meaningful chunks, going beyond simple character or line-based + splitting. This approach is ideal when you need context-aware chunking, such + as dividing content by sections, topics, or other logical boundaries. \\n + You can enable LLM splitting with default settings, or customize it for your + specific needs using these parameters: \\n \\n LLM: Specify the language model + in the generative_model field. Optionally, provide credentials for your chosen + LLM provider in user_keys. \\n Rules: Define natural language instructions + to guide how the text should be split. For example, you might use rules like + Split at each section heading or Separate lists into individual items . \\n + \\n Example configuration: \\n json \\n { \\n name : llm , \\n max_paragraph + : 1000, \\n custom_split : 2, \\n llm_split : { \\n llm : { \\n generative_model + : chatgpt-azure-4o-mini , \\n }, \\n rules : [ \\n Split at each section heading + \\n ] \\n }, \\n } \\n Experiment with different rules and models to optimize + chunking for your use case. Note that using an LLM may increase processing + time and cost, and could slightly alter the extracted text. \\n Manual Splitting + \\n Manual splitting divides text based on a specified delimiter, making it + straightforward and efficient for simpler chunking needs. This method is particularly + useful when your content has clear, consistent separators, such as paragraphs + or bullet points. \\n You can customize manual splitting using the following + parameters: \\n - Splitter: Choose the character or string that indicates + where to split the text. The default is a double newline character ( \\\\n\\\\n + ), but you can set it to any delimiter that suits your content structure. + \\n Example configuration: \\n json \\n { \\n name : manual , \\n custom_split + : 1, \\n manual_split : { \\n splitter : \\\\n \\n } \\n } \\n Strategy creation + and management \\n Before we can use a split strategy, we need to create it. + We can create as many strategies as we want, and just use the right one for + each processing job. Once created we can not modify our strategies, but we + can delete them and inspect the ones we have created for a given kb. \\n Dashboard + \\n To create a strategy, just go to the section AI Models and then to Extract + & split. Then you can click on the button Create configuration , next to Split + configurations to create a new strategy. Once there, you can fill in the necessary + fields with the desired configuration. \\n In the same section, you can also + see the list of strategies you have created, and you can delete them once + you no longer need them. \\n CLI \\n To create a strategy using the CLI, you + can use the command nuclia kb split_strategies add with the desired configuration + in JSON format. You can also list the strategies you have created with nuclia + kb split_strategies list, and delete them with nuclia kb split_strategies + delete. \\n bash \\n nuclia kb split_strategies add --config='{ name : strategy1 + , custom_split : 1, manual_split : { splitter : \\\\n }}' \\n nuclia kb split_strategies + list \\n nuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To create a strategy using the SDK, you can use the add method + of the split_strategies object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the list method, + and delete them with the delete method. \\n python \\n from nuclia import + sdk \\n split_strategies = sdk.NucliaKB().split_strategies \\n print(split_strategies.list()) + \\n id = split_strategies.add(config='{ name : strategy1 , custom_split : + 1, manual_split : { splitter : \\\\n }}') \\n split_strategies.delete(id=id) + \\n Use split strategies for processing \\n Dashboard \\n To use a split strategy + for processing documents, just upload the document normally, enable Customize + data extraction and select the strategy you want to use in the dropdown menu. + Once the document is uploaded, it will be processed using the selected strategy. + \\n CLI \\n To use a split strategy for processing documents using the CLI, + you can use the command nuclia kb upload file with the --split_strategy option, + passing the ID of the strategy you want to use. \\n bash \\n nuclia kb upload + file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n bash \\n nuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To use a split strategy for processing documents using the SDK, + you can use the file method of the NucliaUpload object, passing the path to + the file and the ID of the strategy you want to use. \\n python \\n from nuclia + import sdk \\n upload = sdk.NucliaUpload() \\n upload.file(path=FILE_PATH, + split_strategy= 1361c0c7-918a-4a7f-b44b-ba37437619fb 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Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n 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UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\n\\n# + Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n***\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\\_PROCESSED**: + `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n***\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\n\\n***\\n\\n### + PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"pages_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n***\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**: `\\\"paragraphs_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n***\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\n\\n***\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**: `\\\"resources_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n***\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**: `\\\"searches_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n***\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\n\\n***\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**: `\\\"suggestions_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > 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libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.015625,\"score_type\":\"VECTOR\",\"order\":10,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.01639344262295082,\"score_type\":\"BM25\",\"order\":4,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.014705882352941176,\"score_type\":\"BM25\",\"order\":17,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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+ Box\":\"PRODUCT\",\"Agentic RAG\":\"PRODUCT\",\"LLM\":\"ORG\",\"json\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge + Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\":4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\",\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic + 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Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\",\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.015873015873015872,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.015625,\"score_type\":\"BM25\",\"order\":9,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.015384615384615385,\"score_type\":\"BM25\",\"order\":11,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n 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+ min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"end\":654}]},{\"text\":\"112 + min\",\"label\":\"TIME\",\"positions\":[{\"start\":2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\",\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PERSON/boolean\":{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"},\"TIME/112 + min\":{\"position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.030834914611005692,\"score_type\":\"BOTH\",\"order\":0,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9a6df7db9c6d4036b949d3f8a134ca1c\":{\"id\":\"9a6df7db9c6d4036b949d3f8a134ca1c\",\"slug\":\"docs-develop-python-sdk-02-auth-md\",\"title\":\"docs + > develop > python sdk > 02 auth\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:39.987690\",\"modified\":\"2026-06-09T08:08:06.772770\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/auth\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Authentication\\n\\nIn order to start working you can login either using a + user acount, or using an API key.\\n\\nNote: you can also login with a NUA + key. With the right permissions/scopes, a NUA key can be used not only for + processing features (see [NUA](07-nua.md)), but also for Knowledge Box management + operations (list/get/create/delete) in your authorized account.\\n\\n- CLI: + `nuclia auth nua [NUA_KEY]`\\n- SDK:\\n\\n ```python\\n from nuclia import + sdk\\n sdk.NucliaAuth().nua(NUA_KEY)\\n ```\\n\\nFor private or on-premise + deployments where the API endpoint differs from the token issuer, see [Custom + API endpoint](07-nua.md#custom-api-endpoint-private-or-on-premise-deployments-only).\\n\\n## + User authentication\\n\\nWith user authentication, there is no limitation + to the API endpoints you can use. You manage all the accounts and Knowledge + Boxes you have access to. It also allows to manage local NucliaDBs connected + to your accounts.\\n\\nBut it does not last very long (30 minutes), and it + requires a browser to complete the authentication process.\\nIt is a good + fit for interactive use, but not for long running scripts.\\n\\n- CLI: `nuclia + auth login`, and it will trigger a browser authentication flow, so you can + copy/paste the token in your terminal.\\n\\n- SDK: (even if it is not recommended + for long running scripts, user authentication can be done in the SDK)\\n\\n + \ ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().set_user_token(USER_TOKEN)\\n + \ ```\\n\\n## Personal access token\\n\\nA personal access token is a long-lived + user token that can be used to authenticate to the API. It is a good fit for + long running scripts. It grants the same access as user authentication.\\n\\nYou + define its expiration date (default is 90 days), and you can revoke it at + any time.\\n\\n- CLI:\\n\\n To generate a token, run:\\n\\n ```sh\\n nuclia + auth create_personal_token --description=\\\"My token\\\" --days=30\\n ```\\n\\n + \ To generate a token and use it as authentication in the CLI, run:\\n\\n + \ ```sh\\n nuclia auth create_personal_token --description=\\\"My token\\\" + --days=30 --login\\n ```\\n\\n To list the tokens, run:\\n\\n ```sh\\n + \ nuclia auth list_personal_tokens\\n ```\\n\\n To delete a token, run:\\n\\n + \ ```sh\\n nuclia auth delete_personal_token [TOKEN_ID]\\n ```\\n\\n- SDK:\\n\\n + \ ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().create_personal_token(description=\\\"My + token\\\", days=30)\\n sdk.NucliaAuth().list_personal_tokens()\\n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID)\\n + \ ```\\n\\n## API key\\n\\nAn API key can be generated from the Nuclia Dashboard, + see [Get an API key](https://docs.rag.progress.cloud/docs/guides/getting-started/quick-start/push#get-an-api-key).\\n\\nWhen + authenticating with an API key, you can only access the Knowledge Box that + is associated with this API key.\\nThe authentication will last as long as + the key is valid (potentially forever, but an API key can be revoked from + the Nuclia Dashboard).\\n\\nIt is the recommended way to authenticate for + long running scripts.\\n\\n- CLI: `nuclia auth kb [KB_URL] [API_KEY]`\\n- + SDK:\\n\\n ```python\\n from nuclia import sdk\\n sdk.NucliaAuth().kb(url=KB_URL, + token=API_KEY)\\n ```\\n\\n- Provides access to NucliaDB API. For the managed + service you will need a service token.\\n\\n## Logout\\n\\n```sh\\nnuclia + auth logout\\n```\\n\\n## List configured user accounts, knowledge boxes and + NUA keys\\n\\nYou can list the configured authentication mechanisms using:\\n\\n```bash\\nnuclia + auth show\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"7adfd5cd16971a512ca4db72bb4047d5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Authentication + \\n In order to start working you can login either using a user acount, or + using an API key. \\n Note: you can also login with a NUA key. With the right + permissions/scopes, a NUA key can be used not only for processing features + (see NUA), but also for Knowledge Box management operations (list/get/create/delete) + in your authorized account. \\n \\n CLI: nuclia auth nua [NUA_KEY] \\n SDK: + \\n \\n python \\n from nuclia import sdk \\n sdk.NucliaAuth().nua(NUA_KEY) + \\n For private or on-premise deployments where the API endpoint differs from + the token issuer, see Custom API endpoint. \\n User authentication \\n With + user authentication, there is no limitation to the API endpoints you can use. + You manage all the accounts and Knowledge Boxes you have access to. It also + allows to manage local NucliaDBs connected to your accounts. \\n But it does + not last very long (30 minutes), and it requires a browser to complete the + authentication process. \\n It is a good fit for interactive use, but not + for long running scripts. \\n \\n \\n CLI: nuclia auth login, and it will + trigger a browser authentication flow, so you can copy/paste the token in + your terminal. \\n \\n \\n SDK: (even if it is not recommended for long running + scripts, user authentication can be done in the SDK) \\n \\n \\n python \\n + from nuclia import sdk \\n sdk.NucliaAuth().set_user_token(USER_TOKEN) \\n + Personal access token \\n A personal access token is a long-lived user token + that can be used to authenticate to the API. It is a good fit for long running + scripts. It grants the same access as user authentication. \\n You define + its expiration date (default is 90 days), and you can revoke it at any time. + \\n \\n CLI: \\n \\n To generate a token, run: \\n sh \\n nuclia auth create_personal_token + --description= My token --days=30 \\n To generate a token and use it as authentication + in the CLI, run: \\n sh \\n nuclia auth create_personal_token --description= + My token --days=30 --login \\n To list the tokens, run: \\n sh \\n nuclia + auth list_personal_tokens \\n To delete a token, run: \\n sh \\n nuclia auth + delete_personal_token [TOKEN_ID] \\n \\n SDK: \\n \\n python \\n from nuclia + import sdk \\n sdk.NucliaAuth().create_personal_token(description= My token + , days=30) \\n sdk.NucliaAuth().list_personal_tokens() \\n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID) + \\n API key \\n An API key can be generated from the Nuclia Dashboard, see + Get an API key. \\n When authenticating with an API key, you can only access + the Knowledge Box that is associated with this API key. \\n The authentication + will last as long as the key is valid (potentially forever, but an API key + can be revoked from the Nuclia Dashboard). \\n It is the recommended way to + authenticate for long running scripts. \\n \\n CLI: nuclia auth kb [KB_URL] + [API_KEY] \\n SDK: \\n \\n python \\n from nuclia import sdk \\n sdk.NucliaAuth().kb(url=KB_URL, + token=API_KEY) \\n \\n Provides access to NucliaDB API. For the managed service + you will need a service token. \\n \\n Logout \\n sh \\n nuclia auth logout + \\n List configured user accounts, knowledge boxes and NUA keys \\n You can + list the configured authentication mechanisms using: \\n bash \\n nuclia auth + 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+ days\":\"DATE\",\"30 minutes\":\"TIME\",\"30\":\"DATE\",\"days\":\"DATE\",\"days=30\":\"DATE\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"NucliaAuth\",\"label\":\"ORG\",\"positions\":[{\"start\":436,\"end\":446},{\"start\":1299,\"end\":1309},{\"start\":2179,\"end\":2189},{\"start\":2778,\"end\":2788}]},{\"text\":\"30 + minutes\",\"label\":\"TIME\",\"positions\":[{\"start\":859,\"end\":869}]},{\"text\":\"90 + days\",\"label\":\"DATE\",\"positions\":[{\"start\":1599,\"end\":1606}]},{\"text\":\"days\",\"label\":\"DATE\",\"positions\":[{\"start\":1750,\"end\":1754}]},{\"text\":\"30\",\"label\":\"DATE\",\"positions\":[{\"start\":1755,\"end\":1757},{\"start\":1898,\"end\":1900}]},{\"text\":\"days=30\",\"label\":\"DATE\",\"positions\":[{\"start\":2164,\"end\":2171}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:28.338373Z\",\"last_understanding\":\"2026-06-09T08:08:27.939363Z\",\"last_extract\":\"2026-06-09T08:08:26.245668Z\",\"last_processing_start\":\"2026-06-09T08:08:26.205740Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"DATE/days\":{\"position\":[{\"start\":1750,\"end\":1754}],\"entity\":\"days\"},\"TIME/30 + minutes\":{\"position\":[{\"start\":859,\"end\":869}],\"entity\":\"30 minutes\"},\"DATE/30\":{\"position\":[{\"start\":1755,\"end\":1757},{\"start\":1898,\"end\":1900}],\"entity\":\"30\"},\"ORG/NucliaAuth\":{\"position\":[{\"start\":436,\"end\":446},{\"start\":1299,\"end\":1309},{\"start\":2179,\"end\":2189},{\"start\":2778,\"end\":2788}],\"entity\":\"NucliaAuth\"},\"DATE/90 + days\":{\"position\":[{\"start\":1599,\"end\":1606}],\"entity\":\"90 days\"},\"DATE/days=30\":{\"position\":[{\"start\":2164,\"end\":2171}],\"entity\":\"days=30\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"developer\",\"metadata\":{\"paragraph_id\":\"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363\",\"source_start\":2164,\"source_end\":2171,\"to_start\":2179,\"to_end\":2189},\"from\":{\"value\":\"days=30\",\"type\":\"entity\",\"group\":\"DATE\"},\"to\":{\"value\":\"NucliaAuth\",\"type\":\"entity\",\"group\":\"ORG\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > python sdk > 02 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '11230' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:05 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '178' + x-nuclia-trace-id: + - f9c362fb770df733920f02ca0dfe75d6 + status: + code: 200 + message: OK +- request: + body: '{"question": "\u00bfCu\u00e1l es la URL de la documentaci\u00f3n oficial + de la API de Nuclia relacionada con el par\u00e1metro `max_tokens`?", "user_id": + "arag-ask-rerank", "context": {"6e8250e6b5264156988657a221fd5e94/t/page/0-397": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / Ask / ConsumptionAskResponseItem + \n Interface: ConsumptionAskResponseItem \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n", + "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum + number of input tokens to put in the final context (including the prompt, the + retrieved results and the user question). \n max_output_tokens: the maximum + number of tokens to generate. \n\n", "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / PageToken \n Interface: PageToken + \n Properties \n height \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \n \n text \n \n\n", "4194605cdfbd414f8fa762630d555bc1/t/page/0-358": "@nuclia/core + \u2022 Docs \n \n @nuclia/core / PageStructure \n Interface: PageStructure \n + Properties \n page \n \n page: object \n \n height \n \n height: number \n \n + width \n \n width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 + \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n", + "7b41750917e349598c846526162ebb68/a/title/0-58": "docs > develop > js sdk > + interfaces > NucliaTokensDetails\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": + " ::: \n :::warning \n Enabling reasoning can use additional tokens, which may + increase your usage costs. \n You may need to increase max_tokens to give the + LLM enough room to reason and generate an answer. \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": + " \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n", "1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239": " Key components + of a split strategy include: \n \n Max paragraph size: Sets the maximum size + (in characters or tokens) for each chunk. \n Custom split: Determines the method + used for splitting: \n Manual splitting: Splits content based on a specified + delimiter (default is \\n ). \n\n", "8b3e0ef630a346d1b591143309db87ec/t/page/281-511": + " name: nuclia_tokens \n \n Overrides \n UsageMetric.name \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 + \n \n value \n \n value: number \n \n Overrides \n UsageMetric.value \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:184\n", "8b3e0ef630a346d1b591143309db87ec/a/title/0-57": + "docs > develop > js sdk > interfaces > NucliaTokensMetric\n", "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": + "@nuclia/core \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType + \n Enumeration Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED + \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n", + "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": "@nuclia/core \u2022 Docs \n + \n @nuclia/core / Consumption \n Interface: Consumption \n Properties \n customer_key_tokens + \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 + \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n", "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": + " \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": + " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": + " Use the max_tokens parameter on the /ask endpoint to set hard limits on: \n + - Context size: Limits the amount of retrieved information sent to the LLM \n + - Answer length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n", "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139": + "@nuclia/core / Exports / UsageType \n Enumeration: UsageType \n Table of contents + \n Enumeration Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED + \n MEDIA_FILES_PROCESSED \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED + \n PARAGRAPHS_PROCESSED \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED + \n SLOW_PROCESSING_TIME \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration + Members \n AI_TOKENS_USED \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:190 \n \n BYTES_PROCESSED \n \u2022 + BYTES_PROCESSED = bytes_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:181 + \n \n CHARS_PROCESSED \n \u2022 CHARS_PROCESSED = chars_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:182 \n \n MEDIA_FILES_PROCESSED + \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 + \n \n MEDIA_SECONDS_PROCESSED \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS + \n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n", + "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": + " \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": " \n optional max_tokens: + number \\| object \n \n Defines the maximum number of tokens that the model + will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n", "9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363": + " python \n from nuclia import sdk \n sdk.NucliaAuth().create_personal_token(description= + My token , days=30) \n sdk.NucliaAuth().list_personal_tokens() \n sdk.NucliaAuth().delete_personal_token(token_id=TOKEN_ID) + \n API key \n An API key can be generated from the Nuclia Dashboard, see Get + an API key. \n\n"}}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '10341' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank + response: + body: + string: '{"context_scores":{"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833":0.3312278985977173,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.27464309334754944,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.2632204294204712,"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.22456520795822144,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.19544048607349396,"8b3e0ef630a346d1b591143309db87ec/a/title/0-57":0.19147881865501404,"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139":0.16871994733810425,"7b41750917e349598c846526162ebb68/a/title/0-58":0.11240508407354355,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.10466019809246063,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.04401864856481552,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.03289904072880745,"4194605cdfbd414f8fa762630d555bc1/t/page/0-358":0.029256708920001984,"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349":0.02859835885465145,"6e8250e6b5264156988657a221fd5e94/t/page/0-397":0.026155617088079453,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.02595735713839531,"8b3e0ef630a346d1b591143309db87ec/t/page/281-511":0.0219902191311121,"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299":0.017917344346642494,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709":0.010130411945283413,"9a6df7db9c6d4036b949d3f8a134ca1c/t/page/2066-2363":0.005199880339205265,"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239":0.00029365462251007557}}' + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '1425' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:06 GMT + nuclia-learning-model: + - bge-reranker-v2-m3 + via: + - 1.1 google + x-envoy-upstream-service-time: + - '157' + x-nuclia-trace-id: + - 655b0d92b57e4b5e59793a054029f20a + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", + "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context + and user question, perform the following tasks:\n\n1. Select only information + directly relevant to the question.\n2. Break down compound sentences into simple, + single-idea statements. Preserve original phrasing when possible.\n3. For any + named entity with descriptive details, separate those details into distinct + propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", + \"she\", \"they\", \"this\", \"that\") with the full names of the entities they + reference, and add necessary modifiers to clarify meaning.\n5. The context may + be delimited by tags such as and . Treat + everything between these tags as context.\n6. Assess whether the context sufficiently + answers the question. If it answers it partially, provide the answer; if it + does not answer it fully, specify what information is missing to answer the + question.\n7. If the context does not answer the question at all, just return + the original question as the missing information.\n8. The `citations` field + consists ONLY in a list of block IDs that are relevant to the answer, following + these rules:\n - Use the format: block-AB\n - Just mention the block IDs, + do NOT include any other text.\n - Just mention the blocks actually relevant + and that contain information used in the answer, do NOT include blocks that + are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only + use those provided in the context.\n10. Your output must be a JSON object with + the following fields:\n - \"reason\": Explain your reasoning for the answer + or validation.\n - \"answer\": Provide a partial or complete answer to the + user query strictly from the information in the context. If there isn''t enough + information to even provide a partial answer, leave ''answer'' empty.\n - + \"missing_info_query\": If the context is insufficient, specify what information + is missing in a query shape; otherwise, leave it empty. Just return the query + needed to retrieve the missing information.\n - \"useful\": Indicate if the + context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": + List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", + \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you + MUST follow them carefully when generating the answer field. These instructions + may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfCu\u00e1l + es la URL de la documentaci\u00f3n oficial de la API de Nuclia relacionada con + el par\u00e1metro `max_tokens`?\n\n\n\nContext:\n\n**block-AA**\n\n## + Chunk: 66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\nTags: /k/text\n``` @nuclia/core + \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration + Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED \n \n + BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 + \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n + \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 + \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS + \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 + \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in + \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n + PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 + \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED + \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 + \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME + \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 + \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed + \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS + \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n + ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## + Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n optional max_tokens: + number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use + the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context + size: Limits the amount of retrieved information sent to the LLM \n - Answer + length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n + SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: 8b3e0ef630a346d1b591143309db87ec/a/title/0-57\nTags: + /k/text\n``` docs > develop > js sdk > interfaces > NucliaTokensMetric\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## + Chunk: b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\nTags: /k/text\n``` @nuclia/core + / Exports / UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration + Members \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED + \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED + \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME + \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED + \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 + \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 + CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 + \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED + \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n + libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS + = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 + \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined + in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n + \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 + \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED + \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 + \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n + Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME + \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 + \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed + \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS + \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n + ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 7b41750917e349598c846526162ebb68/a/title/0-58\nTags: + /k/text\n``` docs > develop > js sdk > interfaces > NucliaTokensDetails\n ```\n\n\n---\"\n\n\n**block-AI**\n\n## + Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n optional max_tokens: + number \\| object \n \n Defines the maximum number of tokens that the model + will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": + false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", + "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": + {"title": "validate_or_answer", "description": "Validate or answer", "parameters": + {"type": "object", "properties": {"reason": {"type": "string", "description": + "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": + "Partial or complete answer to the user query from the information in the context."}, + "missing_info_query": {"type": "string", "description": "Query needed to retrieve + the missing information in case the context is not enough to answer the question. + If the context does not answer the question at all, just return the original + question."}, "useful": {"type": "string", "description": "Is the context useful + to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", + "items": {"type": "string", "description": "Block ID cited in the answer, e.g. + block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, + "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, + "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": + {"type": "required"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '10816' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-origin: + - RAO + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context + provides information about the `max_tokens` parameter but does not include + a URL for the official documentation of the Nuclia API. The relevant blocks + discuss the definition and usage of the `max_tokens` parameter but do not + link to any documentation page.\",\"answer\":\"\",\"missing_info_query\":\"\xBFCu\xE1l + es la URL de la documentaci\xF3n oficial de la API de Nuclia relacionada con + el par\xE1metro `max_tokens`?\",\"useful\":\"no\",\"citations\":[]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":75,\"output_tokens\":12,\"timings\":{\"generative\":1.8758880960003808},\"input_nuclia_tokens\":0.075,\"output_nuclia_tokens\":0.012}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.07506,\"output\":0.01176,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:06 GMT + nuclia-learning-id: + - 8550944e19ed4c36b6794f2541ed7041 + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '1881' + x-nuclia-trace-id: + - a082f552cc9275a9b9ae1464899cff4c + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "summarize", "system": "You + are a helpful AI assistant. Your role is to provide accurate, clear, and well-structured + answers based strictly on the information provided to you.\nKey principles:\n- + Answer only using the information in the provided context\n- Do not use external + knowledge, assumptions, or prior experience\n- Maintain a professional and informative + tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge + this clearly\n\nAlways follow any additional instructions provided about format, + style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": + {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## + Question\nComo usar max_tokens.answer. En espa\u00f1ol y dame link a la doucmentaci\u00f3n\n\n## + Provided Context\n[START OF CONTEXT]\n## Retrieval on nucliadb-2 Knowledge Box\n\n# + \u00bfCu\u00e1l es la funci\u00f3n del par\u00e1metro `max_tokens` en el contexto + de las respuestas generadas por la API de Nuclia y c\u00f3mo se utiliza?\n\n + The `max_tokens` parameter in the Nuclia API is used to set hard limits on two + aspects: the context size and the answer length. It limits the amount of retrieved + information sent to the language model (LLM) and also limits the length of the + generated response. Specifically, `max_tokens` refers to the maximum number + of input tokens to include in the final context, which encompasses the prompt, + the retrieved results, and the user question.\n\n## Retrieval on nucliadb-2 + Knowledge Box\n\n# \u00bfD\u00f3nde puedo encontrar la documentaci\u00f3n oficial + de la API de Nuclia relacionada con el par\u00e1metro `max_tokens`?\n\n The + `max_tokens` parameter is defined in the Nuclia API documentation. It is mentioned + in the context of the `/ask` endpoint, where it sets limits on context size + and answer length. However, the exact location of the official documentation + is not provided in the context.\n[END OF CONTEXT]\n\n## Answering Guidelines\n- + Carefully read all context; it may be lengthy or detailed\n- Do not omit or + overlook any relevant information\n- If the context is incomplete or insufficient, + state: \"Not enough data to answer this.\"\n- Read carefully any extra instructions + below if provided and use them to answer\n\nNow provide your answer to the question: + Como usar max_tokens.answer. En espa\u00f1ol y dame link a la doucmentaci\u00f3n"}, + "citations": null, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", + "max_tokens": 5000, "query_context_images": {}, "prefer_markdown": null, "json_schema": + null, "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": + {"type": "auto"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '2655' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-client-ident: + - default + x-message: + - 5c31c1e9240443d6ba1ce9fcbe0aafc9 + x-origin: + - RAO + x-session: + - default_default_session + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: "{\"chunk\":{\"type\":\"text\",\"text\":\"El\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + par\xE1\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"metro\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + `\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"max\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"_tokens\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"`\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + en\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" la\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + API\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" de\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + Nu\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"cl\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\"ia\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + se\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" utiliza\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + para\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" establecer\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" + 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x-nuclia-trace-id: + - a082f552cc9275a9b9ae1464899cff4c + status: + code: 200 + message: OK +version: 1 diff --git a/agents/rephrase/tests/cassettes/test_rephrase/test_rephrase_agent_only_rephrase.yaml b/agents/rephrase/tests/cassettes/test_rephrase/test_rephrase_agent_only_rephrase.yaml new file mode 100644 index 0000000..8238d5b --- /dev/null +++ b/agents/rephrase/tests/cassettes/test_rephrase/test_rephrase_agent_only_rephrase.yaml @@ -0,0 +1,4686 @@ +interactions: +- request: + body: '' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-nuclia-nuakey: + - DUMMY + method: GET + uri: https://europe-1.dp.progress.cloud/api/authorizer/info + response: + body: + string: '{"auth":"nua_key","user":{"identity_type":"nua_key","user_id":"7fe77405-a6de-43cf-ac27-ac09f80b66c6","account_id":"4f9285c7-7151-4431-94e6-3f1fb0d66aca","account_type":"v3enterprise","allow_kb_management":false},"ip_info":null}' + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '227' + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:11 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '5' + status: + code: 200 + message: OK +- request: + body: '{"query": "Como usar max_tokens.answer. En espa\u00f1ol y dame link a la + doucmentaci\u00f3n", "filters": [], "min_score": {"bm25": 1.0}, "resource_filters": + [], "features": ["keyword"]}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '167' + Host: + - europe-1.nuclia.cloud + User-Agent: + - nucliadb-sdk/6.13.1.post6414 + content-type: + - application/json + x-stf-serviceaccount: + - DUMMY + method: POST + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find + response: + body: + string: "{\"resources\":{\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.36206647753715515,\"score_type\":\"RERANKER\",\"order\":0,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\":{\"score\":0.0005112579092383385,\"score_type\":\"RERANKER\",\"order\":18,\"text\":\" + Key components of a split strategy include: \\n \\n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \\n Custom split: + Determines the method used for splitting: \\n Manual splitting: Splits content + based on a specified delimiter (default is \\\\n ). \\n\",\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":963,\"end\":1239,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.01652955636382103,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.007374365348368883,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"774344cbf79648619585dfaa136d2077\":{\"id\":\"774344cbf79648619585dfaa136d2077\",\"slug\":\"docs-develop-dotnet-sdk-IResourceFieldsService-md\",\"title\":\"docs + > develop > dotnet sdk > IResourceFieldsService\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T16:03:49.207565\",\"modified\":\"2026-06-09T08:17:39.086385\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"774344cbf79648619585dfaa136d2077/t/page/11501-11823\":{\"score\":0.0013670255430042744,\"score_type\":\"RERANKER\",\"order\":12,\"text\":\" + ```csharp \\n var linkField = new LinkField( https://example.com/article ) + \\n { \\n Headers = new Dictionary \\n { \\n [ User-Agent ] = MyBot/1.0 \\n + }, \\n Language = en , \\n CssSelector = article.content \\n }; \\n var response + = await client.ResourceFields.AddLinkFieldByIdAsync( \\n resource-123 , \\n + web-link , \\n linkField \\n ); \\n ``` \\n \\n\",\"id\":\"774344cbf79648619585dfaa136d2077/t/page/11501-11823\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":30,\"start\":11501,\"end\":11823,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"774344cbf79648619585dfaa136d2077/t/page/0-269\":{\"score\":0.000337979436153546,\"score_type\":\"RERANKER\",\"order\":19,\"text\":\"IResourceFieldsService + Documentation \\n The IResourceFieldsService provides operations for managing + individual fields within resources, including conversation, file, link, and + text fields. It supports both resource ID and slug-based access patterns for + all operations. \\n\",\"id\":\"774344cbf79648619585dfaa136d2077/t/page/0-269\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":269,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.10669060051441193,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.07081620395183563,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4494369d2ea747c4ac7bcaba4ca87609\":{\"id\":\"4494369d2ea747c4ac7bcaba4ca87609\",\"slug\":\"docs-develop-js-sdk-enumerations-SHORT_FIELD_TYPE-md\",\"title\":\"docs + > develop > js sdk > enumerations > SHORT_FIELD_TYPE\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:04.900061\",\"modified\":\"2026-07-14T12:54:26.542730\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4494369d2ea747c4ac7bcaba4ca87609/t/page/517-687\":{\"score\":0.0007495314348489046,\"score_type\":\"RERANKER\",\"order\":15,\"text\":\" + link: u \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:316 + \\n \\n text \\n \\n text: t \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:314\",\"id\":\"4494369d2ea747c4ac7bcaba4ca87609/t/page/517-687\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":517,\"end\":687,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1d865fe167cb4bcb9aed2f73a3fc831c\":{\"id\":\"1d865fe167cb4bcb9aed2f73a3fc831c\",\"slug\":\"docs-rag-advanced-ask-md\",\"title\":\"docs + > rag > advanced > ask\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:22.545338\",\"modified\":\"2026-06-26T08:59:41.139324\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1d865fe167cb4bcb9aed2f73a3fc831c/t/page/1124-1476\":{\"score\":0.0032476764172315598,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" + answer: The generative answer. \\n metadata: The amount of tokens consumed + by the query and the answer generation and the time taken to produce the response. + \\n citations: The paragraphs actually used to generated the answer (among + the search results initially passed to the generative model) and the positions + of the corresponding parts of the answer. \\n\",\"id\":\"1d865fe167cb4bcb9aed2f73a3fc831c/t/page/1124-1476\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":1124,\"end\":1476,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"3d73bdffb13b4fed8076a1bee384d7c0\":{\"id\":\"3d73bdffb13b4fed8076a1bee384d7c0\",\"slug\":\"docs-rag-rag-strategy-md\",\"title\":\"docs + > rag > rag strategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"hu\",\"cy\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:18.060615\",\"modified\":\"2026-06-09T08:07:32.011816\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"3d73bdffb13b4fed8076a1bee384d7c0/t/page/8831-9141\":{\"score\":0.002074867021292448,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\" + Use the origin url to provide a markdown link to the corresponding result. + \\n Use preferably results that have been published after 2020. \\n These + metadata can also be used through the JSON output to produce next to the generated + answer a rich info card showing images, buttons, or any kind of call-to-action. + \\n\",\"id\":\"3d73bdffb13b4fed8076a1bee384d7c0/t/page/8831-9141\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":21,\"start\":8831,\"end\":9141,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.011687257327139378,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.0043653263710439205,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"a3da5b7efa8944e193c734e7d1138473\":{\"id\":\"a3da5b7efa8944e193c734e7d1138473\",\"slug\":\"docs-rag-how-to-e-commerce-md\",\"title\":\"docs + > rag > how to > e commerce\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-09-02T12:55:15.367587\",\"modified\":\"2026-06-09T08:07:37.132628\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"a3da5b7efa8944e193c734e7d1138473/t/page/5575-5892\":{\"score\":0.000661517377011478,\"score_type\":\"RERANKER\",\"order\":16,\"text\":\" + If you need to render a more complex UI than a simple link, you might not + want to rely on markdown code generated by an LLM. In that case, you can use + the answer_json_schema parameter to retrieve the information in a structured + format that can be easily processed by your front-end (see the Use JSON output + how-to).\",\"id\":\"a3da5b7efa8944e193c734e7d1138473/t/page/5575-5892\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":16,\"start\":5575,\"end\":5892,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"ecabb0862fa24c8fb8e01d4930a3f7a2\":{\"id\":\"ecabb0862fa24c8fb8e01d4930a3f7a2\",\"slug\":\"docs-develop-js-sdk-variables-MAX_FACETS_PER_REQUEST-md\",\"title\":\"docs + > develop > js sdk > variables > MAX_FACETS_PER_REQUEST\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:18.992004\",\"modified\":\"2026-07-14T12:54:35.795661\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/a/title\":{\"paragraphs\":{\"ecabb0862fa24c8fb8e01d4930a3f7a2/a/title/0-60\":{\"score\":0.001269364496693015,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\"docs + > develop > js sdk > variables > MAX_FACETS_PER_REQUEST\",\"id\":\"ecabb0862fa24c8fb8e01d4930a3f7a2/a/title/0-60\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":60,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"a572ce0c7eb949e4a17babae21a03a4a\":{\"id\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"slug\":\"navigation-pages\",\"title\":\"navigation-pages\",\"summary\":\"\",\"icon\":\"application/json\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"fr\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-06-23T10:58:22.812220\",\"modified\":\"2026-06-23T10:58:22.812242\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/kb-tokens-time-usage\":{\"paragraphs\":{\"a572ce0c7eb949e4a17babae21a03a4a/t/kb-tokens-time-usage/0-118\":{\"score\":0.0009290033485740423,\"score_type\":\"RERANKER\",\"order\":14,\"text\":\"id + : kb-tokens-time-usage\\n\\nroute : /at/:account/:zone/:kb/metrics/tokens-and-time-usage\\n\\ntitle + : Tokens & Time Usage\\n\\n\",\"id\":\"a572ce0c7eb949e4a17babae21a03a4a/t/kb-tokens-time-usage/0-118\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":118,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.1440284252166748,\"score_type\":\"RERANKER\",\"order\":1,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"632f90605bd14a39b48acc4c9fa74c01\":{\"id\":\"632f90605bd14a39b48acc4c9fa74c01\",\"slug\":\"docs-develop-js-sdk-enumerations-FIELD_TYPE-md\",\"title\":\"docs + > develop > js sdk > enumerations > FIELD_TYPE\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:09.427987\",\"modified\":\"2026-07-14T12:54:24.739120\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"632f90605bd14a39b48acc4c9fa74c01/t/page/544-726\":{\"score\":0.0005484792636707425,\"score_type\":\"RERANKER\",\"order\":17,\"text\":\" + link: link \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:57 + \\n \\n text \\n \\n text: text \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:55\",\"id\":\"632f90605bd14a39b48acc4c9fa74c01/t/page/544-726\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":544,\"end\":726,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.03321117162704468,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.01590639166533947,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\n + \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[] + \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens? + \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}}},\"relations\":{\"entities\":{}},\"query\":\"Como + usar max_tokens.answer. En espa\xF1ol y dame link a la doucmentaci\xF3n\",\"rephrased_query\":null,\"total\":460,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"],\"min_score\":{\"semantic\":0.0,\"bm25\":1.0},\"best_matches\":[\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"1d865fe167cb4bcb9aed2f73a3fc831c/t/page/1124-1476\",\"3d73bdffb13b4fed8076a1bee384d7c0/t/page/8831-9141\",\"774344cbf79648619585dfaa136d2077/t/page/11501-11823\",\"ecabb0862fa24c8fb8e01d4930a3f7a2/a/title/0-60\",\"a572ce0c7eb949e4a17babae21a03a4a/t/kb-tokens-time-usage/0-118\",\"4494369d2ea747c4ac7bcaba4ca87609/t/page/517-687\",\"a3da5b7efa8944e193c734e7d1138473/t/page/5575-5892\",\"632f90605bd14a39b48acc4c9fa74c01/t/page/544-726\",\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"774344cbf79648619585dfaa136d2077/t/page/0-269\"]}" + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Content-Length: + - '24873' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:11 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '256' + x-nuclia-trace-id: + - d9af6cfa31aa307b9b6d584e030a46ed + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rephrase", "system": null, + "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nYou are an expert at rephrasing + complex questions for an agentic RAG system.\n\nYour task is to review the main + question and any provided context, then rephrase the question to maximize clarity + and focus. Follow these steps:\n\n1. Carefully analyze the main question and + all context provided, including sources, previous questions and answers, and + any other relevant information.\n2. I present, assess whether previous questions + and answers (history) are necessary for rephrasing. Only use history if it is + relevant and improves the clarity or specificity of the main question; otherwise, + ignore it.\n3. If the question can be made clearer or more specific, rephrase + it accordingly. If it is already clear and focused, return it unchanged.\n4. + Only use information present in the provided context. Do not introduce external + knowledge or assumptions.\n5. Return a JSON object with the following fields:\n - + \"rephrased_question\": The rephrased version of the main question, keep the + same question if no rephrasing is needed or possible.\n - \"rules\": Any + rules or guidelines that should be followed when generating the answer.\n - + \"reason\": Explain why the rephrasing was necessary or beneficial.\nReturn + only the JSON object as your response.\n\nAdditional context to assist with + rephrasing:\n# CONTEXT\n\n- ## Extra information to append to the question:\n + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \n + - Context size: Limits the amount of retrieved information sent to the LLM \n + - Answer length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n Key components of a split strategy include: \n + \n Max paragraph size: Sets the maximum size (in characters or tokens) for each + chunk. \n Custom split: Determines the method used for splitting: \n Manual + splitting: Splits content based on a specified delimiter (default is \\n ). + \n\n \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n \n optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n ```csharp \n var linkField = new LinkField( https://example.com/article + ) \n { \n Headers = new Dictionary \n { \n [ User-Agent ] = MyBot/1.0 \n }, + \n Language = en , \n CssSelector = article.content \n }; \n var response = + await client.ResourceFields.AddLinkFieldByIdAsync( \n resource-123 , \n web-link + , \n linkField \n ); \n ``` \n \n\nIResourceFieldsService Documentation \n The + IResourceFieldsService provides operations for managing individual fields within + resources, including conversation, file, link, and text fields. It supports + both resource ID and slug-based access patterns for all operations. \n\n ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n \n SDK: \n \n ```python \n from + nuclia import sdk \n from nucliadb_models.search import AskRequest, Reasoning + \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My question + with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( \n + display=True, # Show reasoning in the response \n effort= low , # Can be low + , medium , or high \n budget_tokens=1024 # How many tokens reasoning can use + \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n link: u \n \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:316 + \n \n text \n \n text: t \n \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:314\n + answer: The generative answer. \n metadata: The amount of tokens consumed by + the query and the answer generation and the time taken to produce the response. + \n citations: The paragraphs actually used to generated the answer (among the + search results initially passed to the generative model) and the positions of + the corresponding parts of the answer. \n\n Use the origin url to provide a + markdown link to the corresponding result. \n Use preferably results that have + been published after 2020. \n These metadata can also be used through the JSON + output to produce next to the generated answer a rich info card showing images, + buttons, or any kind of call-to-action. \n\n \n optional driver: string \n \n + Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 \n \n input_tokens + \n \n input_tokens: object \n \n max \n \n max: number \n \n min? \n \n optional + min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n If you need + to render a more complex UI than a simple link, you might not want to rely on + markdown code generated by an LLM. In that case, you can use the answer_json_schema + parameter to retrieve the information in a structured format that can be easily + processed by your front-end (see the Use JSON output how-to).\ndocs > develop + > js sdk > variables > MAX_FACETS_PER_REQUEST\nid : kb-tokens-time-usage\n\nroute + : /at/:account/:zone/:kb/metrics/tokens-and-time-usage\n\ntitle : Tokens & Time + Usage\n\n\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \n max_output_tokens: the maximum number of tokens to generate. \n\n link: link + \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:57 + \n \n text \n \n text: text \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:55\n + \n optional max_tokens: number \\| object \n \n Defines the maximum number of + tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n \n optional highlight: boolean \n \n Inherited from \n + BaseSearchOptions.highlight \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \n \n keyword_filters? \n \n optional keyword_filters: string[] \\| Filter[] + \n \n Inherited from \n BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n\n- ## Session information:\n\n\n\n\n\n\n# MAIN QUESTION:\nComo + usar max_tokens.answer. 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\":5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-resources\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-search\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-rag-lab\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"account-arag\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-kv-schemas\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"user-profile\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-users\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-widgets\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-remi-analytics\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"agent-users\",\"labels\":[]}],\"facets\":{},\"query\":\"\",\"total\":2013,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"]}" + headers: + Alt-Svc: + - h3=":443"; 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This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers 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RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic + RAG\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.031818181818181815,\"score_type\":\"BOTH\",\"order\":2,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136\":{\"score\":0.015384615384615385,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" + - Restricting context size may result in less relevant answers since the LLM + has less information to work with \\n - Balance between cost control and answer + quality \\n Answer Length Limitations: \\n - The LLM might not complete its + response if it hits the token limit, potentially cutting sentences mid-way + \\n - Recommended approach: Include length requirements in your prompt (e.g., + Please answer in less than 200 words ) rather than relying solely on hard + limits \\n - This allows the LLM to naturally conclude its response within + the desired length \\n How to Monitor Token Consumption \\n Understanding + Token Consumption Data \\n You can receive detailed token consumption information + from the following endpoints that utilize LLM models: ask, chat, remi, query, + sentence, summarize, tokens, and rerank. \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":7,\"start\":3340,\"end\":4136,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\":{\"score\":0.014925373134328358,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\" + \\n Large context: Results from using RAG strategies like Full resource or + Neighbouring paragraphs , or from using the extra_context parameter \\n Long + questions: More detailed or complex questions require more input tokens \\n + Long prompts: Extensive system prompts increase the input token count \\n + Detailed answers: Comprehensive responses require more output tokens \\n Images + in context: When using multimodal models, images included in the retrieved + context significantly increase token consumption \\n \\n How to Limit and + Control Token Consumption \\n Strategy 1: Optimize Your Parameters \\n The + first approach to reducing token consumption is to fine-tune your request + parameters: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/split-strategies\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + split-strategies\\ntitle: Use split strategies\\n---\\n\\n# Split strategies\\n\\nIn + some cases, default chunking methods aren't enough to handle the complexity + of your data.\\nImagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved.\\n\\nTo address this, you can define a split strategy that + controls how content is broken into chunks during ingestion.\\nSplit strategies + are reusable and configurable, allowing you to tailor the chunking process + to your specific needs.\\n\\nOnce defined, these strategies can be applied + across multiple ingestion jobs, giving you flexibility and consistency in + how your data is processed.\\n\\n## What is a split strategy?\\n\\nA split + strategy is a reusable configuration that defines how your data is divided + into chunks during ingestion. By customizing a split strategy, you can optimize + chunking for different types of content and use cases.\\n\\nKey components + of a split strategy include:\\n\\n- **Max paragraph size**: Sets the maximum + size (in characters or tokens) for each chunk.\\n- **Custom split**: Determines + the method used for splitting:\\n - **Manual splitting**: Splits content + based on a specified delimiter (default is `\\\"\\\\n\\\"`).\\n - **LLM splitting**: + Uses a language model to segment text intelligently, with optional rules to + guide the process.\\n\\nNote that you can only choose one splitting method + per strategy, and that you will have to set custom_split to 1 for manual splitting + or 2 for LLM splitting.\\n\\n## LLM Splitting\\n\\nLLM splitting leverages + a language model to intelligently segment text into meaningful chunks, going + beyond simple character or line-based splitting. This approach is ideal when + you need context-aware chunking, such as dividing content by sections, topics, + or other logical boundaries.\\n\\nYou can enable LLM splitting with default + settings, or customize it for your specific needs using these parameters:\\n\\n- + **LLM**: Specify the language model in the **generative_model** field. Optionally, + provide credentials for your chosen LLM provider in **user_keys**.\\n- **Rules**: + Define natural language instructions to guide how the text should be split. + For example, you might use rules like \\\"Split at each section heading\\\" + or \\\"Separate lists into individual items\\\".\\n\\nExample configuration:\\n\\n```json\\n{\\n + \ \\\"name\\\": \\\"llm\\\",\\n \\\"max_paragraph\\\": 1000,\\n + \ \\\"custom_split\\\": 2, \\n \\\"llm_split\\\": {\\n \\\"llm\\\": + {\\n \\\"generative_model\\\": \\\"chatgpt-azure-4o-mini\\\",\\n + \ },\\n \\\"rules\\\": [\\n \\\"Split + at each section heading\\\"\\n ]\\n },\\n }\\n```\\n\\nExperiment + with different rules and models to optimize chunking for your use case. Note + that using an LLM may increase processing time and cost, and could slightly + alter the extracted text.\\n\\n## Manual Splitting\\nManual splitting divides + text based on a specified delimiter, making it straightforward and efficient + for simpler chunking needs. This method is particularly useful when your content + has clear, consistent separators, such as paragraphs or bullet points.\\nYou + can customize manual splitting using the following parameters:\\n- **Splitter**: + Choose the character or string that indicates where to split the text. The + default is a double newline character (`\\\"\\\\n\\\\n\\\"`), but you can + set it to any delimiter that suits your content structure.\\nExample configuration:\\n\\n```json\\n + {\\n \\\"name\\\": \\\"manual\\\",\\n \\\"custom_split\\\": + 1,\\n \\\"manual_split\\\": {\\n \\\"splitter\\\": \\\"\\\\n\\\"\\n + \ }\\n }\\n``` \\n\\n## Strategy creation and management\\n\\nBefore + we can use a split strategy, we need to create it. We can create as many strategies + as we want, and just use the right one for each processing job. Once created + we can not modify our strategies, but we can delete them and inspect the ones + we have created for a given kb.\\n\\n### Dashboard\\n\\nTo create a strategy, + just go to the section **AI Models** and then to **Extract & split**. Then + you can click on the button **Create configuration** , next to **Split configurations** + to create a new strategy. Once there, you can fill in the necessary fields + with the desired configuration.\\nIn the same section, you can also see the + list of strategies you have created, and you can delete them once you no longer + need them.\\n\\n### CLI\\n\\nTo create a strategy using the CLI, you can use + the command `nuclia kb split_strategies add` with the desired configuration + in JSON format. You can also list the strategies you have created with `nuclia + kb split_strategies list`, and delete them with `nuclia kb split_strategies + delete`.\\n\\n```bash\\nnuclia kb split_strategies add --config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}'\\nnuclia kb split_strategies + list\\nnuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo create a strategy using the SDK, you can use the `add` method + of the `split_strategies` object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the `list` + method, and delete them with the `delete` method.\\n\\n```python\\nfrom nuclia + import sdk\\nsplit_strategies = sdk.NucliaKB().split_strategies\\nprint(split_strategies.list())\\nid + = split_strategies.add(config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}')\\nsplit_strategies.delete(id=id)\\n```\\n\\n## + Use split strategies for processing\\n\\n### Dashboard\\n\\nTo use a split + strategy for processing documents, just upload the document normally, enable + **Customize data extraction** and select the strategy you want to use in the + dropdown menu. Once the document is uploaded, it will be processed using the + selected strategy.\\n\\n### CLI\\n\\nTo use a split strategy for processing + documents using the CLI, you can use the command `nuclia kb upload file` with + the `--split_strategy` option, passing the ID of the strategy you want to + use.\\n\\n```bash\\nnuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n```bash\\nnuclia + kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo use a split strategy for processing documents using the SDK, you + can use the `file` method of the `NucliaUpload` object, passing the path to + the file and the ID of the strategy you want to use.\\n\\n```python\\nfrom + nuclia import sdk\\nupload = sdk.NucliaUpload()\\nupload.file(path=FILE_PATH, + split_strategy=\\\"1361c0c7-918a-4a7f-b44b-ba37437619fb\\\")\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"834cf2c7e30daced272772d79b257cf0\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: split-strategies \\n title: Use split strategies \\n \\n Split strategies + \\n In some cases, default chunking methods aren't enough to handle the complexity + of your data. \\n Imagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved. \\n To address this, you can define a split strategy that controls + how content is broken into chunks during ingestion. \\n Split strategies are + reusable and configurable, allowing you to tailor the chunking process to + your specific needs. \\n Once defined, these strategies can be applied across + multiple ingestion jobs, giving you flexibility and consistency in how your + data is processed. \\n What is a split strategy? \\n A split strategy is a + reusable configuration that defines how your data is divided into chunks during + ingestion. By customizing a split strategy, you can optimize chunking for + different types of content and use cases. \\n Key components of a split strategy + include: \\n \\n Max paragraph size: Sets the maximum size (in characters + or tokens) for each chunk. \\n Custom split: Determines the method used for + splitting: \\n Manual splitting: Splits content based on a specified delimiter + (default is \\\\n ). \\n LLM splitting: Uses a language model to segment text + intelligently, with optional rules to guide the process. \\n \\n Note that + you can only choose one splitting method per strategy, and that you will have + to set custom_split to 1 for manual splitting or 2 for LLM splitting. \\n + LLM Splitting \\n LLM splitting leverages a language model to intelligently + segment text into meaningful chunks, going beyond simple character or line-based + splitting. This approach is ideal when you need context-aware chunking, such + as dividing content by sections, topics, or other logical boundaries. \\n + You can enable LLM splitting with default settings, or customize it for your + specific needs using these parameters: \\n \\n LLM: Specify the language model + in the generative_model field. Optionally, provide credentials for your chosen + LLM provider in user_keys. \\n Rules: Define natural language instructions + to guide how the text should be split. For example, you might use rules like + Split at each section heading or Separate lists into individual items . \\n + \\n Example configuration: \\n json \\n { \\n name : llm , \\n max_paragraph + : 1000, \\n custom_split : 2, \\n llm_split : { \\n llm : { \\n generative_model + : chatgpt-azure-4o-mini , \\n }, \\n rules : [ \\n Split at each section heading + \\n ] \\n }, \\n } \\n Experiment with different rules and models to optimize + chunking for your use case. Note that using an LLM may increase processing + time and cost, and could slightly alter the extracted text. \\n Manual Splitting + \\n Manual splitting divides text based on a specified delimiter, making it + straightforward and efficient for simpler chunking needs. This method is particularly + useful when your content has clear, consistent separators, such as paragraphs + or bullet points. \\n You can customize manual splitting using the following + parameters: \\n - Splitter: Choose the character or string that indicates + where to split the text. The default is a double newline character ( \\\\n\\\\n + ), but you can set it to any delimiter that suits your content structure. + \\n Example configuration: \\n json \\n { \\n name : manual , \\n custom_split + : 1, \\n manual_split : { \\n splitter : \\\\n \\n } \\n } \\n Strategy creation + and management \\n Before we can use a split strategy, we need to create it. + We can create as many strategies as we want, and just use the right one for + each processing job. Once created we can not modify our strategies, but we + can delete them and inspect the ones we have created for a given kb. \\n Dashboard + \\n To create a strategy, just go to the section AI Models and then to Extract + & split. Then you can click on the button Create configuration , next to Split + configurations to create a new strategy. Once there, you can fill in the necessary + fields with the desired configuration. \\n In the same section, you can also + see the list of strategies you have created, and you can delete them once + you no longer need them. \\n CLI \\n To create a strategy using the CLI, you + can use the command nuclia kb split_strategies add with the desired configuration + in JSON format. You can also list the strategies you have created with nuclia + kb split_strategies list, and delete them with nuclia kb split_strategies + delete. \\n bash \\n nuclia kb split_strategies add --config='{ name : strategy1 + , custom_split : 1, manual_split : { splitter : \\\\n }}' \\n nuclia kb split_strategies + list \\n nuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To create a strategy using the SDK, you can use the add method + of the split_strategies object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the list method, + and delete them with the delete method. \\n python \\n from nuclia import + sdk \\n split_strategies = sdk.NucliaKB().split_strategies \\n print(split_strategies.list()) + \\n id = split_strategies.add(config='{ name : strategy1 , custom_split : + 1, manual_split : { splitter : \\\\n }}') \\n split_strategies.delete(id=id) + \\n Use split strategies for processing \\n Dashboard \\n To use a split strategy + for processing documents, just upload the document normally, enable Customize + data extraction and select the strategy you want to use in the dropdown menu. + Once the document is uploaded, it will be processed using the selected strategy. + \\n CLI \\n To use a split strategy for processing documents using the CLI, + you can use the command nuclia kb upload file with the --split_strategy option, + passing the ID of the strategy you want to use. \\n bash \\n nuclia kb upload + file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n bash \\n nuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To use a split strategy for processing documents using the SDK, + you can use the file method of the NucliaUpload object, passing the path to + the file and the ID of the strategy you want to use. \\n python \\n from nuclia + import sdk \\n upload = sdk.NucliaUpload() \\n upload.file(path=FILE_PATH, + split_strategy= 1361c0c7-918a-4a7f-b44b-ba37437619fb 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split\"},\"ORG/NucliaUpload\":{\"position\":[{\"start\":5981,\"end\":5993}],\"entity\":\"NucliaUpload\"},\"ORG/JSON\":{\"position\":[{\"start\":4235,\"end\":4239}],\"entity\":\"JSON\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"operating + system\",\"metadata\":{\"paragraph_id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/3968-4393\",\"source_start\":4141,\"source_end\":4144,\"to_start\":4235,\"to_end\":4239},\"from\":{\"value\":\"CLI\",\"type\":\"entity\",\"group\":\"PRODUCT\"},\"to\":{\"value\":\"JSON\",\"type\":\"entity\",\"group\":\"ORG\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > ingestion > how to > split strategies\",\"extracted\":{\"text\":{\"text\":\"docs + > ingestion > how to > split strategies\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\":{\"score\":0.014925373134328358,\"score_type\":\"BM25\",\"order\":13,\"text\":\" + Key components of a split strategy include: \\n \\n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \\n Custom split: + Determines the method used for splitting: \\n Manual splitting: Splits content + based on a specified delimiter (default is \\\\n ). \\n\",\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":963,\"end\":1239,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.030536130536130537,\"score_type\":\"BOTH\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.030330882352941176,\"score_type\":\"BOTH\",\"order\":5,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\":[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\":499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\":\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\":1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"end\":3929,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3650,\"end\":3795,\"key\":\"\"},{\"start\":3795,\"end\":3929,\"key\":\"\"}]},{\"start\":3929,\"end\":4317,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3932,\"end\":4123,\"key\":\"\"},{\"start\":4123,\"end\":4317,\"key\":\"\"}]},{\"start\":4317,\"end\":4520,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4320,\"end\":4520,\"key\":\"\"}]},{\"start\":4520,\"end\":4884,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4523,\"end\":4717,\"key\":\"\"},{\"start\":4717,\"end\":4884,\"key\":\"\"}]},{\"start\":4884,\"end\":5167,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4887,\"end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5167,\"key\":\"\"}]},{\"start\":5167,\"end\":5525,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5170,\"end\":5339,\"key\":\"\"},{\"start\":5339,\"end\":5525,\"key\":\"\"}]},{\"start\":5525,\"end\":5897,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5528,\"end\":5711,\"key\":\"\"},{\"start\":5711,\"end\":5897,\"key\":\"\"}]},{\"start\":5897,\"end\":6194,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5898,\"end\":6071,\"key\":\"\"},{\"start\":6071,\"end\":6194,\"key\":\"\"}]},{\"start\":6194,\"end\":6478,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6197,\"end\":6366,\"key\":\"\"},{\"start\":6366,\"end\":6478,\"key\":\"\"}]},{\"start\":6478,\"end\":6778,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6481,\"end\":6635,\"key\":\"\"},{\"start\":6635,\"end\":6778,\"key\":\"\"}]}],\"ner\":{\"boolean\":\"PERSON\",\"112 + min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"end\":654}]},{\"text\":\"112 + min\",\"label\":\"TIME\",\"positions\":[{\"start\":2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\",\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"TIME/112 + min\":{\"position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"},\"PERSON/boolean\":{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.03225806451612903,\"score_type\":\"BOTH\",\"order\":1,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.02819138376017471,\"score_type\":\"BOTH\",\"order\":8,\"text\":\" + \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\n + \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[] + \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens? + \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"c053e849251b4a5296ffeb79ed00200e\":{\"id\":\"c053e849251b4a5296ffeb79ed00200e\",\"slug\":\"docs-nua-api-mdx\",\"title\":\"docs + > nua api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:43.966941\",\"modified\":\"2026-06-09T08:18:14.251220\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/nua-api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + nua-api\\nhide_title: true\\ntitle: Agentic RAG Understanding API\\nsidebar_label: + Agentic RAG Understanding API\\n---\\n\\nimport ApiDocMdx from \\\"@theme/ApiDocMdx\\\";\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8eea4340dceaec0eed766d13fc001e72\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: nua-api \\n hide_title: true \\n title: Agentic RAG Understanding + API \\n sidebar_label: Agentic RAG Understanding API \\n \\n import ApiDocMdx + from @theme/ApiDocMdx ; \\n \\n {` \\n @media screen and (min-width: 998px) + { \\n [class ='docMainContainer'] > .container { \\n max-width: 100% !important; + \\n } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n + } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":289,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":246,\"key\":\"\"},{\"start\":246,\"end\":277,\"key\":\"\"},{\"start\":277,\"end\":289,\"key\":\"\"}]},{\"start\":289,\"end\":366,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":290,\"end\":337,\"key\":\"\"},{\"start\":337,\"end\":366,\"key\":\"\"}]}],\"ner\":{\"nua-api\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"nua-api\",\"label\":\"PERSON\",\"positions\":[{\"start\":7,\"end\":14}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:20.548849Z\",\"last_understanding\":\"2026-06-09T08:18:19.989241Z\",\"last_extract\":\"2026-06-09T08:18:18.844339Z\",\"last_processing_start\":\"2026-06-09T08:18:18.793432Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PERSON/nua-api\":{\"position\":[{\"start\":7,\"end\":14}],\"entity\":\"nua-api\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > nua api.\",\"extracted\":{\"text\":{\"text\":\"docs > nua api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"c053e849251b4a5296ffeb79ed00200e/t/page/289-366\":{\"score\":0.0136986301369863,\"score_type\":\"BM25\",\"order\":19,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"c053e849251b4a5296ffeb79ed00200e/t/page/289-366\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":289,\"end\":366,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\":{\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\",\"slug\":\"docs-zone-api-mdx\",\"title\":\"docs + > zone api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:43.137585\",\"modified\":\"2026-06-09T08:18:13.619843\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/zone-api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + knowledgebox-zone-api\\nhide_title: true\\ntitle: Knowledge Box & Zone API\\n---\\n\\nimport + ApiDocMdx from '@theme/ApiDocMdx';\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"4060ad5ed8c024c99b505a02edfce76f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: knowledgebox-zone-api \\n hide_title: true \\n title: Knowledge Box + & Zone API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` \\n + @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] > .container + { \\n max-width: 100% !important; \\n } \\n [class ='docItemCol'] { \\n max-width: + 100% !important; \\n } \\n } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":252,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":209,\"key\":\"\"},{\"start\":209,\"end\":240,\"key\":\"\"},{\"start\":240,\"end\":252,\"key\":\"\"}]},{\"start\":252,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":253,\"end\":300,\"key\":\"\"},{\"start\":300,\"end\":329,\"key\":\"\"}]}],\"ner\":{\"ApiDocMdx\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"ApiDocMdx\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":93,\"end\":102}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:20.481514Z\",\"last_understanding\":\"2026-06-09T08:18:19.956554Z\",\"last_extract\":\"2026-06-09T08:18:18.741090Z\",\"last_processing_start\":\"2026-06-09T08:18:18.713420Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/ApiDocMdx\":{\"position\":[{\"start\":93,\"end\":102}],\"entity\":\"ApiDocMdx\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > zone api.\",\"extracted\":{\"text\":{\"text\":\"docs > zone api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329\":{\"score\":0.013888888888888888,\"score_type\":\"BM25\",\"order\":17,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":252,\"end\":329,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.029551337359792925,\"score_type\":\"BOTH\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.027526395173453996,\"score_type\":\"BOTH\",\"order\":9,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"53b91ad0dd5f48a7ac8b59f9775ed7e0\":{\"id\":\"53b91ad0dd5f48a7ac8b59f9775ed7e0\",\"slug\":\"docs-api-mdx\",\"title\":\"docs + > api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\",\"hu\",\"la\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:45.557031\",\"modified\":\"2026-06-09T08:18:15.494834\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + api\\nhide_title: true\\ntitle: NucliaDB REST API\\nsidebar_label: NucliaDB + API\\n---\\n\\nimport ApiDocMdx from '@theme/ApiDocMdx';\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"b50e745a15cde6807eea0d88d476f3d9\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: api \\n hide_title: true \\n title: NucliaDB REST API \\n sidebar_label: + NucliaDB API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` + \\n @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] + > .container { \\n max-width: 100% !important; \\n } \\n [class ='docItemCol'] + { \\n max-width: 100% !important; \\n } \\n } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":214,\"key\":\"\"},{\"start\":214,\"end\":245,\"key\":\"\"},{\"start\":245,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":334,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":258,\"end\":305,\"key\":\"\"},{\"start\":305,\"end\":334,\"key\":\"\"}]}],\"ner\":{\"NucliaDB\":\"ORG\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"NucliaDB\",\"label\":\"ORG\",\"positions\":[{\"start\":39,\"end\":47}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:22.537575Z\",\"last_understanding\":\"2026-06-09T08:18:22.045700Z\",\"last_extract\":\"2026-06-09T08:18:20.890878Z\",\"last_processing_start\":\"2026-06-09T08:18:20.832975Z\",\"language\":\"la\",\"summary\":\"\",\"positions\":{\"ORG/NucliaDB\":{\"position\":[{\"start\":39,\"end\":47}],\"entity\":\"NucliaDB\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > api.\",\"extracted\":{\"text\":{\"text\":\"docs > api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334\":{\"score\":0.014084507042253521,\"score_type\":\"BM25\",\"order\":16,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":257,\"end\":334,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.032266458495966696,\"score_type\":\"BOTH\",\"order\":0,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate + limits are an essential aspect of the Agentic RAG platform, ensuring fair + usage and optimal performance for all users interacting with Agentic RAG APIs. + This document outlines the rate limits enforced by Agentic RAG and provides + guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic + RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: + By default, the sum of all authenticated requests in a Agentic RAG account + cannot exceed 2400 requests per minute. Note that this limit can be customized + on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) + if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic + RAG implements a back-pressure mechanism to manage ingestion pipeline overload. + This mainly affects endpoints for uploading data and creating or updating + resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres + to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) + and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe + official Agentic RAG API clients already have built-in mechanisms for retrying + requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- + [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are + interacting directly with the API, we recommend using an [exponential backoff + retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits + are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response + will include a `try_after` key with an estimated UTC time for retrying the + request. You can use this value for retry logic as an alternative to the exponential + backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an + example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport + time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, + headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n + \ response = requests.get(url, headers=headers)\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n + \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n + \ time.sleep(wait_time)\\n retries += 1\\n else:\\n + \ response.raise_for_status()\\n raise Exception(\\\"Max retries + exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders + = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, + headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's + an example of how to use the try_after key from the response to manage rate + limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport + requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n + \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, + headers=headers)\\n response_body = response.json()\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n + \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n + \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n + \ print(\\n f\\\"Rate limit exceeded. Retrying at + {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n + \ retries += 1\\n else:\\n response.raise_for_status()\\n + \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl + = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer + YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese + examples demonstrate how to handle rate limits effectively, ensuring that + your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate + limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, + ensuring fair usage and optimal performance for all users interacting with + Agentic RAG APIs. This document outlines the rate limits enforced by Agentic + RAG and provides guidelines for handling rate-limited responses effectively. + \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: + \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated + requests in a Agentic RAG account cannot exceed 2400 requests per minute. + Note that this limit can be customized on a per-account basis. Please contact + Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion + back pressure limits: Agentic RAG implements a back-pressure mechanism to + manage ingestion pipeline overload. This mainly affects endpoints for uploading + data and creating or updating resources. \\n \\n \\n Handling Rate-Limited + Responses \\n Agentic RAG adheres to the HTTP standard and will return a response + with a 429 status codes when the limits are exceeded. \\n The official Agentic + RAG API clients already have built-in mechanisms for retrying requests when + rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript + client \\n \\n However, if you are interacting directly with the API, we recommend + using an exponential backoff retry strategy when limits are reached. \\n When + ingestion back pressure rate limits are hit, the response will include a try_after + key with an estimated UTC time for retrying the request. You can use this + value for retry logic as an alternative to the exponential backoff strategy. + \\n Example 1: Regular API rate limits \\n Here's an example of how to implement + an exponential backoff retry strategy in Python: \\n ```python \\n import + time \\n import requests \\n def make_request_with_exponential_backoff(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n if response.status_code + == 200: \\n return response.json() \\n elif response.status_code == 429: \\n + wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit + exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n + retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( + Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n + headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n ```python \\n import time \\n from datetime import + datetime \\n import requests \\n def make_request_with_try_after_info(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n response_body = response.json() + \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code + == 429 and try_after in response_body: \\n try_after = response_body[ try_after + ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n + wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n + f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... + \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries 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+ RAG\"},\"LAW/HTTP\":{\"position\":[{\"start\":998,\"end\":1002}],\"entity\":\"HTTP\"},\"PRODUCT/Max\":{\"position\":[{\"start\":3409,\"end\":3412}],\"entity\":\"Max\"},\"TIME/seconds\":{\"position\":[{\"start\":2199,\"end\":2206}],\"entity\":\"seconds\"},\"PRODUCT/Python\":{\"position\":[{\"start\":1232,\"end\":1238},{\"start\":1773,\"end\":1779}],\"entity\":\"Python\"},\"ORG/Agentic + RAG's\":{\"position\":[{\"start\":668,\"end\":681}],\"entity\":\"Agentic RAG's\"},\"LAW/Handling + Rate-Limited Responses Agentic RAG\":{\"position\":[{\"start\":937,\"end\":982}],\"entity\":\"Handling + Rate-Limited Responses Agentic RAG\"},\"TIME/UTC\":{\"position\":[{\"start\":1526,\"end\":1529}],\"entity\":\"UTC\"},\"PRODUCT/Agentic + RAG API\":{\"position\":[{\"start\":1108,\"end\":1123}],\"entity\":\"Agentic + RAG API\"},\"ORG/Nuclia\":{\"position\":[{\"start\":1225,\"end\":1231},{\"start\":1248,\"end\":1254}],\"entity\":\"Nuclia\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"operating + system\",\"metadata\":{\"paragraph_id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1222-1656\",\"source_start\":1248,\"source_end\":1254,\"to_start\":1232,\"to_end\":1238},\"from\":{\"value\":\"Nuclia\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\":{\"value\":\"Python\",\"type\":\"entity\",\"group\":\"PRODUCT\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > ingestion > how to > rate limiting\",\"extracted\":{\"text\":{\"text\":\"docs + > ingestion > how to > rate limiting\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.014285714285714285,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\" + ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\":{\"score\":0.013888888888888888,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" + retries = 0 \\n while retries < max_retries: \\n response = requests.get(url, + headers=headers) \\n if response.status_code == 200: \\n return response.json() + \\n elif response.status_code == 429: \\n wait_time = 2 retries # Exponential + backoff: 2^retries \\n print(f Rate limit exceeded. Retrying in {wait_time} + seconds... ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1900,\"end\":2653,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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+ > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.030751173708920186,\"score_type\":\"BOTH\",\"order\":3,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.02877846790890269,\"score_type\":\"BOTH\",\"order\":7,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible + LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG + allows you to connect to any OpenAI API compatible LLM. This means that you + can use any LLM that has an API compatible with the OpenAI API which has become + a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, + open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box + configuration you can do so in three manners, through the API, the Nuclia + CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers + the most user-friendly way to modify the configuration of your knowledge box + and we will use it in this example.\\n\\nWe will be setting up a connection + to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers + a wide range of open-source and commercial models compatible with the OpenAI + API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), + the API parameters are located under the **API** tab.\\n\\n1. **Open the AI + Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. + **Select \u201COpenAI API Compatible Model\u201D** \\n From the models + list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n + \ Toggle the option for using you own `OpenAI API Compatible Key` if it is + not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - + **API Key**:\\n - Description: The API key for your LLM. This is the key + that you would use as an authorization header in the API. You may leave this + blank if the endpoint you are connecting to does not require an API key.\\n + \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n + \ - Description: The URL of the API endpoint for your LLM. This may be + shared between multiple models.\\n - Example: For OpenRouter, it is the + same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - + Description: The name of the model you want to use, it needs to exactly match + the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus + in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - + **Maximum supported input tokens**:\\n - Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only.\\n - + Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, + as we want to leave room for the output, we will set the maximum supported + input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output + tokens**:\\n - Description: The maximum number of tokens that the model + can generate as output. Again, we should keep in mind that this value summed + to the **Maximum supported input tokens** should not exceed the total context + size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the + maximum output tokens is specified at `32768`, but we already reserved `31744` + for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - + **Model Features**:\\n - Description: Under this section you will find + multiple toggles related to features supported by the model, these vary from + model to model, but most often the default values are well suited to most + use cases. The most relevant toggle is for `Image Support` which allows you + to use images as input for the model.\\n - Example: Image input is not + supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** + \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample + query in Agentic RAG or via API/CLI. Adjust your prompt templates and token + settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API + compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic + RAG allows you to connect to any OpenAI API compatible LLM. This means that + you can use any LLM that has an API compatible with the OpenAI API which has + become a standard in the industry. \\n Many of the options for self-hosted + LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications. \\n Configuration \\n To modify your knowledge box configuration + you can do so in three manners, through the API, the Nuclia CLI / SDK or the + Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly + way to modify the configuration of your knowledge box and we will use it in + this example. \\n We will be setting up a connection to the Phi 4 Reasoning + Plus model, hosted by OpenRouter which offers a wide range of open-source + and commercial models compatible with the OpenAI API. We can see more information + about this specific model here, the API parameters are located under the API + tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, + click AI Models. \\n Select OpenAI API Compatible Model \\n From the models + list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle + the option for using you own OpenAI API Compatible Key if it is not already + enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: + \\n \\n Description: The API key for your LLM. This is the key that you would + use as an authorization header in the API. You may leave this blank if the + endpoint you are connecting to does not require an API key. \\n Example: We + will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: + The URL of the API endpoint for your LLM. This may be shared between multiple + models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 + \\n \\n \\n Model: \\n Description: The name of the model you want to use, + it needs to exactly match the name of the model in the API. \\n Example: For + Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. + \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n Example: + For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want + to leave room for the output, we will set the maximum supported input tokens + as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified + at 32768, but we already reserved 31744 for the input tokens, so we will set + this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: + Under this section you will find multiple toggles related to features supported + by the model, these vary from model to model, but most often the default values + are well suited to most use cases. The most relevant toggle is for Image Support + which allows you to use images as input for the model. \\n Example: Image + input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. + \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n + Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt templates + and token settings as needed. \\n \\n 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Content-Length: + - '9269' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:14 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '156' + x-nuclia-trace-id: + - d088988e2c1638ee1a0aa43395d7ccf8 + status: + code: 200 + message: OK +- request: + body: '{"question": "Explica c\u00f3mo usar el par\u00e1metro `max_tokens` para + limitar la longitud de la respuesta generada por el modelo, en espa\u00f1ol, + y proporciona un enlace a la documentaci\u00f3n relevante.", "user_id": "arag-ask-rerank", + "context": {"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": " Use the max_tokens + parameter on the /ask endpoint to set hard limits on: \n - Context size: Limits + the amount of retrieved information sent to the LLM \n - Answer length: Limits + the length of the generated response \n Important Considerations \n Context + Limitations: \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136": " - + Restricting context size may result in less relevant answers since the LLM has + less information to work with \n - Balance between cost control and answer quality + \n Answer Length Limitations: \n - The LLM might not complete its response if + it hits the token limit, potentially cutting sentences mid-way \n - Recommended + approach: Include length requirements in your prompt (e.g., Please answer in + less than 200 words ) rather than relying solely on hard limits \n - This allows + the LLM to naturally conclude its response within the desired length \n How + to Monitor Token Consumption \n Understanding Token Consumption Data \n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326": " \n + Large context: Results from using RAG strategies like Full resource or Neighbouring + paragraphs , or from using the extra_context parameter \n Long questions: More + detailed or complex questions require more input tokens \n Long prompts: Extensive + system prompts increase the input token count \n Detailed answers: Comprehensive + responses require more output tokens \n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \n \n How to Limit and Control Token Consumption \n Strategy 1: + Optimize Your Parameters \n The first approach to reducing token consumption + is to fine-tune your request parameters: \n\n", "1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239": + " Key components of a split strategy include: \n \n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \n Custom split: + Determines the method used for splitting: \n Manual splitting: Splits content + based on a specified delimiter (default is \\n ). \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": + " \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": + " \n optional max_tokens: number \\| object \n \n Defines the maximum number + of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2450-2808": + " \n optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight + \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? + \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n + BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n", "c053e849251b4a5296ffeb79ed00200e/t/page/289-366": + " } \n [class =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n + `} \n \n \n", "91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329": " } \n [class + =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n `} \n \n \n", + "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: + string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \n \n + output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": + " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n", "53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334": + " } \n [class =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n + `} \n \n \n", "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n", "dd41482018924facb5dbb87a7d53f122/t/page/3310-3757": + " ) \n time.sleep(wait_time) \n retries += 1 \n else: \n response.raise_for_status() + \n raise Exception( Max retries exceeded ) \n Example usage \n url = https://your-endpoint + \n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, + headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate + limits effectively, ensuring that your application respects the limits and retries + appropriately.\n", "dd41482018924facb5dbb87a7d53f122/t/page/1900-2653": " retries + = 0 \n while retries < max_retries: \n response = requests.get(url, headers=headers) + \n if response.status_code == 200: \n return response.json() \n elif response.status_code + == 429: \n wait_time = 2 retries # Exponential backoff: 2^retries \n print(f + Rate limit exceeded. Retrying in {wait_time} seconds... ) \n time.sleep(wait_time) + \n retries += 1 \n else: \n response.raise_for_status() \n raise Exception( + Max retries exceeded ) \n Example usage \n url = https://your-endpoint \n headers + = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_exponential_backoff(url, + headers) \n print(data) \n ``` \n Example 2: Ingestion back pressure limits + \n Here''s an example of how to use the try_after key from the response to manage + rate limits: \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": " ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": + " \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n", "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: + The maximum number of tokens that the model can generate as output. Again, we + should keep in mind that this value summed to the Maximum supported input tokens + should not exceed the total context size supported by the model. \n\n", "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": + " \n \n Maximum supported input tokens: \n Description: The maximum number of + tokens that the model can accept as input. Be mindful that this takes into account + the tokens used in the prompt, query and context. Also take note that some models + may provide their context window as the total between input and output tokens, + while others may provide it as the input tokens only. \n\n"}}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '8056' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank + response: + body: + string: '{"context_scores":{"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.9534487724304199,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.44607239961624146,"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043":0.418399840593338,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.39338502287864685,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709":0.26913344860076904,"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757":0.2317880541086197,"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575":0.1788124442100525,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.13741667568683624,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.13251455128192902,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.10557883977890015,"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136":0.08540366590023041,"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808":0.07450025528669357,"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653":0.05223085731267929,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.03934250399470329,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.03554439917206764,"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326":0.024798160418868065,"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239":0.004485794343054295,"c053e849251b4a5296ffeb79ed00200e/t/page/289-366":0.00048406238784082234,"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329":0.00048406238784082234,"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334":0.00048406238784082234}}' + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Content-Length: + - '1452' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:14 GMT + nuclia-learning-model: + - bge-reranker-v2-m3 + via: + - 1.1 google + x-envoy-upstream-service-time: + - '61' + x-nuclia-trace-id: + - a082f552cc9275a9b9ae1464899cff4c + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", + "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context + and user question, perform the following tasks:\n\n1. Select only information + directly relevant to the question.\n2. Break down compound sentences into simple, + single-idea statements. Preserve original phrasing when possible.\n3. For any + named entity with descriptive details, separate those details into distinct + propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", + \"she\", \"they\", \"this\", \"that\") with the full names of the entities they + reference, and add necessary modifiers to clarify meaning.\n5. The context may + be delimited by tags such as and . Treat + everything between these tags as context.\n6. Assess whether the context sufficiently + answers the question. If it answers it partially, provide the answer; if it + does not answer it fully, specify what information is missing to answer the + question.\n7. If the context does not answer the question at all, just return + the original question as the missing information.\n8. The `citations` field + consists ONLY in a list of block IDs that are relevant to the answer, following + these rules:\n - Use the format: block-AB\n - Just mention the block IDs, + do NOT include any other text.\n - Just mention the blocks actually relevant + and that contain information used in the answer, do NOT include blocks that + are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only + use those provided in the context.\n10. Your output must be a JSON object with + the following fields:\n - \"reason\": Explain your reasoning for the answer + or validation.\n - \"answer\": Provide a partial or complete answer to the + user query strictly from the information in the context. If there isn''t enough + information to even provide a partial answer, leave ''answer'' empty.\n - + \"missing_info_query\": If the context is insufficient, specify what information + is missing in a query shape; otherwise, leave it empty. Just return the query + needed to retrieve the missing information.\n - \"useful\": Indicate if the + context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": + List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", + \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you + MUST follow them carefully when generating the answer field. These instructions + may specify the format, style, tools to use, or other requirements for the answer.\n\n\nExplica + c\u00f3mo usar el par\u00e1metro `max_tokens` para limitar la longitud de la + respuesta generada por el modelo, en espa\u00f1ol, y proporciona un enlace a + la documentaci\u00f3n relevante.\n\n\n\nContext:\n\n**block-AA**\n\n## + Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use the max_tokens + parameter on the /ask endpoint to set hard limits on: \n - Context size: Limits + the amount of retrieved information sent to the LLM \n - Answer length: Limits + the length of the generated response \n Important Considerations \n Context + Limitations: \n\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n + SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\nTags: + /k/text\n``` Description: The maximum number of tokens that the model can generate + as output. Again, we should keep in mind that this value summed to the Maximum + supported input tokens should not exceed the total context size supported by + the model. \n\n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## + Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\n``` ::: \n :::warning + \n Enabling reasoning can use additional tokens, which may increase your usage + costs. \n You may need to increase max_tokens to give the LLM enough room to + reason and generate an answer. \n\n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: + dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\nTags: /k/text\n``` ) \n + time.sleep(wait_time) \n retries += 1 \n else: \n response.raise_for_status() + \n raise Exception( Max retries exceeded ) \n Example usage \n url = https://your-endpoint + \n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, + headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate + limits effectively, ensuring that your application respects the limits and retries + appropriately.\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: + /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum + number of tokens that the model can accept as input. Be mindful that this takes + into account the tokens used in the prompt, query and context. Also take note + that some models may provide their context window as the total between input + and output tokens, while others may provide it as the input tokens only. \n\n + ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n + optional max_tokens: number \\| object \n \n Defines the maximum number of tokens + that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\n``` \n + optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n + optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n---\"\n\n\n**block-AK**\n\n## Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136\nTags: + /k/text\n``` - Restricting context size may result in less relevant answers + since the LLM has less information to work with \n - Balance between cost control + and answer quality \n Answer Length Limitations: \n - The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way + \n - Recommended approach: Include length requirements in your prompt (e.g., + Please answer in less than 200 words ) rather than relying solely on hard limits + \n - This allows the LLM to naturally conclude its response within the desired + length \n How to Monitor Token Consumption \n Understanding Token Consumption + Data \n You can receive detailed token consumption information from the following + endpoints that utilize LLM models: ask, chat, remi, query, sentence, summarize, + tokens, and rerank. \n\n ```\n\n\n---\"\n\n\n**block-AL**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\n``` \n + optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight + \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? + \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n + BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n**block-AM**\n\n## Chunk: dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\nTags: + /k/text\n``` retries = 0 \n while retries < max_retries: \n response = requests.get(url, + headers=headers) \n if response.status_code == 200: \n return response.json() + \n elif response.status_code == 429: \n wait_time = 2 retries # Exponential + backoff: 2^retries \n print(f Rate limit exceeded. Retrying in {wait_time} seconds... + ) \n time.sleep(wait_time) \n retries += 1 \n else: \n response.raise_for_status() + \n raise Exception( Max retries exceeded ) \n Example usage \n url = https://your-endpoint + \n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_exponential_backoff(url, + headers) \n print(data) \n ``` \n Example 2: Ingestion back pressure limits + \n Here''s an example of how to use the try_after key from the response to manage + rate limits: \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": + false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", + "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": + {"title": "validate_or_answer", "description": "Validate or answer", "parameters": + {"type": "object", "properties": {"reason": {"type": "string", "description": + "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": + "Partial or complete answer to the user query from the information in the context."}, + "missing_info_query": {"type": "string", "description": "Query needed to retrieve + the missing information in case the context is not enough to answer the question. + If the context does not answer the question at all, just return the original + question."}, "useful": {"type": "string", "description": "Is the context useful + to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", + "items": {"type": "string", "description": "Block ID cited in the answer, e.g. + block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, + "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, + "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": + {"type": "required"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '10513' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-origin: + - RAO + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context + provides information on how to use the `max_tokens` parameter to limit the + length of the generated response by the model. It explains that `max_tokens` + sets a hard limit on the answer length and mentions considerations regarding + context size and answer quality. However, it does not provide a direct link + to the relevant documentation.\",\"answer\":\"El par\xE1metro `max_tokens` + se utiliza en el endpoint /ask para establecer l\xEDmites en la longitud de + la respuesta generada por el modelo. Este par\xE1metro limita la cantidad + m\xE1xima de tokens que el modelo puede generar como salida. Es importante + tener en cuenta que el valor de `max_tokens` sumado a los tokens de entrada + no debe exceder el tama\xF1o total del contexto soportado por el modelo. Para + m\xE1s detalles, consulta la documentaci\xF3n en el enlace correspondiente.\",\"missing_info_query\":\"\xBFCu\xE1l + es el enlace a la documentaci\xF3n relevante sobre el uso del par\xE1metro + `max_tokens`?\",\"useful\":\"yes\",\"citations\":[\"block-AA\",\"block-AC\",\"block-AD\",\"block-AK\"]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":71,\"output_tokens\":26,\"timings\":{\"generative\":3.238622928998666},\"input_nuclia_tokens\":0.071,\"output_nuclia_tokens\":0.026}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.07125,\"output\":0.02556,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:14 GMT + nuclia-learning-id: + - a89231042df94af2bfb2ba1c7185b462 + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '3245' + x-nuclia-trace-id: + - cffbbc8824c2f57a77aeb736ae33be12 + status: + code: 200 + message: OK +- request: + body: '{"query": "Explica c\u00f3mo usar el par\u00e1metro `max_tokens` para limitar + la longitud de la respuesta generada por el modelo, en espa\u00f1ol, y proporciona + un enlace a la documentaci\u00f3n relevante.", "filters": [], "show": ["basic", + "origin", "extra", "extracted", "values", "relations"], "extracted": ["text", + "metadata", "file", "link"], "security": {"groups": []}, "features": ["semantic", + "keyword"], "reranker": "noop"}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '393' + Host: + - europe-1.nuclia.cloud + User-Agent: + - nucliadb-sdk/6.13.1.post6414 + content-type: + - application/json + x-stf-serviceaccount: + - DUMMY + method: POST + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find + response: + body: + string: "{\"resources\":{\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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+ Box\":\"PRODUCT\",\"Agentic RAG\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic + RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge + Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\":4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\",\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Knowledge + Box\":{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge Box\"},\"PRODUCT/Agentic + RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic + RAG\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.031818181818181815,\"score_type\":\"BOTH\",\"order\":2,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136\":{\"score\":0.015384615384615385,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" + - Restricting context size may result in less relevant answers since the LLM + has less information to work with \\n - Balance between cost control and answer + quality \\n Answer Length Limitations: \\n - The LLM might not complete its + response if it hits the token limit, potentially cutting sentences mid-way + \\n - Recommended approach: Include length requirements in your prompt (e.g., + Please answer in less than 200 words ) rather than relying solely on hard + limits \\n - This allows the LLM to naturally conclude its response within + the desired length \\n How to Monitor Token Consumption \\n Understanding + Token Consumption Data \\n You can receive detailed token consumption information + from the following endpoints that utilize LLM models: ask, chat, remi, query, + sentence, summarize, tokens, and rerank. \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":7,\"start\":3340,\"end\":4136,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\":{\"score\":0.014925373134328358,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\" + \\n Large context: Results from using RAG strategies like Full resource or + Neighbouring paragraphs , or from using the extra_context parameter \\n Long + questions: More detailed or complex questions require more input tokens \\n + Long prompts: Extensive system prompts increase the input token count \\n + Detailed answers: Comprehensive responses require more output tokens \\n Images + in context: When using multimodal models, images included in the retrieved + context significantly increase token consumption \\n \\n How to Limit and + Control Token Consumption \\n Strategy 1: Optimize Your Parameters \\n The + first approach to reducing token consumption is to fine-tune your request + parameters: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\",\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs + > ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/split-strategies\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + split-strategies\\ntitle: Use split strategies\\n---\\n\\n# Split strategies\\n\\nIn + some cases, default chunking methods aren't enough to handle the complexity + of your data.\\nImagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved.\\n\\nTo address this, you can define a split strategy that + controls how content is broken into chunks during ingestion.\\nSplit strategies + are reusable and configurable, allowing you to tailor the chunking process + to your specific needs.\\n\\nOnce defined, these strategies can be applied + across multiple ingestion jobs, giving you flexibility and consistency in + how your data is processed.\\n\\n## What is a split strategy?\\n\\nA split + strategy is a reusable configuration that defines how your data is divided + into chunks during ingestion. By customizing a split strategy, you can optimize + chunking for different types of content and use cases.\\n\\nKey components + of a split strategy include:\\n\\n- **Max paragraph size**: Sets the maximum + size (in characters or tokens) for each chunk.\\n- **Custom split**: Determines + the method used for splitting:\\n - **Manual splitting**: Splits content + based on a specified delimiter (default is `\\\"\\\\n\\\"`).\\n - **LLM splitting**: + Uses a language model to segment text intelligently, with optional rules to + guide the process.\\n\\nNote that you can only choose one splitting method + per strategy, and that you will have to set custom_split to 1 for manual splitting + or 2 for LLM splitting.\\n\\n## LLM Splitting\\n\\nLLM splitting leverages + a language model to intelligently segment text into meaningful chunks, going + beyond simple character or line-based splitting. This approach is ideal when + you need context-aware chunking, such as dividing content by sections, topics, + or other logical boundaries.\\n\\nYou can enable LLM splitting with default + settings, or customize it for your specific needs using these parameters:\\n\\n- + **LLM**: Specify the language model in the **generative_model** field. Optionally, + provide credentials for your chosen LLM provider in **user_keys**.\\n- **Rules**: + Define natural language instructions to guide how the text should be split. + For example, you might use rules like \\\"Split at each section heading\\\" + or \\\"Separate lists into individual items\\\".\\n\\nExample configuration:\\n\\n```json\\n{\\n + \ \\\"name\\\": \\\"llm\\\",\\n \\\"max_paragraph\\\": 1000,\\n + \ \\\"custom_split\\\": 2, \\n \\\"llm_split\\\": {\\n \\\"llm\\\": + {\\n \\\"generative_model\\\": \\\"chatgpt-azure-4o-mini\\\",\\n + \ },\\n \\\"rules\\\": [\\n \\\"Split + at each section heading\\\"\\n ]\\n },\\n }\\n```\\n\\nExperiment + with different rules and models to optimize chunking for your use case. Note + that using an LLM may increase processing time and cost, and could slightly + alter the extracted text.\\n\\n## Manual Splitting\\nManual splitting divides + text based on a specified delimiter, making it straightforward and efficient + for simpler chunking needs. This method is particularly useful when your content + has clear, consistent separators, such as paragraphs or bullet points.\\nYou + can customize manual splitting using the following parameters:\\n- **Splitter**: + Choose the character or string that indicates where to split the text. The + default is a double newline character (`\\\"\\\\n\\\\n\\\"`), but you can + set it to any delimiter that suits your content structure.\\nExample configuration:\\n\\n```json\\n + {\\n \\\"name\\\": \\\"manual\\\",\\n \\\"custom_split\\\": + 1,\\n \\\"manual_split\\\": {\\n \\\"splitter\\\": \\\"\\\\n\\\"\\n + \ }\\n }\\n``` \\n\\n## Strategy creation and management\\n\\nBefore + we can use a split strategy, we need to create it. We can create as many strategies + as we want, and just use the right one for each processing job. Once created + we can not modify our strategies, but we can delete them and inspect the ones + we have created for a given kb.\\n\\n### Dashboard\\n\\nTo create a strategy, + just go to the section **AI Models** and then to **Extract & split**. Then + you can click on the button **Create configuration** , next to **Split configurations** + to create a new strategy. Once there, you can fill in the necessary fields + with the desired configuration.\\nIn the same section, you can also see the + list of strategies you have created, and you can delete them once you no longer + need them.\\n\\n### CLI\\n\\nTo create a strategy using the CLI, you can use + the command `nuclia kb split_strategies add` with the desired configuration + in JSON format. You can also list the strategies you have created with `nuclia + kb split_strategies list`, and delete them with `nuclia kb split_strategies + delete`.\\n\\n```bash\\nnuclia kb split_strategies add --config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}'\\nnuclia kb split_strategies + list\\nnuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo create a strategy using the SDK, you can use the `add` method + of the `split_strategies` object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the `list` + method, and delete them with the `delete` method.\\n\\n```python\\nfrom nuclia + import sdk\\nsplit_strategies = sdk.NucliaKB().split_strategies\\nprint(split_strategies.list())\\nid + = split_strategies.add(config='{\\\"name\\\":\\\"strategy1\\\",\\\"custom_split\\\": + 1,\\\"manual_split\\\": {\\\"splitter\\\": \\\"\\\\n\\\"}}')\\nsplit_strategies.delete(id=id)\\n```\\n\\n## + Use split strategies for processing\\n\\n### Dashboard\\n\\nTo use a split + strategy for processing documents, just upload the document normally, enable + **Customize data extraction** and select the strategy you want to use in the + dropdown menu. Once the document is uploaded, it will be processed using the + selected strategy.\\n\\n### CLI\\n\\nTo use a split strategy for processing + documents using the CLI, you can use the command `nuclia kb upload file` with + the `--split_strategy` option, passing the ID of the strategy you want to + use.\\n\\n```bash\\nnuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n```bash\\nnuclia + kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb\\n```\\n\\n### + SDK\\n\\nTo use a split strategy for processing documents using the SDK, you + can use the `file` method of the `NucliaUpload` object, passing the path to + the file and the ID of the strategy you want to use.\\n\\n```python\\nfrom + nuclia import sdk\\nupload = sdk.NucliaUpload()\\nupload.file(path=FILE_PATH, + split_strategy=\\\"1361c0c7-918a-4a7f-b44b-ba37437619fb\\\")\\n```\\n\",\"format\":\"MARKDOWN\",\"md5\":\"834cf2c7e30daced272772d79b257cf0\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: split-strategies \\n title: Use split strategies \\n \\n Split strategies + \\n In some cases, default chunking methods aren't enough to handle the complexity + of your data. \\n Imagine working with dense technical documentation, splitting + it effectively can make a big difference in how well the information is indexed + and retrieved. \\n To address this, you can define a split strategy that controls + how content is broken into chunks during ingestion. \\n Split strategies are + reusable and configurable, allowing you to tailor the chunking process to + your specific needs. \\n Once defined, these strategies can be applied across + multiple ingestion jobs, giving you flexibility and consistency in how your + data is processed. \\n What is a split strategy? \\n A split strategy is a + reusable configuration that defines how your data is divided into chunks during + ingestion. By customizing a split strategy, you can optimize chunking for + different types of content and use cases. \\n Key components of a split strategy + include: \\n \\n Max paragraph size: Sets the maximum size (in characters + or tokens) for each chunk. \\n Custom split: Determines the method used for + splitting: \\n Manual splitting: Splits content based on a specified delimiter + (default is \\\\n ). \\n LLM splitting: Uses a language model to segment text + intelligently, with optional rules to guide the process. \\n \\n Note that + you can only choose one splitting method per strategy, and that you will have + to set custom_split to 1 for manual splitting or 2 for LLM splitting. \\n + LLM Splitting \\n LLM splitting leverages a language model to intelligently + segment text into meaningful chunks, going beyond simple character or line-based + splitting. This approach is ideal when you need context-aware chunking, such + as dividing content by sections, topics, or other logical boundaries. \\n + You can enable LLM splitting with default settings, or customize it for your + specific needs using these parameters: \\n \\n LLM: Specify the language model + in the generative_model field. Optionally, provide credentials for your chosen + LLM provider in user_keys. \\n Rules: Define natural language instructions + to guide how the text should be split. For example, you might use rules like + Split at each section heading or Separate lists into individual items . \\n + \\n Example configuration: \\n json \\n { \\n name : llm , \\n max_paragraph + : 1000, \\n custom_split : 2, \\n llm_split : { \\n llm : { \\n generative_model + : chatgpt-azure-4o-mini , \\n }, \\n rules : [ \\n Split at each section heading + \\n ] \\n }, \\n } \\n Experiment with different rules and models to optimize + chunking for your use case. Note that using an LLM may increase processing + time and cost, and could slightly alter the extracted text. \\n Manual Splitting + \\n Manual splitting divides text based on a specified delimiter, making it + straightforward and efficient for simpler chunking needs. This method is particularly + useful when your content has clear, consistent separators, such as paragraphs + or bullet points. \\n You can customize manual splitting using the following + parameters: \\n - Splitter: Choose the character or string that indicates + where to split the text. The default is a double newline character ( \\\\n\\\\n + ), but you can set it to any delimiter that suits your content structure. + \\n Example configuration: \\n json \\n { \\n name : manual , \\n custom_split + : 1, \\n manual_split : { \\n splitter : \\\\n \\n } \\n } \\n Strategy creation + and management \\n Before we can use a split strategy, we need to create it. + We can create as many strategies as we want, and just use the right one for + each processing job. Once created we can not modify our strategies, but we + can delete them and inspect the ones we have created for a given kb. \\n Dashboard + \\n To create a strategy, just go to the section AI Models and then to Extract + & split. Then you can click on the button Create configuration , next to Split + configurations to create a new strategy. Once there, you can fill in the necessary + fields with the desired configuration. \\n In the same section, you can also + see the list of strategies you have created, and you can delete them once + you no longer need them. \\n CLI \\n To create a strategy using the CLI, you + can use the command nuclia kb split_strategies add with the desired configuration + in JSON format. You can also list the strategies you have created with nuclia + kb split_strategies list, and delete them with nuclia kb split_strategies + delete. \\n bash \\n nuclia kb split_strategies add --config='{ name : strategy1 + , custom_split : 1, manual_split : { splitter : \\\\n }}' \\n nuclia kb split_strategies + list \\n nuclia kb split_strategies delete --id=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To create a strategy using the SDK, you can use the add method + of the split_strategies object, passing the desired configuration in JSON + format. You can also list the strategies you have created with the list method, + and delete them with the delete method. \\n python \\n from nuclia import + sdk \\n split_strategies = sdk.NucliaKB().split_strategies \\n print(split_strategies.list()) + \\n id = split_strategies.add(config='{ name : strategy1 , custom_split : + 1, manual_split : { splitter : \\\\n }}') \\n split_strategies.delete(id=id) + \\n Use split strategies for processing \\n Dashboard \\n To use a split strategy + for processing documents, just upload the document normally, enable Customize + data extraction and select the strategy you want to use in the dropdown menu. + Once the document is uploaded, it will be processed using the selected strategy. + \\n CLI \\n To use a split strategy for processing documents using the CLI, + you can use the command nuclia kb upload file with the --split_strategy option, + passing the ID of the strategy you want to use. \\n bash \\n nuclia kb upload + file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n bash \\n nuclia kb upload file --path=FILE_PATH --split_strategy=1361c0c7-918a-4a7f-b44b-ba37437619fb + \\n SDK \\n To use a split strategy for processing documents using the SDK, + you can use the file method of the NucliaUpload object, passing the path to + the file and the ID of the strategy you want to use. \\n python \\n from nuclia + import sdk \\n upload = sdk.NucliaUpload() \\n upload.file(path=FILE_PATH, + split_strategy= 1361c0c7-918a-4a7f-b44b-ba37437619fb 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strategies\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\":{\"score\":0.014925373134328358,\"score_type\":\"BM25\",\"order\":13,\"text\":\" + Key components of a split strategy include: \\n \\n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \\n Custom split: + Determines the method used for splitting: \\n Manual splitting: Splits content + based on a specified delimiter (default is \\\\n ). \\n\",\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":963,\"end\":1239,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.030536130536130537,\"score_type\":\"BOTH\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.030330882352941176,\"score_type\":\"BOTH\",\"order\":5,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\":[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\":499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\":\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\":1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"end\":3929,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3650,\"end\":3795,\"key\":\"\"},{\"start\":3795,\"end\":3929,\"key\":\"\"}]},{\"start\":3929,\"end\":4317,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3932,\"end\":4123,\"key\":\"\"},{\"start\":4123,\"end\":4317,\"key\":\"\"}]},{\"start\":4317,\"end\":4520,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4320,\"end\":4520,\"key\":\"\"}]},{\"start\":4520,\"end\":4884,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4523,\"end\":4717,\"key\":\"\"},{\"start\":4717,\"end\":4884,\"key\":\"\"}]},{\"start\":4884,\"end\":5167,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4887,\"end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5167,\"key\":\"\"}]},{\"start\":5167,\"end\":5525,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5170,\"end\":5339,\"key\":\"\"},{\"start\":5339,\"end\":5525,\"key\":\"\"}]},{\"start\":5525,\"end\":5897,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5528,\"end\":5711,\"key\":\"\"},{\"start\":5711,\"end\":5897,\"key\":\"\"}]},{\"start\":5897,\"end\":6194,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5898,\"end\":6071,\"key\":\"\"},{\"start\":6071,\"end\":6194,\"key\":\"\"}]},{\"start\":6194,\"end\":6478,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6197,\"end\":6366,\"key\":\"\"},{\"start\":6366,\"end\":6478,\"key\":\"\"}]},{\"start\":6478,\"end\":6778,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6481,\"end\":6635,\"key\":\"\"},{\"start\":6635,\"end\":6778,\"key\":\"\"}]}],\"ner\":{\"boolean\":\"PERSON\",\"112 + min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"end\":654}]},{\"text\":\"112 + min\",\"label\":\"TIME\",\"positions\":[{\"start\":2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\",\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"TIME/112 + min\":{\"position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"},\"PERSON/boolean\":{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.03225806451612903,\"score_type\":\"BOTH\",\"order\":1,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.02819138376017471,\"score_type\":\"BOTH\",\"order\":8,\"text\":\" + \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\n + \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[] + \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens? + \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"c053e849251b4a5296ffeb79ed00200e\":{\"id\":\"c053e849251b4a5296ffeb79ed00200e\",\"slug\":\"docs-nua-api-mdx\",\"title\":\"docs + > nua api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:43.966941\",\"modified\":\"2026-06-09T08:18:14.251220\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/nua-api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + nua-api\\nhide_title: true\\ntitle: Agentic RAG Understanding API\\nsidebar_label: + Agentic RAG Understanding API\\n---\\n\\nimport ApiDocMdx from \\\"@theme/ApiDocMdx\\\";\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8eea4340dceaec0eed766d13fc001e72\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: nua-api \\n hide_title: true \\n title: Agentic RAG Understanding + API \\n sidebar_label: Agentic RAG Understanding API \\n \\n import ApiDocMdx + from @theme/ApiDocMdx ; \\n \\n {` \\n @media screen and (min-width: 998px) + { \\n [class ='docMainContainer'] > .container { \\n max-width: 100% !important; + \\n } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n + } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":289,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":246,\"key\":\"\"},{\"start\":246,\"end\":277,\"key\":\"\"},{\"start\":277,\"end\":289,\"key\":\"\"}]},{\"start\":289,\"end\":366,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":290,\"end\":337,\"key\":\"\"},{\"start\":337,\"end\":366,\"key\":\"\"}]}],\"ner\":{\"nua-api\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"nua-api\",\"label\":\"PERSON\",\"positions\":[{\"start\":7,\"end\":14}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:20.548849Z\",\"last_understanding\":\"2026-06-09T08:18:19.989241Z\",\"last_extract\":\"2026-06-09T08:18:18.844339Z\",\"last_processing_start\":\"2026-06-09T08:18:18.793432Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PERSON/nua-api\":{\"position\":[{\"start\":7,\"end\":14}],\"entity\":\"nua-api\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > nua api.\",\"extracted\":{\"text\":{\"text\":\"docs > nua api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"c053e849251b4a5296ffeb79ed00200e/t/page/289-366\":{\"score\":0.0136986301369863,\"score_type\":\"BM25\",\"order\":19,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"c053e849251b4a5296ffeb79ed00200e/t/page/289-366\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":289,\"end\":366,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\":{\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd\",\"slug\":\"docs-zone-api-mdx\",\"title\":\"docs + > zone api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:43.137585\",\"modified\":\"2026-06-09T08:18:13.619843\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/zone-api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + knowledgebox-zone-api\\nhide_title: true\\ntitle: Knowledge Box & Zone API\\n---\\n\\nimport + ApiDocMdx from '@theme/ApiDocMdx';\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"4060ad5ed8c024c99b505a02edfce76f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: knowledgebox-zone-api \\n hide_title: true \\n title: Knowledge Box + & Zone API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` \\n + @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] > .container + { \\n max-width: 100% !important; \\n } \\n [class ='docItemCol'] { \\n max-width: + 100% !important; \\n } \\n } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":252,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":209,\"key\":\"\"},{\"start\":209,\"end\":240,\"key\":\"\"},{\"start\":240,\"end\":252,\"key\":\"\"}]},{\"start\":252,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":253,\"end\":300,\"key\":\"\"},{\"start\":300,\"end\":329,\"key\":\"\"}]}],\"ner\":{\"ApiDocMdx\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"ApiDocMdx\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":93,\"end\":102}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:20.481514Z\",\"last_understanding\":\"2026-06-09T08:18:19.956554Z\",\"last_extract\":\"2026-06-09T08:18:18.741090Z\",\"last_processing_start\":\"2026-06-09T08:18:18.713420Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/ApiDocMdx\":{\"position\":[{\"start\":93,\"end\":102}],\"entity\":\"ApiDocMdx\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > zone api.\",\"extracted\":{\"text\":{\"text\":\"docs > zone api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329\":{\"score\":0.013888888888888888,\"score_type\":\"BM25\",\"order\":17,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":252,\"end\":329,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.029551337359792925,\"score_type\":\"BOTH\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.027526395173453996,\"score_type\":\"BOTH\",\"order\":9,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"53b91ad0dd5f48a7ac8b59f9775ed7e0\":{\"id\":\"53b91ad0dd5f48a7ac8b59f9775ed7e0\",\"slug\":\"docs-api-mdx\",\"title\":\"docs + > api.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"ca\",\"en\",\"hu\",\"la\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T15:30:45.557031\",\"modified\":\"2026-06-09T08:18:15.494834\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/api\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + api\\nhide_title: true\\ntitle: NucliaDB REST API\\nsidebar_label: NucliaDB + API\\n---\\n\\nimport ApiDocMdx from '@theme/ApiDocMdx';\\n\\n\\n\\n\\n\",\"format\":\"MARKDOWN\",\"md5\":\"b50e745a15cde6807eea0d88d476f3d9\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: api \\n hide_title: true \\n title: NucliaDB REST API \\n sidebar_label: + NucliaDB API \\n \\n import ApiDocMdx from '@theme/ApiDocMdx'; \\n \\n {` + \\n @media screen and (min-width: 998px) { \\n [class ='docMainContainer'] + > .container { \\n max-width: 100% !important; \\n } \\n [class ='docItemCol'] + { \\n max-width: 100% !important; \\n } \\n } \\n `} \\n \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"end\":214,\"key\":\"\"},{\"start\":214,\"end\":245,\"key\":\"\"},{\"start\":245,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":334,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":258,\"end\":305,\"key\":\"\"},{\"start\":305,\"end\":334,\"key\":\"\"}]}],\"ner\":{\"NucliaDB\":\"ORG\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"NucliaDB\",\"label\":\"ORG\",\"positions\":[{\"start\":39,\"end\":47}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:22.537575Z\",\"last_understanding\":\"2026-06-09T08:18:22.045700Z\",\"last_extract\":\"2026-06-09T08:18:20.890878Z\",\"last_processing_start\":\"2026-06-09T08:18:20.832975Z\",\"language\":\"la\",\"summary\":\"\",\"positions\":{\"ORG/NucliaDB\":{\"position\":[{\"start\":39,\"end\":47}],\"entity\":\"NucliaDB\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > api.\",\"extracted\":{\"text\":{\"text\":\"docs > api.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334\":{\"score\":0.014084507042253521,\"score_type\":\"BM25\",\"order\":16,\"text\":\" + } \\n [class ='docItemCol'] { \\n max-width: 100% !important; \\n } \\n } + \\n `} \\n \\n \",\"id\":\"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\":257,\"end\":334,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.032266458495966696,\"score_type\":\"BOTH\",\"order\":0,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate + limits are an essential aspect of the Agentic RAG platform, ensuring fair + usage and optimal performance for all users interacting with Agentic RAG APIs. + This document outlines the rate limits enforced by Agentic RAG and provides + guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic + RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: + By default, the sum of all authenticated requests in a Agentic RAG account + cannot exceed 2400 requests per minute. Note that this limit can be customized + on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) + if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic + RAG implements a back-pressure mechanism to manage ingestion pipeline overload. + This mainly affects endpoints for uploading data and creating or updating + resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres + to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) + and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe + official Agentic RAG API clients already have built-in mechanisms for retrying + requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- + [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are + interacting directly with the API, we recommend using an [exponential backoff + retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits + are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response + will include a `try_after` key with an estimated UTC time for retrying the + request. You can use this value for retry logic as an alternative to the exponential + backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an + example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport + time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, + headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n + \ response = requests.get(url, headers=headers)\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n + \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n + \ time.sleep(wait_time)\\n retries += 1\\n else:\\n + \ response.raise_for_status()\\n raise Exception(\\\"Max retries + exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders + = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, + headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's + an example of how to use the try_after key from the response to manage rate + limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport + requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n + \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, + headers=headers)\\n response_body = response.json()\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n + \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n + \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n + \ print(\\n f\\\"Rate limit exceeded. Retrying at + {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n + \ retries += 1\\n else:\\n response.raise_for_status()\\n + \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl + = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer + YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese + examples demonstrate how to handle rate limits effectively, ensuring that + your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate + limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, + ensuring fair usage and optimal performance for all users interacting with + Agentic RAG APIs. This document outlines the rate limits enforced by Agentic + RAG and provides guidelines for handling rate-limited responses effectively. + \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: + \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated + requests in a Agentic RAG account cannot exceed 2400 requests per minute. + Note that this limit can be customized on a per-account basis. Please contact + Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion + back pressure limits: Agentic RAG implements a back-pressure mechanism to + manage ingestion pipeline overload. This mainly affects endpoints for uploading + data and creating or updating resources. \\n \\n \\n Handling Rate-Limited + Responses \\n Agentic RAG adheres to the HTTP standard and will return a response + with a 429 status codes when the limits are exceeded. \\n The official Agentic + RAG API clients already have built-in mechanisms for retrying requests when + rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript + client \\n \\n However, if you are interacting directly with the API, we recommend + using an exponential backoff retry strategy when limits are reached. \\n When + ingestion back pressure rate limits are hit, the response will include a try_after + key with an estimated UTC time for retrying the request. You can use this + value for retry logic as an alternative to the exponential backoff strategy. + \\n Example 1: Regular API rate limits \\n Here's an example of how to implement + an exponential backoff retry strategy in Python: \\n ```python \\n import + time \\n import requests \\n def make_request_with_exponential_backoff(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n if response.status_code + == 200: \\n return response.json() \\n elif response.status_code == 429: \\n + wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit + exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n + retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( + Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n + headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n ```python \\n import time \\n from datetime import + datetime \\n import requests \\n def make_request_with_try_after_info(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n response_body = response.json() + \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code + == 429 and try_after in response_body: \\n try_after = response_body[ try_after + ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n + wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n + f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... + \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries 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+ RAG\"},\"LAW/HTTP\":{\"position\":[{\"start\":998,\"end\":1002}],\"entity\":\"HTTP\"},\"PRODUCT/Max\":{\"position\":[{\"start\":3409,\"end\":3412}],\"entity\":\"Max\"},\"TIME/seconds\":{\"position\":[{\"start\":2199,\"end\":2206}],\"entity\":\"seconds\"},\"PRODUCT/Python\":{\"position\":[{\"start\":1232,\"end\":1238},{\"start\":1773,\"end\":1779}],\"entity\":\"Python\"},\"ORG/Agentic + RAG's\":{\"position\":[{\"start\":668,\"end\":681}],\"entity\":\"Agentic RAG's\"},\"LAW/Handling + Rate-Limited Responses Agentic RAG\":{\"position\":[{\"start\":937,\"end\":982}],\"entity\":\"Handling + Rate-Limited Responses Agentic RAG\"},\"TIME/UTC\":{\"position\":[{\"start\":1526,\"end\":1529}],\"entity\":\"UTC\"},\"PRODUCT/Agentic + RAG API\":{\"position\":[{\"start\":1108,\"end\":1123}],\"entity\":\"Agentic + RAG API\"},\"ORG/Nuclia\":{\"position\":[{\"start\":1225,\"end\":1231},{\"start\":1248,\"end\":1254}],\"entity\":\"Nuclia\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"operating + system\",\"metadata\":{\"paragraph_id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1222-1656\",\"source_start\":1248,\"source_end\":1254,\"to_start\":1232,\"to_end\":1238},\"from\":{\"value\":\"Nuclia\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\":{\"value\":\"Python\",\"type\":\"entity\",\"group\":\"PRODUCT\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > ingestion > how to > rate limiting\",\"extracted\":{\"text\":{\"text\":\"docs + > ingestion > how to > rate limiting\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.014285714285714285,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\" + ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\":{\"score\":0.013888888888888888,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" + retries = 0 \\n while retries < max_retries: \\n response = requests.get(url, + headers=headers) \\n if response.status_code == 200: \\n return response.json() + \\n elif response.status_code == 429: \\n wait_time = 2 retries # Exponential + backoff: 2^retries \\n print(f Rate limit exceeded. Retrying in {wait_time} + seconds... ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1900,\"end\":2653,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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+ > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.030751173708920186,\"score_type\":\"BOTH\",\"order\":3,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.02877846790890269,\"score_type\":\"BOTH\",\"order\":7,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible + LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG + allows you to connect to any OpenAI API compatible LLM. This means that you + can use any LLM that has an API compatible with the OpenAI API which has become + a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, + open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box + configuration you can do so in three manners, through the API, the Nuclia + CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers + the most user-friendly way to modify the configuration of your knowledge box + and we will use it in this example.\\n\\nWe will be setting up a connection + to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers + a wide range of open-source and commercial models compatible with the OpenAI + API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), + the API parameters are located under the **API** tab.\\n\\n1. **Open the AI + Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. + **Select \u201COpenAI API Compatible Model\u201D** \\n From the models + list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n + \ Toggle the option for using you own `OpenAI API Compatible Key` if it is + not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - + **API Key**:\\n - Description: The API key for your LLM. This is the key + that you would use as an authorization header in the API. You may leave this + blank if the endpoint you are connecting to does not require an API key.\\n + \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n + \ - Description: The URL of the API endpoint for your LLM. This may be + shared between multiple models.\\n - Example: For OpenRouter, it is the + same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - + Description: The name of the model you want to use, it needs to exactly match + the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus + in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - + **Maximum supported input tokens**:\\n - Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only.\\n - + Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, + as we want to leave room for the output, we will set the maximum supported + input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output + tokens**:\\n - Description: The maximum number of tokens that the model + can generate as output. Again, we should keep in mind that this value summed + to the **Maximum supported input tokens** should not exceed the total context + size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the + maximum output tokens is specified at `32768`, but we already reserved `31744` + for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - + **Model Features**:\\n - Description: Under this section you will find + multiple toggles related to features supported by the model, these vary from + model to model, but most often the default values are well suited to most + use cases. The most relevant toggle is for `Image Support` which allows you + to use images as input for the model.\\n - Example: Image input is not + supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** + \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample + query in Agentic RAG or via API/CLI. Adjust your prompt templates and token + settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API + compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic + RAG allows you to connect to any OpenAI API compatible LLM. This means that + you can use any LLM that has an API compatible with the OpenAI API which has + become a standard in the industry. \\n Many of the options for self-hosted + LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications. \\n Configuration \\n To modify your knowledge box configuration + you can do so in three manners, through the API, the Nuclia CLI / SDK or the + Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly + way to modify the configuration of your knowledge box and we will use it in + this example. \\n We will be setting up a connection to the Phi 4 Reasoning + Plus model, hosted by OpenRouter which offers a wide range of open-source + and commercial models compatible with the OpenAI API. We can see more information + about this specific model here, the API parameters are located under the API + tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, + click AI Models. \\n Select OpenAI API Compatible Model \\n From the models + list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle + the option for using you own OpenAI API Compatible Key if it is not already + enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: + \\n \\n Description: The API key for your LLM. This is the key that you would + use as an authorization header in the API. You may leave this blank if the + endpoint you are connecting to does not require an API key. \\n Example: We + will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: + The URL of the API endpoint for your LLM. This may be shared between multiple + models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 + \\n \\n \\n Model: \\n Description: The name of the model you want to use, + it needs to exactly match the name of the model in the API. \\n Example: For + Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. + \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n Example: + For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want + to leave room for the output, we will set the maximum supported input tokens + as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified + at 32768, but we already reserved 31744 for the input tokens, so we will set + this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: + Under this section you will find multiple toggles related to features supported + by the model, these vary from model to model, but most often the default values + are well suited to most use cases. The most relevant toggle is for Image Support + which allows you to use images as input for the model. \\n Example: Image + input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. + \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n + Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt templates + and token settings as needed. \\n \\n 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Content-Length: + - '9269' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:18 GMT + via: + - 1.1 google + x-envoy-upstream-service-time: + - '133' + x-nuclia-trace-id: + - f3a808f002b4bb6f3fa7cdb4de549a84 + status: + code: 200 + message: OK +- request: + body: '{"question": "\u00bfCu\u00e1l es el enlace a la documentaci\u00f3n relevante + sobre el uso del par\u00e1metro `max_tokens`?", "user_id": "arag-ask-rerank", + "context": {"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": " Use the max_tokens + parameter on the /ask endpoint to set hard limits on: \n - Context size: Limits + the amount of retrieved information sent to the LLM \n - Answer length: Limits + the length of the generated response \n Important Considerations \n Context + Limitations: \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136": " - + Restricting context size may result in less relevant answers since the LLM has + less information to work with \n - Balance between cost control and answer quality + \n Answer Length Limitations: \n - The LLM might not complete its response if + it hits the token limit, potentially cutting sentences mid-way \n - Recommended + approach: Include length requirements in your prompt (e.g., Please answer in + less than 200 words ) rather than relying solely on hard limits \n - This allows + the LLM to naturally conclude its response within the desired length \n How + to Monitor Token Consumption \n Understanding Token Consumption Data \n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326": " \n + Large context: Results from using RAG strategies like Full resource or Neighbouring + paragraphs , or from using the extra_context parameter \n Long questions: More + detailed or complex questions require more input tokens \n Long prompts: Extensive + system prompts increase the input token count \n Detailed answers: Comprehensive + responses require more output tokens \n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \n \n How to Limit and Control Token Consumption \n Strategy 1: + Optimize Your Parameters \n The first approach to reducing token consumption + is to fine-tune your request parameters: \n\n", "1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239": + " Key components of a split strategy include: \n \n Max paragraph size: Sets + the maximum size (in characters or tokens) for each chunk. \n Custom split: + Determines the method used for splitting: \n Manual splitting: Splits content + based on a specified delimiter (default is \\n ). \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": + " \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: + string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": + " \n optional max_tokens: number \\| object \n \n Defines the maximum number + of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2450-2808": + " \n optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight + \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? + \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n + BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n", "c053e849251b4a5296ffeb79ed00200e/t/page/289-366": + " } \n [class =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n + `} \n \n \n", "91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329": " } \n [class + =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n `} \n \n \n", + "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: + string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \n \n + output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional + default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": + " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n", "53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334": + " } \n [class =''docItemCol''] { \n max-width: 100% !important; \n } \n } \n + `} \n \n \n", "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n", "dd41482018924facb5dbb87a7d53f122/t/page/3310-3757": + " ) \n time.sleep(wait_time) \n retries += 1 \n else: \n response.raise_for_status() + \n raise Exception( Max retries exceeded ) \n Example usage \n url = https://your-endpoint + \n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, + headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate + limits effectively, ensuring that your application respects the limits and retries + appropriately.\n", "dd41482018924facb5dbb87a7d53f122/t/page/1900-2653": " retries + = 0 \n while retries < max_retries: \n response = requests.get(url, headers=headers) + \n if response.status_code == 200: \n return response.json() \n elif response.status_code + == 429: \n wait_time = 2 retries # Exponential backoff: 2^retries \n print(f + Rate limit exceeded. Retrying in {wait_time} seconds... ) \n time.sleep(wait_time) + \n retries += 1 \n else: \n response.raise_for_status() \n raise Exception( + Max retries exceeded ) \n Example usage \n url = https://your-endpoint \n headers + = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_exponential_backoff(url, + headers) \n print(data) \n ``` \n Example 2: Ingestion back pressure limits + \n Here''s an example of how to use the try_after key from the response to manage + rate limits: \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": " ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": + " \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n", "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: + The maximum number of tokens that the model can generate as output. Again, we + should keep in mind that this value summed to the Maximum supported input tokens + should not exceed the total context size supported by the model. \n\n", "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": + " \n \n Maximum supported input tokens: \n Description: The maximum number of + tokens that the model can accept as input. Be mindful that this takes into account + the tokens used in the prompt, query and context. Also take note that some models + may provide their context window as the total between input and output tokens, + while others may provide it as the input tokens only. \n\n"}}' + headers: + Accept: + - '*/*' + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '7970' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank + response: + body: + string: '{"context_scores":{"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.6302298903465271,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.5369200110435486,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.2701912820339203,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.20291663706302643,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.10070160776376724,"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808":0.09807933121919632,"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575":0.08803311735391617,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709":0.0652465671300888,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.06325051933526993,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.05165378004312515,"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043":0.041617799550294876,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.03422932326793671,"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757":0.011288073845207691,"4039d76b0fff4962900836ab3fdec9f7/t/page/3340-4136":0.0035518845543265343,"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239":0.0028118586633354425,"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653":0.0027149852830916643,"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326":0.0022606407292187214,"c053e849251b4a5296ffeb79ed00200e/t/page/289-366":0.000317508791340515,"91d9ee7dc2bd47e5b4a1011ca8e42bfd/t/page/252-329":0.000317508791340515,"53b91ad0dd5f48a7ac8b59f9775ed7e0/t/page/257-334":0.000317508791340515}}' + headers: + Alt-Svc: + - h3=":443"; ma=2592000 + Content-Length: + - '1455' + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/json + date: + - Wed, 15 Jul 2026 07:56:19 GMT + nuclia-learning-model: + - bge-reranker-v2-m3 + via: + - 1.1 google + x-envoy-upstream-service-time: + - '58' + x-nuclia-trace-id: + - 60830709e5c9f4d8eeab2982663e779e + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", + "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": + {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context + and user question, perform the following tasks:\n\n1. Select only information + directly relevant to the question.\n2. Break down compound sentences into simple, + single-idea statements. Preserve original phrasing when possible.\n3. For any + named entity with descriptive details, separate those details into distinct + propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", + \"she\", \"they\", \"this\", \"that\") with the full names of the entities they + reference, and add necessary modifiers to clarify meaning.\n5. The context may + be delimited by tags such as and . Treat + everything between these tags as context.\n6. Assess whether the context sufficiently + answers the question. If it answers it partially, provide the answer; if it + does not answer it fully, specify what information is missing to answer the + question.\n7. If the context does not answer the question at all, just return + the original question as the missing information.\n8. The `citations` field + consists ONLY in a list of block IDs that are relevant to the answer, following + these rules:\n - Use the format: block-AB\n - Just mention the block IDs, + do NOT include any other text.\n - Just mention the blocks actually relevant + and that contain information used in the answer, do NOT include blocks that + are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only + use those provided in the context.\n10. Your output must be a JSON object with + the following fields:\n - \"reason\": Explain your reasoning for the answer + or validation.\n - \"answer\": Provide a partial or complete answer to the + user query strictly from the information in the context. If there isn''t enough + information to even provide a partial answer, leave ''answer'' empty.\n - + \"missing_info_query\": If the context is insufficient, specify what information + is missing in a query shape; otherwise, leave it empty. Just return the query + needed to retrieve the missing information.\n - \"useful\": Indicate if the + context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": + List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", + \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you + MUST follow them carefully when generating the answer field. These instructions + may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfCu\u00e1l + es el enlace a la documentaci\u00f3n relevante sobre el uso del par\u00e1metro + `max_tokens`?\n\n\n\nContext:\n\n**block-AA**\n\n## Chunk: + 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use the max_tokens parameter + on the /ask endpoint to set hard limits on: \n - Context size: Limits the amount + of retrieved information sent to the LLM \n - Answer length: Limits the length + of the generated response \n Important Considerations \n Context Limitations: + \n\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## + Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n optional max_tokens: + number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n + optional max_tokens: number \\| object \n \n Defines the maximum number of tokens + that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\n``` \n + optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\n``` \n + optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight + \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? + \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n + BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: + /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum + number of tokens that the model can accept as input. Be mindful that this takes + into account the tokens used in the prompt, query and context. Also take note + that some models may provide their context window as the total between input + and output tokens, while others may provide it as the input tokens only. \n\n + ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\n``` ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n ```\n\n\n---\"\n\n\n**block-AI**\n\n## + Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n SDK: \n \n + ```python \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, + Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My + question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\n``` \n + optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": + false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", + "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": + {"title": "validate_or_answer", "description": "Validate or answer", "parameters": + {"type": "object", "properties": {"reason": {"type": "string", "description": + "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": + "Partial or complete answer to the user query from the information in the context."}, + "missing_info_query": {"type": "string", "description": "Query needed to retrieve + the missing information in case the context is not enough to answer the question. + If the context does not answer the question at all, just return the original + question."}, "useful": {"type": "string", "description": "Is the context useful + to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", + "items": {"type": "string", "description": "Block ID cited in the answer, e.g. + block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, + "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, + "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": + {"type": "required"}, "reasoning": false, "seed": null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '8066' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-origin: + - RAO + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: '{"chunk":{"type":"object","object":{"reason":"The context provides + information about the `max_tokens` parameter, including its definition and + usage in various contexts. However, it does not provide a direct link to the + relevant documentation. Therefore, the answer is partially complete, but lacks + the specific documentation link requested in the question.","answer":"","missing_info_query":"What + is the link to the documentation relevant to the `max_tokens` parameter?","useful":"yes","citations":["block-AA","block-AB","block-AC","block-AD","block-AE","block-AF","block-AG","block-AH","block-AI","block-AJ"]}}} + + {"chunk":{"type":"status","code":"0"}} + + {"chunk":{"type":"meta","input_tokens":54,"output_tokens":15,"timings":{"generative":2.3304300750023685},"input_nuclia_tokens":0.054,"output_nuclia_tokens":0.015}} + + {"chunk":{"normalized_tokens":{"input":0.05403,"output":0.01548,"image":0.0},"customer_key_tokens":{"input":0.0,"output":0.0,"image":0.0},"type":"consumption"}} + + ' + headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:19 GMT + nuclia-learning-id: + - 29d3a6e56ed74ba4b11b25cb517ebacd + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '2336' + x-nuclia-trace-id: + - 09a5475bf38d208b9a57ccf3c63f4c8e + status: + code: 200 + message: OK +- request: + body: '{"question": "", "retrieval": true, "user_id": "summarize", "system": "You + are a helpful AI assistant. Your role is to provide accurate, clear, and well-structured + answers based strictly on the information provided to you.\nKey principles:\n- + Answer only using the information in the provided context\n- Do not use external + knowledge, assumptions, or prior experience\n- Maintain a professional and informative + tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge + this clearly\n\nAlways follow any additional instructions provided about format, + style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": + {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## + Question\nExplica c\u00f3mo usar el par\u00e1metro `max_tokens` para limitar + la longitud de la respuesta generada por el modelo, en espa\u00f1ol, y proporciona + un enlace a la documentaci\u00f3n relevante.\n\n## Provided Context\n[START + OF CONTEXT]\n## Retrieval on nucliadb-2 Knowledge Box\n\n# Explica c\u00f3mo + usar el par\u00e1metro `max_tokens` para limitar la longitud de la respuesta + generada por el modelo, en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n + relevante.\n\n El par\u00e1metro `max_tokens` se utiliza en el endpoint /ask + para establecer l\u00edmites en la longitud de la respuesta generada por el + modelo. Este par\u00e1metro limita la cantidad m\u00e1xima de tokens que el + modelo puede generar como salida. Es importante tener en cuenta que el valor + de `max_tokens` sumado a los tokens de entrada no debe exceder el tama\u00f1o + total del contexto soportado por el modelo. Para m\u00e1s detalles, consulta + la documentaci\u00f3n en el enlace correspondiente.\n\n## Retrieval on nucliadb-2 + Knowledge Box\n\n\n\n\n## Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use + the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context + size: Limits the amount of retrieved information sent to the LLM \n - Answer + length: Limits the length of the generated response \n Important Considerations + \n Context Limitations: \n\n ```\n\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: + the maximum number of input tokens to put in the final context (including the + prompt, the retrieved results and the user question). \n max_output_tokens: + the maximum number of tokens to generate. \n\n ```\n\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n + optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined + in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? + \n\n ```\n\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n + optional max_tokens: number \\| object \n \n Defines the maximum number of tokens + that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \n \n min_score? \n\n ```\n\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\n``` \n + optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \n \n max_tokens? \n\n ```\n\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\n``` \n + optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight + \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? + \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n + BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 + \n \n max_tokens? \n\n ```\n\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: + /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum + number of tokens that the model can accept as input. Be mindful that this takes + into account the tokens used in the prompt, query and context. Also take note + that some models may provide their context window as the total between input + and output tokens, while others may provide it as the input tokens only. \n\n + ```\n\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\n``` ::: + \n :::warning \n Enabling reasoning can use additional tokens, which may increase + your usage costs. \n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \n\n ```\n\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n + SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search + import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( + \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( + \n display=True, # Show reasoning in the response \n effort= low , # Can be + low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can + use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning + Options: \n\n ```\n\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\n``` \n + optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n + \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \n \n max_images? \n\n ```\n\n\n[END OF CONTEXT]\n\n## Answering Guidelines\n- + Carefully read all context; it may be lengthy or detailed\n- Do not omit or + overlook any relevant information\n- If the context is incomplete or insufficient, + state: \"Not enough data to answer this.\"\n- Read carefully any extra instructions + below if provided and use them to answer\n\nNow provide your answer to the question: + Explica c\u00f3mo usar el par\u00e1metro `max_tokens` para limitar la longitud + de la respuesta generada por el modelo, en espa\u00f1ol, y proporciona un enlace + a la documentaci\u00f3n relevante."}, "citations": null, "citation_threshold": + null, "generative_model": "chatgpt-azure-4o-mini", "max_tokens": 5000, "query_context_images": + {}, "prefer_markdown": null, "json_schema": null, "format_prompt": false, "rerank_context": + false, "tools": [], "tool_choice": {"type": "auto"}, "reasoning": false, "seed": + null}' + headers: + Accept: + - application/x-ndjson + Accept-Encoding: + - gzip, deflate + Connection: + - keep-alive + Content-Length: + - '6291' + Content-Type: + - application/json + Host: + - europe-1.dp.progress.cloud + User-Agent: + - nuclia.py/4.11.5 + x-client-ident: + - default + x-message: + - 5d63febedbc24517be4b991a586e17dc + x-origin: + - RAO + x-session: + - default_default_session + x-show-consumption: + - 'true' + x-stf-nuakey: + - DUMMY + method: POST + uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat + response: + body: + string: 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+ headers: + Alt-Svc: + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + Transfer-Encoding: + - chunked + access-control-expose-headers: + - X-NUCLIA-TRACE-ID + content-type: + - application/x-ndjson + date: + - Wed, 15 Jul 2026 07:56:21 GMT + nuclia-learning-id: + - c52889d1c3444febba6812355f5ffa0f + nuclia-learning-model: + - chatgpt-azure-4o-mini + via: + - 1.1 google + x-envoy-upstream-service-time: + - '439' + x-nuclia-trace-id: + - 3912f0dd166796808fcd566a52276262 + status: + code: 200 + message: OK +version: 1 diff --git a/agents/rephrase/tests/test_rephrase.py b/agents/rephrase/tests/test_rephrase.py index 76f5960..10f3447 100644 --- a/agents/rephrase/tests/test_rephrase.py +++ b/agents/rephrase/tests/test_rephrase.py @@ -18,9 +18,7 @@ "KB_DF8B4C24_2807_4888_AD6C_AE97357A638B" ) or cassette_nua_key("https://europe-1.nuclia.cloud/") pytestmark = [ - pytest.mark.vcr( - ignore_localhost=True, ignore_hosts=["test", "europe-1.nuclia.cloud", "europe-1.dp.progress.cloud"] - ), + pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["test"]), pytest.mark.asyncio, ] From 60cd064114861d9ee6508c147f0520a34036b065 Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Wed, 15 Jul 2026 10:05:52 +0200 Subject: [PATCH 09/10] format --- .../nucliadb/src/hyperforge_nucliadb/sync/config_driver.py | 1 - agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py | 3 +-- agents/nucliadb/tests/test_sync.py | 5 ++++- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py b/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py index 1635781..a2395e2 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/sync/config_driver.py @@ -8,7 +8,6 @@ NucliaDBConnection, ) - SYNC_HTTP_TIMEOUT = Timeout(connect=5.0, read=30.0, write=10.0, pool=5.0) diff --git a/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py b/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py index 01994c7..72a6fcb 100644 --- a/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py +++ b/agents/nucliadb/src/hyperforge_nucliadb/sync/driver.py @@ -1,5 +1,5 @@ -import datetime import asyncio +import datetime import random import time from typing import Any, Dict, List, cast @@ -35,7 +35,6 @@ SyncDriverConfig, ) - SYNC_VALIDATE_ATTEMPTS = 2 SYNC_VALIDATE_RETRY_BASE_SECONDS = 0.5 SYNC_VALIDATE_RETRY_JITTER_SECONDS = 0.25 diff --git a/agents/nucliadb/tests/test_sync.py b/agents/nucliadb/tests/test_sync.py index 0f29f42..225f5a2 100644 --- a/agents/nucliadb/tests/test_sync.py +++ b/agents/nucliadb/tests/test_sync.py @@ -22,7 +22,10 @@ "KB_E103CAF3_F8CB_4161_A57C_AAD1192D0666" ) or cassette_nua_key("https://europe-1.nuclia.cloud/") -pytestmark = [pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]), pytest.mark.asyncio] +pytestmark = [ + pytest.mark.vcr(ignore_localhost=True, ignore_hosts=["europe-1.dp.progress.cloud"]), + pytest.mark.asyncio, +] CONFIG = { "drivers": [ From fa227d24c24d9df8c41493df891375c6810d6495 Mon Sep 17 00:00:00 2001 From: Carles Onielfa Date: Wed, 15 Jul 2026 10:12:29 +0200 Subject: [PATCH 10/10] rerecord nucliadb --- .../test_nucliadb_agent_basic_ask.yaml | 3471 +--- .../test_nucliadb_agent_simple.yaml | 13062 +++------------ ...nt_simple_disable_ai_parameter_search.yaml | 13117 +++++----------- .../cassettes/test_sync/test_sync_agent.yaml | 11757 +++----------- 4 files changed, 7531 insertions(+), 33876 deletions(-) diff --git a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml index b8fb944..26812c1 100644 --- a/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml +++ b/agents/nucliadb/tests/cassettes/test_nucliadb/test_nucliadb_agent_basic_ask.yaml @@ -23,82 +23,23 @@ interactions: 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"{\"chunk\":{\"type\":\"object\",\"object\":{\"rephrased_question\"\ - :\"\xBFC\xF3mo se utiliza el par\xE1metro `max_tokens` para controlar la longitud\ - \ de las respuestas en espa\xF1ol, y d\xF3nde puedo encontrar la documentaci\xF3\ - n oficial?\",\"rules\":[\"Proporcionar una explicaci\xF3n clara sobre el uso\ - \ de `max_tokens`.\",\"Incluir un enlace directo a la documentaci\xF3n relevante.\"\ - ,\"La respuesta debe estar en espa\xF1ol.\"],\"reason\":\"La pregunta original\ - \ es un poco ambigua al preguntar 'c\xF3mo usar max_tokens.answer'. La reformulaci\xF3\ - n especifica que se refiere al uso del par\xE1metro `max_tokens` para controlar\ - \ la longitud de las respuestas, que es su funci\xF3n principal. Adem\xE1\ - s, solicita expl\xEDcitamente la documentaci\xF3n oficial, lo que hace la\ - \ petici\xF3n m\xE1s precisa.\"}}}\n{\"chunk\":{\"type\":\"status\",\"code\"\ - :\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":7,\"output_tokens\"\ - :13,\"timings\":{\"generative\":1.010955389996525},\"input_nuclia_tokens\"\ - :0.007,\"output_nuclia_tokens\":0.013}}\n{\"chunk\":{\"normalized_tokens\"\ - :{\"input\":0.0071,\"output\":0.01344,\"image\":0.0},\"customer_key_tokens\"\ - :{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Length: - - '133' + - '111' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 15 Jul 2026 07:57:29 GMT + - Wed, 15 Jul 2026 08:11:22 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '12' + - '15' x-nuclia-trace-id: - - 4031c7b360f8b69d15c3fafc5ef91c75 + - 5fb40576d4d8cf1a6cb3c5e23df20600 status: code: 200 message: OK @@ -120,36 +61,33 @@ interactions: x-stf-serviceaccount: - 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- h3=":443"; ma=2592000 + - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 Content-Length: - - '575' + - '434' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 15 Jul 2026 07:57:30 GMT + - Wed, 15 Jul 2026 08:11:22 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '15' + - '14' x-nuclia-trace-id: - - 7e940b2f60aba5c8b4b9300c73d4fe5e + - 6d25f6a520f85405e56545eb3847fca9 status: code: 200 message: OK - request: - body: '' + body: '{"prefixes": [{"prefix": "/s/p"}]}' headers: Accept: - '*/*' @@ -157,6 +95,8 @@ interactions: - gzip, deflate Connection: - keep-alive + Content-Length: + - '32' Host: - europe-1.nuclia.cloud User-Agent: @@ -165,29 +105,28 @@ interactions: - application/json x-stf-serviceaccount: - DUMMY - method: GET - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b + method: POST + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/catalog/facets response: body: - 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\":5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-resources\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-search\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-rag-lab\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"account-arag\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-kv-schemas\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"user-profile\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-users\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-widgets\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"kb-remi-analytics\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\":\"agent-users\",\"labels\":[]}],\"facets\":{},\"query\":\"\",\"total\":2013,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"]}" headers: Alt-Svc: - 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keep-alive Content-Length: - - '195' + - '198' Host: - europe-1.nuclia.cloud User-Agent: @@ -915,2293 +576,209 @@ interactions: uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/ask response: body: - string: "{\"item\":{\"type\":\"answer\",\"text\":\"El\"}}\n{\"item\":{\"type\"\ - :\"answer\",\"text\":\" par\xE1\"}}\n{\"item\":{\"type\":\"answer\",\"text\"\ - :\"metro\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" `\"}}\n{\"item\"\ - :{\"type\":\"answer\",\"text\":\"max\"}}\n{\"item\":{\"type\":\"answer\",\"\ - text\":\"_tokens\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"`\"}}\n{\"\ - item\":{\"type\":\"answer\",\"text\":\" se\"}}\n{\"item\":{\"type\":\"answer\"\ - ,\"text\":\" utiliza\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\" en\"\ - }}\n{\"item\":{\"type\":\"answer\",\"text\":\" el\"}}\n{\"item\":{\"type\"\ - :\"answer\",\"text\":\" endpoint\"}}\n{\"item\":{\"type\":\"answer\",\"text\"\ - :\" `/\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\"ask\"}}\n{\"item\":{\"\ - 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:{\"type\":\"answer\",\"text\":\" en\"}}\n{\"item\":{\"type\":\"answer\",\"\ - text\":\" preguntar\"}}\n{\"item\":{\"type\":\"answer\",\"text\":\".\"}}\n\ - {\"item\":{\"type\":\"retrieval\",\"results\":{\"resources\":{\"1ceff32ecafc4f559c9636e9d1e8cd89\"\ - :{\"id\":\"1ceff32ecafc4f559c9636e9d1e8cd89\",\"slug\":\"docs-develop-js-sdk-interfaces-QAOperation-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > QAOperation\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:10.613432\"\ - ,\"modified\":\"2026-06-02T15:39:44.624338\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/0-277\"\ - :{\"score\":0.003403005190193653,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / QAOperation \\n Interface:\ - \ QAOperation \\n Extends \\n \\n BaseOperation \\n \\n Properties \\n generate_answers_prompt?\ - \ \\n \\n optional generate_answers_prompt: string \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/task/task.models.ts:102 \\n \\n max_questions?\ - \ \\n\",\"id\":\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/0-277\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":277,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"\ - id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\"\ - ,\"title\":\"docs > rag > advanced > openai api compatible models\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2025-08-27T15:33:06.862963\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - :{\"score\":0.0065388246439397335,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - :{\"score\":0.003839395008981228,\"score_type\":\"RERANKER\",\"order\":16,\"\ - text\":\" Description: The maximum number of tokens that the model can generate\ - \ as output. Again, we should keep in mind that this value summed to the Maximum\ - \ supported input tokens should not exceed the total context size supported\ - \ by the model. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - :{\"score\":0.0023322338238358498,\"score_type\":\"RERANKER\",\"order\":18,\"\ - text\":\" Example: For Phi 4 Reasoning Plus, the total context size is 32768\ - \ tokens, as we want to leave room for the output, we will set the maximum\ - \ supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported\ - \ output tokens: \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :2575,\"end\":2804,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"e8525e64c5b44982b958d32cf6090613\"\ - :{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ChatOptions\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\"\ - ,\"modified\":\"2026-06-02T15:40:39.731007\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\"\ - :{\"score\":0.022201228886842728,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the\ - \ maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2808,\"end\":3011,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\"\ - :{\"score\":0.015306354500353336,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\\ - n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2450,\"end\":2808,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"\ - id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\"\ - ,\"title\":\"docs > rag > advanced > consumption.\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\"\ - ,\"modified\":\"2026-06-02T22:04:59.702465\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - :{\"score\":0.8172829747200012,\"score_type\":\"RERANKER\",\"order\":0,\"\ - text\":\" Use the max_tokens parameter on the /ask endpoint to set hard limits\ - \ on: \\n - Context size: Limits the amount of retrieved information sent\ - \ to the LLM \\n - Answer length: Limits the length of the generated response\ - \ \\n Important Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":3075,\"\ - end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"9f7036a7a6694700b72d52eb58a8326c\"\ - :{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\"\ - ,\"title\":\"docs > rag > advanced > widget > features\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\"\ - ,\"modified\":\"2025-10-27T14:35:08.325400\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.19269122183322906,\"score_type\":\"RERANKER\",\"order\":1,\"\ - text\":\" max_tokens: the maximum number of input tokens to put in the final\ - \ context (including the prompt, the retrieved results and the user question).\ - \ \\n max_output_tokens: the maximum number of tokens to generate. \\n\",\"\ - id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":5203,\"end\":5412,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\"\ - ,\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs > ingestion\ - \ > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2025-08-27T15:32:35.381741\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"\ - score\":0.013955709524452686,\"score_type\":\"RERANKER\",\"order\":8,\"text\"\ - :\" ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status()\ - \ \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url =\ - \ https://your-endpoint \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN\ - \ } \\n data = make_request_with_try_after_info(url, headers) \\n print(data)\ - \ \\n ``` \\n These examples demonstrate how to handle rate limits effectively,\ - \ ensuring that your application respects the limits and retries appropriately.\"\ - ,\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":3310,\"end\"\ - :3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false},\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\"\ - :{\"score\":0.003976228181272745,\"score_type\":\"RERANKER\",\"order\":15,\"\ - text\":\" retries = 0 \\n while retries < max_retries: \\n response = requests.get(url,\ - \ headers=headers) \\n if response.status_code == 200: \\n return response.json()\ - \ \\n elif response.status_code == 429: \\n wait_time = 2 retries # Exponential\ - \ backoff: 2^retries \\n print(f Rate limit exceeded. Retrying in {wait_time}\ - \ seconds... ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status()\ - \ \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url =\ - \ https://your-endpoint \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN\ - \ } \\n data = make_request_with_exponential_backoff(url, headers) \\n print(data)\ - \ \\n ``` \\n Example 2: Ingestion back pressure limits \\n Here's an example\ - \ of how to use the try_after key from the response to manage rate limits:\ - \ \\n\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":1900,\"\ - end\":2653,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"b14cf452a3434839a04c111f2ea4dc51\"\ - :{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\"\ - ,\"title\":\"docs > develop > js sdk > enums > UsageType\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\"\ - ,\"modified\":\"2025-04-15T14:51:41.582119\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\"\ - :{\"score\":0.007204769179224968,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\"@nuclia/core / Exports / UsageType \\n Enumeration: UsageType \\\ - n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED\ - \ \\n CHARS_PROCESSED \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED\ - \ \\n NUCLIA_TOKENS \\n PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME\ - \ \\n RESOURCES_PROCESSED \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME\ - \ \\n SUGGESTIONS_PERFORMED \\n TRAIN_SECONDS \\n \\n Enumeration Members\ - \ \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:190 \\n \\n BYTES_PROCESSED\ - \ \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:181\ - \ \\n \\n CHARS_PROCESSED \\n \u2022 CHARS_PROCESSED = chars_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 \\n \\n MEDIA_FILES_PROCESSED\ - \ \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED\ - \ \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\\ - n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191\ - \ \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED\ - \ \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\\ - n \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178\ - \ \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED\ - \ \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED\ - \ \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\"\ - ,\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"666b3a9d01f74323b6ca6c9939834bec\":{\"\ - id\":\"666b3a9d01f74323b6ca6c9939834bec\",\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\"\ - ,\"title\":\"docs > develop > js sdk > enums > StatsType\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\",\"sv\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.005040\"\ - ,\"modified\":\"2025-04-15T14:51:41.005050\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\"\ - :{\"score\":0.005752544850111008,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\"@nuclia/core / Exports / StatsType \\n Enumeration: StatsType \\\ - n Deprecated \\n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED\ - \ \\n BYTES \\n CHARS \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME\ - \ \\n RESOURCES \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration\ - \ Members \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\\ - n \u2022 BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200\ - \ \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\\ - n \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199\ - \ \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202\ - \ \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES\ - \ \\n \u2022 RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197\ - \ \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\"\ - ,\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ModelConfig\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-06-02T15:43:13.632521\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.004556113854050636,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:586 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":212,\"end\":480,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"\ - score\":0.001995538594201207,\"score_type\":\"RERANKER\",\"order\":19,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":480,\"\ - end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"4be29234ea764ce7889f78e60fe8ec78\"\ - :{\"id\":\"4be29234ea764ce7889f78e60fe8ec78\",\"slug\":\"docs-rag-advanced-models-md\"\ - ,\"title\":\"docs > rag > advanced > models\",\"summary\":\"\",\"icon\":\"\ - text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2024-08-19T16:35:49.576447\"\ - ,\"modified\":\"2025-08-27T15:33:08.935390\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\"\ - :{\"score\":0.007907291874289513,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n id: models \\n title: Semantic models \\n \\n | Model Name |\ - \ Size | Threshold | Max Tokens | Matryoshka | Multilingual | External | \\\ - n | --------------------------- | -------- | ------------- | --------------\ - \ | -------------- | ---------------- | ------------ | \\n | en-2024-04-24\ - \ | 768 | 0.47 | 2048 | No | No | No | \\n | multilingual-2023-08-16 | 1024\ - \ | 0.7 | 512 | No | Yes | No | \\n | multilingual-2024-05-06 | 1024 | 0.4\ - \ | 2048 | No | Yes | No | \\n | Open AI small | 1536 | 0.5 | 8192 | Yes |\ - \ No | No | \\n | Open AI large | 3072 | 0.5 | 8192 | Yes | No | Yes | \\\ - n | Google multilingual Gecko | 768 | 0.55 | 3072 | Yes | Yes | Yes | \\n\ - \ | Hugging Face | N/A | N/A | N/A | N/A | N/A | Yes | \\n Embeddings models\ - \ \\n Embeddings are like fingerprints for words or data. They help computers\ - \ understand the similarities and differences between them, making it easier\ - \ for machines to perform tasks like understanding language or recognizing\ - \ patterns. \\n\",\"id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":935,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - :{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\"\ - ,\"title\":\"docs > develop > python sdk > 05 search\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\"\ - ,\"modified\":\"2025-09-18T11:01:18.918196\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\"\ - :{\"score\":0.03448852524161339,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" ::: \\n :::warning \\n Enabling reasoning can use additional tokens,\ - \ which may increase your usage costs. \\n You may need to increase max_tokens\ - \ to give the LLM enough room to reason and generate an answer. \\n\",\"id\"\ - :\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\"\ - :{\"page_number\":0,\"index\":0,\"start\":2505,\"end\":2709,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":0.02779562398791313,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":1790,\"\ - end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\"\ - :{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-06-02T15:39:28.012340\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"\ - score\":0.03775027394294739,\"score_type\":\"RERANKER\",\"order\":2,\"text\"\ - :\" \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":977,\"\ - end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\"\ - :{\"score\":0.014785407111048698,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":739,\"end\":977,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"\ - id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\"\ - ,\"title\":\"docs > develop > js sdk > enumerations > UsageType\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\"\ - ,\"modified\":\"2026-06-02T15:38:04.014988\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\"\ - :{\"score\":0.00821969285607338,\"score_type\":\"RERANKER\",\"order\":9,\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration:\ - \ UsageType \\n Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED:\ - \ ai_tokens_used \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206\ - \ \\n \\n BYTES_PROCESSED \\n \\n BYTES_PROCESSED: bytes_processed \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 \\n \\n CHARS_PROCESSED\ - \ \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - 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- '1131' - x-nuclia-trace-id: - - 713209dff1a8573f8eb4a9979bba3e31 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-basic_ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfC\u00f3mo - se utiliza el par\u00e1metro `max_tokens` para controlar la longitud de las - respuestas en espa\u00f1ol, y d\u00f3nde puedo encontrar la documentaci\u00f3n - oficial?\n\n\n\nContext:\n\n**block-AA**\n\n## Chunk: docs - > rag > advanced > consumption.\n``` Use the max_tokens parameter on the /ask - endpoint to set hard limits on: \n - Context size: Limits the amount of retrieved - information sent to the LLM \n - Answer length: Limits the length of the generated - response \n Important Considerations \n Context Limitations: \n ```\n\n\n---\"\n\n\n**block-AB**\n\n## - Chunk: docs > develop > js sdk > interfaces > PredictAnswerOptions\n``` \n - optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: docs > rag > advanced > openai - api compatible models\n``` \n \n Maximum supported input tokens: \n Description: - The maximum number of tokens that the model can accept as input. Be mindful - that this takes into account the tokens used in the prompt, query and context. - Also take note that some models may provide their context window as the total - between input and output tokens, while others may provide it as the input tokens - only. \n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: docs > develop > python - sdk > 05 search\n``` \n SDK: \n \n ```python \n from nuclia import sdk \n from - nucliadb_models.search import AskRequest, Reasoning \n search = sdk.NucliaSearch() - \n query = AskRequest( \n query= My question with extra reasoning effort , \n - max_tokens=5000, \n reasoning=Reasoning( \n display=True, # Show reasoning in - the response \n effort= low , # Can be low , medium , or high \n budget_tokens=1024 - # How many tokens reasoning can use \n ), \n ) \n search.ask(query=query) \n - ``` \n Model Support for Reasoning Options: \n ```\n\n\n---\"\n\n\n**block-AE**\n\n## - Chunk: docs > rag > advanced > widget > features\n``` max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: - docs > develop > js sdk > interfaces > ChatOptions\n``` \n optional highlight: - boolean \n \n Inherited from \n BaseSearchOptions.highlight \n Defined in \n - libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? \n - \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n BaseSearchOptions.keyword_filters - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 \n \n max_tokens? - \n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: docs > develop > js sdk > interfaces - > ChatOptions\n``` \n optional max_tokens: number \\| object \n \n Defines - the maximum number of tokens that the model will take as context. \n Defined - in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 \n \n min_score? - \n ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: docs > develop > js sdk > interfaces - > PredictAnswerOptions\n``` \n optional generative_model: string \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \n \n json_schema? \n - \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: docs > ingestion - > how to > rate limiting\n``` ) \n time.sleep(wait_time) \n retries += 1 \n - else: \n response.raise_for_status() \n raise Exception( Max retries exceeded - ) \n Example usage \n url = https://your-endpoint \n headers = { Authorization - : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, - headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate - limits effectively, ensuring that your application respects the limits and retries - appropriately. ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: docs > rag > advanced - > openai api compatible models\n``` Description: The maximum number of tokens - that the model can generate as output. Again, we should keep in mind that this - value summed to the Maximum supported input tokens should not exceed the total - context size supported by the model. \n ```\n\n\n---\"\n\n\n\n\n\n\n"}, - "citations": false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 2000, "query_context_images": {}, "prefer_markdown": null, "json_schema": - {"title": "validate_or_answer", "description": "Validate or answer", "parameters": - {"type": "object", "properties": {"reason": {"type": "string", "description": - "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": - "Partial or complete answer to the user query from the information in the context."}, - "missing_info_query": {"type": "string", "description": "Query needed to retrieve - the missing information in case the context is not enough to answer the question. - If the context does not answer the question at all, just return the original - question."}, "useful": {"type": "string", "description": "Is the context useful - to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", - "items": {"type": "string", "description": "Block ID cited in the answer, e.g. - block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, - "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, - "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "required"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '8294' - Content-Type: - - application/json - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-origin: - - RAO - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context\ - \ provides information on how to use the `max_tokens` parameter to control\ - \ the length of responses. It specifies that `max_tokens` can limit the length\ - \ of the generated response and the amount of input tokens. However, the context\ - \ does not provide a direct link or reference to the official documentation.\"\ - ,\"answer\":\"El par\xE1metro `max_tokens` se utiliza en el endpoint /ask\ - \ para establecer l\xEDmites en la longitud de las respuestas generadas. Este\ - \ par\xE1metro puede limitar la longitud de la respuesta generada y tambi\xE9\ - n el tama\xF1o del contexto. Para encontrar la documentaci\xF3n oficial, se\ - \ necesita informaci\xF3n adicional.\",\"missing_info_query\":\"\xBFD\xF3\ - nde puedo encontrar la documentaci\xF3n oficial sobre el uso del par\xE1metro\ - \ `max_tokens`?\",\"useful\":\"yes\",\"citations\":[\"block-AA\",\"block-AE\"\ - ]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\"\ - :\"meta\",\"input_tokens\":50,\"output_tokens\":19,\"timings\":{\"generative\"\ - :2.20798238000134},\"input_nuclia_tokens\":0.05,\"output_nuclia_tokens\":0.019}}\n\ - {\"chunk\":{\"normalized_tokens\":{\"input\":0.04956,\"output\":0.01932,\"\ - image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"image\"\ - :0.0},\"type\":\"consumption\"}}\n" - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 03 Jun 2026 07:41:19 GMT - nuclia-learning-id: - - 8c27804426164f02bf178a8d671e911a - nuclia-learning-model: - - chatgpt-azure-4o-mini - via: - - 1.1 google - x-envoy-upstream-service-time: - - '2213' - x-nuclia-trace-id: - - 82e7eca5c06374a195e3471467b3631b - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "summarize", "system": "You - are a helpful AI assistant. Your role is to provide accurate, clear, and well-structured - answers based strictly on the information provided to you.\nKey principles:\n- - Answer only using the information in the provided context\n- Do not use external - knowledge, assumptions, or prior experience\n- Maintain a professional and informative - tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge - this clearly\n\nAlways follow any additional instructions provided about format, - style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": - {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## - Question\n\u00bfC\u00f3mo se utiliza el par\u00e1metro `max_tokens` para controlar - la longitud de las respuestas en espa\u00f1ol, y d\u00f3nde puedo encontrar - la documentaci\u00f3n oficial?\n\n## Provided Context\n[START OF CONTEXT]\n## - Retrieval on nuclia-docs Knowledge Box\n\n# \u00bfC\u00f3mo se utiliza el par\u00e1metro - `max_tokens` para controlar la longitud de las respuestas en espa\u00f1ol, y - d\u00f3nde puedo encontrar la documentaci\u00f3n oficial?\n\n El par\u00e1metro - `max_tokens` se utiliza en el endpoint /ask para establecer l\u00edmites en - la longitud de las respuestas generadas. Este par\u00e1metro puede limitar la - longitud de la respuesta generada y tambi\u00e9n el tama\u00f1o del contexto. - Para encontrar la documentaci\u00f3n oficial, se necesita informaci\u00f3n adicional.\n[END - OF CONTEXT]\n\n## Answering Guidelines\n- Carefully read all context; it may - be lengthy or detailed\n- Do not omit or overlook any relevant information\n- - If the context is incomplete or insufficient, state: \"Not enough data to answer - this.\"\n- Read carefully any extra instructions below if provided and use them - to answer\n\nNow provide your answer to the question: \u00bfC\u00f3mo se utiliza - el par\u00e1metro `max_tokens` para controlar la longitud de las respuestas - en espa\u00f1ol, y d\u00f3nde puedo encontrar la documentaci\u00f3n oficial?"}, - "citations": null, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 5000, "query_context_images": {}, "prefer_markdown": null, "json_schema": - null, "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - 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Please\ - \ reach out to your account manager for details.\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"\ - 007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n Inbound \u2014 the IP addresses that Progress Agentic RAG\ - \ receives requests on. 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It is very important that you provide\ - \ an extensive description of each registered agent, so that the Smart Agent\ - \ can understand what each source is about and when to use it. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n reactive: The Smart Agent will decide what\ - \ to do first, and will plan the next steps based on the information it retrieves.\ - \ It is expected to be faster. \\n plan_execute: The Smart Agent will plan\ - \ all the steps in advance, and will execute them. It will be slower but more\ - \ accurate when processing complex questions. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" Also select the proper function for each registered agent, depending\ - \ on the type of source (MCP agents will not need one, the functions are provided\ - \ dynamically by the MCP server). \\n In the Smart Agent configuration, you\ - \ can select the planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"\ - end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" In the Summarize Agent,\ - \ enable the Conversational mode. This will allow the Summarize Agent to generate\ - \ a final answer that will not repeat the information already provided in\ - \ the previous conversation, and will be more natural for a chat interface.\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" Custom frontend \\n You can also create\ - \ a custom frontend for your Retrieval Agent. 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Allowlist\ - \ these if you restrict inbound traffic to your infrastructure. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n :::note \\n The current list of IP addresses\ - \ is also available in machine-readable format: JSON | YAML. These lists may\ - \ change over time, so we recommend periodically fetching and applying updates\ - \ to your firewall rules to ensure uninterrupted service. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n id: public-ips \\n title: Public IP Addresses\ - \ \\n \\n If your firewall restricts network traffic, you may need to allowlist\ - \ the following IP addresses. They are grouped by region and split into two\ - \ categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" ::: \\n Europe (GCP) \\n | Inbound |\ - \ Outbound | \\n | ------------------ | --------------------- | \\n | 34.111.1.187/32\ - \ | 35.204.65.155/32 | \\n | 136.68.112.29/32 | 34.91.38.151/32 | \\n | |\ - \ 35.204.139.129/32 | \\n | | 35.204.108.221/32 | \\n Australia (AWS) \\n\ - \ | Inbound | Outbound | \\n | ------------------- | ---------------------\ - \ | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n | 3.25.20.9/32 | 3.24.144.83/32\ - \ | \\n | 52.64.202.159/32 | 3.24.81.206/32 | \\n Europe (AWS) \\n | Inbound\ - \ | Outbound | \\n | ------------------- | --------------------- | \\n | 63.182.151.234/32\ - \ | 63.179.23.139/32 | \\n | 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32\ - \ | 3.66.123.24/32 | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | -------------------\ - \ | --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\\ - n | 16.164.50.185/32 | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32\ - \ | \\n United States (AWS) \\n | Inbound | Outbound | \\n | --------------------\ - \ | -------------------- | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\\ - n | 18.119.145.185/32 | 18.225.104.91/32 | \\n | 3.21.239.244/32 | 16.59.115.61/32\ - \ | \\n Private connectivity (AWS PrivateLink) \\n For AWS-hosted regions,\ - \ it is possible to expose access privately through an AWS endpoint service\ - \ (AWS PrivateLink) instead of over the public internet, allowing connections\ - \ to be established from your VPC without traversing public IP addresses.\ - \ This option is not available through self-service configuration. Please\ - \ reach out to your account manager for details.\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"\ - 007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n Inbound \u2014 the IP addresses that Progress Agentic RAG\ - \ receives requests on. Allowlist these if you restrict outbound traffic from\ - \ your infrastructure and need to reach our services (e.g., calling our APIs).\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"a\",\"field\":\"title\",\"text\":\"docs > management > security > 5 public\ - \ ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - a\",\"field\":\"title\",\"text\":\"docs > agentic > how to > agentic retrieval\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - t\",\"field\":\"page\",\"text\":\" \\n id: agentic-retrieval \\n title: Agentic\ - \ retrieval with the Smart agent \\n \\n Agentic retrieval with the Smart\ - \ agent \\n The Smart agent is a powerful tool that enhances the capabilities\ - \ of traditional retrieval-augmented generation (RAG) systems. Regular RAG's\ - \ fixed search-then-generate process is limiting for complex questions that\ - \ need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"\ - start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n Different models\ - \ can be chosen for different tasks: \\n \\n Context validation happens when\ - \ the Prune context option is enabled (recommended), we recommend using a\ - \ fast model - planning or the execution model (depending on the planning\ - \ mode you have selected): we recommend using a more powerful model. \\n\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" \\n Note: the rephrasing model is only\ - \ used in more complex workflow, it can be ignored in the present case. \\\ - n Finally, add a Summarize agent in the Generation step to generate a final\ - \ answer from the retrieved information. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"\ - start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" The Smart Agent plans the answer: picks the most appropriate\ - \ sources, splits the question into sub-questions, evaluates relevancy, and\ - \ iterates autonomously until the information is sufficient. \\n Basic usage\ - \ \\n To set up a Smart Agent, declare all the sources you want to use (Knowledge\ - \ Boxes, Perplexity, etc.) in the Sources section of the left menu. Then,\ - \ create a new workflow in the Workflows section and add a Smart Agent in\ - \ the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Chat mode \\n If you\ - \ want to use the Smart Agent in a chat interface, you need the following:\ - \ \\n \\n Enable the Session history option in the Smart Agent configuration.\ - \ This will allow the Smart Agent to take into account the previous conversation\ - \ when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"\ - end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Then connect your sources\ - \ as Registered Agents to the Smart Agent. It is very important that you provide\ - \ an extensive description of each registered agent, so that the Smart Agent\ - \ can understand what each source is about and when to use it. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n reactive: The Smart Agent will decide what\ - \ to do first, and will plan the next steps based on the information it retrieves.\ - \ It is expected to be faster. \\n plan_execute: The Smart Agent will plan\ - \ all the steps in advance, and will execute them. It will be slower but more\ - \ accurate when processing complex questions. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" Also select the proper function for each registered agent, depending\ - \ on the type of source (MCP agents will not need one, the functions are provided\ - \ dynamically by the MCP server). \\n In the Smart Agent configuration, you\ - \ can select the planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"\ - end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" In the Summarize Agent,\ - \ enable the Conversational mode. This will allow the Summarize Agent to generate\ - \ a final answer that will not repeat the information already provided in\ - \ the previous conversation, and will be more natural for a chat interface.\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" Custom frontend \\n You can also create\ - \ a custom frontend for your Retrieval Agent. This allows you to have full\ - \ control over the user interface and user experience. \\n You can directly\ - \ implement the API calls to your Retrieval Agent in your frontend code (see\ - \ the Websocket API section for more details), or you can use the JavaScript\ - \ SDK provided by Agentic to simplify the integration. \\n\",\"labels\":[],\"\ - start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"\ - index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"\ - rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"text\":\" MCP \\n If you want to integrate your Retrieval Agent\ - \ with a broader AI-based system (for example Claude Cowork or Copilot), it\ - \ is reachable throught MCP. \\n The MCP endpoint is visible on the agent\ - \ home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n id: deploy \\n title:\ - \ Deploy \\n \\n Deploy \\n Your Agentic Retrieval Agent can be deployed in\ - \ different ways, depending on your needs and the environment you are working\ - \ in. \\n Ready-to-use widget \\n The easiest way to deploy your Retrieval\ - \ Agent is to use the ready-to-use widget. 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:{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - :{\"score\":0.0326513797044754,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - :{\"score\":0.020606474950909615,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" Description: The maximum number of tokens that the model can generate\ - \ as output. Again, we should keep in mind that this value summed to the Maximum\ - \ supported input tokens should not exceed the total context size supported\ - \ by the model. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\"\ - :{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"\ - score\":0.1108555942773819,\"score_type\":\"RERANKER\",\"order\":2,\"text\"\ - :\" \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":977,\"\ - end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\"\ - :{\"score\":0.06233103200793266,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":739,\"end\":977,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"4e12158442054714a4caff0af00da2f5\":{\"\ - id\":\"4e12158442054714a4caff0af00da2f5\",\"slug\":\"docs-develop-js-sdk-interfaces-QueryInfo-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > QueryInfo\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:36.036743\"\ - ,\"modified\":\"2026-07-14T12:52:03.497025\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4e12158442054714a4caff0af00da2f5/t/page/0-396\"\ - :{\"score\":0.008283626288175583,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / QueryInfo \\n Interface:\ - \ QueryInfo \\n Properties \\n entities? \\n \\n optional entities: TokenSearch\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:423 \\n \\\ - n language \\n \\n language: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:418\ - \ \\n \\n max_context \\n \\n max_context: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:422\ - \ \\n \\n query \\n \\n\",\"id\":\"4e12158442054714a4caff0af00da2f5/t/page/0-396\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":396,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\"\ - :{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > PageToken\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\"\ - 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:{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\"\ - ,\"title\":\"docs > rag > advanced > widget > features\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\"\ - ,\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.30923226475715637,\"score_type\":\"RERANKER\",\"order\":1,\"\ - text\":\" max_tokens: the maximum number of input tokens to put in the final\ - \ context (including the prompt, the retrieved results and the user question).\ - \ \\n max_output_tokens: the maximum number of tokens to generate. \\n\",\"\ - id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":5203,\"end\":5412,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"\ - score\":0.06828853487968445,\"score_type\":\"RERANKER\",\"order\":4,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":1790,\"\ - end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\"\ - :{\"score\":0.06210312619805336,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\" ::: \\n :::warning \\n Enabling reasoning can use additional tokens,\ - \ which may increase your usage costs. \\n You may need to increase max_tokens\ - \ to give the LLM enough room to reason and generate an answer. \\n\",\"id\"\ - :\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\"\ - :{\"page_number\":0,\"index\":0,\"start\":2505,\"end\":2709,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\"\ - ,\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs > ingestion\ - \ > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"\ - score\":0.037326887249946594,\"score_type\":\"RERANKER\",\"order\":9,\"text\"\ - :\" ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status()\ - \ \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url =\ - \ https://your-endpoint \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN\ - \ } \\n data = make_request_with_try_after_info(url, headers) \\n print(data)\ - \ \\n ``` \\n These examples demonstrate how to handle rate limits effectively,\ - \ ensuring that your application respects the limits and retries appropriately.\"\ - ,\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":3310,\"end\"\ - :3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"4194605cdfbd414f8fa762630d555bc1\":{\"\ - id\":\"4194605cdfbd414f8fa762630d555bc1\",\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > PageStructure\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\"\ - ,\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\"\ - :{\"score\":0.0024726230185478926,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageStructure \\\ - n Interface: PageStructure \\n Properties \\n page \\n \\n page: object \\\ - n \\n height \\n \\n height: number \\n \\n width \\n \\n width: number \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238\ - \ \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\"\ - ,\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\"\ - :{\"score\":0.0018748616566881537,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface:\ - \ Consumption \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens:\ - \ TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"328ed5b87692439a881303c7c1d4eefc\":{\"id\":\"328ed5b87692439a881303c7c1d4eefc\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-Ask-AskTokens-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > Ask.AskTokens\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:53.076880\"\ - ,\"modified\":\"2026-06-09T08:08:31.130765\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/a/title\":{\"paragraphs\":{\"328ed5b87692439a881303c7c1d4eefc/a/title/0-52\"\ - :{\"score\":0.001734208082780242,\"score_type\":\"RERANKER\",\"order\":19,\"\ - text\":\"docs > develop > js sdk > interfaces > Ask.AskTokens\",\"id\":\"\ - 328ed5b87692439a881303c7c1d4eefc/a/title/0-52\",\"labels\":[\"/k/text\"],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":52,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"1cf976a5ca5947cd89a2f2a047b71ce9\":{\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9\"\ - ,\"slug\":\"docs-ingestion-how-to-split-strategies-md\",\"title\":\"docs >\ - \ ingestion > how to > split strategies\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-09-12T08:39:42.634556\",\"modified\":\"2026-06-09T08:18:07.009807\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\":{\"\ - score\":0.0023504828568547964,\"score_type\":\"RERANKER\",\"order\":15,\"\ - text\":\" Key components of a split strategy include: \\n \\n Max paragraph\ - \ size: Sets the maximum size (in characters or tokens) for each chunk. \\\ - n Custom split: Determines the method used for splitting: \\n Manual splitting:\ - \ Splits content based on a specified delimiter (default is \\\\n ). \\n\"\ - ,\"id\":\"1cf976a5ca5947cd89a2f2a047b71ce9/t/page/963-1239\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":963,\"end\":1239,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\"\ - ,\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\"\ - :{\"score\":0.08913693577051163,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the\ - \ maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2808,\"end\":3011,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\"\ - :{\"score\":0.04611974582076073,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\\ - n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2450,\"end\":2808,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"is_a_table\":false}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ModelConfig\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"queue\":\"\ - private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.06742377579212189,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":212,\"end\":480,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - is_a_table\":false},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"\ - score\":0.026657739654183388,\"score_type\":\"RERANKER\",\"order\":11,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":480,\"\ - end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}},\"6e8250e6b5264156988657a221fd5e94\"\ - :{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\"\ - ,\"title\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\"\ - ,\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\"\ - :0.002225670963525772,\"score_type\":\"RERANKER\",\"order\":16,\"text\":\"\ - @nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"is_a_table\":false}}}}}},\"query\":\"Explica c\xF3\ - mo usar el par\xE1metro `max_tokens` en espa\xF1ol, incluyendo un enlace a\ - \ la documentaci\xF3n oficial.\",\"total\":20,\"page_number\":0,\"page_size\"\ - :20,\"next_page\":false,\"autofilters\":[],\"min_score\":{\"bm25\":0.0},\"\ - best_matches\":[\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - 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ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.0019418168812990189,\"score_type\":\"RERANKER\",\"order\":18,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption 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models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.023197626695036888,\"score_type\":\"RERANKER\",\"order\":12,\"text\":\" + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.022499969229102135,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\" + \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.0018675660248845816,\"score_type\":\"RERANKER\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.06359858065843582,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.03126191347837448,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.08647765219211578,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\":{\"score\":0.053502149879932404,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\" + \\n optional highlight: boolean \\n \\n Inherited from \\n BaseSearchOptions.highlight + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 \\n + \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| Filter[] + \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n max_tokens? + \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.09982066601514816,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.05582314357161522,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.456288605928421,\"score_type\":\"RERANKER\",\"order\":0,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs + > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\":{\"score\":0.029986508190631866,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.1041124165058136,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.04401864856481552,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.042562730610370636,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"ecabb0862fa24c8fb8e01d4930a3f7a2\":{\"id\":\"ecabb0862fa24c8fb8e01d4930a3f7a2\",\"slug\":\"docs-develop-js-sdk-variables-MAX_FACETS_PER_REQUEST-md\",\"title\":\"docs + > develop > js sdk > variables > MAX_FACETS_PER_REQUEST\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:18.992004\",\"modified\":\"2026-07-14T12:54:35.795661\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/a/title\":{\"paragraphs\":{\"ecabb0862fa24c8fb8e01d4930a3f7a2/a/title/0-60\":{\"score\":0.00220838887616992,\"score_type\":\"RERANKER\",\"order\":17,\"text\":\"docs + > develop > js sdk > variables > MAX_FACETS_PER_REQUEST\",\"id\":\"ecabb0862fa24c8fb8e01d4930a3f7a2/a/title/0-60\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":60,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"is_a_table\":false}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"queue\":\"private\",\"hidden\":false,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.40833574533462524,\"score_type\":\"RERANKER\",\"order\":1,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. 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headers: Alt-Svc: - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 @@ -3212,339 +789,15 @@ interactions: content-type: - application/x-ndjson date: - - Wed, 15 Jul 2026 07:06:13 GMT - nuclia-learning-id: - - 7511dfbd290a412c825f838287abd709 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '1086' - x-nuclia-trace-id: - - b32b7dd8cdab6fdb36261a96fe70fd64 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-basic_ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. 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Your role is to provide accurate, clear, and well-structured - answers based strictly on the information provided to you.\nKey principles:\n- - Answer only using the information in the provided context\n- Do not use external - knowledge, assumptions, or prior experience\n- Maintain a professional and informative - tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge - this clearly\n\nAlways follow any additional instructions provided about format, - style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": - {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## - Question\nExplica c\u00f3mo usar el par\u00e1metro `max_tokens` en espa\u00f1ol, - incluyendo un enlace a la documentaci\u00f3n oficial.\n\n## Provided Context\n[START - OF CONTEXT]\n## Retrieval on nuclia-docs Knowledge Box\n\n# Explica c\u00f3mo - usar el par\u00e1metro `max_tokens` en espa\u00f1ol, incluyendo un enlace a - la documentaci\u00f3n oficial.\n\n El par\u00e1metro `max_tokens` se utiliza - en el endpoint /ask para establecer l\u00edmites en el tama\u00f1o del contexto - y la longitud de la respuesta generada. 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. You can customize its appearance, and then copy the generated + code snippet to embed it in your website or application. 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from the internet, you may need to allowlist the following IP addresses.\ - \ These are the source IPs that Progress Agentic RAG uses when making outbound\ - \ connections (e.g., webhooks, sync agents, or other integrations calling\ - \ your infrastructure). \\n\",\"labels\":[],\"position\":{\"page_number\"\ - :0,\"index\":0,\"start\":0,\"end\":341},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\",\"field_type\":\"a\",\"\ - field\":\"title\",\"text\":\"docs > management > security > 5 outbound ips\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":45},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n optional security:\ - \ object \\n \\n groups \\n \\n groups: string[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:166\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n 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KeyValueFieldData\",\"labels\":[],\"position\":{\"page_number\"\ - :0,\"index\":0,\"start\":0,\"end\":56},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n optional errors: IError[] \\n \\n Inherited\ - \ from \\n IFieldDataBase.errors \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:150\ - \ \\n \\n extracted? \\n \\n optional extracted: ExtractedData \\n \\n Inherited\ - \ from \\n IFieldDataBase.extracted \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:148\ - \ \\n \\n status? \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\"\ - :1,\"start\":295,\"end\":623},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"701e9133181a4002bc373eaa1254daf8\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n optional status: null \\\\| string \\n \\n Inherited from\ - \ \\n IFieldDataBase.status \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:151\ - \ \\n \\n value? \\n \\n optional value: object \\n \\n Index Signature \\\ - n [key: string]: KVValue \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:606\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":2,\"start\":623,\"\ - end\":928},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs\ - \ \\n \\n @nuclia/core / KeyValueFieldData \\n Interface: KeyValueFieldData\ - \ \\n Extends \\n \\n IFieldDataBase \\n \\n Properties \\n error? \\n \\\ - n optional error: IError \\n \\n Inherited from \\n IFieldDataBase.error \\\ - n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:149\ - \ \\n \\n errors? \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\"\ - :0,\"start\":0,\"end\":295},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - 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message: OK - request: - body: '{"prefixes": [{"prefix": "/n/i", "depth": 2}]}' + body: '{"query": "Esboniwch sut i ddefnyddio''r paramedr ''max_tokens'' mewn atebion, + a darparwch ddolen i''r ddogfennaeth swyddogol.", "filters": [], "show": ["basic", + "origin", "extra", "extracted", "values", "relations"], "extracted": ["text", + "metadata", "file", "link"], "security": {"groups": []}, "features": ["keyword"], + "reranker": "noop", "keyword_filters": ["max_tokens", "par\u00e1metro"]}' headers: Accept: - '*/*' @@ -745,7 +611,7 @@ interactions: Connection: - keep-alive Content-Length: - - '42' + - '361' Host: - europe-1.nuclia.cloud User-Agent: @@ -755,32 +621,39 @@ interactions: x-stf-serviceaccount: - DUMMY method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/catalog/facets + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find response: body: - string: 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del par\u00e1metro ''max_tokens'' en las respuestas + y proporciona un enlace a la documentaci\u00f3n oficial.", "filters": [], "min_score": + {"semantic": 0.4}, "show": ["basic", "origin", "extra", "extracted", "values", + "relations"], "extracted": ["text", "metadata", "file", "link"], "vectorset": + "multilingual-2024-05-06", "security": {"groups": []}, "features": ["semantic"], + "reranker": "noop"}' headers: Accept: - '*/*' @@ -789,7 +662,7 @@ interactions: Connection: - keep-alive Content-Length: - - '42' + - '385' Host: - europe-1.nuclia.cloud User-Agent: @@ -799,32 +672,1646 @@ interactions: x-stf-serviceaccount: - DUMMY method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/catalog/facets + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find response: body: - string: '{"facets":{"/s/p":974,"/s/p/ca":3,"/s/p/cy":1,"/s/p/da":1,"/s/p/en":955,"/s/p/eo":4,"/s/p/la":7,"/s/p/nb":1,"/s/p/tl":1,"/s/p/yo":1}}' 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ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.6163696646690369,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.7432252764701843,\"score_type\":\"VECTOR\",\"order\":0,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\",\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.599377453327179,\"score_type\":\"VECTOR\",\"order\":17,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\",\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs + > develop > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Search and answer generation\\n\\n## Search\\n\\nNuclia supports 2 different + search endpoints:\\n\\n- `search`: returns several result sets according the + different search techniques (full-text, fuzzy, semantic).\\n- `find`: returns + a single result set where all different results are merged into a hierarchical + structure.\\n\\nBoth endpoints support the same query parameters.\\n\\n- CLI:\\n\\n + \ ```bash\\n nuclia kb search search --query=\\\"My search\\\"\\n nuclia + kb search find --query=\\\"My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf', + '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\")\\n + \ ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\"My + search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb search + search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative answer\\n\\nBased + on a `find` request, Nuclia uses a generative AI to answer the question based + on the context without hallucinations and with the find result and relations.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search ask --query=\\\"My question\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\")\\n ```\\n\\n + \ You can also use the `AskRequest` item to configure the request with all + the parameters supported:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(query=\\\"My question\\\", prefer_markdown=True, citations=True)\\n + \ search.ask(query=query)\\n ```\\n\\n### Reasoning\\n\\nSome LLMs support + reasoning. In some models, reasoning is enabled by default, while in others + it must be explicitly requested. You can control this behavior using the reasoning + parameter.\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from + nucliadb_models.search import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n + \ query = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\",\\n + \ max_tokens=5000,\\n reasoning=Reasoning(\\n display=True, # + Show reasoning in the response\\n effort=\\\"low\\\", # Can be + \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024 + # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\n + \ ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models** \u2192 + support `effort` only.\\n* **Google & Anthropic models** \u2192 support `budget_tokens` + only.\\n\\n:::tip\\nIf you send just one of these values (`effort` or `budget_tokens`), + Nuclia will automatically fill in the other for you.\\n:::\\n\\n:::warning\\n* + Enabling reasoning can use additional tokens, which may increase your usage + costs.\\n* You may need to increase `max_tokens` to give the LLM enough room + to reason and generate an answer.\\n:::\\n\\n## Filtering\\n\\nAny endpoint + that involves search (`search`, `find` and `ask`) also support more advanced + filtering expressions. Expressions can have one of the following operators:\\n\\n- + `all`: this is the default. Will make search return results containing all + specified filter labels.\\n- `any`: returns results containing at least one + of the labels.\\n- `none`: returns results that do not contain any of the + labels.\\n- `not_all`: returns results that do not contain all specified labels.\\n\\nNote + that multiple expressions can be chained in the `filters` parameter and the + conjunction of all of them will be computed.\\n\\nHere are some examples:\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search + import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\"My + question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\n + \ )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used to + improve the quality of the answers by extending the search results passed + to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask + --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\"}]'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\"name\\\": + \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy) + for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows to + use all the options supported by the `/find` and `/ask` endpoints,\\nbut not + all of the options can be passed as specific parameter.\\nIn these cases, + you can just pass your query as a dictionnary in the `query` parameter.\\n\\n- + CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\": \\\"My + search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]}'\\n + \ nuclia kb search ask --query='{\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": + 5}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search + = sdk.NucliaSearch()\\n search.find(query={\\\"query\\\": \\\"My search\\\", + \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\n + \ search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\n + \ ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported + by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph + can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb + search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}}}'\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search = + sdk.NucliaSearch()\\n search.graph(\\n query={\\n \\\"query\\\": + {\\n \\\"prop\\\": \\\"path\\\",\\n \\\"source\\\": + {\\\"value\\\": \\\"Rust\\\"},\\n \\\"destination\\\": {\\\"value\\\": + \\\"Python\\\"}\\n }\\n }\\n )\\n ```\\n\\nFor more information + about graph querying, please refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph) + or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Search + and answer generation \\n Search \\n Nuclia supports 2 different search endpoints: + \\n \\n search: returns several result sets according the different search + techniques (full-text, fuzzy, semantic). \\n find: returns a single result + set where all different results are merged into a hierarchical structure. + \\n \\n Both endpoints support the same query parameters. \\n \\n CLI: \\n + \\n bash \\n nuclia kb search search --query= My search \\n nuclia kb search + find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia'] + \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia']) + \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia + kb search find --query= My search --json \\n Get YAML output: \\n bash \\n + nuclia kb search search --query= My search --yaml \\n Generative answer \\n + Based on a find request, Nuclia uses a generative AI to answer the question + based on the context without hallucinations and with the find result and relations. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My question \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.ask(query= My question ) \\n You can also use the AskRequest item + to configure the request with all the parameters supported: \\n ```python + \\n from nuclia import sdk \\n from nucliadb_models.search import AskRequest + \\n search = sdk.NucliaSearch() \\n query = AskRequest(query= My question + , prefer_markdown=True, citations=True) \\n search.ask(query=query) \\n ``` + \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning is + enabled by default, while in others it must be explicitly requested. You can + control this behavior using the reasoning parameter. \\n \\n SDK: \\n \\n + ```python \\n from nuclia import sdk \\n from nucliadb_models.search import + AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192 + support effort only. \\n Google & Anthropic models \u2192 support budget_tokens + only. \\n \\n :::tip \\n If you send just one of these values (effort or budget_tokens), + Nuclia will automatically fill in the other for you. \\n ::: \\n :::warning + \\n Enabling reasoning can use additional tokens, which may increase your + usage costs. \\n You may need to increase max_tokens to give the LLM enough + room to reason and generate an answer. \\n ::: \\n Filtering \\n Any endpoint + that involves search (search, find and ask) also support more advanced filtering + expressions. Expressions can have one of the following operators: \\n \\n + all: this is the default. Will make search return results containing all specified + filter labels. \\n any: returns results containing at least one of the labels. + \\n none: returns results that do not contain any of the labels. \\n not_all: + returns results that do not contain all specified labels. \\n \\n Note that + multiple expressions can be chained in the filters parameter and the conjunction + of all of them will be computed. \\n Here are some examples: \\n \\n CLI: + \\n \\n bash \\n nuclia kb search find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}] + \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query= My + question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])], + \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to improve + the quality of the answers by extending the search results passed to the LLM + as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My + question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK: \\n \\n python + \\n from nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.ask(query= + My question , rag_strategies=[{ name : hierarchy }]) \\n See the RAG strategies + documentation for more information. \\n Complex queries \\n The Python SDK + allows to use all the options supported by the /find and /ask endpoints, \\n + but not all of the options can be passed as specific parameter. \\n In these + cases, you can just pass your query as a dictionnary in the query parameter. + \\n \\n CLI: \\n \\n bash \\n nuclia kb search find --query='{ query : My + search , filters : [ /icon/application/pdf , /classification.labels/region/Asia + ]}' \\n nuclia kb search ask --query='{ query : My search , top_k : 5}' \\n + \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch() + \\n search.find(query={ query : My search , filters : [ /icon/application/pdf + , /classification.labels/region/Asia ]}) \\n search.ask(query={ query : My + search , top_k : 5}) \\n Graph queries \\n The Python SDK allows graph queries + supported by the /graph endpoint. Although \\n a bit cumbersome, the knowledge + graph can be queried as in this example: \\n \\n CLI: \\n \\n bash \\n nuclia + kb search graph --query='{ query : { prop : path , source : { value : Rust + }, destination : { value : Python }}}' \\n \\n SDK: \\n \\n python \\n from + nuclia import sdk \\n search = sdk.NucliaSearch() \\n search.graph( \\n query={ + \\n query : { \\n prop : path , \\n source : { value : Rust }, \\n destination + : { value : Python } \\n } \\n } \\n ) \\n For more information about graph + querying, please refer to Nuclia's graph \\n doc or the API 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end\":4864}],\"entity\":\"Asia\"},\"PRODUCT/OpenAI\":{\"position\":[{\"start\":2279,\"end\":2285}],\"entity\":\"OpenAI\"},\"LOC/Europe\":{\"position\":[{\"start\":3677,\"end\":3683}],\"entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\":{\"score\":0.6185709238052368,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\" + \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search + import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query = AskRequest( + \\n query= My question with extra reasoning effort , \\n max_tokens=5000, + \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response + \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024 # + How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query) + \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\":{\"score\":0.5997583270072937,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\" + ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which + may increase your usage costs. \\n You may need to increase max_tokens to + give the LLM enough room to reason and generate an answer. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\",\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs + > develop > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2026-06-09T08:08:04.176136\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Retrieval Agents Orchestrator\\n\\nInteract with Nuclia's Retrieval Agents + Orchestrator to have intelligent conversations over several knowledge sources + with persistent session management and real-time streaming responses.\\n\\n## + Prerequisites\\n\\nInstall the Nuclia SDK:\\n\\n```sh\\npip install nuclia\\n```\\n\\nEnsure + you have:\\n- A valid Nuclia authentication token (see [Authentication](02-auth.md))\\n- + Access to a configured Retrieval Agent\\n\\n## Overview\\n\\nThe nuclia.py + library provides several ways to interact with your Retrieval Agents Orchestrators:\\n\\n- + **Interactive CLI**: A rich, user-friendly terminal interface (recommended)\\n- + **Standard CLI**: Direct access to raw websocket messages for debugging\\n- + **Session Management**: Create and manage persistent conversation sessions\\n- + **Programmatic API**: Python SDK for building custom applications\\n\\n\\n## + Listing Available Agents\\n\\nDiscover what Retrieval Agents Orchestrators + you have access to.\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents list\\n ```\\n\\n- + SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n all_agents = agents.list()\\n\\n for agent + in all_agents:\\n print(f\\\"Agent: {agent.title} ({agent.id})\\\")\\n + \ print(f\\\" Slug: {agent.slug}\\\")\\n print(f\\\" Zone: {agent.zone}\\\")\\n + \ ```\\n\\n### Getting a Specific Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents get --account=\\\"my-account\\\" --id=\\\"agent-uuid\\\" --zone=\\\"europe-1\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n agent_details = agents.get(\\n account=\\\"my-account\\\",\\n + \ id=\\\"agent-uuid\\\",\\n zone=\\\"europe-1\\\"\\n )\\n print(agent_details)\\n + \ ```\\n\\n### Setting a Default Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents default [AGENT_SLUG or AGENT_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agents import NucliaAgents\\n\\n agents = NucliaAgents()\\n + \ agents.default(\\\"my-agent\\\")\\n ```\\n\\nThis sets the default agent + for all subsequent operations.\\n\\n## Interactive CLI (Recommended)\\n\\nThe + interactive CLI provides a beautiful, real-time interface for conversing with + your Retrieval Agents Orchestrator.\\n\\n### Starting the Interactive CLI\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent cli interact\\n ```\\n\\n- SDK:\\n\\n + \ ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ agent.cli.interact()\\n ```\\n\\nThis launches an interactive terminal + session where you can:\\n- Ask questions and see streaming responses\\n- View + processing steps in real-time\\n- Manage conversation sessions\\n- See retrieved + context and citations\\n\\n### Interactive CLI Commands\\n\\nThe CLI supports + several commands (prefix with `/`):\\n\\n| Command | Description |\\n|---------|-------------|\\n| + `/help` | Show available commands |\\n| `/new_session` | Create a new persistent + session |\\n| `/list_sessions` | List all your sessions |\\n| `/change_session` + | Switch to a different session, use 'ephemeral' for a temporary session |\\n| + `/clear` | Clear the screen |\\n| `/exit` | Exit the CLI |\\n\\nPlease note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option **Agent with memory** enabled during creation.\\n\\n## Session + Management\\n\\nSessions allow you to maintain conversation context across + multiple interactions.\\n\\n> This feature will only be available if you checked + **Agent with memory** during the creation of your Retrieval Agents Orchestrator.\\n\\n### + Creating a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent session new --name=\\\"My + Research Session\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n session_uuid = agent.session.new(\\\"My + Research Session\\\")\\n print(f\\\"Created session: {session_uuid}\\\")\\n + \ ```\\n\\n### Listing Sessions\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session list\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n sessions = agent.session.list()\\n + \ for session in sessions.resources:\\n print(f\\\"{session.title}: {session.id}\\\")\\n + \ ```\\n\\n### Getting a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session get --session_uuid=[SESSION_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ session = agent.session.get(session_uuid)\\n print(f\\\"Session: {session.title}\\\")\\n + \ print(f\\\"Created: {session.created}\\\")\\n ```\\n\\n### Deleting a Session\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent session delete --session_uuid=[SESSION_UUID]\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n + \ agent = NucliaAgent()\\n agent.session.delete(session_uuid)\\n ```\\n\\n## + Interaction\\n\\nAside from the interactive CLI, you can interact with your + Retrieval Agents Orchestrator with the simple CLI or programmatically using + the SDK.\\n\\n### Basic Interaction\\n\\n\\n**CLI:**\\n```bash\\nnuclia agent + interact \\\"What is Eric known for?\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over streaming responses\\nfor response in agent.interact(\\n question=\\\"What + is Eric known for?\\\"\\n):\\n if response.operation == \\\"ANSWER\\\" + and response.answer:\\n print(response.answer)\\n elif response.step:\\n + \ print(f\\\"Processing: {response.step.module}\\\")\\n```\\n\\nNot + supplying a `session_uuid` when calling `interact` will use an ephemeral session + by default. To maintain context, provide a persistent session UUID.\\n\\n### + Using Persistent Sessions\\n\\n**CLI:**\\n```bash\\nnuclia agent sessions + new \\\"Customer Support Chat\\\"\\n# Note the session UUID returned\\nnuclia + agent interact \\\"What are your business hours?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\nnuclia + agent interact \\\"Are you open on weekends?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Create + a session\\nsession_uuid = agent.session.new(\\\"Customer Support Chat\\\")\\n\\n# + Have a conversation with context\\nfor response in agent.interact(\\n session_uuid=session_uuid,\\n + \ question=\\\"What are your business hours?\\\"\\n):\\n if response.answer:\\n + \ print(response.answer)\\n\\n# Follow-up question maintains context\\nfor + response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\"Are + you open on weekends?\\\"\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\n## + Understanding Response Types\\n\\nWhen interacting with an agent, you receive + a stream of `AragAnswer` objects with different operations:\\n\\n| Operation + | Description |\\n|-----------|-------------|\\n| `START` | Interaction has + begun |\\n| `ANSWER` | Processing step or partial answer |\\n| `DONE` | Interaction + complete |\\n| `ERROR` | An error occurred |\\n| `AGENT_REQUEST` | Agent needs + user feedback |\\n\\n### Response Attributes\\n\\nEach response may contain:\\n\\n- + **`step`**: Information about the current processing step\\n - `module`: + The module being executed (e.g., \\\"rephrase\\\", \\\"basic_ask\\\", \\\"remi\\\")\\n + \ - `title`: Display title for the step\\n - `value`: Result of the step\\n + \ - `reason`: Explanation for the step\\n - `timeit`: Time taken in seconds\\n + \ - `input_nuclia_tokens`/`output_nuclia_tokens`: Token usage\\n\\n- **`context`**: + Retrieved context from the knowledge base\\n - `chunks`: List of retrieved + text chunks with sources\\n - `summary`: Summary of the context or partial + answer\\n\\n- **`answer`**: The final answer text (Markdown formatted)\\n\\n- + **`generated_text`**: Intermediate generated text\\n\\n- **`possible_answer`**: + Alternative answer being considered\\n\\n- **`exception`**: Error details + if something went wrong\\n\\n### Processing Responses\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\nfrom nuclia_models.agent.interaction + import AnswerOperation\\n\\nagent = NucliaAgent()\\n\\nfor response in agent.interact(question=\\\"Tell + me about AI\\\"):\\n if response.operation == AnswerOperation.START:\\n + \ print(\\\"Starting...\\\")\\n \\n elif response.step:\\n print(f\\\"Step: + {response.step.module} ({response.step.timeit:.2f}s)\\\")\\n \\n elif + response.context:\\n print(f\\\"Retrieved {len(response.context.chunks)} + chunks\\\")\\n for chunk in response.context.chunks:\\n print(f\\\" + \ - {chunk.title}: {chunk.text[:100]}...\\\")\\n \\n elif response.answer:\\n + \ print(f\\\"\\\\nFinal Answer:\\\\n{response.answer}\\\")\\n \\n + \ elif response.operation == AnswerOperation.DONE:\\n print(\\\"Complete!\\\")\\n + \ \\n elif response.operation == AnswerOperation.ERROR:\\n print(f\\\"Error: + {response.exception.detail if response.exception else 'Unknown'}\\\")\\n```\\n\\n## + Standard CLI for Raw Messages\\n\\nFor debugging or advanced use cases, you + can access raw websocket messages programmatically:\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over all messages\\nfor message in agent.interact(\\n question=\\\"What + is RAO?\\\"\\n):\\n # message is an AragAnswer object with all raw data\\n + \ print(f\\\"Operation: {message.operation}\\\")\\n print(f\\\"Raw message: + {message.model_dump_json(indent=2)}\\\")\\n```\\n\\nThis gives you direct + access to all websocket message data for debugging or custom processing.\\n\\n## + Advanced Features\\n\\n### Agent Feedback Requests\\n\\nAgents can request + additional input from users during processing:\\n\\n```python\\nfrom nuclia.sdk.agent + import NucliaAgent\\nfrom nuclia_models.agent.interaction import AnswerOperation\\n\\nagent + = NucliaAgent()\\ngenerator = agent.interact(question=\\\"Help me with X\\\")\\n\\nfor + response in generator:\\n if response.operation == AnswerOperation.AGENT_REQUEST:\\n + \ # Agent is requesting user input\\n user_input = input(f\\\"Agent + asks: {response.feedback.question}\\\\n> \\\")\\n # Send response back\\n + \ generator.send(user_input)\\n elif response.answer:\\n print(response.answer)\\n```\\n\\n### + Error Handling\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom + nuclia.exceptions import RaoAPIException\\n\\nagent = NucliaAgent()\\n\\ntry:\\n + \ for response in agent.interact(question=\\\"Hello?\\\"):\\n if + response.exception:\\n print(f\\\"Agent error: {response.exception.detail}\\\")\\n + \ elif response.answer:\\n print(response.answer)\\nexcept + RaoAPIException as e:\\n print(f\\\"API error: {e.detail}\\\")\\nexcept + Exception as e:\\n print(f\\\"Unexpected error: {e}\\\")\\n```\\n\\n### + Passing Custom Headers to MCP\\n\\nIf your Retrieval Agents Orchestrator requires + custom headers for MCP Agents, you can pass them as follows:\\n\\n**CLI:**\\n```bash\\nnuclia + agent interact \\\"What is AI?\\\" --headers '{\\\"X-Custom-Header\\\": \\\"value\\\"}'\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\nfor response + in agent.interact(\\n question=\\\"What is AI?\\\",\\n headers={\\\"X-Custom-Header\\\": + \\\"value\\\"}\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\nPlease + ensure that the 'Allowed Headers' configuration in your MCP agent includes + any custom headers you wish to use.\\n\\n## Best Practices\\n\\n1. **Use Sessions + for Context**: Create sessions when you need multi-turn conversations with + context retention\\n2. **Use Ephemeral Sessions for One-offs**: Don't supply + a session UUID for using agents in a stateless manner.\\n3. **Stream for UX**: + Process responses as they arrive for better user experience\\n4. **Handle + All Operations**: Check for different operation types (START, ANSWER, DONE, + ERROR) when processing responses\\n5. **Clean Up Sessions**: Delete sessions + when done to avoid clutter\\n6. **Use Interactive CLI**: For manual testing + and exploration, the interactive CLI provides the best experience\\n\",\"format\":\"MARKDOWN\",\"md5\":\"bbaaf8cccd2b664ba4f7daf47d1f2bf4\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Retrieval + Agents Orchestrator \\n Interact with Nuclia's Retrieval Agents Orchestrator + to have intelligent conversations over several knowledge sources with persistent + session management and real-time streaming responses. \\n Prerequisites \\n + Install the Nuclia SDK: \\n sh \\n pip install nuclia \\n Ensure you have: + \\n - A valid Nuclia authentication token (see Authentication) \\n - Access + to a configured Retrieval Agent \\n Overview \\n The nuclia.py library provides + several ways to interact with your Retrieval Agents Orchestrators: \\n \\n + Interactive CLI: A rich, user-friendly terminal interface (recommended) \\n + Standard CLI: Direct access to raw websocket messages for debugging \\n Session + Management: Create and manage persistent conversation sessions \\n Programmatic + API: Python SDK for building custom applications \\n \\n Listing Available + Agents \\n Discover what Retrieval Agents Orchestrators you have access to. + \\n \\n CLI: \\n \\n sh \\n nuclia agents list \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() + \\n all_agents = agents.list() \\n for agent in all_agents: \\n print(f Agent: + {agent.title} ({agent.id}) ) \\n print(f Slug: {agent.slug} ) \\n print(f + Zone: {agent.zone} ) \\n ``` \\n Getting a Specific Agent \\n \\n CLI: \\n + \\n sh \\n nuclia agents get --account= my-account --id= agent-uuid --zone= + europe-1 \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agents import + NucliaAgents \\n agents = NucliaAgents() \\n agent_details = agents.get( \\n + account= my-account , \\n id= agent-uuid , \\n zone= europe-1 \\n ) \\n print(agent_details) + \\n ``` \\n Setting a Default Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents + default [AGENT_SLUG or AGENT_UUID] \\n \\n SDK: \\n \\n ```python \\n from + nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() \\n agents.default( + my-agent ) \\n ``` \\n This sets the default agent for all subsequent operations. + \\n Interactive CLI (Recommended) \\n The interactive CLI provides a beautiful, + real-time interface for conversing with your Retrieval Agents Orchestrator. + \\n Starting the Interactive CLI \\n \\n CLI: \\n \\n sh \\n nuclia agent + cli interact \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n agent.cli.interact() \\n ``` \\n + This launches an interactive terminal session where you can: \\n - Ask questions + and see streaming responses \\n - View processing steps in real-time \\n - + Manage conversation sessions \\n - See retrieved context and citations \\n + Interactive CLI Commands \\n The CLI supports several commands (prefix with + /): \\n | Command | Description | \\n |---------|-------------| \\n | /help + | Show available commands | \\n | /new_session | Create a new persistent session + | \\n | /list_sessions | List all your sessions | \\n | /change_session | + Switch to a different session, use 'ephemeral' for a temporary session | \\n + | /clear | Clear the screen | \\n | /exit | Exit the CLI | \\n Please note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option Agent with memory enabled during creation. \\n Session Management + \\n Sessions allow you to maintain conversation context across multiple interactions. + \\n \\n This feature will only be available if you checked Agent with memory + during the creation of your Retrieval Agents Orchestrator. \\n \\n Creating + a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session new --name= My + Research Session \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session_uuid = agent.session.new( + My Research Session ) \\n print(f Created session: {session_uuid} ) \\n ``` + \\n Listing Sessions \\n \\n CLI: \\n \\n sh \\n nuclia agent session list + \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import NucliaAgent + \\n agent = NucliaAgent() \\n sessions = agent.session.list() \\n for session + in sessions.resources: \\n print(f {session.title}: {session.id} ) \\n ``` + \\n Getting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session get + --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session = agent.session.get(session_uuid) + \\n print(f Session: {session.title} ) \\n print(f Created: {session.created} + ) \\n ``` \\n Deleting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent + session delete --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n + agent.session.delete(session_uuid) \\n ``` \\n Interaction \\n Aside from + the interactive CLI, you can interact with your Retrieval Agents Orchestrator + with the simple CLI or programmatically using the SDK. \\n Basic Interaction + \\n CLI: \\n bash \\n nuclia agent interact What is Eric known for? \\n SDK: + \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() + \\n Iterate over streaming responses \\n for response in agent.interact( \\n + question= What is Eric known for? \\n ): \\n if response.operation == ANSWER + and response.answer: \\n print(response.answer) \\n elif response.step: \\n + print(f Processing: {response.step.module} ) \\n ``` \\n Not supplying a session_uuid + when calling interact will use an ephemeral session by default. To maintain + context, provide a persistent session UUID. \\n Using Persistent Sessions + \\n CLI: \\n ```bash \\n nuclia agent sessions new Customer Support Chat \\n + Note the session UUID returned \\n nuclia agent interact What are your business + hours? --session_uuid= SESSION_UUID \\n nuclia agent interact Are you open + on weekends? --session_uuid= SESSION_UUID \\n ``` \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n Create + a session \\n session_uuid = agent.session.new( Customer Support Chat ) \\n + Have a conversation with context \\n for response in agent.interact( \\n session_uuid=session_uuid, + \\n question= What are your business hours? \\n ): \\n if response.answer: + \\n print(response.answer) \\n Follow-up question maintains context \\n for + response in agent.interact( \\n session_uuid=session_uuid, \\n question= Are + you open on weekends? \\n ): \\n if response.answer: \\n print(response.answer) + \\n ``` \\n Understanding Response Types \\n When interacting with an agent, + you receive a stream of AragAnswer objects with different operations: \\n + | Operation | Description | \\n |-----------|-------------| \\n | START | + Interaction has begun | \\n | ANSWER | Processing step or partial answer | + \\n | DONE | Interaction complete | \\n | ERROR | An error occurred | \\n + | AGENT_REQUEST | Agent needs user feedback | \\n Response Attributes \\n + Each response may contain: \\n \\n step: Information about the current processing + step \\n module: The module being executed (e.g., rephrase , basic_ask , remi + ) \\n title: Display title for the step \\n value: Result of the step \\n + reason: Explanation for the step \\n timeit: Time taken in seconds \\n \\n + input_nuclia_tokens/output_nuclia_tokens: Token usage \\n \\n \\n context: + Retrieved context from the knowledge base \\n \\n chunks: List of retrieved + text chunks with sources \\n \\n summary: Summary of the context or partial + answer \\n \\n \\n answer: The final answer text (Markdown formatted) \\n + \\n \\n generated_text: Intermediate generated text \\n \\n \\n possible_answer: + Alternative answer being considered \\n \\n \\n exception: Error details if + something went wrong \\n \\n \\n Processing Responses \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n for response in agent.interact(question= + Tell me about AI ): \\n if response.operation == AnswerOperation.START: \\n + print( Starting... ) \\n elif response.step: \\n print(f Step: {response.step.module} + ({response.step.timeit:.2f}s) ) \\n \\n elif response.context: \\n print(f + Retrieved {len(response.context.chunks)} chunks ) \\n for chunk in response.context.chunks: + \\n print(f - {chunk.title}: {chunk.text[:100]}... ) \\n \\n elif response.answer: + \\n print(f \\\\nFinal Answer:\\\\n{response.answer} ) \\n \\n elif response.operation + == AnswerOperation.DONE: \\n print( Complete! ) \\n \\n elif response.operation + == AnswerOperation.ERROR: \\n print(f Error: {response.exception.detail if + response.exception else 'Unknown'} ) \\n \\n ``` \\n Standard CLI for Raw + Messages \\n For debugging or advanced use cases, you can access raw websocket + messages programmatically: \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n Iterate over all messages \\n for + message in agent.interact( \\n question= What is RAO? \\n ): \\n # message + is an AragAnswer object with all raw data \\n print(f Operation: {message.operation} + ) \\n print(f Raw message: {message.model_dump_json(indent=2)} ) \\n ``` \\n + This gives you direct access to all websocket message data for debugging or + custom processing. \\n Advanced Features \\n Agent Feedback Requests \\n Agents + can request additional input from users during processing: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n generator = agent.interact(question= + Help me with X ) \\n for response in generator: \\n if response.operation + == AnswerOperation.AGENT_REQUEST: \\n # Agent is requesting user input \\n + user_input = input(f Agent asks: {response.feedback.question}\\\\n> ) \\n + # Send response back \\n generator.send(user_input) \\n elif response.answer: + \\n print(response.answer) \\n ``` \\n Error Handling \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia.exceptions import RaoAPIException + \\n agent = NucliaAgent() \\n try: \\n for response in agent.interact(question= + Hello? ): \\n if response.exception: \\n print(f Agent error: {response.exception.detail} + ) \\n elif response.answer: \\n print(response.answer) \\n except RaoAPIException + as e: \\n print(f API error: {e.detail} ) \\n except Exception as e: \\n print(f + Unexpected error: {e} ) \\n ``` \\n Passing Custom Headers to MCP \\n If your + Retrieval Agents Orchestrator requires custom headers for MCP Agents, you + can pass them as follows: \\n CLI: \\n bash \\n nuclia agent interact What + is AI? --headers '{ X-Custom-Header : value }' \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n for + response in agent.interact( \\n question= What is AI? , \\n headers={ X-Custom-Header + : value } \\n ): \\n if response.answer: \\n print(response.answer) \\n ``` + \\n Please ensure that the 'Allowed Headers' configuration in your MCP agent + includes any custom headers you wish to use. \\n Best Practices \\n \\n Use + Sessions for Context: Create sessions when you need multi-turn conversations + with context retention \\n Use Ephemeral Sessions for One-offs: Don't supply + a session UUID for using agents in a stateless manner. \\n Stream for UX: + Process responses as they arrive for better user experience \\n Handle All + Operations: Check for different operation types (START, ANSWER, DONE, ERROR) + when processing responses \\n Clean Up Sessions: Delete sessions when done + to avoid clutter \\n Use Interactive CLI: For manual testing and exploration, + the interactive CLI provides the best experience \\n 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Properties\\n\\n### attachments\\\\_images\\n\\n> + **attachments\\\\_images**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:385](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L385)\\n\\n***\\n\\n### + attachments\\\\_text\\n\\n> **attachments\\\\_text**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:384](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L384)\\n\\n***\\n\\n### + full\\n\\n> **full**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:386](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L386)\\n\\n***\\n\\n### + max\\\\_messages?\\n\\n> `optional` **max\\\\_messages**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:387](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L387)\\n\\n***\\n\\n### + name\\n\\n> **name**: [`CONVERSATION`](../enumerations/RagStrategyName.md#conversation)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:383](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L383)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"57a22a6451d1dd8d7ef574dfc6cae218\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ConversationalStrategy \\n Interface: ConversationalStrategy + \\n Properties \\n attachments_images \\n \\n attachments_images: boolean + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:385 \\n \\n + attachments_text \\n \\n attachments_text: boolean \\n \\n Defined in \\n + 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budget\\\\_tokens?\\n\\n> + `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\n\\n***\\n\\n### + effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n 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AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: 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in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > 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libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.6027967929840088,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic + RAG\"},\"PRODUCT/Knowledge Box\":{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge + Box\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"score\":0.6896387338638306,\"score_type\":\"VECTOR\",\"order\":2,\"text\":\" + Use the max_tokens parameter on the /ask endpoint to set hard limits on: \\n + - Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\":{\"score\":0.6072185635566711,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" + \\n Large context: Results from using RAG strategies like Full resource or + Neighbouring paragraphs , or from using the extra_context parameter \\n Long + questions: More detailed or complex questions require more input tokens \\n + Long prompts: Extensive system prompts increase the input token count \\n + Detailed answers: Comprehensive responses require more output tokens \\n Images + in context: When using multimodal models, images included in the retrieved + context significantly increase token consumption \\n \\n How to Limit and + Control Token Consumption \\n Strategy 1: Optimize Your Parameters \\n The + first approach to reducing token consumption is to fine-tune your request + parameters: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible + LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG + allows you to connect to any OpenAI API compatible LLM. This means that you + can use any LLM that has an API compatible with the OpenAI API which has become + a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, + open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box + configuration you can do so in three manners, through the API, the Nuclia + CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers + the most user-friendly way to modify the configuration of your knowledge box + and we will use it in this example.\\n\\nWe will be setting up a connection + to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers + a wide range of open-source and commercial models compatible with the OpenAI + API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), + the API parameters are located under the **API** tab.\\n\\n1. **Open the AI + Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. + **Select \u201COpenAI API Compatible Model\u201D** \\n From the models + list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n + \ Toggle the option for using you own `OpenAI API Compatible Key` if it is + not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - + **API Key**:\\n - Description: The API key for your LLM. This is the key + that you would use as an authorization header in the API. You may leave this + blank if the endpoint you are connecting to does not require an API key.\\n + \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n + \ - Description: The URL of the API endpoint for your LLM. This may be + shared between multiple models.\\n - Example: For OpenRouter, it is the + same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - + Description: The name of the model you want to use, it needs to exactly match + the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus + in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - + **Maximum supported input tokens**:\\n - Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only.\\n - + Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, + as we want to leave room for the output, we will set the maximum supported + input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output + tokens**:\\n - Description: The maximum number of tokens that the model + can generate as output. Again, we should keep in mind that this value summed + to the **Maximum supported input tokens** should not exceed the total context + size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the + maximum output tokens is specified at `32768`, but we already reserved `31744` + for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - + **Model Features**:\\n - Description: Under this section you will find + multiple toggles related to features supported by the model, these vary from + model to model, but most often the default values are well suited to most + use cases. The most relevant toggle is for `Image Support` which allows you + to use images as input for the model.\\n - Example: Image input is not + supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** + \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample + query in Agentic RAG or via API/CLI. Adjust your prompt templates and token + settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API + compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic + RAG allows you to connect to any OpenAI API compatible LLM. This means that + you can use any LLM that has an API compatible with the OpenAI API which has + become a standard in the industry. \\n Many of the options for self-hosted + LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications. \\n Configuration \\n To modify your knowledge box configuration + you can do so in three manners, through the API, the Nuclia CLI / SDK or the + Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly + way to modify the configuration of your knowledge box and we will use it in + this example. \\n We will be setting up a connection to the Phi 4 Reasoning + Plus model, hosted by OpenRouter which offers a wide range of open-source + and commercial models compatible with the OpenAI API. We can see more information + about this specific model here, the API parameters are located under the API + tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, + click AI Models. \\n Select OpenAI API Compatible Model \\n From the models + list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle + the option for using you own OpenAI API Compatible Key if it is not already + enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: + \\n \\n Description: The API key for your LLM. This is the key that you would + use as an authorization header in the API. You may leave this blank if the + endpoint you are connecting to does not require an API key. \\n Example: We + will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: + The URL of the API endpoint for your LLM. This may be shared between multiple + models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 + \\n \\n \\n Model: \\n Description: The name of the model you want to use, + it needs to exactly match the name of the model in the API. \\n Example: For + Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. + \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n Example: + For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want + to leave room for the output, we will set the maximum supported input tokens + as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified + at 32768, but we already reserved 31744 for the input tokens, so we will set + this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: + Under this section you will find multiple toggles related to features supported + by the model, these vary from model to model, but most often the default values + are well suited to most use cases. The most relevant toggle is for Image Support + which allows you to use images as input for the model. \\n Example: Image + input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. + \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n + Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt templates + and token settings as needed. \\n \\n 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Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\":0.6517809629440308,\"score_type\":\"VECTOR\",\"order\":5,\"text\":\" + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.6226930022239685,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.5966856479644775,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate + limits are an essential aspect of the Agentic RAG platform, ensuring fair + usage and optimal performance for all users interacting with Agentic RAG APIs. + This document outlines the rate limits enforced by Agentic RAG and provides + guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic + RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: + By default, the sum of all authenticated requests in a Agentic RAG account + cannot exceed 2400 requests per minute. Note that this limit can be customized + on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) + if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic + RAG implements a back-pressure mechanism to manage ingestion pipeline overload. + This mainly affects endpoints for uploading data and creating or updating + resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres + to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) + and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe + official Agentic RAG API clients already have built-in mechanisms for retrying + requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- + [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are + interacting directly with the API, we recommend using an [exponential backoff + retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits + are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response + will include a `try_after` key with an estimated UTC time for retrying the + request. You can use this value for retry logic as an alternative to the exponential + backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an + example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport + time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, + headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n + \ response = requests.get(url, headers=headers)\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n + \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n + \ time.sleep(wait_time)\\n retries += 1\\n else:\\n + \ response.raise_for_status()\\n raise Exception(\\\"Max retries + exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders + = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, + headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's + an example of how to use the try_after key from the response to manage rate + limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport + requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n + \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, + headers=headers)\\n response_body = response.json()\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n + \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n + \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n + \ print(\\n f\\\"Rate limit exceeded. Retrying at + {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n + \ retries += 1\\n else:\\n response.raise_for_status()\\n + \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl + = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer + YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese + examples demonstrate how to handle rate limits effectively, ensuring that + your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate + limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, + ensuring fair usage and optimal performance for all users interacting with + Agentic RAG APIs. This document outlines the rate limits enforced by Agentic + RAG and provides guidelines for handling rate-limited responses effectively. + \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: + \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated + requests in a Agentic RAG account cannot exceed 2400 requests per minute. + Note that this limit can be customized on a per-account basis. Please contact + Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion + back pressure limits: Agentic RAG implements a back-pressure mechanism to + manage ingestion pipeline overload. This mainly affects endpoints for uploading + data and creating or updating resources. \\n \\n \\n Handling Rate-Limited + Responses \\n Agentic RAG adheres to the HTTP standard and will return a response + with a 429 status codes when the limits are exceeded. \\n The official Agentic + RAG API clients already have built-in mechanisms for retrying requests when + rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript + client \\n \\n However, if you are interacting directly with the API, we recommend + using an exponential backoff retry strategy when limits are reached. \\n When + ingestion back pressure rate limits are hit, the response will include a try_after + key with an estimated UTC time for retrying the request. You can use this + value for retry logic as an alternative to the exponential backoff strategy. + \\n Example 1: Regular API rate limits \\n Here's an example of how to implement + an exponential backoff retry strategy in Python: \\n ```python \\n import + time \\n import requests \\n def make_request_with_exponential_backoff(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n if response.status_code + == 200: \\n return response.json() \\n elif response.status_code == 429: \\n + wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit + exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n + retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( + Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n + headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n ```python \\n import time \\n from datetime import + datetime \\n import requests \\n def make_request_with_try_after_info(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n response_body = response.json() + \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code + == 429 and try_after in response_body: \\n try_after = response_body[ try_after + ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n + wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n + f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... + \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries 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limiting\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.6079242825508118,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\" + ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.6562845706939697,\"score_type\":\"VECTOR\",\"order\":4,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.6046878099441528,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\":[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\":499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\":\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\":1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"end\":3929,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3650,\"end\":3795,\"key\":\"\"},{\"start\":3795,\"end\":3929,\"key\":\"\"}]},{\"start\":3929,\"end\":4317,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3932,\"end\":4123,\"key\":\"\"},{\"start\":4123,\"end\":4317,\"key\":\"\"}]},{\"start\":4317,\"end\":4520,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4320,\"end\":4520,\"key\":\"\"}]},{\"start\":4520,\"end\":4884,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4523,\"end\":4717,\"key\":\"\"},{\"start\":4717,\"end\":4884,\"key\":\"\"}]},{\"start\":4884,\"end\":5167,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4887,\"end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5167,\"key\":\"\"}]},{\"start\":5167,\"end\":5525,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5170,\"end\":5339,\"key\":\"\"},{\"start\":5339,\"end\":5525,\"key\":\"\"}]},{\"start\":5525,\"end\":5897,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5528,\"end\":5711,\"key\":\"\"},{\"start\":5711,\"end\":5897,\"key\":\"\"}]},{\"start\":5897,\"end\":6194,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5898,\"end\":6071,\"key\":\"\"},{\"start\":6071,\"end\":6194,\"key\":\"\"}]},{\"start\":6194,\"end\":6478,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6197,\"end\":6366,\"key\":\"\"},{\"start\":6366,\"end\":6478,\"key\":\"\"}]},{\"start\":6478,\"end\":6778,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6481,\"end\":6635,\"key\":\"\"},{\"start\":6635,\"end\":6778,\"key\":\"\"}]}],\"ner\":{\"boolean\":\"PERSON\",\"112 + min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"end\":654}]},{\"text\":\"112 + min\",\"label\":\"TIME\",\"positions\":[{\"start\":2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\",\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PERSON/boolean\":{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"},\"TIME/112 + min\":{\"position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.69486403465271,\"score_type\":\"VECTOR\",\"order\":1,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}}},\"relations\":{\"entities\":{}},\"query\":\"Explica + el uso del par\xE1metro 'max_tokens' en las respuestas y proporciona un enlace + a la documentaci\xF3n 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storage sync\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2026-04-13T08:05:37.518460\",\"modified\":\"2026-05-05T14:56:07.367333\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false}},\"paragraphs\":{\"results\":[{\"score\":0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Europe (AWS) \\n \\n\ - \ 63.179.23.139/32 \\n 3.78.13.149/32 \\n 3.66.123.24/32 \\n \\n Europe (GCP)\ - \ \\n \\n 35.204.65.155/32 \\n 34.91.38.151/32 \\n 35.204.139.129/32 \\n 35.204.108.221/32\ - \ \\n \\n Israel \\n \\n 51.17.150.97/32 \\n \\n United States \\n \\n 3.137.28.168/32\ - \ \\n \",\"labels\":[],\"position\":{\"page_number\":0,\"index\":1,\"start\"\ - :341,\"end\":578},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n id: outbound-ips\ - \ \\n title: Outbound IP Addresses \\n \\n If your firewall blocks inbound\ - \ requests from the internet, you may need to allowlist the following IP addresses.\ - \ These are the source IPs that Progress Agentic RAG uses when making outbound\ - \ connections (e.g., webhooks, sync agents, or other integrations calling\ - \ your infrastructure). \\n\",\"labels\":[],\"position\":{\"page_number\"\ - :0,\"index\":0,\"start\":0,\"end\":341},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"256f6b95e3fb476c8f47b6b057d02fa0\",\"field_type\":\"a\",\"\ - field\":\"title\",\"text\":\"docs > management > security > 5 outbound ips\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":45},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n optional security:\ - \ object \\n \\n groups \\n \\n groups: string[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:166\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:165\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":1,\"start\":225,\"end\":477},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs\ - \ \\n \\n @nuclia/core / SuggestOptions \\n Interface: SuggestOptions \\n\ - \ Properties \\n highlight? \\n \\n optional highlight: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:164 \\n \\n security?\ - \ \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - 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\\n \\\ - n optional error: IError \\n \\n Inherited from \\n IFieldDataBase.error \\\ - n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:149\ - \ \\n \\n errors? \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\"\ - :0,\"start\":0,\"end\":295},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"a\",\"field\":\"title\"\ - ,\"text\":\"docs > develop > js sdk > interfaces > KVSchema\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":47},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core\ - \ / KVSchema \\n Interface: KVSchema \\n Properties \\n description? \\n \\\ - n optional description: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:597\ - \ \\n \\n fields \\n \\n fields: KVSchemaField[] \\n \\n 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Note that\ - \ this limit can be customized on a per-account basis. Please contact [Agentic\ - \ RAG's support team](mailto:support@nuclia.com) if you need an increase.\\\ - n\\n- **Ingestion back pressure limits**: Agentic RAG implements a back-pressure\ - \ mechanism to manage ingestion pipeline overload. This mainly affects endpoints\ - \ for uploading data and creating or updating resources.\\n\\n## Handling\ - \ Rate-Limited Responses\\n\\nAgentic RAG adheres to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4)\ - \ and will return a response with a `429` status codes when the limits are\ - \ exceeded.\\n\\nThe official Agentic RAG API clients already have built-in\ - \ mechanisms for retrying requests when rate limits are encountered:\\n\\\ - n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- [Nuclia JavaScript\ - \ client](/docs/develop/js-sdk/)\\n\\nHowever, if you are interacting directly\ - \ with the API, we recommend using an [exponential backoff retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff)\ - \ when limits are reached.\\n\\nWhen ingestion back pressure rate limits are\ - \ hit, the response will include a `try_after` key with an estimated UTC time\ - \ for retrying the request. You can use this value for retry logic as an alternative\ - \ to the exponential backoff strategy.\\n\\n## Example 1: Regular API rate\ - \ limits\\n\\nHere's an example of how to implement an exponential backoff\ - \ retry strategy in Python:\\n\\n```python\\nimport time\\nimport requests\\\ - n\\ndef make_request_with_exponential_backoff(url, headers, max_retries=5):\\\ - n retries = 0\\n while retries < max_retries:\\n response = requests.get(url,\ - \ headers=headers)\\n if response.status_code == 200:\\n \ - \ return response.json()\\n elif response.status_code == 429:\\n \ - \ wait_time = 2 ** retries # Exponential backoff: 2^retries\\n\ - \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\ - \")\\n time.sleep(wait_time)\\n retries += 1\\n \ - \ else:\\n response.raise_for_status()\\n raise Exception(\\\ - \"Max retries exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\ - \"\\nheaders = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\\ - ndata = make_request_with_exponential_backoff(url, headers)\\nprint(data)\\\ - n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's an example\ - \ of how to use the try_after key from the response to manage rate limits:\\\ - n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport requests\\\ - n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\\ - n retries = 0\\n while retries < max_retries:\\n response = requests.get(url,\ - \ headers=headers)\\n response_body = response.json()\\n if\ - \ response.status_code == 200:\\n return response.json()\\n \ - \ elif response.status_code == 429 and \\\"try_after\\\" in response_body:\\\ - n try_after = response_body[\\\"try_after\\\"]\\n retry_time\ - \ = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n \ - \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n \ - \ print(\\n f\\\"Rate limit exceeded. Retrying at {retry_time}\ - \ (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\\ - n retries += 1\\n else:\\n response.raise_for_status()\\\ - n raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\\ - nurl = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\ - \"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url,\ - \ headers)\\nprint(data)\\n```\\n\\nThese examples demonstrate how to handle\ - \ rate limits effectively, ensuring that your application respects the limits\ - \ and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\"\ - ,\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\"\ - :{\"text\":\" \\n id: rate-limiting \\n title: Manage rate limiting \\n \\\ - n Manage rate limiting \\n Rate limits are an essential aspect of the Agentic\ - \ RAG platform, ensuring fair usage and optimal performance for all users\ - \ interacting with Agentic RAG APIs. This document outlines the rate limits\ - \ enforced by Agentic RAG and provides guidelines for handling rate-limited\ - \ responses effectively. \\n Introduction \\n Agentic RAG can apply two types\ - \ of limits to its APIs: \\n \\n \\n Regular API rate limits: By default,\ - \ the sum of all authenticated requests in a Agentic RAG account cannot exceed\ - \ 2400 requests per minute. Note that this limit can be customized on a per-account\ - \ basis. Please contact Agentic RAG's support team if you need an increase.\ - \ \\n \\n \\n Ingestion back pressure limits: Agentic RAG implements a back-pressure\ - \ mechanism to manage ingestion pipeline overload. This mainly affects endpoints\ - \ for uploading data and creating or updating resources. \\n \\n \\n Handling\ - \ Rate-Limited Responses \\n Agentic RAG adheres to the HTTP standard and\ - \ will return a response with a 429 status codes when the limits are exceeded.\ - \ \\n The official Agentic RAG API clients already have built-in mechanisms\ - \ for retrying requests when rate limits are encountered: \\n \\n Nuclia Python\ - \ client \\n Nuclia JavaScript client \\n \\n However, if you are interacting\ - \ directly with the API, we recommend using an exponential backoff retry strategy\ - \ when limits are reached. \\n When ingestion back pressure rate limits are\ - \ hit, the response will include a try_after key with an estimated UTC time\ - \ for retrying the request. You can use this value for retry logic as an alternative\ - \ to the exponential backoff strategy. \\n Example 1: Regular API rate limits\ - \ \\n Here's an example of how to implement an exponential backoff retry strategy\ - \ in Python: \\n ```python \\n import time \\n import requests \\n def make_request_with_exponential_backoff(url,\ - \ headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries:\ - \ \\n response = requests.get(url, headers=headers) \\n if response.status_code\ - \ == 200: \\n return response.json() \\n elif response.status_code == 429:\ - \ \\n wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate\ - \ limit exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time)\ - \ \\n retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception(\ - \ Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint\ - \ \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url,\ - \ headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure\ - \ limits \\n Here's an example of how to use the try_after key from the response\ - \ to manage rate limits: \\n ```python \\n import time \\n from datetime import\ - \ datetime \\n import requests \\n def make_request_with_try_after_info(url,\ - \ headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries:\ - \ \\n response = requests.get(url, headers=headers) \\n response_body = response.json()\ - \ \\n if response.status_code == 200: \\n return response.json() \\n elif\ - \ response.status_code == 429 and try_after in response_body: \\n try_after\ - \ = response_body[ try_after ] \\n retry_time = datetime.strptime(try_after,\ - \ %Y-%m-%dT%H:%M:%S.%fZ ) \\n wait_time = (retry_time - datetime.utcnow()).total_seconds()\ - \ \\n print( \\n f Rate limit exceeded. 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n- [output](Ask.AskTokens.md#output)\\n\\n## Properties\\n\\n### input\\n\\\ - n\u2022 **input**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:107](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L107)\\\ - n\\n___\\n\\n### output\\n\\n\u2022 **output**: `number`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:108](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L108)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"0e6f19eeb8dee8b2914160e8f0d162ae\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / Ask / AskTokens \\n Interface: AskTokens\ - \ \\n Ask.AskTokens \\n Table of contents \\n Properties \\n \\n input \\\ - n output \\n \\n Properties \\n input \\n \u2022 input: number \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:107 \\n \\n output\ - \ \\n \u2022 output: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:108\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":333,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":333,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:51:24.304417Z\"\ - ,\"last_understanding\":\"2025-04-15T14:50:53.884881Z\",\"last_extract\":\"\ - 2025-04-15T14:50:53.690369Z\",\"last_processing_start\":\"2025-04-15T14:50:53.665841Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Ask.AskTokens\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > Ask.AskTokens\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"328ed5b87692439a881303c7c1d4eefc/t/page/0-333\"\ - :{\"score\":0.5672594904899597,\"score_type\":\"VECTOR\",\"order\":15,\"text\"\ - :\"@nuclia/core / Exports / Ask / AskTokens \\n Interface: AskTokens \\n Ask.AskTokens\ - \ \\n Table of contents \\n Properties \\n \\n input \\n output \\n \\n Properties\ - \ \\n input \\n \u2022 input: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:107\ - \ \\n \\n output \\n \u2022 output: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:108\"\ - ,\"id\":\"328ed5b87692439a881303c7c1d4eefc/t/page/0-333\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":333,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\"\ - ,\"modified\":\"2026-06-02T15:43:14.212428\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\\ - n\\n# Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\\ - n\\n> `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\\ - n\\n***\\n\\n### llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\\ - n\\n***\\n\\n### manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**:\ - \ `object`\\n\\n#### splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\\ - n\\n***\\n\\n### max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\\ - n\\n***\\n\\n### name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:534](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L534)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"7074857dc3154de0e287bf1a4a31f240\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\\ - n Interface: SplitStrategy \\n Properties \\n custom_split? \\n \\n optional\ - \ custom_split: CustomSplitStrategy \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536\ - \ \\n \\n llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n manual_split?\ - \ \\n \\n optional manual_split: object \\n \\n splitter \\n \\n splitter:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:538\ - \ \\n \\n max_paragraph? \\n \\n optional max_paragraph: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 \\n \\n name? \\n \\\ - n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\"\ - :234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\"\ - :\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"\ - end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:44:43.409066Z\"\ - ,\"last_understanding\":\"2026-06-02T15:44:17.926743Z\",\"last_extract\":\"\ - 2026-06-02T15:44:17.298950Z\",\"last_processing_start\":\"2026-06-02T15:44:17.276971Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\"\ - :{\"score\":0.5749672651290894,\"score_type\":\"VECTOR\",\"order\":11,\"text\"\ - :\" \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\ - \ \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\"\ - ,\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\"\ - :696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\"\ - ,\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\"\ - :\"docs > rag > advanced > openai api compatible models\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2025-08-27T15:33:06.862963\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: openai-api-compatible-models\\\ - ntitle: Connect to any OpenAI API compatible LLM\\n---\\n\\n# Connect to any\ - \ OpenAI API compatible LLM\\n\\nAgentic RAG allows you to connect to any\ - \ OpenAI API compatible LLM. This means that you can use any LLM that has\ - \ an API compatible with the OpenAI API which has become a standard in the\ - \ industry.\\n\\nMany of the options for self-hosted LLMs, open-source LLMs\ - \ hosted by cloud providers or commercial LLMs are compatible with the OpenAI\ - \ API. This means that you can use them with Agentic RAG without any modifications.\\\ - n\\n## Configuration\\n\\nTo modify your knowledge box configuration you can\ - \ do so in three manners, through the API, the Nuclia CLI / SDK or the Agentic\ - \ RAG dashboard.\\n\\nThe Agentic RAG dashboard offers the most user-friendly\ - \ way to modify the configuration of your knowledge box and we will use it\ - \ in this example.\\n\\nWe will be setting up a connection to the **Phi 4\ - \ Reasoning Plus** model, hosted by **OpenRouter** which offers a wide range\ - \ of open-source and commercial models compatible with the OpenAI API. We\ - \ can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free),\ - \ the API parameters are located under the **API** tab.\\n\\n1. **Open the\ - \ AI Models page** \\n In the left sidebar under **Advanced**, click **AI\ - \ Models**.\\n2. **Select \u201COpenAI API Compatible Model\u201D** \\n \ - \ From the models list, choose **OpenAI API Compatible Model**.\\n3. **Enable\ - \ custom Key** \\n Toggle the option for using you own `OpenAI API Compatible\ - \ Key` if it is not already enabled.\\n4. **Fill in the configuration parameters**\\\ - n\\n - **API Key**:\\n - Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key.\\n - Example: We will set this to our OpenRouter API key.\\\ - n - **API URL**:\\n - Description: The URL of the API endpoint for your\ - \ LLM. This may be shared between multiple models.\\n - Example: For OpenRouter,\ - \ it is the same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\\ - n - Description: The name of the model you want to use, it needs to exactly\ - \ match the name of the model in the API.\\n - Example: For Phi 4 Reasoning\ - \ Plus in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\\ - n - **Maximum supported input tokens**:\\n - Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only.\\n - Example: For Phi 4 Reasoning Plus, the total context\ - \ size is `32768` tokens, as we want to leave room for the output, we will\ - \ set the maximum supported input tokens as `32768 - 1024 = 31744`.\\n -\ - \ **Maximum supported output tokens**:\\n - Description: The maximum number\ - \ of tokens that the model can generate as output. Again, we should keep in\ - \ mind that this value summed to the **Maximum supported input tokens** should\ - \ not exceed the total context size supported by the model.\\n - Example:\ - \ For Phi 4 Reasoning Plus, the maximum output tokens is specified at `32768`,\ - \ but we already reserved `31744` for the input tokens, so we will set this\ - \ to `32768 - 31744 = 1024`.\\n - **Model Features**:\\n - Description:\ - \ Under this section you will find multiple toggles related to features supported\ - \ by the model, these vary from model to model, but most often the default\ - \ values are well suited to most use cases. The most relevant toggle is for\ - \ `Image Support` which allows you to use images as input for the model.\\\ - n - Example: Image input is not supported by Phi 4 Reasoning Plus, so\ - \ we will leave it disabled.\\n\\n5. **Save** \\n Click **Save changes**.\\\ - n\\n6. **Test your model** \\n Run a sample query in Agentic RAG or via\ - \ API/CLI. Adjust your prompt templates and token settings as needed.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ openai-api-compatible-models \\n title: Connect to any OpenAI API compatible\ - \ LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic RAG allows\ - \ you to connect to any OpenAI API compatible LLM. This means that you can\ - \ use any LLM that has an API compatible with the OpenAI API which has become\ - \ a standard in the industry. \\n Many of the options for self-hosted LLMs,\ - \ open-source LLMs hosted by cloud providers or commercial LLMs are compatible\ - \ with the OpenAI API. This means that you can use them with Agentic RAG without\ - \ any modifications. \\n Configuration \\n To modify your knowledge box configuration\ - \ you can do so in three manners, through the API, the Nuclia CLI / SDK or\ - \ the Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most\ - \ user-friendly way to modify the configuration of your knowledge box and\ - \ we will use it in this example. \\n We will be setting up a connection to\ - \ the Phi 4 Reasoning Plus model, hosted by OpenRouter which offers a wide\ - \ range of open-source and commercial models compatible with the OpenAI API.\ - \ We can see more information about this specific model here, the API parameters\ - \ are located under the API tab. \\n \\n Open the AI Models page \\n In the\ - \ left sidebar under Advanced, click AI Models. \\n Select OpenAI API Compatible\ - \ Model \\n From the models list, choose OpenAI API Compatible Model. \\n\ - \ Enable custom Key \\n Toggle the option for using you own OpenAI API Compatible\ - \ Key if it is not already enabled. \\n \\n Fill in the configuration parameters\ - \ \\n \\n \\n API Key: \\n \\n Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key. \\n Example: We will set this to our OpenRouter API key. \\\ - n \\n \\n API URL: \\n Description: The URL of the API endpoint for your LLM.\ - \ This may be shared between multiple models. \\n Example: For OpenRouter,\ - \ it is the same for all models: https://openrouter.ai/api/v1 \\n \\n \\n\ - \ Model: \\n Description: The name of the model you want to use, it needs\ - \ to exactly match the name of the model in the API. \\n Example: For Phi\ - \ 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free.\ - \ \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n Example: For Phi 4 Reasoning Plus, the total context size\ - \ is 32768 tokens, as we want to leave room for the output, we will set the\ - \ maximum supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum\ - \ supported output tokens: \\n Description: The maximum number of tokens that\ - \ the model can generate as output. Again, we should keep in mind that this\ - \ value summed to the Maximum supported input tokens should not exceed the\ - \ total context size supported by the model. \\n Example: For Phi 4 Reasoning\ - \ Plus, the maximum output tokens is specified at 32768, but we already reserved\ - \ 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024.\ - \ \\n \\n \\n \\n Model Features: \\n \\n Description: Under this section\ - \ you will find multiple toggles related to features supported by the model,\ - \ these vary from model to model, but most often the default values are well\ - \ suited to most use cases. The most relevant toggle is for Image Support\ - \ which allows you to use images as input for the model. \\n Example: Image\ - \ input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled.\ - \ \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model\ - \ \\n Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt\ - \ templates and token settings as needed. \\n \\n \",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"https://openrouter.ai/api/v1\"\ - ],\"paragraphs\":[{\"start\":0,\"end\":330,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"\ - end\":201,\"key\":\"\"},{\"start\":201,\"end\":330,\"key\":\"\"}]},{\"start\"\ - :330,\"end\":549,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":331,\"end\":471,\"key\"\ - :\"\"},{\"start\":471,\"end\":549,\"key\":\"\"}]},{\"start\":549,\"end\":858,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":550,\"end\":711,\"key\":\"\"},{\"start\":711,\"\ - end\":858,\"key\":\"\"}]},{\"start\":858,\"end\":1156,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":859,\"end\":1045,\"key\":\"\"},{\"start\":1045,\"end\":1156,\"key\"\ - :\"\"}]},{\"start\":1156,\"end\":1450,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1159,\"\ - end\":1240,\"key\":\"\"},{\"start\":1240,\"end\":1337,\"key\":\"\"},{\"start\"\ - :1337,\"end\":1450,\"key\":\"\"}]},{\"start\":1450,\"end\":1715,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1453,\"end\":1548,\"key\":\"\"},{\"start\":1548,\"end\":1622,\"\ - key\":\"\"},{\"start\":1622,\"end\":1715,\"key\":\"\"}]},{\"start\":1715,\"\ - end\":1977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1716,\"end\":1775,\"key\":\"\"},{\"start\":1775,\"\ - end\":1841,\"key\":\"\"},{\"start\":1841,\"end\":1887,\"key\":\"\"},{\"start\"\ - :1887,\"end\":1977,\"key\":\"\"}]},{\"start\":1977,\"end\":2202,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1978,\"end\":2101,\"key\":\"\"},{\"start\":2101,\"end\":2202,\"\ - 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:509,\"end\":520},{\"start\":715,\"end\":726},{\"start\":3738,\"end\":3749}],\"\ - entity\":\"Agentic RAG\"},\"ORG/OpenRouter\":{\"position\":[{\"start\":939,\"\ - end\":949},{\"start\":1749,\"end\":1759},{\"start\":1900,\"end\":1910}],\"\ - entity\":\"OpenRouter\"},\"LAW/Phi 4 Reasoning Plus\":{\"position\":[{\"start\"\ - :901,\"end\":921},{\"start\":3605,\"end\":3625}],\"entity\":\"Phi 4 Reasoning\ - \ Plus\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\"\ - :{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs >\ - \ rag > advanced > openai api compatible models\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > rag > advanced > openai api compatible models\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - :{\"score\":0.6304314136505127,\"score_type\":\"VECTOR\",\"order\":1,\"text\"\ - :\" \\n \\n Maximum supported input tokens: \\n Description: The maximum number\ - \ of tokens that the model can accept as input. Be mindful that this takes\ - \ into account the tokens used in the prompt, query and context. Also take\ - \ note that some models may provide their context window as the total between\ - \ input and output tokens, while others may provide it as the input tokens\ - \ only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - :{\"score\":0.6280757188796997,\"score_type\":\"VECTOR\",\"order\":2,\"text\"\ - :\" Description: The maximum number of tokens that the model can generate\ - \ as output. Again, we should keep in mind that this value summed to the Maximum\ - \ supported input tokens should not exceed the total context size supported\ - \ by the model. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"\ - start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - :{\"score\":0.5632287263870239,\"score_type\":\"VECTOR\",\"order\":17,\"text\"\ - :\" Example: For Phi 4 Reasoning Plus, the total context size is 32768 tokens,\ - \ as we want to leave room for the output, we will set the maximum supported\ - \ input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output\ - \ tokens: \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\"\ - 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,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\\ - n\\n# Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\\ - n\\n***\\n\\n### line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\\ - n\\n***\\n\\n### text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\\ - n\\n***\\n\\n### width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\\ - n\\n***\\n\\n### x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\\ - n\\n***\\n\\n### y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"b174ec97138cd1778c102918672d3f07\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface:\ - \ PageToken \\n Properties \\n height \\n \\n height: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 \\n \\n\ - \ line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250\ - \ \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248\ - \ \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246\ - \ \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"\ - processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:48.900917Z\"\ - ,\"last_understanding\":\"2026-06-02T15:43:43.037157Z\",\"last_extract\":\"\ - 2026-06-02T15:43:42.655587Z\",\"last_processing_start\":\"2026-06-02T15:43:42.629857Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ PageToken\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\"\ - :0.5863822102546692,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\\ - n Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249\ - \ \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - 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entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"\ - split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"\ - value\":\"docs > develop > python sdk > 05 search\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\"\ - :{\"score\":0.5834914445877075,\"score_type\":\"VECTOR\",\"order\":8,\"text\"\ - :\" ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n\",\"id\"\ - :\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"\ - 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relevant_relations\":null}}}}},\"0c8acfaf951e48a6a31b52234305fa57\":{\"id\"\ - :\"0c8acfaf951e48a6a31b52234305fa57\",\"slug\":\"docs-develop-js-sdk-interfaces-Ask-MetadataAskResponseItem-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > Ask.MetadataAskResponseItem\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-04-15T14:49:45.541827\",\"modified\":\"2025-04-15T14:49:45.541836\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Ask.MetadataAskResponseItem\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / [Ask](../modules/Ask.md) / MetadataAskResponseItem\\\ - n\\n# Interface: MetadataAskResponseItem\\n\\n[Ask](../modules/Ask.md).MetadataAskResponseItem\\\ - n\\n## Table of contents\\n\\n### Properties\\n\\n- [timings](Ask.MetadataAskResponseItem.md#timings)\\\ - n- [tokens](Ask.MetadataAskResponseItem.md#tokens)\\n- [type](Ask.MetadataAskResponseItem.md#type)\\\ - n\\n## Properties\\n\\n### timings\\n\\n\u2022 **timings**: [`AskTimings`](Ask.AskTimings.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:71](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L71)\\\ - n\\n___\\n\\n### tokens\\n\\n\u2022 **tokens**: [`AskTokens`](Ask.AskTokens.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:70](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L70)\\\ - n\\n___\\n\\n### type\\n\\n\u2022 **type**: ``\\\"metadata\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:69](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/ask.models.ts#L69)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"158d00991b6256aa585f95f97d90e8c6\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / Ask / MetadataAskResponseItem \\n Interface:\ - \ MetadataAskResponseItem \\n Ask.MetadataAskResponseItem \\n Table of contents\ - \ \\n Properties \\n \\n timings \\n tokens \\n type \\n \\n Properties \\\ - n timings \\n \u2022 timings: AskTimings \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:71\ - \ \\n \\n tokens \\n \u2022 tokens: AskTokens \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:70\ - \ \\n \\n type \\n \u2022 type: metadata \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:69\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":485,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":485,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:50:09.285312Z\"\ - ,\"last_understanding\":\"2025-04-15T14:49:46.781240Z\",\"last_extract\":\"\ - 2025-04-15T14:49:46.422568Z\",\"last_processing_start\":\"2025-04-15T14:49:46.390071Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Ask.MetadataAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs >\ - \ develop > js sdk > interfaces > Ask.MetadataAskResponseItem\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0c8acfaf951e48a6a31b52234305fa57/t/page/0-485\"\ - :{\"score\":0.5690771341323853,\"score_type\":\"VECTOR\",\"order\":14,\"text\"\ - :\"@nuclia/core / Exports / Ask / MetadataAskResponseItem \\n Interface: MetadataAskResponseItem\ - \ \\n Ask.MetadataAskResponseItem \\n Table of contents \\n Properties \\\ - n \\n timings \\n tokens \\n type \\n \\n Properties \\n timings \\n \u2022\ - \ timings: AskTimings \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:71\ - \ \\n \\n tokens \\n \u2022 tokens: AskTokens \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:70\ - \ \\n \\n type \\n \u2022 type: metadata \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:69\"\ - ,\"id\":\"0c8acfaf951e48a6a31b52234305fa57/t/page/0-485\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":485,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 828f2dc506434dad90774c5d9e055100\":{\"id\":\"828f2dc506434dad90774c5d9e055100\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-Search-Pagination-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > Search.Pagination\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:13.065988\"\ - ,\"modified\":\"2025-04-15T14:49:13.065998\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Search.Pagination\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / [Search](../modules/Search.md) / Pagination\\\ - n\\n# Interface: Pagination\\n\\n[Search](../modules/Search.md).Pagination\\\ - n\\n## Hierarchy\\n\\n- **`Pagination`**\\n\\n \u21B3 [`Sentences`](Search.Sentences.md)\\\ - n\\n \u21B3 [`Fulltext`](Search.Fulltext.md)\\n\\n \u21B3 [`Paragraphs`](Search.Paragraphs.md)\\\ - n\\n## Table of contents\\n\\n### Properties\\n\\n- [next\\\\_page](Search.Pagination.md#next_page)\\\ - n- [page\\\\_number](Search.Pagination.md#page_number)\\n- [page\\\\_size](Search.Pagination.md#page_size)\\\ - n- [total](Search.Pagination.md#total)\\n\\n## Properties\\n\\n### next\\\\\ - _page\\n\\n\u2022 **next\\\\_page**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:213](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L213)\\\ - n\\n___\\n\\n### page\\\\_number\\n\\n\u2022 **page\\\\_number**: `number`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:211](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L211)\\\ - n\\n___\\n\\n### page\\\\_size\\n\\n\u2022 **page\\\\_size**: `number`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:212](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L212)\\\ - n\\n___\\n\\n### total\\n\\n\u2022 **total**: `number`\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:210](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/search/search.models.ts#L210)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"e9273e51e5f1a29264acd1644519affd\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / Search / Pagination \\n Interface: Pagination\ - \ \\n Search.Pagination \\n Hierarchy \\n \\n Pagination \\n \\n \u21B3 Sentences\ - \ \\n \u21B3 Fulltext \\n \u21B3 Paragraphs \\n Table of contents \\n Properties\ - \ \\n \\n next_page \\n page_number \\n page_size \\n total \\n \\n Properties\ - \ \\n next_page \\n \u2022 next_page: boolean \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:213\ - \ \\n \\n page_number \\n \u2022 page_number: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:211\ - \ \\n \\n page_size \\n \u2022 page_size: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:212\ - \ \\n \\n total \\n \u2022 total: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:210\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":244,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":244,\"key\":\"\"}]},{\"start\":244,\"end\":667,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":245,\"end\":667,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:49:31.578135Z\"\ - ,\"last_understanding\":\"2025-04-15T14:49:16.415424Z\",\"last_extract\":\"\ - 2025-04-15T14:49:15.976719Z\",\"last_processing_start\":\"2025-04-15T14:49:15.943627Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Search.Pagination\",\"extracted\":{\"text\":{\"text\":\"docs > develop >\ - \ js sdk > interfaces > Search.Pagination\"}}}}},\"security\":null,\"fields\"\ - :{\"/t/page\":{\"paragraphs\":{\"828f2dc506434dad90774c5d9e055100/t/page/244-667\"\ - :{\"score\":0.5596579313278198,\"score_type\":\"VECTOR\",\"order\":19,\"text\"\ - :\" Properties \\n next_page \\n \u2022 next_page: boolean \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/search.models.ts:213 \\n \\n page_number\ - \ \\n \u2022 page_number: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:211\ - \ \\n \\n page_size \\n \u2022 page_size: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:212\ - \ \\n \\n total \\n \u2022 total: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:210\"\ - ,\"id\":\"828f2dc506434dad90774c5d9e055100/t/page/244-667\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":244,\"end\"\ - :667,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\"\ - ,\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs > develop\ - \ > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2025-12-30T08:56:44.797830\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Retrieval Agents Orchestrator\\\ - n\\nInteract with Nuclia's Retrieval Agents Orchestrator to have intelligent\ - \ conversations over several knowledge sources with persistent session management\ - \ and real-time streaming responses.\\n\\n## Prerequisites\\n\\nInstall the\ - \ Nuclia SDK:\\n\\n```sh\\npip install nuclia\\n```\\n\\nEnsure you have:\\\ - n- A valid Nuclia authentication token (see [Authentication](02-auth.md))\\\ - n- Access to a configured Retrieval Agent\\n\\n## Overview\\n\\nThe nuclia.py\ - \ library provides several ways to interact with your Retrieval Agents Orchestrators:\\\ - n\\n- **Interactive CLI**: A rich, user-friendly terminal interface (recommended)\\\ - n- **Standard CLI**: Direct access to raw websocket messages for debugging\\\ - n- **Session Management**: Create and manage persistent conversation sessions\\\ - n- **Programmatic API**: Python SDK for building custom applications\\n\\\ - n\\n## Listing Available Agents\\n\\nDiscover what Retrieval Agents Orchestrators\ - \ you have access to.\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents list\\n \ - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\\ - n\\n agents = NucliaAgents()\\n all_agents = agents.list()\\n\\n for agent\ - \ in all_agents:\\n print(f\\\"Agent: {agent.title} ({agent.id})\\\"\ - )\\n print(f\\\" Slug: {agent.slug}\\\")\\n print(f\\\" Zone:\ - \ {agent.zone}\\\")\\n ```\\n\\n### Getting a Specific Agent\\n\\n- CLI:\\\ - n\\n ```sh\\n nuclia agents get --account=\\\"my-account\\\" --id=\\\"agent-uuid\\\ - \" --zone=\\\"europe-1\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents\ - \ import NucliaAgents\\n\\n agents = NucliaAgents()\\n agent_details = agents.get(\\\ - n account=\\\"my-account\\\",\\n id=\\\"agent-uuid\\\",\\n \ - \ zone=\\\"europe-1\\\"\\n )\\n print(agent_details)\\n ```\\n\\n### Setting\ - \ a Default Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents default [AGENT_SLUG\ - \ or AGENT_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents\ - \ import NucliaAgents\\n\\n agents = NucliaAgents()\\n agents.default(\\\ - \"my-agent\\\")\\n ```\\n\\nThis sets the default agent for all subsequent\ - \ operations.\\n\\n## Interactive CLI (Recommended)\\n\\nThe interactive CLI\ - \ provides a beautiful, real-time interface for conversing with your Retrieval\ - \ Agents Orchestrator.\\n\\n### Starting the Interactive CLI\\n\\n- CLI:\\\ - n\\n ```sh\\n nuclia agent cli interact\\n ```\\n\\n- SDK:\\n\\n ```python\\\ - n from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\\ - n agent.cli.interact()\\n ```\\n\\nThis launches an interactive terminal\ - \ session where you can:\\n- Ask questions and see streaming responses\\n-\ - \ View processing steps in real-time\\n- Manage conversation sessions\\n-\ - \ See retrieved context and citations\\n\\n### Interactive CLI Commands\\\ - n\\nThe CLI supports several commands (prefix with `/`):\\n\\n| Command |\ - \ Description |\\n|---------|-------------|\\n| `/help` | Show available commands\ - \ |\\n| `/new_session` | Create a new persistent session |\\n| `/list_sessions`\ - \ | List all your sessions |\\n| `/change_session` | Switch to a different\ - \ session, use 'ephemeral' for a temporary session |\\n| `/clear` | Clear\ - \ the screen |\\n| `/exit` | Exit the CLI |\\n\\nPlease note that all commands\ - \ related to sessions require a Retrieval Agent Orchestrator with the option\ - \ **Agent with memory** enabled during creation.\\n\\n## Session Management\\\ - n\\nSessions allow you to maintain conversation context across multiple interactions.\\\ - n\\n> This feature will only be available if you checked **Agent with memory**\ - \ during the creation of your Retrieval Agents Orchestrator.\\n\\n### Creating\ - \ a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent session new --name=\\\ - \"My Research Session\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent\ - \ import NucliaAgent\\n\\n agent = NucliaAgent()\\n session_uuid = agent.session.new(\\\ - \"My Research Session\\\")\\n print(f\\\"Created session: {session_uuid}\\\ - \")\\n ```\\n\\n### Listing Sessions\\n\\n- CLI:\\n\\n ```sh\\n nuclia\ - \ agent session list\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent\ - \ import NucliaAgent\\n\\n agent = NucliaAgent()\\n sessions = agent.session.list()\\\ - n for session in sessions.resources:\\n print(f\\\"{session.title}:\ - \ {session.id}\\\")\\n ```\\n\\n### Getting a Session\\n\\n- CLI:\\n\\n \ - \ ```sh\\n nuclia agent session get --session_uuid=[SESSION_UUID]\\n ```\\\ - n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\\ - n\\n agent = NucliaAgent()\\n session = agent.session.get(session_uuid)\\\ - n print(f\\\"Session: {session.title}\\\")\\n print(f\\\"Created: {session.created}\\\ - \")\\n ```\\n\\n### Deleting a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia\ - \ agent session delete --session_uuid=[SESSION_UUID]\\n ```\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n agent\ - \ = NucliaAgent()\\n agent.session.delete(session_uuid)\\n ```\\n\\n## Interaction\\\ - n\\nAside from the interactive CLI, you can interact with your Retrieval Agents\ - \ Orchestrator with the simple CLI or programmatically using the SDK.\\n\\\ - n### Basic Interaction\\n\\n\\n**CLI:**\\n```bash\\nnuclia agent interact\ - \ \\\"What is Eric known for?\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom nuclia.sdk.agent\ - \ import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate over streaming\ - \ responses\\nfor response in agent.interact(\\n question=\\\"What is Eric\ - \ known for?\\\"\\n):\\n if response.operation == \\\"ANSWER\\\" and response.answer:\\\ - n print(response.answer)\\n elif response.step:\\n print(f\\\ - \"Processing: {response.step.module}\\\")\\n```\\n\\nNot supplying a `session_uuid`\ - \ when calling `interact` will use an ephemeral session by default. To maintain\ - \ context, provide a persistent session UUID.\\n\\n### Using Persistent Sessions\\\ - n\\n**CLI:**\\n```bash\\nnuclia agent sessions new \\\"Customer Support Chat\\\ - \"\\n# Note the session UUID returned\\nnuclia agent interact \\\"What are\ - \ your business hours?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\nnuclia agent\ - \ interact \\\"Are you open on weekends?\\\" --session_uuid=\\\"SESSION_UUID\\\ - \"\\n```\\n\\n**SDK:**\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\\ - n\\nagent = NucliaAgent()\\n\\n# Create a session\\nsession_uuid = agent.session.new(\\\ - \"Customer Support Chat\\\")\\n\\n# Have a conversation with context\\nfor\ - \ response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\ - \"What are your business hours?\\\"\\n):\\n if response.answer:\\n \ - \ print(response.answer)\\n\\n# Follow-up question maintains context\\\ - nfor response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\ - \"Are you open on weekends?\\\"\\n):\\n if response.answer:\\n print(response.answer)\\\ - n```\\n\\n## Understanding Response Types\\n\\nWhen interacting with an agent,\ - \ you receive a stream of `AragAnswer` objects with different operations:\\\ - n\\n| Operation | Description |\\n|-----------|-------------|\\n| `START`\ - \ | Interaction has begun |\\n| `ANSWER` | Processing step or partial answer\ - \ |\\n| `DONE` | Interaction complete |\\n| `ERROR` | An error occurred |\\\ - n| `AGENT_REQUEST` | Agent needs user feedback |\\n\\n### Response Attributes\\\ - n\\nEach response may contain:\\n\\n- **`step`**: Information about the current\ - \ processing step\\n - `module`: The module being executed (e.g., \\\"rephrase\\\ - \", \\\"basic_ask\\\", \\\"remi\\\")\\n - `title`: Display title for the\ - \ step\\n - `value`: Result of the step\\n - `reason`: Explanation for the\ - \ step\\n - `timeit`: Time taken in seconds\\n - `input_nuclia_tokens`/`output_nuclia_tokens`:\ - \ Token usage\\n\\n- **`context`**: Retrieved context from the knowledge base\\\ - n - `chunks`: List of retrieved text chunks with sources\\n - `summary`:\ - \ Summary of the context or partial answer\\n\\n- **`answer`**: The final\ - \ answer text (Markdown formatted)\\n\\n- **`generated_text`**: Intermediate\ - \ generated text\\n\\n- **`possible_answer`**: Alternative answer being considered\\\ - n\\n- **`exception`**: Error details if something went wrong\\n\\n### Processing\ - \ Responses\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom\ - \ nuclia_models.agent.interaction import AnswerOperation\\n\\nagent = NucliaAgent()\\\ - n\\nfor response in agent.interact(question=\\\"Tell me about AI\\\"):\\n\ - \ if response.operation == AnswerOperation.START:\\n print(\\\"\ - Starting...\\\")\\n \\n elif response.step:\\n print(f\\\"Step:\ - \ {response.step.module} ({response.step.timeit:.2f}s)\\\")\\n \\n elif\ - \ response.context:\\n print(f\\\"Retrieved {len(response.context.chunks)}\ - \ chunks\\\")\\n for chunk in response.context.chunks:\\n \ - \ print(f\\\" - {chunk.title}: {chunk.text[:100]}...\\\")\\n \\n \ - \ elif response.answer:\\n print(f\\\"\\\\nFinal Answer:\\\\n{response.answer}\\\ - \")\\n \\n elif response.operation == AnswerOperation.DONE:\\n \ - \ print(\\\"Complete!\\\")\\n \\n elif response.operation == AnswerOperation.ERROR:\\\ - n print(f\\\"Error: {response.exception.detail if response.exception\ - \ else 'Unknown'}\\\")\\n```\\n\\n## Standard CLI for Raw Messages\\n\\nFor\ - \ debugging or advanced use cases, you can access raw websocket messages programmatically:\\\ - n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\\ - n\\n# Iterate over all messages\\nfor message in agent.interact(\\n question=\\\ - \"What is RAO?\\\"\\n):\\n # message is an AragAnswer object with all raw\ - \ data\\n print(f\\\"Operation: {message.operation}\\\")\\n print(f\\\ - \"Raw message: {message.model_dump_json(indent=2)}\\\")\\n```\\n\\nThis gives\ - \ you direct access to all websocket message data for debugging or custom\ - \ processing.\\n\\n## Advanced Features\\n\\n### Agent Feedback Requests\\\ - n\\nAgents can request additional input from users during processing:\\n\\\ - n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom nuclia_models.agent.interaction\ - \ import AnswerOperation\\n\\nagent = NucliaAgent()\\ngenerator = agent.interact(question=\\\ - \"Help me with X\\\")\\n\\nfor response in generator:\\n if response.operation\ - \ == AnswerOperation.AGENT_REQUEST:\\n # Agent is requesting user input\\\ - n user_input = input(f\\\"Agent asks: {response.feedback.question}\\\ - \\n> \\\")\\n # Send response back\\n generator.send(user_input)\\\ - n elif response.answer:\\n print(response.answer)\\n```\\n\\n###\ - \ Error Handling\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\\ - nfrom nuclia.exceptions import RaoAPIException\\n\\nagent = NucliaAgent()\\\ - n\\ntry:\\n for response in agent.interact(question=\\\"Hello?\\\"):\\\ - n if response.exception:\\n print(f\\\"Agent error: {response.exception.detail}\\\ - \")\\n elif response.answer:\\n print(response.answer)\\\ - nexcept RaoAPIException as e:\\n print(f\\\"API error: {e.detail}\\\")\\\ - nexcept Exception as e:\\n print(f\\\"Unexpected error: {e}\\\")\\n```\\\ - n\\n### Passing Custom Headers to MCP\\n\\nIf your Retrieval Agents Orchestrator\ - \ requires custom headers for MCP Agents, you can pass them as follows:\\\ - n\\n**CLI:**\\n```bash\\nnuclia agent interact \\\"What is AI?\\\" --headers\ - \ '{\\\"X-Custom-Header\\\": \\\"value\\\"}'\\n```\\n\\n**SDK:**\\n```python\\\ - nfrom nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\nfor\ - \ response in agent.interact(\\n question=\\\"What is AI?\\\",\\n headers={\\\ - \"X-Custom-Header\\\": \\\"value\\\"}\\n):\\n if response.answer:\\n \ - \ print(response.answer)\\n```\\n\\nPlease ensure that the 'Allowed Headers'\ - \ configuration in your MCP agent includes any custom headers you wish to\ - \ use.\\n\\n## Best Practices\\n\\n1. **Use Sessions for Context**: Create\ - \ sessions when you need multi-turn conversations with context retention\\\ - n2. **Use Ephemeral Sessions for One-offs**: Don't supply a session UUID for\ - \ using agents in a stateless manner.\\n3. **Stream for UX**: Process responses\ - \ as they arrive for better user experience\\n4. **Handle All Operations**:\ - \ Check for different operation types (START, ANSWER, DONE, ERROR) when processing\ - \ responses\\n5. **Clean Up Sessions**: Delete sessions when done to avoid\ - \ clutter\\n6. **Use Interactive CLI**: For manual testing and exploration,\ - \ the interactive CLI provides the best experience\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"bbaaf8cccd2b664ba4f7daf47d1f2bf4\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Retrieval Agents\ - \ Orchestrator \\n Interact with Nuclia's Retrieval Agents Orchestrator to\ - \ have intelligent conversations over several knowledge sources with persistent\ - \ session management and real-time streaming responses. \\n Prerequisites\ - \ \\n Install the Nuclia SDK: \\n sh \\n pip install nuclia \\n Ensure you\ - \ have: \\n - A valid Nuclia authentication token (see Authentication) \\\ - n - Access to a configured Retrieval Agent \\n Overview \\n The nuclia.py\ - \ library provides several ways to interact with your Retrieval Agents Orchestrators:\ - \ \\n \\n Interactive CLI: A rich, user-friendly terminal interface (recommended)\ - \ \\n Standard CLI: Direct access to raw websocket messages for debugging\ - \ \\n Session Management: Create and manage persistent conversation sessions\ - \ \\n Programmatic API: Python SDK for building custom applications \\n \\\ - n Listing Available Agents \\n Discover what Retrieval Agents Orchestrators\ - \ you have access to. \\n \\n CLI: \\n \\n sh \\n nuclia agents list \\n \\\ - n SDK: \\n \\n ```python \\n from nuclia.sdk.agents import NucliaAgents \\\ - n agents = NucliaAgents() \\n all_agents = agents.list() \\n for agent in\ - \ all_agents: \\n print(f Agent: {agent.title} ({agent.id}) ) \\n print(f\ - \ Slug: {agent.slug} ) \\n print(f Zone: {agent.zone} ) \\n ``` \\n Getting\ - \ a Specific Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents get --account=\ - \ my-account --id= agent-uuid --zone= europe-1 \\n \\n SDK: \\n \\n ```python\ - \ \\n from nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents()\ - \ \\n agent_details = agents.get( \\n account= my-account , \\n id= agent-uuid\ - \ , \\n zone= europe-1 \\n ) \\n print(agent_details) \\n ``` \\n Setting\ - \ a Default Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents default [AGENT_SLUG\ - \ or AGENT_UUID] \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agents\ - \ import NucliaAgents \\n agents = NucliaAgents() \\n agents.default( my-agent\ - \ ) \\n ``` \\n This sets the default agent for all subsequent operations.\ - \ \\n Interactive CLI (Recommended) \\n The interactive CLI provides a beautiful,\ - \ real-time interface for conversing with your Retrieval Agents Orchestrator.\ - \ \\n Starting the Interactive CLI \\n \\n CLI: \\n \\n sh \\n nuclia agent\ - \ cli interact \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import\ - \ NucliaAgent \\n agent = NucliaAgent() \\n agent.cli.interact() \\n ``` \\\ - n This launches an interactive terminal session where you can: \\n - Ask questions\ - \ and see streaming responses \\n - View processing steps in real-time \\\ - n - Manage conversation sessions \\n - See retrieved context and citations\ - \ \\n Interactive CLI Commands \\n The CLI supports several commands (prefix\ - \ with /): \\n | Command | Description | \\n |---------|-------------| \\\ - n | /help | Show available commands | \\n | /new_session | Create a new persistent\ - \ session | \\n | /list_sessions | List all your sessions | \\n | /change_session\ - \ | Switch to a different session, use 'ephemeral' for a temporary session\ - \ | \\n | /clear | Clear the screen | \\n | /exit | Exit the CLI | \\n Please\ - \ note that all commands related to sessions require a Retrieval Agent Orchestrator\ - \ with the option Agent with memory enabled during creation. \\n Session Management\ - \ \\n Sessions allow you to maintain conversation context across multiple\ - \ interactions. \\n \\n This feature will only be available if you checked\ - \ Agent with memory during the creation of your Retrieval Agents Orchestrator.\ - \ \\n \\n Creating a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session\ - \ new --name= My Research Session \\n \\n SDK: \\n \\n ```python \\n from\ - \ nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n session_uuid\ - \ = agent.session.new( My Research Session ) \\n print(f Created session:\ - \ {session_uuid} ) \\n ``` \\n Listing Sessions \\n \\n CLI: \\n \\n sh \\\ - n nuclia agent session list \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent\ - \ import NucliaAgent \\n agent = NucliaAgent() \\n sessions = agent.session.list()\ - \ \\n for session in sessions.resources: \\n print(f {session.title}: {session.id}\ - \ ) \\n ``` \\n Getting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent\ - \ session get --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python\ - \ \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\\ - n session = agent.session.get(session_uuid) \\n print(f Session: {session.title}\ - \ ) \\n print(f Created: {session.created} ) \\n ``` \\n Deleting a Session\ - \ \\n \\n CLI: \\n \\n sh \\n nuclia agent session delete --session_uuid=[SESSION_UUID]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import NucliaAgent\ - \ \\n agent = NucliaAgent() \\n agent.session.delete(session_uuid) \\n ```\ - \ \\n Interaction \\n Aside from the interactive CLI, you can interact with\ - \ your Retrieval Agents Orchestrator with the simple CLI or programmatically\ - \ using the SDK. \\n Basic Interaction \\n CLI: \\n bash \\n nuclia agent\ - \ interact What is Eric known for? \\n SDK: \\n ```python \\n from nuclia.sdk.agent\ - \ import NucliaAgent \\n agent = NucliaAgent() \\n Iterate over streaming\ - \ responses \\n for response in agent.interact( \\n question= What is Eric\ - \ known for? \\n ): \\n if response.operation == ANSWER and response.answer:\ - \ \\n print(response.answer) \\n elif response.step: \\n print(f Processing:\ - \ {response.step.module} ) \\n ``` \\n Not supplying a session_uuid when calling\ - \ interact will use an ephemeral session by default. To maintain context,\ - \ provide a persistent session UUID. \\n Using Persistent Sessions \\n CLI:\ - \ \\n ```bash \\n nuclia agent sessions new Customer Support Chat \\n Note\ - \ the session UUID returned \\n nuclia agent interact What are your business\ - \ hours? --session_uuid= SESSION_UUID \\n nuclia agent interact Are you open\ - \ on weekends? --session_uuid= SESSION_UUID \\n ``` \\n SDK: \\n ```python\ - \ \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\\ - n Create a session \\n session_uuid = agent.session.new( Customer Support\ - \ Chat ) \\n Have a conversation with context \\n for response in agent.interact(\ - \ \\n session_uuid=session_uuid, \\n question= What are your business hours?\ - \ \\n ): \\n if response.answer: \\n print(response.answer) \\n Follow-up\ - \ question maintains context \\n for response in agent.interact( \\n session_uuid=session_uuid,\ - \ \\n question= Are you open on weekends? \\n ): \\n if response.answer: \\\ - n print(response.answer) \\n ``` \\n Understanding Response Types \\n When\ - \ interacting with an agent, you receive a stream of AragAnswer objects with\ - \ different operations: \\n | Operation | Description | \\n |-----------|-------------|\ - \ \\n | START | Interaction has begun | \\n | ANSWER | Processing step or\ - \ partial answer | \\n | DONE | Interaction complete | \\n | ERROR | An error\ - \ occurred | \\n | AGENT_REQUEST | Agent needs user feedback | \\n Response\ - \ Attributes \\n Each response may contain: \\n \\n step: Information about\ - \ the current processing step \\n module: The module being executed (e.g.,\ - \ rephrase , basic_ask , remi ) \\n title: Display title for the step \\n\ - \ value: Result of the step \\n reason: Explanation for the step \\n timeit:\ - \ Time taken in seconds \\n \\n input_nuclia_tokens/output_nuclia_tokens:\ - \ Token usage \\n \\n \\n context: Retrieved context from the knowledge base\ - \ \\n \\n chunks: List of retrieved text chunks with sources \\n \\n summary:\ - \ Summary of the context or partial answer \\n \\n \\n answer: The final answer\ - \ text (Markdown formatted) \\n \\n \\n generated_text: Intermediate generated\ - \ text \\n \\n \\n possible_answer: Alternative answer being considered \\\ - n \\n \\n exception: Error details if something went wrong \\n \\n \\n Processing\ - \ Responses \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\\ - n from nuclia_models.agent.interaction import AnswerOperation \\n agent =\ - \ NucliaAgent() \\n for response in agent.interact(question= Tell me about\ - \ AI ): \\n if response.operation == AnswerOperation.START: \\n print( Starting...\ - \ ) \\n elif response.step: \\n print(f Step: {response.step.module} ({response.step.timeit:.2f}s)\ - \ ) \\n \\n elif response.context: \\n print(f Retrieved {len(response.context.chunks)}\ - \ chunks ) \\n for chunk in response.context.chunks: \\n print(f - {chunk.title}:\ - \ {chunk.text[:100]}... ) \\n \\n elif response.answer: \\n print(f \\\\nFinal\ - \ Answer:\\\\n{response.answer} ) \\n \\n elif response.operation == AnswerOperation.DONE:\ - \ \\n print( Complete! ) \\n \\n elif response.operation == AnswerOperation.ERROR:\ - \ \\n print(f Error: {response.exception.detail if response.exception else\ - \ 'Unknown'} ) \\n \\n ``` \\n Standard CLI for Raw Messages \\n For debugging\ - \ or advanced use cases, you can access raw websocket messages programmatically:\ - \ \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent()\ - \ \\n Iterate over all messages \\n for message in agent.interact( \\n question=\ - \ What is RAO? \\n ): \\n # message is an AragAnswer object with all raw data\ - \ \\n print(f Operation: {message.operation} ) \\n print(f Raw message: {message.model_dump_json(indent=2)}\ - \ ) \\n ``` \\n This gives you direct access to all websocket message data\ - \ for debugging or custom processing. \\n Advanced Features \\n Agent Feedback\ - \ Requests \\n Agents can request additional input from users during processing:\ - \ \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction\ - \ import AnswerOperation \\n agent = NucliaAgent() \\n generator = agent.interact(question=\ - \ Help me with X ) \\n for response in generator: \\n if response.operation\ - \ == AnswerOperation.AGENT_REQUEST: \\n # Agent is requesting user input \\\ - n user_input = input(f Agent asks: {response.feedback.question}\\\\n> ) \\\ - n # Send response back \\n generator.send(user_input) \\n elif response.answer:\ - \ \\n print(response.answer) \\n ``` \\n Error Handling \\n ```python \\n\ - \ from nuclia.sdk.agent import NucliaAgent \\n from nuclia.exceptions import\ - \ RaoAPIException \\n agent = NucliaAgent() \\n try: \\n for response in agent.interact(question=\ - \ Hello? ): \\n if response.exception: \\n print(f Agent error: {response.exception.detail}\ - \ ) \\n elif response.answer: \\n print(response.answer) \\n except RaoAPIException\ - \ as e: \\n print(f API error: {e.detail} ) \\n except Exception as e: \\\ - n print(f Unexpected error: {e} ) \\n ``` \\n Passing Custom Headers to MCP\ - 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:{\"score\":0.5773901343345642,\"score_type\":\"VECTOR\",\"order\":10,\"text\"\ - :\" \\n step: Information about the current processing step \\n module: The\ - \ module being executed (e.g., rephrase , basic_ask , remi ) \\n title: Display\ - \ title for the step \\n value: Result of the step \\n reason: Explanation\ - \ for the step \\n timeit: Time taken in seconds \\n \\n input_nuclia_tokens/output_nuclia_tokens:\ - \ Token usage \\n \\n \\n\",\"id\":\"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":19,\"\ - start\":6346,\"end\":6668,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"1ceff32ecafc4f559c9636e9d1e8cd89\":{\"id\"\ - :\"1ceff32ecafc4f559c9636e9d1e8cd89\",\"slug\":\"docs-develop-js-sdk-interfaces-QAOperation-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > QAOperation\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:10.613432\"\ - ,\"modified\":\"2026-06-02T15:39:44.624338\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/QAOperation\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / QAOperation\\\ - n\\n# Interface: QAOperation\\n\\n## Extends\\n\\n- `BaseOperation`\\n\\n##\ - \ Properties\\n\\n### generate\\\\_answers\\\\_prompt?\\n\\n> `optional` **generate\\\ - \\_answers\\\\_prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:102](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L102)\\\ - n\\n***\\n\\n### max\\\\_questions?\\n\\n> `optional` **max\\\\_questions**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:106](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L106)\\\ - n\\n***\\n\\n### question\\\\_generator\\\\_prompt?\\n\\n> `optional` **question\\\ - \\_generator\\\\_prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:103](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L103)\\\ - n\\n***\\n\\n### summary\\\\_prompt?\\n\\n> `optional` **summary\\\\_prompt**:\ - \ `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:104](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L104)\\\ - n\\n***\\n\\n### system\\\\_question\\\\_generator\\\\_prompt?\\n\\n> `optional`\ - \ **system\\\\_question\\\\_generator\\\\_prompt**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:105](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L105)\\\ - n\\n***\\n\\n### triggers?\\n\\n> `optional` **triggers**: [`TaskTrigger`](TaskTrigger.md)[]\\\ - n\\n#### Inherited from\\n\\n`BaseOperation.triggers`\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/task/task.models.ts:59](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/task/task.models.ts#L59)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"b73bf2eeb6305feec55587493b42f69d\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / QAOperation \\n Interface:\ - \ QAOperation \\n Extends \\n \\n BaseOperation \\n \\n Properties \\n generate_answers_prompt?\ - \ \\n \\n optional generate_answers_prompt: string \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/task/task.models.ts:102 \\n \\n max_questions?\ - \ \\n \\n optional max_questions: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:106\ - \ \\n \\n question_generator_prompt? \\n \\n optional question_generator_prompt:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:103\ - \ \\n \\n summary_prompt? \\n \\n optional summary_prompt: string \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:104 \\n \\n\ - \ system_question_generator_prompt? \\n \\n optional system_question_generator_prompt:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:105\ - \ \\n \\n triggers? \\n \\n optional triggers: TaskTrigger[] \\n \\n Inherited\ - \ from \\n BaseOperation.triggers \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:59\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":277,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":150,\"key\":\"\"},{\"start\":150,\"end\":277,\"key\":\"\"}]},{\"start\"\ - :277,\"end\":542,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":280,\"end\":412,\"key\"\ - :\"\"},{\"start\":412,\"end\":542,\"key\":\"\"}]},{\"start\":542,\"end\":816,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":545,\"end\":685,\"key\":\"\"},{\"start\":685,\"\ - end\":816,\"key\":\"\"}]},{\"start\":816,\"end\":959,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":819,\"end\":959,\"key\":\"\"}]}],\"ner\":{\"TaskTrigger\":\"PRODUCT\"\ - },\"entities\":{\"processor\":{\"entities\":[{\"text\":\"TaskTrigger\",\"\ - label\":\"PRODUCT\",\"positions\":[{\"start\":838,\"end\":849}]}]}},\"classifications\"\ - :[],\"last_index\":\"2026-06-02T15:40:39.013422Z\",\"last_understanding\"\ - :\"2026-06-02T15:40:37.940589Z\",\"last_extract\":\"2026-06-02T15:40:37.441350Z\"\ - ,\"last_processing_start\":\"2026-06-02T15:40:37.418586Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{\"PRODUCT/TaskTrigger\":{\"position\"\ - :[{\"start\":838,\"end\":849}],\"entity\":\"TaskTrigger\"}},\"relations\"\ - :[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\"\ - :[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces\ - \ > QAOperation\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > QAOperation\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/277-542\":{\"\ - score\":0.5726113319396973,\"score_type\":\"VECTOR\",\"order\":12,\"text\"\ - :\" \\n optional max_questions: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:106\ - \ \\n \\n question_generator_prompt? \\n \\n optional question_generator_prompt:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/task/task.models.ts:103\ - \ \\n \\n summary_prompt? \\n\",\"id\":\"1ceff32ecafc4f559c9636e9d1e8cd89/t/page/277-542\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\"\ - :277,\"end\":542,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\"\ - ,\"title\":\"docs > rag > advanced > widget > features\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\"\ - ,\"modified\":\"2025-10-27T14:35:08.325400\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. It must not be used in production. \\n preferMarkdown:\ - \ require the generative answer to be formatted in Markdown. \\n openNewTab:\ - \ open the link in a new tab when clicking on the result. \\n orFilterLogic:\ - \ use the OR logic for filters instead of the default AND logic. \\n noChatHistory:\ - \ the previous questions and answers in the chat mode will not be passed as\ - \ context when generating a new answer. \\n showHidden: display hidden resources\ - \ in the search results. \\n showAttachedImages: display images attached to\ - \ the matching paragraphs in the search results. \\n \\n Other parameters\ - \ \\n \\n backend: the URL of the backend to use. Useful if you use your own\ - \ proxy to access the Agentic RAG API. \\n zone: the zone to use. \\n knowledgebox:\ - \ the Knowledge Box id. \\n placeholder: the text displayed in the search\ - \ bar when it is empty. \\n lang: the language of the widget. Currently supported:\ - \ ca, fr, en, es. Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. It must use {context} and {question} variables.\ - \ \\n system_prompt: the system prompt to use for the generative model. \\\ - n rephrase_prompt: the prompt to use when optimizing the user query to get\ - \ the best search results. \\n generativemodel: the generative model to use\ - \ for the answer generation. \\n rag_strategies: the RAG strategies to apply\ - \ to the retrieved paragraphs. \\n rag_images_strategies: the RAG strategies\ - \ to apply to the retrieved images. \\n not_enough_data_message: the message\ - \ to display when there is not enough data to generate an answer. \\n ask_to_resource:\ - \ the resource ID to use as context for the generative model. \\n max_tokens:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question). \\n max_output_tokens:\ - \ the maximum number of tokens to generate. \\n max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n vectorset: the embedding model to use for the semantic search.\ - \ \\n chat_placeholder: the placeholder to display in the chat input. \\n\ - \ audit_metadata: custom metatada to add in API calls for auditing purposes.\ - \ \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\"\ - :{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":213,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":213,\"key\":\"\"}]},{\"start\":213,\"end\":467,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":214,\"end\":402,\"key\":\"\"},{\"start\":402,\"end\":467,\"key\"\ - 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,\"Markdown\":\"ORG\",\"the Knowledge Box\":\"ORG\",\"NucliaDB\":\"ORG\",\"\ - CSS\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"\ - Agentic RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"\ - start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\"\ - :[{\"start\":3046,\"end\":3054}]},{\"text\":\"the Knowledge Box\",\"label\"\ - :\"ORG\",\"positions\":[{\"start\":4031,\"end\":4048}]},{\"text\":\"NucliaDB\"\ - ,\"label\":\"ORG\",\"positions\":[{\"start\":4100,\"end\":4108}]},{\"text\"\ - :\"CSS\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":4476,\"end\":4479}]},{\"\ - text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"\ - start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2025-10-27T14:35:15.052135Z\"\ - ,\"last_understanding\":\"2025-10-27T14:35:14.504231Z\",\"last_extract\":\"\ - 2025-10-27T14:35:09.724520Z\",\"last_processing_start\":\"2025-10-27T14:35:09.621790Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic RAG\"\ - :{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"\ - entity\":\"Agentic RAG\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"\ - end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\"\ - :5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"\ - ORG/the Knowledge Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\"\ - :\"the Knowledge Box\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"\ - end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\"\ - :3046,\"end\":3054}],\"entity\":\"Markdown\"}},\"relations\":[],\"mime_type\"\ - :\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\"\ - :{\"text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.7153109312057495,\"score_type\":\"VECTOR\",\"order\":0,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"\ - 9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\":{\"score\":0.5605948567390442,\"\ - score_type\":\"VECTOR\",\"order\":18,\"text\":\" max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":21,\"\ - start\":5412,\"end\":5678,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\"\ - :\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ModelConfig\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-06-02T15:43:13.632521\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\\ - n\\n# Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\\ - n\\n> `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\\ - n\\n***\\n\\n### driver?\\n\\n> `optional` **driver**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\\ - n\\n***\\n\\n### input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:586](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L586)\\\ - n\\n***\\n\\n### max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\\ - n\\n***\\n\\n### output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\\ - n\\n#### default\\\\_max?\\n\\n> `optional` **default\\\\_max**: `number`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\\ - n\\n***\\n\\n### prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"34d1ff190433791d9bec5987606d5826\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface:\ - \ ModelConfig \\n Properties \\n assume_role? \\n \\n optional assume_role:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n driver? \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:586 \\n \\n max_images? \\n \\\ - n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587\ - \ \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"\ - key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\"\ - :480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"\ - end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\ - \"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\"\ - :806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-06-02T15:44:43.530490Z\",\"last_understanding\":\"2026-06-02T15:44:18.042002Z\"\ - ,\"last_extract\":\"2026-06-02T15:44:17.294916Z\",\"last_processing_start\"\ - :\"2026-06-02T15:44:17.270585Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - :{\"score\":0.5890693068504333,\"score_type\":\"VECTOR\",\"order\":5,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\"\ - :480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.5841667056083679,\"score_type\":\"VECTOR\",\"order\":7,\"text\"\ - :\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:586 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\"\ - ,\"modified\":\"2026-06-02T15:41:37.990484\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\\ - n\\n# Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**:\ - \ `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\\ - n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\\ - n\\n***\\n\\n### tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d64df098b6c544fe4fcdfd0b8d22a271\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageStructure \\\ - n Interface: PageStructure \\n Properties \\n page \\n \\n page: object \\\ - n \\n height \\n \\n height: number \\n \\n width \\n \\n width: number \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238\ - \ \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:47.288115Z\"\ - ,\"last_understanding\":\"2026-06-02T15:43:41.818455Z\",\"last_extract\":\"\ - 2026-06-02T15:43:41.624860Z\",\"last_processing_start\":\"2026-06-02T15:43:41.606570Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > 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,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\\ - n\\n# Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\ - \\_USED\\n\\n> **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n***\\n\\n### BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"\ - bytes_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n***\\n\\n### CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"\ - chars_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n***\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\\ - _PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L200)\\\ - n\\n***\\n\\n### MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\ - \\_PROCESSED**: `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L199)\\\ - n\\n***\\n\\n### NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L207)\\\ - n\\n***\\n\\n### PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"\ - pages_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L201)\\\ - n\\n***\\n\\n### PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**:\ - \ `\\\"paragraphs_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L202)\\\ - n\\n***\\n\\n### PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\ - \\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### 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in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L195)\\\ - n\\n***\\n\\n### SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**:\ - \ `\\\"suggestions_performed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L205)\\\ - n\\n***\\n\\n### TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L203)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"43e50d28c841e949cb601b09d908eae2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n 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MEDIA_SECONDS_PROCESSED: media_seconds_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\\ - n NUCLIA_TOKENS \\n \\n NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:207 \\n \\n PAGES_PROCESSED\ - \ \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n PARAGRAPHS_PROCESSED \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\\ - n PRE_PROCESSING_TIME \\n \\n PRE_PROCESSING_TIME: pre_processing_time \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 \\n \\n RESOURCES_PROCESSED\ - \ \\n \\n RESOURCES_PROCESSED: resources_processed \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED \\\ - n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204\ - \ 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- headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '10271' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 03 Jun 2026 07:40:59 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '141' - x-nuclia-trace-id: - - 019c1af795ad76d5ce74d15bb87629ff - status: - code: 200 - message: OK -- request: - body: '{"question": "Explica c\u00f3mo usar el par\u00e1metro `max_tokens` para - controlar la longitud de las respuestas en espa\u00f1ol, y proporciona un enlace - a la documentaci\u00f3n oficial.", "user_id": "arag-ask-rerank", "context": - {"dd41482018924facb5dbb87a7d53f122/t/page/1900-2653": " retries = 0 \n while - retries < max_retries: \n response = requests.get(url, headers=headers) \n if - response.status_code == 200: \n return response.json() \n elif response.status_code - == 429: \n wait_time = 2 retries # Exponential backoff: 2^retries \n print(f - Rate limit exceeded. Retrying in {wait_time} seconds... ) \n time.sleep(wait_time) - \n retries += 1 \n else: \n response.raise_for_status() \n raise Exception( - Max retries exceeded ) \n Example usage \n url = https://your-endpoint \n headers - = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_exponential_backoff(url, - headers) \n print(data) \n ``` \n Example 2: Ingestion back pressure limits - \n Here''s an example of how to use the try_after key from the response to manage - rate limits: \n\n\n\nLink: https://docs.nuclia.dev/docs/ingestion/how-to/rate-limiting\n", - "dd41482018924facb5dbb87a7d53f122/t/page/3310-3757": " ) \n time.sleep(wait_time) - \n retries += 1 \n else: \n response.raise_for_status() \n raise Exception( - Max retries exceeded ) \n Example usage \n url = https://your-endpoint \n headers - = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, - headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate - limits effectively, ensuring that your application respects the limits and retries - appropriately.\n\n\nLink: https://docs.nuclia.dev/docs/ingestion/how-to/rate-limiting\n", - "328ed5b87692439a881303c7c1d4eefc/t/page/0-333": "@nuclia/core / Exports / Ask - / AskTokens \n Interface: AskTokens \n Ask.AskTokens \n Table of contents \n - Properties \n \n input \n output \n \n Properties \n input \n \u2022 input: - number \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:107 \n - \n output \n \u2022 output: number \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:108\n\n\nLink: - https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Ask.AskTokens\n", "42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696": - " \n optional max_paragraph: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 - \n \n name? \n \n optional name: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": " \n \n Maximum supported - input tokens: \n Description: The maximum number of tokens that the model can - accept as input. Be mindful that this takes into account the tokens used in - the prompt, query and context. Also take note that some models may provide their - context window as the total between input and output tokens, while others may - provide it as the input tokens only. \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: The maximum - number of tokens that the model can generate as output. Again, we should keep - in mind that this value summed to the Maximum supported input tokens should - not exceed the total context size supported by the model. \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804": " Example: For Phi 4 Reasoning - Plus, the total context size is 32768 tokens, as we want to leave room for the - output, we will set the maximum supported input tokens as 32768 - 1024 = 31744. - \n \n \n Maximum supported output tokens: \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / PageToken \n Interface: PageToken \n Properties \n height - \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \n \n text \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": " ::: \n :::warning \n - Enabling reasoning can use additional tokens, which may increase your usage - costs. \n You may need to increase max_tokens to give the LLM enough room to - reason and generate an answer. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n ```python - \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, - Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My - question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "0c8acfaf951e48a6a31b52234305fa57/t/page/0-485": "@nuclia/core / Exports / Ask - / MetadataAskResponseItem \n Interface: MetadataAskResponseItem \n Ask.MetadataAskResponseItem - \n Table of contents \n Properties \n \n timings \n tokens \n type \n \n Properties - \n timings \n \u2022 timings: AskTimings \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:71 - \n \n tokens \n \u2022 tokens: AskTokens \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:70 - \n \n type \n \u2022 type: metadata \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:69\n\n\nLink: - https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Ask.MetadataAskResponseItem\n", - "828f2dc506434dad90774c5d9e055100/t/page/244-667": " Properties \n next_page - \n \u2022 next_page: boolean \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:213 - \n \n page_number \n \u2022 page_number: number \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:211 - \n \n page_size \n \u2022 page_size: number \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:212 - \n \n total \n \u2022 total: number \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:210\n\n\nLink: - https://docs.nuclia.dev/docs/develop/js-sdk/interfaces/Search.Pagination\n", - "44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668": " \n step: Information - about the current processing step \n module: The module being executed (e.g., - rephrase , basic_ask , remi ) \n title: Display title for the step \n value: - Result of the step \n reason: Explanation for the step \n timeit: Time taken - in seconds \n \n input_nuclia_tokens/output_nuclia_tokens: Token usage \n \n - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\n", - "1ceff32ecafc4f559c9636e9d1e8cd89/t/page/277-542": " \n optional max_questions: - number \n \n Defined in \n libs/sdk-core/src/lib/db/task/task.models.ts:106 - \n \n question_generator_prompt? \n \n optional question_generator_prompt: string - \n \n Defined in \n libs/sdk-core/src/lib/db/task/task.models.ts:103 \n \n summary_prompt? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/QAOperation\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678": " max_paragraphs: the maximum - number of paragraphs to pass in the context to the generative model (default: - 20). \n query_prepend: the hard-coded text to prepend to the user query. \n - json_schema: the JSON schema to use to get a JSON answer from the generative - model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: - string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 \n \n - output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": " \n optional driver: string - \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:589 \n \n input_tokens - \n \n input_tokens: object \n \n max \n \n max: number \n \n min? \n \n optional - min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:586 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "4194605cdfbd414f8fa762630d555bc1/t/page/0-358": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / PageStructure \n Interface: PageStructure \n Properties \n - page \n \n page: object \n \n height \n \n height: number \n \n width \n \n - width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 - \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\n", - "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": "@nuclia/core \u2022 Docs - \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration Members - \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 - \n \n BYTES_PROCESSED \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:197 \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: - chars_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n MEDIA_FILES_PROCESSED \n \n MEDIA_FILES_PROCESSED: media_files_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n \n MEDIA_SECONDS_PROCESSED - \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 - \n \n NUCLIA_TOKENS \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:207 \n \n PAGES_PROCESSED \n \n - PAGES_PROCESSED: pages_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 - \n \n PARAGRAPHS_PROCESSED \n \n PARAGRAPHS_PROCESSED: paragraphs_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 \n \n PRE_PROCESSING_TIME - \n \n PRE_PROCESSING_TIME: pre_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 - \n \n RESOURCES_PROCESSED \n \n RESOURCES_PROCESSED: resources_processed \n - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n SEARCHES_PERFORMED - \n \n SEARCHES_PERFORMED: searches_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n SLOW_PROCESSING_TIME \n \n SLOW_PROCESSING_TIME: slow_processing_time - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 \n \n SUGGESTIONS_PERFORMED - \n \n SUGGESTIONS_PERFORMED: suggestions_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 - 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Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. 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They are grouped by region and split into two\ - \ categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" ::: \\n Europe (GCP) \\n | Inbound |\ - \ Outbound | \\n | ------------------ | --------------------- | \\n | 34.111.1.187/32\ - \ | 35.204.65.155/32 | \\n | 136.68.112.29/32 | 34.91.38.151/32 | \\n | |\ - \ 35.204.139.129/32 | \\n | | 35.204.108.221/32 | \\n Australia (AWS) \\n\ - \ | Inbound | Outbound | \\n | ------------------- | ---------------------\ - \ | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n | 3.25.20.9/32 | 3.24.144.83/32\ - \ | \\n | 52.64.202.159/32 | 3.24.81.206/32 | \\n Europe (AWS) \\n | Inbound\ - \ | Outbound | \\n | ------------------- | --------------------- | \\n | 63.182.151.234/32\ - \ | 63.179.23.139/32 | \\n | 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32\ - \ | 3.66.123.24/32 | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | -------------------\ - \ | --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\\ - n | 16.164.50.185/32 | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32\ - \ | \\n United States (AWS) \\n | Inbound | Outbound | \\n | --------------------\ - \ | -------------------- | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\\ - n | 18.119.145.185/32 | 18.225.104.91/32 | \\n | 3.21.239.244/32 | 16.59.115.61/32\ - \ | \\n Private connectivity (AWS PrivateLink) \\n For AWS-hosted regions,\ - \ it is possible to expose access privately through an AWS endpoint service\ - \ (AWS PrivateLink) instead of over the public internet, allowing connections\ - \ to be established from your VPC without traversing public IP addresses.\ - \ This option is not available through self-service configuration. Please\ - \ reach out to your account manager for details.\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"\ - 007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n Inbound \u2014 the IP addresses that Progress Agentic RAG\ - \ receives requests on. Allowlist these if you restrict outbound traffic from\ - \ your infrastructure and need to reach our services (e.g., calling our APIs).\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"a\",\"field\":\"title\",\"text\":\"docs > management > security > 5 public\ - \ ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - a\",\"field\":\"title\",\"text\":\"docs > agentic > how to > agentic retrieval\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - t\",\"field\":\"page\",\"text\":\" \\n id: agentic-retrieval \\n title: Agentic\ - \ retrieval with the Smart agent \\n \\n Agentic retrieval with the Smart\ - \ agent \\n The Smart agent is a powerful tool that enhances the capabilities\ - \ of traditional retrieval-augmented generation (RAG) systems. Regular RAG's\ - \ fixed search-then-generate process is limiting for complex questions that\ - \ need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"\ - start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n Different models\ - \ can be chosen for different tasks: \\n \\n Context validation happens when\ - \ the Prune context option is enabled (recommended), we recommend using a\ - \ fast model - planning or the execution model (depending on the planning\ - \ mode you have selected): we recommend using a more powerful model. \\n\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" \\n Note: the rephrasing model is only\ - \ used in more complex workflow, it can be ignored in the present case. \\\ - n Finally, add a Summarize agent in the Generation step to generate a final\ - \ answer from the retrieved information. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"\ - start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" The Smart Agent plans the answer: picks the most appropriate\ - \ sources, splits the question into sub-questions, evaluates relevancy, and\ - \ iterates autonomously until the information is sufficient. \\n Basic usage\ - \ \\n To set up a Smart Agent, declare all the sources you want to use (Knowledge\ - \ Boxes, Perplexity, etc.) in the Sources section of the left menu. Then,\ - \ create a new workflow in the Workflows section and add a Smart Agent in\ - \ the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Chat mode \\n If you\ - \ want to use the Smart Agent in a chat interface, you need the following:\ - \ \\n \\n Enable the Session history option in the Smart Agent configuration.\ - \ This will allow the Smart Agent to take into account the previous conversation\ - \ when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"\ - end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Then connect your sources\ - \ as Registered Agents to the Smart Agent. It is very important that you provide\ - \ an extensive description of each registered agent, so that the Smart Agent\ - \ can understand what each source is about and when to use it. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n reactive: The Smart Agent will decide what\ - \ to do first, and will plan the next steps based on the information it retrieves.\ - \ It is expected to be faster. \\n plan_execute: The Smart Agent will plan\ - \ all the steps in advance, and will execute them. It will be slower but more\ - \ accurate when processing complex questions. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" Also select the proper function for each registered agent, depending\ - \ on the type of source (MCP agents will not need one, the functions are provided\ - \ dynamically by the MCP server). \\n In the Smart Agent configuration, you\ - \ can select the planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"\ - end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" In the Summarize Agent,\ - \ enable the Conversational mode. This will allow the Summarize Agent to generate\ - \ a final answer that will not repeat the information already provided in\ - \ the previous conversation, and will be more natural for a chat interface.\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" Custom frontend \\n You can also create\ - \ a custom frontend for your Retrieval Agent. This allows you to have full\ - \ control over the user interface and user experience. \\n You can directly\ - \ implement the API calls to your Retrieval Agent in your frontend code (see\ - \ the Websocket API section for more details), or you can use the JavaScript\ - \ SDK provided by Agentic to simplify the integration. \\n\",\"labels\":[],\"\ - start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"\ - index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"\ - rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"text\":\" MCP \\n If you want to integrate your Retrieval Agent\ - \ with a broader AI-based system (for example Claude Cowork or Copilot), it\ - \ is reachable throught MCP. \\n The MCP endpoint is visible on the agent\ - \ home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n id: deploy \\n title:\ - \ Deploy \\n \\n Deploy \\n Your Agentic Retrieval Agent can be deployed in\ - \ different ways, depending on your needs and the environment you are working\ - \ in. \\n Ready-to-use widget \\n The easiest way to deploy your Retrieval\ - \ Agent is to use the ready-to-use widget. You can embed it in your website\ - \ or application, and it will provide a chat user interface for interacting\ - \ with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Go to the Widgets section\ - \ in the left menu, and click on Create widget to create a new widget. You\ - \ can customize its appearance, and then copy the generated code snippet to\ - \ embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"\ - start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\"\ - :5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"\ - next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\"\ - ,\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\"\ - :\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\",\"field_type\":\"a\",\"field\"\ - :\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"f13642b2862f4a1ea2144c9eb2abb2a3\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"ed6fda7434aa43a3bd5138b95e7fe081\"\ - ,\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\"\ - :\"kb-resources\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\"\ - ,\"field_type\":\"t\",\"field\":\"kb-search\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\"\ - :\"kb-rag-lab\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\"\ - ,\"field_type\":\"t\",\"field\":\"account-arag\",\"labels\":[]},{\"score\"\ - :0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"\ - field\":\"kb-kv-schemas\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\"\ - ,\"field_type\":\"t\",\"field\":\"user-profile\",\"labels\":[]},{\"score\"\ - :0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"\ - field\":\"kb-users\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\"\ - ,\"field_type\":\"t\",\"field\":\"kb-widgets\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"a572ce0c7eb949e4a17babae21a03a4a\",\"field_type\":\"t\",\"field\"\ - :\"kb-remi-analytics\",\"labels\":[]},{\"score\":0.0,\"rid\":\"a572ce0c7eb949e4a17babae21a03a4a\"\ - ,\"field_type\":\"t\",\"field\":\"agent-users\",\"labels\":[]}],\"facets\"\ - :{},\"query\":\"\",\"total\":2013,\"page_number\":0,\"page_size\":20,\"next_page\"\ - :true,\"min_score\":0.0},\"shards\":[\"306cbabb-72a5-417c-827f-7874e205c858\"\ - ]}" - headers: - Alt-Svc: - - h3=":443"; 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ma=2592000 - Content-Length: - - '115' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:41 GMT - nuclia-learning-model: - - multilingual - via: - - 1.1 google - x-envoy-upstream-service-time: - - '25' - x-nuclia-trace-id: - - 2ef545811530a8075c864178ac72d01d - status: - code: 200 - message: OK -- request: - body: '{"user_id": "arag-ask", "texts": ["Explica c\u00f3mo utilizar el par\u00e1metro - `max_tokens` en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n - oficial."]}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '150' - Host: - - europe-1.nuclia.cloud - User-Agent: - - nucliadb-sdk/6.13.1.post6414 - content-type: - - application/json - x-stf-serviceaccount: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/predict/run-agents-text - response: - body: - string: '{"results":[{"input_nuclia_tokens":0.0,"output_nuclia_tokens":0.0,"time":0.0001270771026611328,"payloads":[]}]}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '111' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:41 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '237' - x-nuclia-trace-id: - - 7405e0c1209d4e9a896cf914bcedcafd - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "arag-ask", "system": null, - "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\n\nInformation about the KB:\n\n# - nuclia-docs\n\ndescription: Documentation of the Nuclia API, recipies, reference - \n\nAnd given the question: Explica c\u00f3mo utilizar el par\u00e1metro `max_tokens` - en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n oficial.\n\n## - labels: {''pmm'': [''Videos'', ''Partner Content'', ''Softcat'', ''Sales Enablement - Assets'', ''KO 26'', ''Data Sheets'', ''Progress Agentic RAG Training Materials - 2026'']}\n\n## Facets:\n\n## Content Types\nThe following content types are - available in the KB:\n\n- /n/i: 986\n- application/json: 1\n- text/markdown: - 985\nThe following languages are available in the KB:\n\n- ca: 3\n- cy: 1\n- - da: 1\n- en: 967\n- eo: 4\n- la: 7\n- nb: 1\n- tl: 1\n- yo: 1\n\n\n# Important - rules to follow\n\n\nprompt=''Be polite''\n"}, "citations": false, "citation_threshold": - null, "generative_model": "gemini-2.5-flash", "max_tokens": 8192, "query_context_images": - {}, "prefer_markdown": null, "json_schema": {"title": "ask_configuration", "description": - "Configuration extracted from reasoning engine", "parameters": {"type": "object", - "properties": {"link": {"type": "boolean", "description": "The user wants link - reference to the answer?"}, "knowledge_scan": {"type": "string", "description": - "If the query requires a knowledge aggregation or scan search to answer define - the entities, labels and relations to query in the KB. Example queries: How - many ..."}, "semantic_query": {"type": "string", "description": "Rephrase this - question so its better for semantic retrieval, and keep the rephrased question - in the same language as the original. Please define ONLY the question without - any explanation. JUST A SENTENCE. DO NOT ADD ANY EXTRA NOTES AT THE END, JUST - ONE SENTENCE"}, "lexical_query": {"type": "string", "description": "Rephrase - this question so its better for lexical retrieval, translate the lexical rephrased - question to cy. Please define ONLY the question without any explanation. JUST - A SENTENCE. DO NOT ADD ANY EXTRA NOTES AT THE END, JUST ONE SENTENCE"}, "visual": - {"type": "boolean", "description": "Is required an analysis of an image to answer - this question, answer with false or true"}, "keywords_filter": {"type": "array", - "items": {"type": "string"}, "description": "Extract if any the keywords that - should appear on the retrieved results and its a must match, make sure that - are keywords that are not common words, and that are not the same as the question - or answer. Please define ONLY the keywords without any explanation. Only one - or two words maximum. JUST A LIST OF KEYWORDS. DO NOT ADD ANY EXTRA NOTES AT - THE END, JUST A LIST OF KEYWORDS"}, "reason": {"type": "string"}, "entities": - {"type": "array", "description": "Entities related to the user question to query - in the KB", "items": {"type": "string"}}, "relations": {"type": "array", "description": - "Relations related to the user question to query in the KB", "items": {"type": - "string"}}, "pre_queries": {"type": "array", "items": {"type": "string"}, "description": - "Pre queries to run before the main query to gather more information"}}, "required": - ["semantic_query", "lexical_query", "visual", "keywords_filter", "reason", "pre_queries"]}}, - "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "required"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '3380' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-client-ident: - - default - x-message: - - 2c0bd763788348ec99dcf3d51774421a - x-origin: - - RAO - x-session: - - default_default_session - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"semantic_query\":\"C\xF3\ - mo se usa el par\xE1metro max_tokens y su documentaci\xF3n oficial.\",\"lexical_query\"\ - :\"Sut mae'r paramedr max_tokens yn cael ei ddefnyddio a'i ddogfennaeth swyddogol.\"\ - ,\"visual\":false,\"keywords_filter\":[\"max_tokens\",\"par\xE1metro\"],\"\ - reason\":\"The user is asking how to use the 'max_tokens' parameter and for\ - \ a link to its official documentation in Spanish. I should provide a semantic\ - \ query and a lexical query for retrieval, and extract relevant keywords.\ - \ I also need to specify that no visual analysis is required and pre-queries\ - \ are not needed as the information should be directly retrievable.\",\"pre_queries\"\ - :[]}}}\n{\"chunk\":{\"type\":\"status\",\"code\":\"0\"}}\n{\"chunk\":{\"type\"\ - :\"meta\",\"input_tokens\":18,\"output_tokens\":127,\"timings\":{\"generative\"\ - :1.656766301006428},\"input_nuclia_tokens\":0.018,\"output_nuclia_tokens\"\ - :0.127}}\n{\"chunk\":{\"normalized_tokens\":{\"input\":0.03528,\"output\"\ - :0.0905,\"image\":0.0},\"customer_key_tokens\":{\"input\":0.0,\"output\":0.0,\"\ - image\":0.0},\"type\":\"consumption\"}}\n" - headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 15 Jul 2026 07:05:42 GMT - nuclia-learning-id: - - cfa9098327a44720a1e13e72dd7cb133 - 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n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\\ - n\\n***\\n\\n### type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\"\ - ,\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"\ - 2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > namespaces >\ - \ Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - :{\"score\":0.5780298113822937,\"score_type\":\"VECTOR\",\"order\":14,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\"\ - ,\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs > rag\ - \ > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. 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Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. 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:\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\"\ - :{\"text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.6769846677780151,\"score_type\":\"VECTOR\",\"order\":1,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 1b2a9e67b9f14a0cb81efaa05b8793b8\":{\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-AugmentedField-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > AugmentedField\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T10:00:05.937520\"\ - ,\"modified\":\"2026-07-14T12:49:32.163110\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AugmentedField\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / AugmentedField\\\ - n\\n# Interface: AugmentedField\\n\\n## Properties\\n\\n### applied\\\\_data\\\ - \\_augmentation\\n\\n> **applied\\\\_data\\\\_augmentation**: [`AppliedDataAugmentation`](AppliedDataAugmentation.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:571](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L571)\\\ - n\\n***\\n\\n### input\\\\_nuclia\\\\_tokens\\n\\n> **input\\\\_nuclia\\\\\ - _tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:572](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L572)\\\ - n\\n***\\n\\n### metadata\\n\\n> **metadata**: [`FieldMetadata`](FieldMetadata.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:570](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L570)\\\ - n\\n***\\n\\n### output\\\\_nuclia\\\\_tokens\\n\\n> **output\\\\_nuclia\\\ - \\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:573](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L573)\\\ - n\\n***\\n\\n### time\\n\\n> **time**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:574](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L574)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"1e8e95b886439059ae4ec717c9bcf283\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\\ - n Interface: AugmentedField \\n Properties \\n applied_data_augmentation \\\ - n \\n applied_data_augmentation: AppliedDataAugmentation \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 \\n \\n input_nuclia_tokens\ - \ \\n \\n input_nuclia_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572\ - \ \\n \\n metadata \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/resource/resource.models.ts:570 \\n \\n output_nuclia_tokens\ - \ \\n \\n output_nuclia_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573\ - \ \\n \\n time \\n \\n time: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:574\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":641,\"key\":\"\"}]},{\"start\":641,\"end\":729,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":642,\"end\":729,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:33.585675Z\"\ - ,\"last_understanding\":\"2026-07-14T12:49:33.340252Z\",\"last_extract\":\"\ - 2026-07-14T12:49:32.941746Z\",\"last_processing_start\":\"2026-07-14T12:49:32.917812Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ AugmentedField\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > AugmentedField\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\"\ - :{\"score\":0.572655200958252,\"score_type\":\"VECTOR\",\"order\":15,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface:\ - \ AugmentedField \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation:\ - \ AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571\ - \ \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572\ - \ \\n \\n metadata \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/resource/resource.models.ts:570 \\n \\n output_nuclia_tokens\ - \ \\n \\n output_nuclia_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573\ - \ \\n \\n time \\n \\n\",\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - \ : My search , filters : [ /icon/application/pdf , /classification.labels/region/Asia\ - \ ]}) \\n search.ask(query={ query : My search , top_k : 5}) \\n Graph queries\ - \ \\n The Python SDK allows graph queries supported by the /graph endpoint.\ - \ Although \\n a bit cumbersome, the knowledge graph can be queried as in\ - \ this example: \\n \\n CLI: \\n \\n bash \\n nuclia kb search graph --query='{\ - \ query : { prop : path , source : { value : Rust }, destination : { value\ - \ : Python }}}' \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\\ - n search = sdk.NucliaSearch() \\n search.graph( \\n query={ \\n query : {\ - \ \\n prop : path , \\n source : { value : Rust }, \\n destination : { value\ - \ : Python } \\n } \\n } \\n ) \\n For more information about graph querying,\ - \ please refer to Nuclia's graph \\n doc or the API reference\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":305,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - 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:\"OpenAI\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":2279,\"end\":2285}]},{\"\ - text\":\"Europe\",\"label\":\"LOC\",\"positions\":[{\"start\":3677,\"end\"\ - :3683}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:25.563584Z\"\ - ,\"last_understanding\":\"2026-06-09T08:08:25.117490Z\",\"last_extract\":\"\ - 2026-06-09T08:08:22.113416Z\",\"last_processing_start\":\"2026-06-09T08:08:22.055332Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/Nuclia\":{\"position\"\ - :[{\"start\":955,\"end\":961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"\ - end\":5507}],\"entity\":\"Nuclia\"},\"ORG/JSON\":{\"position\":[{\"start\"\ - :760,\"end\":764}],\"entity\":\"JSON\"},\"LOC/Asia\":{\"position\":[{\"start\"\ - :531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"end\":3720},{\"\ - start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}],\"entity\":\"Asia\"\ - },\"PRODUCT/OpenAI\":{\"position\":[{\"start\":2279,\"end\":2285}],\"entity\"\ - :\"OpenAI\"},\"LOC/Europe\":{\"position\":[{\"start\":3677,\"end\":3683}],\"\ - entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"\ - split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"\ - value\":\"docs > develop > python sdk > 05 search\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":0.5631030201911926,\"score_type\":\"VECTOR\",\"order\":19,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\"\ - :\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ReasoningConfig\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\"\ - ,\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\\ - n\\n# Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\\ - n\\n> `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\\ - n\\n***\\n\\n### effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\\ - n Interface: ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional\ - \ budget_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n \\n optional effort: NumericReasoningEffort \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"\ - key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\"\ - :329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\"\ - ,\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\"\ - :\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - :{\"score\":0.5699529051780701,\"score_type\":\"VECTOR\",\"order\":16,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface:\ - \ ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\"\ - ,\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs\ - \ > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\"\ - ,\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\\ - n\\n# Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\ - \\_USED\\n\\n> **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n***\\n\\n### BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"\ - bytes_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n***\\n\\n### CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"\ - chars_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n***\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\\ - _PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\\ - n\\n***\\n\\n### MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\ - \\_PROCESSED**: `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\\ - n\\n***\\n\\n### NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\\ - n\\n***\\n\\n### PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"\ - pages_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\\ - n\\n***\\n\\n### PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**:\ - \ `\\\"paragraphs_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\\ - n\\n***\\n\\n### PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\ - \\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\\ - n\\n***\\n\\n### RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**:\ - \ `\\\"resources_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\\ - n\\n***\\n\\n### SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**:\ - \ `\\\"searches_performed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\\ - n\\n***\\n\\n### SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\ - \\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\\ - n\\n***\\n\\n### SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**:\ - \ `\\\"suggestions_performed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\\ - n\\n***\\n\\n### TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration:\ - \ UsageType \\n Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED:\ - \ ai_tokens_used \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206\ - \ \\n \\n BYTES_PROCESSED \\n \\n BYTES_PROCESSED: bytes_processed \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 \\n \\n CHARS_PROCESSED\ - \ \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n MEDIA_FILES_PROCESSED \\n \\n MEDIA_FILES_PROCESSED: media_files_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\\ - n MEDIA_SECONDS_PROCESSED \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\\ - n NUCLIA_TOKENS \\n \\n NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:207 \\n \\n PAGES_PROCESSED\ - \ \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n PARAGRAPHS_PROCESSED \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\\ - n PRE_PROCESSING_TIME \\n \\n PRE_PROCESSING_TIME: pre_processing_time \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 \\n \\n RESOURCES_PROCESSED\ - \ \\n \\n RESOURCES_PROCESSED: resources_processed \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED \\\ - n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\\ - n SUGGESTIONS_PERFORMED \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\\ - n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: train_seconds \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\"\ - :[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"\ - end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\"\ - ,\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\"\ - :\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > enumerations > UsageType\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\"\ - :{\"score\":0.6148133873939514,\"score_type\":\"VECTOR\",\"order\":6,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration:\ - \ UsageType \\n Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED:\ - \ ai_tokens_used \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206\ - \ \\n \\n BYTES_PROCESSED \\n \\n BYTES_PROCESSED: bytes_processed \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 \\n \\n CHARS_PROCESSED\ - \ \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n MEDIA_FILES_PROCESSED \\n \\n MEDIA_FILES_PROCESSED: media_files_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\\ - n MEDIA_SECONDS_PROCESSED \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\\ - n NUCLIA_TOKENS \\n \\n NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:207 \\n \\n PAGES_PROCESSED\ - \ \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n PARAGRAPHS_PROCESSED \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\\ - n PRE_PROCESSING_TIME \\n \\n PRE_PROCESSING_TIME: pre_processing_time \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 \\n \\n RESOURCES_PROCESSED\ - \ \\n \\n RESOURCES_PROCESSED: resources_processed \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED \\\ - n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\\ - n SUGGESTIONS_PERFORMED \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\\ - n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: train_seconds \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"666b3a9d01f74323b6ca6c9939834bec\":{\"id\"\ - :\"666b3a9d01f74323b6ca6c9939834bec\",\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\"\ - ,\"title\":\"docs > develop > js sdk > enums > StatsType\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"sv\",\"en\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.005040\"\ - ,\"modified\":\"2026-06-09T08:13:37.365717\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/StatsType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / StatsType\\n\\n# Enumeration: StatsType\\n\\\ - n**`Deprecated`**\\n\\n## Table of contents\\n\\n### Enumeration Members\\\ - n\\n- [AI\\\\_TOKENS\\\\_USED](StatsType.md#ai_tokens_used)\\n- [BYTES](StatsType.md#bytes)\\\ - n- [CHARS](StatsType.md#chars)\\n- [DOCS\\\\_NO\\\\_MEDIA](StatsType.md#docs_no_media)\\\ - n- [MEDIA\\\\_SECONDS](StatsType.md#media_seconds)\\n- [PAGES](StatsType.md#pages)\\\ - n- [PROCESSING\\\\_TIME](StatsType.md#processing_time)\\n- [RESOURCES](StatsType.md#resources)\\\ - n- [SEARCHES](StatsType.md#searches)\\n- [SUGGESTIONS](StatsType.md#suggestions)\\\ - n- [TRAIN\\\\_SECONDS](StatsType.md#train_seconds)\\n\\n## Enumeration Members\\\ - n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\ - \"ai_tokens_used\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n___\\n\\n### BYTES\\n\\n\u2022 **BYTES** = ``\\\"bytes\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L200)\\\ - n\\n___\\n\\n### CHARS\\n\\n\u2022 **CHARS** = ``\\\"chars\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n___\\n\\n### DOCS\\\\_NO\\\\_MEDIA\\n\\n\u2022 **DOCS\\\\_NO\\\\_MEDIA**\ - \ = ``\\\"docs_no_media\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L205)\\\ - n\\n___\\n\\n### MEDIA\\\\_SECONDS\\n\\n\u2022 **MEDIA\\\\_SECONDS** = ``\\\ - \"media_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L199)\\\ - n\\n___\\n\\n### PAGES\\n\\n\u2022 **PAGES** = ``\\\"pages\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L202)\\\ - n\\n___\\n\\n### PROCESSING\\\\_TIME\\n\\n\u2022 **PROCESSING\\\\_TIME** =\ - \ ``\\\"processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L196)\\\ - n\\n___\\n\\n### RESOURCES\\n\\n\u2022 **RESOURCES** = ``\\\"resources\\\"\ - ``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L201)\\\ - n\\n___\\n\\n### SEARCHES\\n\\n\u2022 **SEARCHES** = ``\\\"searches\\\"``\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n___\\n\\n### SUGGESTIONS\\n\\n\u2022 **SUGGESTIONS** = ``\\\"suggestions\\\ - \"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L204)\\\ - n\\n___\\n\\n### TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\ - \"train_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L203)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"13077c5570e4437fb58c1c5ab75d310b\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / StatsType \\n Enumeration: StatsType \\\ - n Deprecated \\n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED\ - \ \\n BYTES \\n CHARS \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME\ - \ \\n RESOURCES \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration\ - \ Members \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\\ - n \u2022 BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200\ - \ \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\\ - n \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199\ - \ \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202\ - \ \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES\ - \ \\n \u2022 RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197\ - \ \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":1386,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:40.544839Z\"\ - ,\"last_understanding\":\"2026-06-09T08:13:38.735743Z\",\"last_extract\":\"\ - 2026-06-09T08:13:38.231630Z\",\"last_processing_start\":\"2026-06-09T08:13:38.196724Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > enums > StatsType\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > enums > StatsType\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\"\ - :{\"score\":0.6067970991134644,\"score_type\":\"VECTOR\",\"order\":8,\"text\"\ - :\"@nuclia/core / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated\ - \ \\n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\\ - n BYTES \\n CHARS \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME\ - \ \\n RESOURCES \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration\ - \ Members \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\\ - n \u2022 BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200\ - \ \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\\ - n \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199\ - \ \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202\ - \ \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES\ - \ \\n \u2022 RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197\ - \ \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\"\ - ,\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\\ - n\\n# Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\\ - n\\n> `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\\ - n\\n***\\n\\n### driver?\\n\\n> `optional` **driver**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\\ - n\\n***\\n\\n### input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\\ - n\\n***\\n\\n### max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\\ - n\\n***\\n\\n### output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\\ - n\\n#### default\\\\_max?\\n\\n> `optional` **default\\\\_max**: `number`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\\ - n\\n***\\n\\n### prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface:\ - \ ModelConfig \\n Properties \\n assume_role? \\n \\n optional assume_role:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592\ - \ \\n \\n driver? \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n \\\ - n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588\ - \ \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"\ - key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\"\ - :480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"\ - end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\ - \"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\"\ - :806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\"\ - ,\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\"\ - :\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - :{\"score\":0.6270709037780762,\"score_type\":\"VECTOR\",\"order\":5,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\"\ - :480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.605417013168335,\"score_type\":\"VECTOR\",\"order\":9,\"text\"\ - :\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\"\ - ,\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs > rag > advanced\ - \ > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: consumption\\\ - ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\"\ - ;\\nimport TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\\ - n\\nAgentic RAG is a license and consumption-based service. This means that\ - \ you pay for the computational resources you consume. The consumption is\ - \ measured in **Agentic RAG tokens**.\\nAll public 3rd-party LLMs base their\ - \ pricing on the number of tokens consumed. In the LLM world, a token is around\ - \ 4-5 characters on average, which might fit an entire word or be split into\ - \ parts. The number of tokens is proportional to the amount of text, measured\ - \ in chunks of 4-5 characters. It closely relates to words but not entirely.\ - \ The longer a sentence is, the more tokens it will consume to read or to\ - \ generate it.\\nSince all these 3rd-party LLMs have different pricing, Agentic\ - \ RAG tokens serve to normalize the cost across all of them.\\n\\n## How Tokens\ - \ Are Consumed in RAG\\n\\nWhen a user asks a question to your Knowledge Box,\ - \ the token consumption process follows these steps:\\n\\n1. **Question Processing**:\ - \ The system finds the most relevant paragraphs to answer the question\\n2.\ - \ **Context Assembly**: These paragraphs are used as context when calling\ - \ the LLM model\\n3. **Prompt Creation**: Agentic RAG assembles the **prompt**,\ - \ **context**, and **question** into a single string\\n4. **LLM Processing**:\ - \ This complete string is sent to the LLM, corresponding to a certain number\ - \ of **input tokens**\\n5. **Answer Generation**: The LLM generates the answer,\ - \ which corresponds to a certain number of **output tokens**\\n\\n**Total\ - \ consumption** = Input tokens + Output tokens + Image tokens\\n\\n### Factors\ - \ That Impact Token Consumption\\n\\nToken consumption is directly affected\ - \ by:\\n\\n- **Large context**: Results from using RAG strategies like \\\"\ - Full resource\\\" or \\\"Neighbouring paragraphs\\\", or from using the `extra_context`\ - \ parameter\\n- **Long questions**: More detailed or complex questions require\ - \ more input tokens\\n- **Long prompts**: Extensive system prompts increase\ - \ the input token count\\n- **Detailed answers**: Comprehensive responses\ - \ require more output tokens\\n- **Images in context**: When using multimodal\ - \ models, images included in the retrieved context significantly increase\ - \ token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\\ - n### Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing\ - \ token consumption is to fine-tune your request parameters:\\n\\n- **Optimize\ - \ prompts**: Ensure your prompts are concise and focused, avoiding unnecessary\ - \ verbosity\\n- **Control resource scope**: When using the \\\"Full resource\\\ - \" strategy, use the `count` attribute to limit the number of resources returned\\\ - n- **Tune neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy,\ - \ optimize the `before` and `after` attributes to balance context quality\ - \ with token efficiency\\n- **Manage summary length**: When using the \\\"\ - Hierarchical\\\" strategy, ensure that resource summaries are appropriately\ - \ sized\\n- **Choose efficient models**: Select LLMs that offer better token\ - \ efficiency (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT\ - \ 4o)\\n\\n### Strategy 2: Set Hard Limits\\n\\nYou can implement safeguards\ - \ against excessive token consumption:\\n\\nUse the `max_tokens` parameter\ - \ on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post)\ - \ to set hard limits on:\\n- **Context size**: Limits the amount of retrieved\ - \ information sent to the LLM\\n- **Answer length**: Limits the length of\ - \ the generated response\\n\\n#### Important Considerations\\n\\n**Context\ - \ Limitations**:\\n- Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with\\n- Balance between\ - \ cost control and answer quality\\n\\n**Answer Length Limitations**:\\n-\ - \ The LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way\\n- **Recommended approach**: Include length requirements\ - \ in your prompt (e.g., \\\"Please answer in less than 200 words\\\") rather\ - \ than relying solely on hard limits\\n- This allows the LLM to naturally\ - \ conclude its response within the desired length\\n\\n## How to Monitor Token\ - \ Consumption\\n\\n### Understanding Token Consumption Data\\n\\nYou can receive\ - \ detailed token consumption information from the following endpoints that\ - \ utilize LLM models: `ask`, `chat`, `remi`, `query`, `sentence`, `summarize`,\ - \ `tokens`, and `rerank`.\\n\\n:::note\\nThe `rephrase` endpoint currently\ - \ does not support token consumption monitoring.\\n:::\\n\\nTo receive token\ - \ consumption data, you must include the following header in your request:\\\ - n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption data is provided\ - \ in different formats depending on the response type:\\n- **Streaming responses**\ - \ (`application/x-ndjson`): Token consumption appears as a separate JSON chunk\ - \ with type \\\"consumption\\\"\\n- **Standard responses** (`application/json`):\ - \ Token consumption is included in a \\\"consumption\\\" field within the\ - \ main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": {\\\ - n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n\\n\\n### Understanding Token Types\\n\\n**Normalized\ - \ Tokens** (`normalized_tokens`):\\n- These represent the number of Agentic\ - \ RAG tokens consumed and that you will be billed for\\n- Values are normalized\ - \ across different LLM providers for consistent billing\\n- Include separate\ - \ counts for:\\n - `input`: Tokens used for the prompt, context, and question\\\ - n - `output`: Tokens used for the generated response\\n - `image`: Tokens\ - \ used for image processing (when applicable)\\n\\n**Customer Key Tokens**\ - \ (`customer_key_tokens`):\\n- These represent tokens consumed when using\ - \ your own LLM API keys\\n- These tokens are **not billed** by Agentic RAG\ - \ since you're using your own API keys\\n- Values are also normalized for\ - \ comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id: consumption\ - \ \\n title: Token consumption \\n \\n import Tabs from @theme/Tabs ; \\n\ - \ import TabItem from @theme/TabItem ; \\n Token consumption \\n Agentic RAG\ - \ is a license and consumption-based service. This means that you pay for\ - \ the computational resources you consume. The consumption is measured in\ - \ Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on\ - \ the number of tokens consumed. In the LLM world, a token is around 4-5 characters\ - \ on average, which might fit an entire word or be split into parts. The number\ - \ of tokens is proportional to the amount of text, measured in chunks of 4-5\ - \ characters. It closely relates to words but not entirely. The longer a sentence\ - \ is, the more tokens it will consume to read or to generate it. \\n Since\ - \ all these 3rd-party LLMs have different pricing, Agentic RAG tokens serve\ - \ to normalize the cost across all of them. \\n How Tokens Are Consumed in\ - \ RAG \\n When a user asks a question to your Knowledge Box, the token consumption\ - \ process follows these steps: \\n \\n Question Processing: The system finds\ - \ the most relevant paragraphs to answer the question \\n Context Assembly:\ - \ These paragraphs are used as context when calling the LLM model \\n Prompt\ - \ Creation: Agentic RAG assembles the prompt, context, and question into a\ - \ single string \\n LLM Processing: This complete string is sent to the LLM,\ - \ corresponding to a certain number of input tokens \\n Answer Generation:\ - \ The LLM generates the answer, which corresponds to a certain number of output\ - \ tokens \\n \\n Total consumption = Input tokens + Output tokens + Image\ - \ tokens \\n Factors That Impact Token Consumption \\n Token consumption is\ - \ directly affected by: \\n \\n Large context: Results from using RAG strategies\ - \ like Full resource or Neighbouring paragraphs , or from using the extra_context\ - \ parameter \\n Long questions: More detailed or complex questions require\ - \ more input tokens \\n Long prompts: Extensive system prompts increase the\ - \ input token count \\n Detailed answers: Comprehensive responses require\ - \ more output tokens \\n Images in context: When using multimodal models,\ - \ images included in the retrieved context significantly increase token consumption\ - \ \\n \\n How to Limit and Control Token Consumption \\n Strategy 1: Optimize\ - \ Your Parameters \\n The first approach to reducing token consumption is\ - \ to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure your\ - \ prompts are concise and focused, avoiding unnecessary verbosity \\n Control\ - \ resource scope: When using the Full resource strategy, use the count attribute\ - \ to limit the number of resources returned \\n Tune neighboring context:\ - \ For the Neighbouring paragraphs strategy, optimize the before and after\ - \ attributes to balance context quality with token efficiency \\n Manage summary\ - \ length: When using the Hierarchical strategy, ensure that resource summaries\ - \ are appropriately sized \\n Choose efficient models: Select LLMs that offer\ - \ better token efficiency (typically, ChatGPT 4o-mini is more cost-effective\ - \ than ChatGPT 4o) \\n \\n Strategy 2: Set Hard Limits \\n You can implement\ - \ safeguards against excessive token consumption: \\n Use the max_tokens parameter\ - \ on the /ask endpoint to set hard limits on: \\n - Context size: Limits the\ - \ amount of retrieved information sent to the LLM \\n - Answer length: Limits\ - \ the length of the generated response \\n Important Considerations \\n Context\ - \ Limitations: \\n - Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with \\n - Balance between\ - \ cost control and answer quality \\n Answer Length Limitations: \\n - The\ - \ LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way \\n - Recommended approach: Include length requirements\ - \ in your prompt (e.g., Please answer in less than 200 words ) rather than\ - \ relying solely on hard limits \\n - This allows the LLM to naturally conclude\ - \ its response within the desired length \\n How to Monitor Token Consumption\ - \ \\n Understanding Token Consumption Data \\n You can receive detailed token\ - \ consumption information from the following endpoints that utilize LLM models:\ - \ ask, chat, remi, query, sentence, summarize, tokens, and rerank. \\n :::note\ - \ \\n The rephrase endpoint currently does not support token consumption monitoring.\ - \ \\n ::: \\n To receive token consumption data, you must include the following\ - \ header in your request: \\n X-SHOW-CONSUMPTION: true \\n The token consumption\ - \ data is provided in different formats depending on the response type: \\\ - n - Streaming responses (application/x-ndjson): Token consumption appears\ - \ as a separate JSON chunk with type consumption \\n - Standard responses\ - \ (application/json): Token consumption is included in a consumption field\ - \ within the main response \\n Token Consumption Response Format \\n \\n \\\ - n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens :\ - \ { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\\ - n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens):\ - \ \\n - These represent the number of Agentic RAG tokens consumed and that\ - \ you will be billed for \\n - Values are normalized across different LLM\ - \ providers for consistent billing \\n - Include separate counts for: \\n\ - \ - input: Tokens used for the prompt, context, and question \\n - output:\ - \ Tokens used for the generated response \\n - image: Tokens used for image\ - \ processing (when applicable) \\n Customer Key Tokens (customer_key_tokens):\ - \ \\n - These represent tokens consumed when using your own LLM API keys \\\ - n - These tokens are not billed by Agentic RAG since you're using your own\ - \ API keys \\n - Values are also normalized for comparison purposes across\ - \ different providers\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\"\ - :{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":319,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":198,\"key\":\"\"},{\"start\":198,\"end\":267,\"key\"\ - :\"\"},{\"start\":267,\"end\":319,\"key\":\"\"}]},{\"start\":319,\"end\":747,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - 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\ Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"\ - text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\"\ - :4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\"\ - ,\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"\ - 2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic RAG\"\ - :{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"\ - start\":5718,\"end\":5729}],\"entity\":\"Agentic RAG\"},\"PRODUCT/Knowledge\ - \ Box\":{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge\ - \ Box\"},\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"\ - entity\":\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"\ - start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"\ - }},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > rag >\ - \ advanced > consumption.\",\"extracted\":{\"text\":{\"text\":\"docs > rag\ - \ > advanced > consumption.\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\":{\"\ - score\":0.6338599920272827,\"score_type\":\"VECTOR\",\"order\":3,\"text\"\ - :\" Use the max_tokens parameter on the /ask endpoint to set hard limits on:\ - \ \\n - Context size: Limits the amount of retrieved information sent to the\ - \ LLM \\n - Answer length: Limits the length of the generated response \\\ - n Important Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932\"\ - :{\"score\":0.5667079091072083,\"score_type\":\"VECTOR\",\"order\":17,\"text\"\ - :\" json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens\ - \ : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"\ - start\":4728,\"end\":4932,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\"\ - :\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\"\ - ,\"title\":\"docs > develop > js sdk > enums > UsageType\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\"\ - ,\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\\ - n## Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\ - \\_USED](UsageType.md#ai_tokens_used)\\n- [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\\ - n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- [MEDIA\\\\_FILES\\\ - \\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\ - \\_PROCESSED](UsageType.md#media_seconds_processed)\\n- [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\\ - n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- [PARAGRAPHS\\\\\ - _PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\\ - _TIME](UsageType.md#pre_processing_time)\\n- [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\\ - n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- [SLOW\\\\\ - _PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\ - \\_PERFORMED](UsageType.md#suggestions_performed)\\n- [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\\ - n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\ - \\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\\ - n\\n___\\n\\n### BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** =\ - \ ``\\\"bytes_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\\ - n\\n___\\n\\n### CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** =\ - \ ``\\\"chars_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\\ - n\\n___\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\ - \\_PROCESSED** = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\\ - n\\n___\\n\\n### MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\ - \\_PROCESSED** = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\\ - n\\n___\\n\\n### NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\ - \"nuclia_tokens_billed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\\ - n\\n___\\n\\n### PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** =\ - \ ``\\\"pages_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\\ - n\\n___\\n\\n### PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED**\ - \ = ``\\\"paragraphs_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\\ - n\\n___\\n\\n### PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\ - \\_TIME** = ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\\ - n\\n___\\n\\n### RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED**\ - \ = ``\\\"resources_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\\ - n\\n___\\n\\n### SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED**\ - \ = ``\\\"searches_performed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\\ - n\\n___\\n\\n### SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\ - \\_TIME** = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\\ - n\\n___\\n\\n### SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED**\ - \ = ``\\\"suggestions_performed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\\ - n\\n___\\n\\n### TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\ - \"train_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / UsageType \\n Enumeration: UsageType \\\ - n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED\ - \ \\n CHARS_PROCESSED \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED\ - \ \\n NUCLIA_TOKENS \\n PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME\ - \ \\n RESOURCES_PROCESSED \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME\ - \ \\n SUGGESTIONS_PERFORMED \\n TRAIN_SECONDS \\n \\n Enumeration Members\ - \ \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:190 \\n \\n BYTES_PROCESSED\ - \ \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:181\ - \ \\n \\n CHARS_PROCESSED \\n \u2022 CHARS_PROCESSED = chars_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 \\n \\n MEDIA_FILES_PROCESSED\ - \ \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED\ - \ \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\\ - n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191\ - \ \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED\ - \ \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\\ - n \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178\ - \ \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED\ - \ \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED\ - \ \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\"\ - ,\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"\ - 2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > enums > UsageType\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > enums > UsageType\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\"\ - :{\"score\":0.6117294430732727,\"score_type\":\"VECTOR\",\"order\":7,\"text\"\ - :\"@nuclia/core / Exports / UsageType \\n Enumeration: UsageType \\n Table\ - \ of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED\ - \ \\n CHARS_PROCESSED \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED\ - \ \\n NUCLIA_TOKENS \\n PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME\ - \ \\n RESOURCES_PROCESSED \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME\ - \ \\n SUGGESTIONS_PERFORMED \\n TRAIN_SECONDS \\n \\n Enumeration Members\ - \ \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:190 \\n \\n BYTES_PROCESSED\ - \ \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:181\ - \ \\n \\n CHARS_PROCESSED \\n \u2022 CHARS_PROCESSED = chars_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 \\n \\n MEDIA_FILES_PROCESSED\ - \ \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED\ - \ \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\\ - n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191\ - \ \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED\ - \ \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\\ - n \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178\ - \ \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED\ - \ \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED\ - \ \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\"\ - ,\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\"\ - ,\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\"\ - :\"docs > rag > advanced > openai api compatible models\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: openai-api-compatible-models\\\ - ntitle: Connect to any OpenAI API compatible LLM\\n---\\n\\n# Connect to any\ - \ OpenAI API compatible LLM\\n\\nAgentic RAG allows you to connect to any\ - \ OpenAI API compatible LLM. This means that you can use any LLM that has\ - \ an API compatible with the OpenAI API which has become a standard in the\ - \ industry.\\n\\nMany of the options for self-hosted LLMs, open-source LLMs\ - \ hosted by cloud providers or commercial LLMs are compatible with the OpenAI\ - \ API. This means that you can use them with Agentic RAG without any modifications.\\\ - n\\n## Configuration\\n\\nTo modify your knowledge box configuration you can\ - \ do so in three manners, through the API, the Nuclia CLI / SDK or the Agentic\ - \ RAG dashboard.\\n\\nThe Agentic RAG dashboard offers the most user-friendly\ - \ way to modify the configuration of your knowledge box and we will use it\ - \ in this example.\\n\\nWe will be setting up a connection to the **Phi 4\ - \ Reasoning Plus** model, hosted by **OpenRouter** which offers a wide range\ - \ of open-source and commercial models compatible with the OpenAI API. We\ - \ can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free),\ - \ the API parameters are located under the **API** tab.\\n\\n1. **Open the\ - \ AI Models page** \\n In the left sidebar under **Advanced**, click **AI\ - \ Models**.\\n2. **Select \u201COpenAI API Compatible Model\u201D** \\n \ - \ From the models list, choose **OpenAI API Compatible Model**.\\n3. **Enable\ - \ custom Key** \\n Toggle the option for using you own `OpenAI API Compatible\ - \ Key` if it is not already enabled.\\n4. **Fill in the configuration parameters**\\\ - n\\n - **API Key**:\\n - Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key.\\n - Example: We will set this to our OpenRouter API key.\\\ - n - **API URL**:\\n - Description: The URL of the API endpoint for your\ - \ LLM. This may be shared between multiple models.\\n - Example: For OpenRouter,\ - \ it is the same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\\ - n - Description: The name of the model you want to use, it needs to exactly\ - \ match the name of the model in the API.\\n - Example: For Phi 4 Reasoning\ - \ Plus in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\\ - n - **Maximum supported input tokens**:\\n - Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only.\\n - Example: For Phi 4 Reasoning Plus, the total context\ - \ size is `32768` tokens, as we want to leave room for the output, we will\ - \ set the maximum supported input tokens as `32768 - 1024 = 31744`.\\n -\ - \ **Maximum supported output tokens**:\\n - Description: The maximum number\ - \ of tokens that the model can generate as output. Again, we should keep in\ - \ mind that this value summed to the **Maximum supported input tokens** should\ - \ not exceed the total context size supported by the model.\\n - Example:\ - \ For Phi 4 Reasoning Plus, the maximum output tokens is specified at `32768`,\ - \ but we already reserved `31744` for the input tokens, so we will set this\ - \ to `32768 - 31744 = 1024`.\\n - **Model Features**:\\n - Description:\ - \ Under this section you will find multiple toggles related to features supported\ - \ by the model, these vary from model to model, but most often the default\ - \ values are well suited to most use cases. The most relevant toggle is for\ - \ `Image Support` which allows you to use images as input for the model.\\\ - n - Example: Image input is not supported by Phi 4 Reasoning Plus, so\ - \ we will leave it disabled.\\n\\n5. **Save** \\n Click **Save changes**.\\\ - n\\n6. **Test your model** \\n Run a sample query in Agentic RAG or via\ - \ API/CLI. Adjust your prompt templates and token settings as needed.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ openai-api-compatible-models \\n title: Connect to any OpenAI API compatible\ - \ LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic RAG allows\ - \ you to connect to any OpenAI API compatible LLM. This means that you can\ - \ use any LLM that has an API compatible with the OpenAI API which has become\ - \ a standard in the industry. \\n Many of the options for self-hosted LLMs,\ - \ open-source LLMs hosted by cloud providers or commercial LLMs are compatible\ - \ with the OpenAI API. This means that you can use them with Agentic RAG without\ - \ any modifications. \\n Configuration \\n To modify your knowledge box configuration\ - \ you can do so in three manners, through the API, the Nuclia CLI / SDK or\ - \ the Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most\ - \ user-friendly way to modify the configuration of your knowledge box and\ - \ we will use it in this example. \\n We will be setting up a connection to\ - \ the Phi 4 Reasoning Plus model, hosted by OpenRouter which offers a wide\ - \ range of open-source and commercial models compatible with the OpenAI API.\ - \ We can see more information about this specific model here, the API parameters\ - \ are located under the API tab. \\n \\n Open the AI Models page \\n In the\ - \ left sidebar under Advanced, click AI Models. \\n Select OpenAI API Compatible\ - \ Model \\n From the models list, choose OpenAI API Compatible Model. \\n\ - \ Enable custom Key \\n Toggle the option for using you own OpenAI API Compatible\ - \ Key if it is not already enabled. \\n \\n Fill in the configuration parameters\ - \ \\n \\n \\n API Key: \\n \\n Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key. \\n Example: We will set this to our OpenRouter API key. \\\ - n \\n \\n API URL: \\n Description: The URL of the API endpoint for your LLM.\ - \ This may be shared between multiple models. \\n Example: For OpenRouter,\ - \ it is the same for all models: https://openrouter.ai/api/v1 \\n \\n \\n\ - \ Model: \\n Description: The name of the model you want to use, it needs\ - \ to exactly match the name of the model in the API. \\n Example: For Phi\ - \ 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free.\ - \ \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n Example: For Phi 4 Reasoning Plus, the total context size\ - \ is 32768 tokens, as we want to leave room for the output, we will set the\ - \ maximum supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum\ - \ supported output tokens: \\n Description: The maximum number of tokens that\ - \ the model can generate as output. Again, we should keep in mind that this\ - \ value summed to the Maximum supported input tokens should not exceed the\ - \ total context size supported by the model. \\n Example: For Phi 4 Reasoning\ - \ Plus, the maximum output tokens is specified at 32768, but we already reserved\ - \ 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024.\ - \ \\n \\n \\n \\n Model Features: \\n \\n Description: Under this section\ - \ you will find multiple toggles related to features supported by the model,\ - \ these vary from model to model, but most often the default values are well\ - \ suited to most use cases. The most relevant toggle is for Image Support\ - \ which allows you to use images as input for the model. \\n Example: Image\ - \ input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled.\ - \ \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model\ - \ \\n Run a sample query in Agentic RAG or via API/CLI. 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Again,\ - \ we should keep in mind that this value summed to the Maximum supported input\ - \ tokens should not exceed the total context size supported by the model.\ - \ \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"\ - end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.5899083614349365,\"\ - score_type\":\"VECTOR\",\"order\":11,\"text\":\" \\n \\n Maximum supported\ - \ input tokens: \\n Description: The maximum number of tokens that the model\ - \ can accept as input. Be mindful that this takes into account the tokens\ - \ used in the prompt, query and context. Also take note that some models may\ - \ provide their context window as the total between input and output tokens,\ - \ while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\"\ - :\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > Consumption\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\"\ - ,\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\\ - n\\n# Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\ - \\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\\ - n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface:\ - \ Consumption \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens:\ - \ TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"\ - 2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Consumption\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\"\ - :0.58526611328125,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption\ - \ \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\"\ - ,\"modified\":\"2026-07-14T12:51:02.450318\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\\ - n\\n# Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\\ - n\\n> `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\\ - n\\n***\\n\\n### llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\\ - n\\n***\\n\\n### manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**:\ - \ `object`\\n\\n#### splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:539](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L539)\\\ - n\\n***\\n\\n### max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\\ - n\\n***\\n\\n### name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d24d884112df2f67b327ac6d6302ddf1\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\\ - n Interface: SplitStrategy \\n Properties \\n custom_split? \\n \\n optional\ - \ custom_split: CustomSplitStrategy \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:537\ - \ \\n \\n llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:538 \\n \\n manual_split?\ - \ \\n \\n optional manual_split: object \\n \\n splitter \\n \\n splitter:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:539\ - \ \\n \\n max_paragraph? \\n \\n optional max_paragraph: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 \\n \\n name? \\n \\\ - n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\"\ - :234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\"\ - :\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"\ - end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:04.701538Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:04.225754Z\",\"last_extract\":\"\ - 2026-07-14T12:51:03.819024Z\",\"last_processing_start\":\"2026-07-14T12:51:03.798124Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\"\ - :{\"score\":0.5642594695091248,\"score_type\":\"VECTOR\",\"order\":18,\"text\"\ - :\" \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536\ - \ \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\"\ - ,\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\"\ - :696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\"\ - ,\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\\ - n\\n# Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\\ - _history?\\n\\n> `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\\ - n\\n***\\n\\n### context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\\ - n\\n***\\n\\n### format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\\ - n\\n***\\n\\n### json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\\ - n\\n***\\n\\n### query\\\\_context?\\n\\n> `optional` **query\\\\_context**:\ - \ `string`[] \\\\| `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\ - \\_context\\\\_images**: `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**:\ - \ `string`\\n\\n#### content\\\\_type\\n\\n> **content\\\\_type**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\\ - _context\\\\_order**: `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`:\ - \ `string`\\\\]: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\\ - n\\n***\\n\\n### rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\\ - n\\n***\\n\\n### retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\\ - n\\n***\\n\\n### system?\\n\\n> `optional` **system**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\\ - n\\n***\\n\\n### truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\\ - n\\n***\\n\\n### user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\\ - n\\n#### prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions\ - \ \\n Interface: PredictAnswerOptions \\n Properties \\n chat_history? \\\ - n \\n optional chat_history: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242\ - \ \\n \\n citation_threshold? \\n \\n optional citation_threshold: number\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 \\n \\n context? \\\ - n \\n optional context: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243\ - \ \\n \\n format_prompt? \\n \\n optional format_prompt: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 \\n \\n generative_model?\ - \ \\n \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n \\n optional query_context: string[] \\\\| object \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\\ - n \\n query_context_images? \\n \\n optional query_context_images: object\ - \ \\n \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\\ - n content_type: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252\ - \ \\n \\n query_context_order? \\n \\n optional query_context_order: object\ - \ \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context?\ - \ \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259\ - \ \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\\ - n \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241\ - \ \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt?\ - \ \\n \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\"\ - :257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\"\ - :\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"\ - end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\ - \"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\"\ - :1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\"\ - :1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\"\ - :\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\"\ - :1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"\ - end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"\ - key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"\ - end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"\ - start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"\ - key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"\ - classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\"\ - :\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\"\ - ,\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > interfaces\ - \ > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"\ - score\":0.6527847647666931,\"score_type\":\"VECTOR\",\"order\":2,\"text\"\ - :\" \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\"\ - :977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\"\ - :{\"score\":0.6318063139915466,\"score_type\":\"VECTOR\",\"order\":4,\"text\"\ - :\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"\ - end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\"\ - ,\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\\ - n\\n# Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\\ - n\\n## Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\ - \\_json\\\\_schema**: `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\\ - n\\n***\\n\\n### audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**:\ - \ `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean` \\\\\ - | `\\\"none\\\"` \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\\ - nIt will return the text blocks that have been effectively used to build each\ - \ section of the answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\\ - n\\n***\\n\\n### debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\\ - n\\n***\\n\\n### extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**:\ - \ `string`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\\ - n\\n***\\n\\n### extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\ - \\_context\\\\_images**: `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\\ - n\\n***\\n\\n### ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\\ - n\\n#### Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\\ - n\\n***\\n\\n### features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\\ - n\\n***\\n\\n### field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\\ - _type\\\\_filter**: [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\\ - n\\n***\\n\\n### fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\\ - n\\n***\\n\\n### filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**:\ - \ [`FilterExpression`](FilterExpression.md)\\n\\n#### Inherited from\\n\\\ - n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\\ - n\\n***\\n\\n### filters?\\n\\n> `optional` **filters**: `string`[] \\\\|\ - \ [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\\ - n\\n***\\n\\n### highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\\ - n\\n***\\n\\n### keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**:\ - \ `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\ - \ \\\\| `object`\\n\\nDefines the maximum number of tokens that the model\ - \ will take as context.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\\ - n\\n***\\n\\n### min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number`\ - \ \\\\| [`MinScore`](MinScore.md)\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\\ - n\\n***\\n\\n### prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\\ - n\\n***\\n\\n### query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\\ - n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\\ - _type\\n\\n> **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\\ - n\\n***\\n\\n### rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\\ - _images\\\\_strategies**: [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\\ - n\\n***\\n\\n### rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**:\ - \ [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\\ - _creation\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\ - \\_creation\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\ - \\_modification\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\ - \\_modification\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\\ - n\\n***\\n\\n### rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\\ - n\\n***\\n\\n### rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\\ - n\\n***\\n\\n### reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\\ - n\\n***\\n\\n### resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**:\ - \ `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\\ - n\\n***\\n\\n### search\\\\_configuration?\\n\\n> `optional` **search\\\\\ - _configuration**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\\ - n\\n***\\n\\n### security?\\n\\n> `optional` **security**: `object`\\n\\n####\ - \ groups\\n\\n> **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\\ - n\\n***\\n\\n### show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\\ - n\\n***\\n\\n### show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\\ - n\\n***\\n\\n### show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\\ - n\\n***\\n\\n### synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\\ - n\\n***\\n\\n### top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\\ - n\\n***\\n\\n### vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface:\ - \ ChatOptions \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\\ - n answer_json_schema? \\n \\n optional answer_json_schema: object \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 \\n\ - \ \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index\ - \ Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\\ - n \\n citation_threshold? \\n \\n optional citation_threshold: number \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\\\| none \\\\|\ - \ default \\\\| llm_footnotes \\n \\n It will return the text blocks that\ - \ have been effectively used to build each section of the answer. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 \\n \\n debug?\ - \ \\n \\n optional debug: boolean \\n \\n Inherited from \\n BaseSearchOptions.debug\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 \\\ - n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images?\ - \ \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118\ - \ \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[]\ - \ \\n \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\\ - n features? \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117\ - \ \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields?\ - \ \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\\ - n \\n filter_expression? \\n \\n optional filter_expression: FilterExpression\ - \ \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters?\ - \ \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63\ - \ \\n \\n generative_model? \\n \\n optional generative_model: string \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108\ - \ \\n \\n highlight? \\n \\n optional highlight: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76\ - \ \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\\ - | Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\\ - n max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines\ - \ the maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from\ - \ \\n BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n\ - \ \\n prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image?\ - \ \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded:\ - \ string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies?\ - \ \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies?\ - \ \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106\ - \ \\n \\n range_creation_end? \\n \\n optional range_creation_end: string\ - \ \\n \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start?\ - \ \\n \\n optional range_creation_start: string \\n \\n Inherited from \\\ - n BaseSearchOptions.range_creation_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67\ - \ \\n \\n range_modification_end? \\n \\n optional range_modification_end:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\\ - n \\n range_modification_start? \\n \\n optional range_modification_start:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_start\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 \\\ - n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited\ - \ from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase?\ - \ \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\\ - n \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from\ - \ \\n BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83\ - \ \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\\ - n \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration?\ - \ \\n \\n optional search_configuration: string \\n \\n Inherited from \\\ - n BaseSearchOptions.search_configuration \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86\ - \ \\n \\n security? \\n \\n optional security: object \\n \\n groups \\n \\\ - n groups: string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\\ - n show? \\n \\n optional show: ResourceProperties[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.show \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71\ - \ \\n \\n show_consumption? \\n \\n optional show_consumption: boolean \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.show_hidden \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80\ - \ \\n \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k?\ - \ \\n \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\\ - n \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from\ - \ \\n BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\"\ - :[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"\ - key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\"\ - :499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"\ - end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"\ - end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\"\ - :\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"\ - end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\"\ - :1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"\ - key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"\ - end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"\ - start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"\ - key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"\ - end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"\ - end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"\ - key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"\ - end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"\ - start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"\ - key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"\ - end\":3929,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3650,\"end\":3795,\"key\":\"\"},{\"start\":3795,\"\ - end\":3929,\"key\":\"\"}]},{\"start\":3929,\"end\":4317,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3932,\"end\":4123,\"key\":\"\"},{\"start\":4123,\"end\":4317,\"\ - key\":\"\"}]},{\"start\":4317,\"end\":4520,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4320,\"\ - end\":4520,\"key\":\"\"}]},{\"start\":4520,\"end\":4884,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4523,\"end\":4717,\"key\":\"\"},{\"start\":4717,\"end\":4884,\"\ - key\":\"\"}]},{\"start\":4884,\"end\":5167,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4887,\"\ - end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5167,\"key\":\"\"}]},{\"\ - start\":5167,\"end\":5525,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5170,\"end\":5339,\"\ - key\":\"\"},{\"start\":5339,\"end\":5525,\"key\":\"\"}]},{\"start\":5525,\"\ - end\":5897,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5528,\"end\":5711,\"key\":\"\"},{\"start\":5711,\"\ - 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- headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Content-Length: - - '10692' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:45 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '319' - x-nuclia-trace-id: - - f685cc7103cc1fb55ddc129828897dd0 - status: - code: 200 - message: OK -- request: - body: '{"question": "Explica c\u00f3mo utilizar el par\u00e1metro `max_tokens` - en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n oficial.", - "user_id": "arag-ask-rerank", "context": {"6e8250e6b5264156988657a221fd5e94/t/page/0-397": - "@nuclia/core \u2022 Docs \n \n @nuclia/core / Ask / ConsumptionAskResponseItem - \n Interface: ConsumptionAskResponseItem \n Properties \n customer_key_tokens - \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 - \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n\n", "1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641": - "@nuclia/core \u2022 Docs \n \n @nuclia/core / AugmentedField \n Interface: - AugmentedField \n Properties \n applied_data_augmentation \n \n applied_data_augmentation: - AppliedDataAugmentation \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 - \n \n input_nuclia_tokens \n \n input_nuclia_tokens: number \n \n Defined in - \n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \n \n metadata \n - \n metadata: FieldMetadata \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 - \n \n output_nuclia_tokens \n \n output_nuclia_tokens: number \n \n Defined - in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \n \n time \n - \n\n", "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n - ```python \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, - Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My - question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n", "0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224": "@nuclia/core - \u2022 Docs \n \n @nuclia/core / ReasoningConfig \n Interface: ReasoningConfig - \n Properties \n budget_tokens? \n \n optional budget_tokens: number \n \n Defined - in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \n \n effort? \n\n", "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": - "@nuclia/core \u2022 Docs \n \n @nuclia/core / UsageType \n Enumeration: UsageType - \n Enumeration Members \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES_PROCESSED - \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 - \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: chars_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:198 \n \n MEDIA_FILES_PROCESSED \n - \n MEDIA_FILES_PROCESSED: media_files_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 - \n \n MEDIA_SECONDS_PROCESSED \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n \n NUCLIA_TOKENS - \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:207 - \n \n PAGES_PROCESSED \n \n PAGES_PROCESSED: pages_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n PARAGRAPHS_PROCESSED \n \n - PARAGRAPHS_PROCESSED: paragraphs_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 - \n \n PRE_PROCESSING_TIME \n \n PRE_PROCESSING_TIME: pre_processing_time \n - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 \n \n RESOURCES_PROCESSED - \n \n RESOURCES_PROCESSED: resources_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 - \n \n SEARCHES_PERFORMED \n \n SEARCHES_PERFORMED: searches_performed \n \n - Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 \n \n SLOW_PROCESSING_TIME - \n \n SLOW_PROCESSING_TIME: slow_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 - \n \n SUGGESTIONS_PERFORMED \n \n SUGGESTIONS_PERFORMED: suggestions_performed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 \n \n TRAIN_SECONDS - \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n", - "666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386": "@nuclia/core / Exports / - StatsType \n Enumeration: StatsType \n Deprecated \n Table of contents \n Enumeration - Members \n \n AI_TOKENS_USED \n BYTES \n CHARS \n DOCS_NO_MEDIA \n MEDIA_SECONDS - \n PAGES \n PROCESSING_TIME \n RESOURCES \n SEARCHES \n SUGGESTIONS \n TRAIN_SECONDS - \n \n Enumeration Members \n AI_TOKENS_USED \n \u2022 AI_TOKENS_USED = ai_tokens_used - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 \n \n BYTES \n \u2022 - BYTES = bytes \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n - \n CHARS \n \u2022 CHARS = chars \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n DOCS_NO_MEDIA \n \u2022 DOCS_NO_MEDIA = docs_no_media \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:205 \n \n MEDIA_SECONDS \n \u2022 MEDIA_SECONDS - = media_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n - \n PAGES \n \u2022 PAGES = pages \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 - \n \n PROCESSING_TIME \n \u2022 PROCESSING_TIME = processing_time \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n RESOURCES \n \u2022 RESOURCES - = resources \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n - SEARCHES \n \u2022 SEARCHES = searches \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 - \n \n SUGGESTIONS \n \u2022 SUGGESTIONS = suggestions \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n TRAIN_SECONDS \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:203\n", "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": - " \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 - \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": - " \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 - \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n - \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": - " Use the max_tokens parameter on the /ask endpoint to set hard limits on: \n - - Context size: Limits the amount of retrieved information sent to the LLM \n - - Answer length: Limits the length of the generated response \n Important Considerations - \n Context Limitations: \n\n", "4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932": - " json \n { \n item : { \n type : consumption , \n normalized_tokens : { \n - input : 13, \n output : 34, \n image : 0.0 \n }, \n customer_key_tokens : { - \n input : 0.0, \n output : 0.0, \n image : 0.0 \n } \n } \n } \n \n \n\n", - "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139": "@nuclia/core / Exports / - UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration Members - \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED - \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED - \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME - \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED - \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 - \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 - CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 - \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED - \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS - = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 - \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n - \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 - \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED - \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 - \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n - Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME - \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 - \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS - \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: The maximum - number of tokens that the model can generate as output. Again, we should keep - in mind that this value summed to the Maximum supported input tokens should - not exceed the total context size supported by the model. \n\n", "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": - " \n \n Maximum supported input tokens: \n Description: The maximum number of - tokens that the model can accept as input. Be mindful that this takes into account - the tokens used in the prompt, query and context. Also take note that some models - may provide their context window as the total between input and output tokens, - while others may provide it as the input tokens only. \n\n", "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": - "@nuclia/core \u2022 Docs \n \n @nuclia/core / Consumption \n Interface: Consumption - \n Properties \n customer_key_tokens \n \n customer_key_tokens: TokenConsumption - \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \n \n - normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined in - \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n", "42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696": - " \n optional max_paragraph: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 - \n \n name? \n \n optional name: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": " \n optional max_tokens: - number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: - string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": - " \n optional max_tokens: number \\| object \n \n Defines the maximum number - of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n", "e8525e64c5b44982b958d32cf6090613/t/page/2450-2808": - " \n optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? - \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n - BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 - \n \n max_tokens? \n\n"}}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '12310' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank - response: - body: - string: '{"context_scores":{"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.4981260597705841,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.43572142720222473,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.12808825075626373,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.10320506244897842,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.08419108390808105,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.07835980504751205,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.06119922548532486,"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808":0.05399883911013603,"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575":0.0361519381403923,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.03448852524161339,"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043":0.0333368256688118,"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139":0.024145422503352165,"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833":0.02157406695187092,"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386":0.01411789283156395,"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641":0.0029694451950490475,"6e8250e6b5264156988657a221fd5e94/t/page/0-397":0.002378123812377453,"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349":0.0022694687359035015,"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224":0.0017546144081279635,"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696":0.0011424495605751872,"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932":0.0010322310263291001}}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Content-Length: - - '1435' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:45 GMT - nuclia-learning-model: - - bge-reranker-v2-m3 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '158' - x-nuclia-trace-id: - - 5a97d7bc611f9ea705a61b2345903424 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\nExplica - c\u00f3mo utilizar el par\u00e1metro `max_tokens` en espa\u00f1ol, y proporciona - un enlace a la documentaci\u00f3n oficial.\n\n\n\nContext:\n\n**block-AA**\n\n## - Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\nTags: /k/text\n``` Use - the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context - size: Limits the amount of retrieved information sent to the LLM \n - Answer - length: Limits the length of the generated response \n Important Considerations - \n Context Limitations: \n\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\nTags: - /k/text\n``` max_tokens: the maximum number of input tokens to put in the final - context (including the prompt, the retrieved results and the user question). - \n max_output_tokens: the maximum number of tokens to generate. \n\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## - Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\nTags: /k/text\n``` \n - optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\nTags: - /k/text\n``` \n optional max_tokens: number \\| object \n \n Defines the maximum - number of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\nTags: - /k/text\n``` \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search - import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( - \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\nTags: - /k/text\n``` \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 - \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n - \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\nTags: - /k/text\n``` \n optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\nTags: - /k/text\n``` \n optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? - \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n - BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 - \n \n max_tokens? \n\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": - false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": - {"title": "validate_or_answer", "description": "Validate or answer", "parameters": - {"type": "object", "properties": {"reason": {"type": "string", "description": - "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": - "Partial or complete answer to the user query from the information in the context."}, - "missing_info_query": {"type": "string", "description": "Query needed to retrieve - the missing information in case the context is not enough to answer the question. - If the context does not answer the question at all, just return the original - question."}, "useful": {"type": "string", "description": "Is the context useful - to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", - "items": {"type": "string", "description": "Block ID cited in the answer, e.g. - block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, - "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, - "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "required"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '7401' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-origin: - - RAO - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"object\",\"object\":{\"reason\":\"The context\ - \ provides information on how to use the `max_tokens` parameter, including\ - \ its purpose and examples of its application. However, it does not provide\ - \ a link to the official documentation.\",\"answer\":\"El par\xE1metro `max_tokens`\ - \ se utiliza en el endpoint /ask para establecer l\xEDmites en: 1. El tama\xF1\ - o del contexto: limita la cantidad de informaci\xF3n recuperada enviada al\ - \ modelo de lenguaje. 2. La longitud de la respuesta generada: limita la longitud\ - \ de la respuesta generada. El valor de `max_tokens` define el n\xFAmero m\xE1\ - ximo de tokens de entrada que se incluir\xE1n en el contexto final, incluyendo\ - \ el aviso, los resultados recuperados y la pregunta del usuario. Tambi\xE9\ - n se puede definir un n\xFAmero m\xE1ximo de tokens a generar con `max_output_tokens`.\"\ - ,\"missing_info_query\":\"\xBFCu\xE1l es el enlace a la documentaci\xF3n oficial\ - \ sobre el par\xE1metro `max_tokens`?\",\"useful\":\"yes\",\"citations\":[\"\ - block-AA\",\"block-AB\",\"block-AD\"]}}}\n{\"chunk\":{\"type\":\"status\"\ - ,\"code\":\"0\"}}\n{\"chunk\":{\"type\":\"meta\",\"input_tokens\":49,\"output_tokens\"\ - :24,\"timings\":{\"generative\":3.051026222994551},\"input_nuclia_tokens\"\ - :0.049,\"output_nuclia_tokens\":0.024}}\n{\"chunk\":{\"normalized_tokens\"\ - :{\"input\":0.04929,\"output\":0.02424,\"image\":0.0},\"customer_key_tokens\"\ - :{\"input\":0.0,\"output\":0.0,\"image\":0.0},\"type\":\"consumption\"}}\n" - headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 15 Jul 2026 07:05:45 GMT - nuclia-learning-id: - - 7bca0c3a9de945dd88323f93f909fd48 - nuclia-learning-model: - - chatgpt-azure-4o-mini - via: - - 1.1 google - x-envoy-upstream-service-time: - - '3058' - x-nuclia-trace-id: - - 5a97d7bc611f9ea705a61b2345903424 - status: - code: 200 - message: OK -- request: - body: '' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-stf-nuakey: - - DUMMY - method: GET - uri: https://europe-1.dp.progress.cloud/api/v1/predict/tokens?model=multilingual&text=Explica+c%C3%B3mo+utilizar+el+par%C3%A1metro+%60max_tokens%60+en+espa%C3%B1ol%2C+y+proporciona+un+enlace+a+la+documentaci%C3%B3n+oficial. - response: - body: - string: "{\"tokens\":[{\"text\":\"espa\xF1ol\",\"ner\":\"LANGUAGE\",\"start\"\ - :51,\"end\":58}],\"time\":0.008199930191040039,\"input_tokens\":29}" - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '115' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:49 GMT - nuclia-learning-model: - - multilingual - via: - - 1.1 google - x-envoy-upstream-service-time: - - '14' - x-nuclia-trace-id: - - 5a97d7bc611f9ea705a61b2345903424 - status: - code: 200 - message: OK -- request: - body: '{"user_id": "arag-ask", "texts": ["Explica c\u00f3mo utilizar el par\u00e1metro - `max_tokens` en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n - oficial."]}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '150' - Host: - - europe-1.nuclia.cloud - User-Agent: - - nucliadb-sdk/6.13.1.post6414 - content-type: - - application/json - x-stf-serviceaccount: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/predict/run-agents-text - response: - body: - string: '{"results":[{"input_nuclia_tokens":0.0,"output_nuclia_tokens":0.0,"time":0.0001087188720703125,"payloads":[]}]}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Content-Length: - - '111' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:48 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '17' - x-nuclia-trace-id: - - ee6874a5ba4b96c98621208bea5e9aed - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "arag-ask", "system": null, - "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\n\nInformation about the KB:\n\n# - nuclia-docs\n\ndescription: Documentation of the Nuclia API, recipies, reference - \n\nAnd given the question: Explica c\u00f3mo utilizar el par\u00e1metro `max_tokens` - en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n oficial.\n\n## - labels: {''pmm'': [''Videos'', ''Partner Content'', ''Softcat'', ''Sales Enablement - Assets'', ''KO 26'', ''Data Sheets'', ''Progress Agentic RAG Training Materials - 2026'']}\n\n## Facets:\n\n## Content Types\nThe following content types are - available in the KB:\n\n- /n/i: 986\n- application/json: 1\n- text/markdown: - 985\nThe following languages are available in the KB:\n\n- ca: 3\n- cy: 1\n- - da: 1\n- en: 967\n- eo: 4\n- la: 7\n- nb: 1\n- tl: 1\n- yo: 1\n\n\n# Important - rules to follow\n\n\nprompt=''Be polite''\n"}, "citations": false, "citation_threshold": - null, "generative_model": "gemini-2.5-flash", "max_tokens": 8192, "query_context_images": - {}, "prefer_markdown": null, "json_schema": {"title": "ask_configuration", "description": - "Configuration extracted from reasoning engine", "parameters": {"type": "object", - "properties": {"link": {"type": "boolean", "description": "The user wants link - reference to the answer?"}, "knowledge_scan": {"type": "string", "description": - "If the query requires a knowledge aggregation or scan search to answer define - the entities, labels and relations to query in the KB. 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,\"modified\":\"2026-07-14T12:49:19.441566\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\\ - n\\n# Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**:\ - \ `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\\ - n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\\ - n\\n***\\n\\n### tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"9a5a602361bcf49d7619fc3995646f40\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageStructure \\\ - n Interface: PageStructure \\n Properties \\n page \\n \\n page: object \\\ - n \\n height \\n \\n height: number \\n \\n width \\n \\n width: number \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238\ - \ \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\"\ - 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\ PageStructure \\n Properties \\n page \\n \\n page: object \\n \\n height\ - \ \\n \\n height: number \\n \\n width \\n \\n width: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 \\n \\n\ - \ tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\"\ - ,\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs > rag > advanced\ - \ > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: consumption\\\ - ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\"\ - ;\\nimport TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\\ - n\\nAgentic RAG is a license and consumption-based service. This means that\ - \ you pay for the computational resources you consume. The consumption is\ - \ measured in **Agentic RAG tokens**.\\nAll public 3rd-party LLMs base their\ - \ pricing on the number of tokens consumed. In the LLM world, a token is around\ - \ 4-5 characters on average, which might fit an entire word or be split into\ - \ parts. The number of tokens is proportional to the amount of text, measured\ - \ in chunks of 4-5 characters. It closely relates to words but not entirely.\ - \ The longer a sentence is, the more tokens it will consume to read or to\ - \ generate it.\\nSince all these 3rd-party LLMs have different pricing, Agentic\ - \ RAG tokens serve to normalize the cost across all of them.\\n\\n## How Tokens\ - \ Are Consumed in RAG\\n\\nWhen a user asks a question to your Knowledge Box,\ - \ the token consumption process follows these steps:\\n\\n1. **Question Processing**:\ - \ The system finds the most relevant paragraphs to answer the question\\n2.\ - \ **Context Assembly**: These paragraphs are used as context when calling\ - \ the LLM model\\n3. **Prompt Creation**: Agentic RAG assembles the **prompt**,\ - \ **context**, and **question** into a single string\\n4. **LLM Processing**:\ - \ This complete string is sent to the LLM, corresponding to a certain number\ - \ of **input tokens**\\n5. **Answer Generation**: The LLM generates the answer,\ - \ which corresponds to a certain number of **output tokens**\\n\\n**Total\ - \ consumption** = Input tokens + Output tokens + Image tokens\\n\\n### Factors\ - \ That Impact Token Consumption\\n\\nToken consumption is directly affected\ - \ by:\\n\\n- **Large context**: Results from using RAG strategies like \\\"\ - Full resource\\\" or \\\"Neighbouring paragraphs\\\", or from using the `extra_context`\ - \ parameter\\n- **Long questions**: More detailed or complex questions require\ - \ more input tokens\\n- **Long prompts**: Extensive system prompts increase\ - \ the input token count\\n- **Detailed answers**: Comprehensive responses\ - \ require more output tokens\\n- **Images in context**: When using multimodal\ - \ models, images included in the retrieved context significantly increase\ - \ token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\\ - n### Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing\ - \ token consumption is to fine-tune your request parameters:\\n\\n- **Optimize\ - \ prompts**: Ensure your prompts are concise and focused, avoiding unnecessary\ - \ verbosity\\n- **Control resource scope**: When using the \\\"Full resource\\\ - \" strategy, use the `count` attribute to limit the number of resources returned\\\ - n- **Tune neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy,\ - \ optimize the `before` and `after` attributes to balance context quality\ - \ with token efficiency\\n- **Manage summary length**: When using the \\\"\ - Hierarchical\\\" strategy, ensure that resource summaries are appropriately\ - \ sized\\n- **Choose efficient models**: Select LLMs that offer better token\ - \ efficiency (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT\ - \ 4o)\\n\\n### Strategy 2: Set Hard Limits\\n\\nYou can implement safeguards\ - \ against excessive token consumption:\\n\\nUse the `max_tokens` parameter\ - \ on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post)\ - \ to set hard limits on:\\n- **Context size**: Limits the amount of retrieved\ - \ information sent to the LLM\\n- **Answer length**: Limits the length of\ - \ the generated response\\n\\n#### Important Considerations\\n\\n**Context\ - \ Limitations**:\\n- Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with\\n- Balance between\ - \ cost control and answer quality\\n\\n**Answer Length Limitations**:\\n-\ - \ The LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way\\n- **Recommended approach**: Include length requirements\ - \ in your prompt (e.g., \\\"Please answer in less than 200 words\\\") rather\ - \ than relying solely on hard limits\\n- This allows the LLM to naturally\ - \ conclude its response within the desired length\\n\\n## How to Monitor Token\ - \ Consumption\\n\\n### Understanding Token Consumption Data\\n\\nYou can receive\ - \ detailed token consumption information from the following endpoints that\ - \ utilize LLM models: `ask`, `chat`, `remi`, `query`, `sentence`, `summarize`,\ - \ `tokens`, and `rerank`.\\n\\n:::note\\nThe `rephrase` endpoint currently\ - \ does not support token consumption monitoring.\\n:::\\n\\nTo receive token\ - \ consumption data, you must include the following header in your request:\\\ - n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption data is provided\ - \ in different formats depending on the response type:\\n- **Streaming responses**\ - \ (`application/x-ndjson`): Token consumption appears as a separate JSON chunk\ - \ with type \\\"consumption\\\"\\n- **Standard responses** (`application/json`):\ - \ Token consumption is included in a \\\"consumption\\\" field within the\ - \ main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": {\\\ - n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n\\n\\n### Understanding Token Types\\n\\n**Normalized\ - \ Tokens** (`normalized_tokens`):\\n- These represent the number of Agentic\ - \ RAG tokens consumed and that you will be billed for\\n- Values are normalized\ - \ across different LLM providers for consistent billing\\n- Include separate\ - \ counts for:\\n - `input`: Tokens used for the prompt, context, and question\\\ - n - `output`: Tokens used for the generated response\\n - `image`: Tokens\ - \ used for image processing (when applicable)\\n\\n**Customer Key Tokens**\ - \ (`customer_key_tokens`):\\n- These represent tokens consumed when using\ - \ your own LLM API keys\\n- These tokens are **not billed** by Agentic RAG\ - \ since you're using your own API keys\\n- Values are also normalized for\ - \ comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id: consumption\ - \ \\n title: Token consumption \\n \\n import Tabs from @theme/Tabs ; \\n\ - \ import TabItem from @theme/TabItem ; \\n Token consumption \\n Agentic RAG\ - \ is a license and consumption-based service. This means that you pay for\ - \ the computational resources you consume. The consumption is measured in\ - \ Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on\ - \ the number of tokens consumed. In the LLM world, a token is around 4-5 characters\ - \ on average, which might fit an entire word or be split into parts. The number\ - \ of tokens is proportional to the amount of text, measured in chunks of 4-5\ - \ characters. It closely relates to words but not entirely. The longer a sentence\ - \ is, the more tokens it will consume to read or to generate it. \\n Since\ - \ all these 3rd-party LLMs have different pricing, Agentic RAG tokens serve\ - \ to normalize the cost across all of them. \\n How Tokens Are Consumed in\ - \ RAG \\n When a user asks a question to your Knowledge Box, the token consumption\ - \ process follows these steps: \\n \\n Question Processing: The system finds\ - \ the most relevant paragraphs to answer the question \\n Context Assembly:\ - \ These paragraphs are used as context when calling the LLM model \\n Prompt\ - \ Creation: Agentic RAG assembles the prompt, context, and question into a\ - \ single string \\n LLM Processing: This complete string is sent to the LLM,\ - \ corresponding to a certain number of input tokens \\n Answer Generation:\ - \ The LLM generates the answer, which corresponds to a certain number of output\ - \ tokens \\n \\n Total consumption = Input tokens + Output tokens + Image\ - \ tokens \\n Factors That Impact Token Consumption \\n Token consumption is\ - \ directly affected by: \\n \\n Large context: Results from using RAG strategies\ - \ like Full resource or Neighbouring paragraphs , or from using the extra_context\ - \ parameter \\n Long questions: More detailed or complex questions require\ - \ more input tokens \\n Long prompts: Extensive system prompts increase the\ - \ input token count \\n Detailed answers: Comprehensive responses require\ - \ more output tokens \\n Images in context: When using multimodal models,\ - \ images included in the retrieved context significantly increase token consumption\ - \ \\n \\n How to Limit and Control Token Consumption \\n Strategy 1: Optimize\ - \ Your Parameters \\n The first approach to reducing token consumption is\ - \ to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure your\ - \ prompts are concise and focused, avoiding unnecessary verbosity \\n Control\ - \ resource scope: When using the Full resource strategy, use the count attribute\ - \ to limit the number of resources returned \\n Tune neighboring context:\ - \ For the Neighbouring paragraphs strategy, optimize the before and after\ - \ attributes to balance context quality with token efficiency \\n Manage summary\ - \ length: When using the Hierarchical strategy, ensure that resource summaries\ - \ are appropriately sized \\n Choose efficient models: Select LLMs that offer\ - \ better token efficiency (typically, ChatGPT 4o-mini is more cost-effective\ - \ than ChatGPT 4o) \\n \\n Strategy 2: Set Hard Limits \\n You can implement\ - \ safeguards against excessive token consumption: \\n Use the max_tokens parameter\ - \ on the /ask endpoint to set hard limits on: \\n - Context size: Limits the\ - \ amount of retrieved information sent to the LLM \\n - Answer length: Limits\ - \ the length of the generated response \\n Important Considerations \\n Context\ - \ Limitations: \\n - Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with \\n - Balance between\ - \ cost control and answer quality \\n Answer Length Limitations: \\n - The\ - \ LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way \\n - Recommended approach: Include length requirements\ - \ in your prompt (e.g., Please answer in less than 200 words ) rather than\ - \ relying solely on hard limits \\n - This allows the LLM to naturally conclude\ - \ its response within the desired length \\n How to Monitor Token Consumption\ - \ \\n Understanding Token Consumption Data \\n You can receive detailed token\ - \ consumption information from the following endpoints that utilize LLM models:\ - \ ask, chat, remi, query, sentence, summarize, tokens, and rerank. \\n :::note\ - \ \\n The rephrase endpoint currently does not support token consumption monitoring.\ - \ \\n ::: \\n To receive token consumption data, you must include the following\ - \ header in your request: \\n X-SHOW-CONSUMPTION: true \\n The token consumption\ - \ data is provided in different formats depending on the response type: \\\ - n - Streaming responses (application/x-ndjson): Token consumption appears\ - \ as a separate JSON chunk with type consumption \\n - Standard responses\ - \ (application/json): Token consumption is included in a consumption field\ - \ within the main response \\n Token Consumption Response Format \\n \\n \\\ - n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens :\ - \ { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\\ - n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens):\ - \ \\n - These represent the number of Agentic RAG tokens consumed and that\ - \ you will be billed for \\n - Values are normalized across different LLM\ - \ providers for consistent billing \\n - Include separate counts for: \\n\ - \ - input: Tokens used for the prompt, context, and question \\n - output:\ - \ Tokens used for the generated response \\n - image: Tokens used for image\ - \ processing (when applicable) \\n Customer Key Tokens (customer_key_tokens):\ - \ \\n - These represent tokens consumed when using your own LLM API keys \\\ - n - These tokens are not billed by Agentic RAG since you're using your own\ - \ API keys \\n - Values are also normalized for comparison purposes across\ - \ different providers\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\"\ - :{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":319,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":198,\"key\":\"\"},{\"start\":198,\"end\":267,\"key\"\ - :\"\"},{\"start\":267,\"end\":319,\"key\":\"\"}]},{\"start\":319,\"end\":747,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":320,\"end\":399,\"key\":\"\"},{\"start\":399,\"\ - end\":517,\"key\":\"\"},{\"start\":517,\"end\":615,\"key\":\"\"},{\"start\"\ - :615,\"end\":661,\"key\":\"\"},{\"start\":661,\"end\":747,\"key\":\"\"}]},{\"\ - start\":747,\"end\":1008,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":748,\"end\":872,\"\ - key\":\"\"},{\"start\":872,\"end\":1008,\"key\":\"\"}]},{\"start\":1008,\"\ - end\":1650,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1011,\"end\":1650,\"key\":\"\"}]},{\"start\"\ - :1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1653,\"end\":2326,\"key\"\ - :\"\"}]},{\"start\":2326,\"end\":3075,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2329,\"\ - end\":3075,\"key\":\"\"}]},{\"start\":3075,\"end\":3340,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3076,\"end\":3340,\"key\":\"\"}]},{\"start\":3340,\"end\":4136,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3341,\"end\":4136,\"key\":\"\"}]},{\"start\"\ - :4136,\"end\":4446,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":4137,\"end\":4228,\"key\"\ - :\"\"},{\"start\":4228,\"end\":4446,\"key\":\"\"}]},{\"start\":4446,\"end\"\ - :4728,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":4447,\"end\":4728,\"key\":\"\"}]},{\"start\"\ - :4728,\"end\":4932,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":4729,\"end\":4932,\"key\"\ - :\"\"}]},{\"start\":4932,\"end\":5189,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4933,\"\ - end\":5189,\"key\":\"\"}]},{\"start\":5189,\"end\":5396,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":5190,\"end\":5396,\"key\":\"\"}]},{\"start\":5396,\"end\":5615,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5397,\"end\":5615,\"key\":\"\"}]},{\"start\"\ - :5615,\"end\":5849,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":5616,\"end\":5849,\"key\"\ - :\"\"}]}],\"ner\":{\"LLM\":\"ORG\",\"json\":\"PERSON\",\"Knowledge Box\":\"\ - PRODUCT\",\"Agentic RAG\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\"\ - :[{\"text\":\"Agentic RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\"\ - :298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"\ - text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"\ - start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge\ - \ Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"\ - text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\"\ - :4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:18:26.967788Z\"\ - ,\"last_understanding\":\"2026-06-09T08:18:26.517989Z\",\"last_extract\":\"\ - 2026-06-09T08:18:20.828386Z\",\"last_processing_start\":\"2026-06-09T08:18:20.761441Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Knowledge Box\"\ - :{\"position\":[{\"start\":941,\"end\":954}],\"entity\":\"Knowledge Box\"\ - },\"PRODUCT/Agentic RAG\":{\"position\":[{\"start\":298,\"end\":309},{\"start\"\ - :5222,\"end\":5233},{\"start\":5718,\"end\":5729}],\"entity\":\"Agentic RAG\"\ - },\"PERSON/json\":{\"position\":[{\"start\":4729,\"end\":4733}],\"entity\"\ - :\"json\"},\"ORG/LLM\":{\"position\":[{\"start\":406,\"end\":409},{\"start\"\ - :4058,\"end\":4061},{\"start\":5325,\"end\":5328}],\"entity\":\"LLM\"}},\"\ - relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\"\ - :[]}}}},\"generics\":{\"title\":{\"value\":\"docs > rag > advanced > consumption.\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > rag > advanced > consumption.\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - :{\"score\":0.6475687026977539,\"score_type\":\"VECTOR\",\"order\":2,\"text\"\ - :\" Use the max_tokens parameter on the /ask endpoint to set hard limits on:\ - \ \\n - Context size: Limits the amount of retrieved information sent to the\ - \ LLM \\n - Answer length: Limits the length of the generated response \\\ - n Important Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\"\ - :{\"score\":0.5797980427742004,\"score_type\":\"VECTOR\",\"order\":16,\"text\"\ - :\" \\n Large context: Results from using RAG strategies like Full resource\ - \ or Neighbouring paragraphs , or from using the extra_context parameter \\\ - n Long questions: More detailed or complex questions require more input tokens\ - \ \\n Long prompts: Extensive system prompts increase the input token count\ - \ \\n Detailed answers: Comprehensive responses require more output tokens\ - \ \\n Images in context: When using multimodal models, images included in\ - \ the retrieved context significantly increase token consumption \\n \\n How\ - \ to Limit and Control Token Consumption \\n Strategy 1: Optimize Your Parameters\ - \ \\n The first approach to reducing token consumption is to fine-tune your\ - \ request parameters: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\"\ - :1650,\"end\":2326,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\"\ - :\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\\ - n\\n# Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\\ - _history?\\n\\n> `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\\ - n\\n***\\n\\n### context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\\ - n\\n***\\n\\n### format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\\ - n\\n***\\n\\n### json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\\ - n\\n***\\n\\n### query\\\\_context?\\n\\n> `optional` **query\\\\_context**:\ - \ `string`[] \\\\| `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\ - \\_context\\\\_images**: `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**:\ - \ `string`\\n\\n#### content\\\\_type\\n\\n> **content\\\\_type**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\\ - _context\\\\_order**: `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`:\ - \ `string`\\\\]: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\\ - n\\n***\\n\\n### rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\\ - n\\n***\\n\\n### retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\\ - n\\n***\\n\\n### system?\\n\\n> `optional` **system**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\\ - n\\n***\\n\\n### truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\\ - n\\n***\\n\\n### user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\\ - n\\n#### prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions\ - \ \\n Interface: PredictAnswerOptions \\n Properties \\n chat_history? \\\ - n \\n optional chat_history: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242\ - \ \\n \\n citation_threshold? \\n \\n optional citation_threshold: number\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 \\n \\n context? \\\ - n \\n optional context: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243\ - \ \\n \\n format_prompt? \\n \\n optional format_prompt: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 \\n \\n generative_model?\ - \ \\n \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n \\n optional query_context: string[] \\\\| object \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\\ - n \\n query_context_images? \\n \\n optional query_context_images: object\ - \ \\n \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\\ - n content_type: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252\ - \ \\n \\n query_context_order? \\n \\n optional query_context_order: object\ - \ \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context?\ - \ \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259\ - \ \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\\ - n \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241\ - \ \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt?\ - \ \\n \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\"\ - :257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\"\ - :\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"\ - end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\ - \"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\"\ - :1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\"\ - :1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\"\ - :\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\"\ - :1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"\ - end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"\ - key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"\ - end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"\ - start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"\ - key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"\ - classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\"\ - :\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\"\ - ,\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > interfaces\ - \ > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"\ - score\":0.637296199798584,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\"\ - \ \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\"\ - :977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\"\ - :{\"score\":0.5942636728286743,\"score_type\":\"VECTOR\",\"order\":8,\"text\"\ - :\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"\ - end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"8b3e0ef630a346d1b591143309db87ec\":{\"id\":\"8b3e0ef630a346d1b591143309db87ec\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensMetric-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > NucliaTokensMetric\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"la\",\"en\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:48.423622\"\ - ,\"modified\":\"2026-07-14T12:51:37.835367\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensMetric\\\ - n\\n# Interface: NucliaTokensMetric\\n\\n## Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\\ - n\\n## Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\\ - n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\\ - n\\n***\\n\\n### name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\\ - n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\\ - n\\n***\\n\\n### value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\\ - n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric\ - \ \\n Interface: NucliaTokensMetric \\n Extends \\n \\n UsageMetric \\n \\\ - n Properties \\n details \\n \\n details: NucliaTokensDetails[] \\n \\n Overrides\ - \ \\n UsageMetric.details \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185\ - \ \\n \\n name \\n \\n name: nuclia_tokens \\n \\n Overrides \\n UsageMetric.name\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n value\ - \ \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"UsageMetric.name\"\ - ],\"paragraphs\":[{\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":281,\"key\":\"\"}]},{\"start\":281,\"end\":511,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":282,\"end\":511,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:39.809701Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:39.578340Z\",\"last_extract\":\"\ - 2026-07-14T12:51:39.320533Z\",\"last_processing_start\":\"2026-07-14T12:51:39.300652Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs > develop\ - \ > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\"\ - :{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\"\ - :{\"score\":0.5772987604141235,\"score_type\":\"VECTOR\",\"order\":17,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n\ - \ Interface: NucliaTokensMetric \\n Extends \\n \\n UsageMetric \\n \\n Properties\ - \ \\n details \\n \\n details: NucliaTokensDetails[] \\n \\n Overrides \\\ - n UsageMetric.details \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185\ - \ \\n \\n name \\n \\n\",\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - 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:\"OpenAI\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":2279,\"end\":2285}]},{\"\ - text\":\"Europe\",\"label\":\"LOC\",\"positions\":[{\"start\":3677,\"end\"\ - :3683}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:25.563584Z\"\ - ,\"last_understanding\":\"2026-06-09T08:08:25.117490Z\",\"last_extract\":\"\ - 2026-06-09T08:08:22.113416Z\",\"last_processing_start\":\"2026-06-09T08:08:22.055332Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"LOC/Europe\":{\"position\"\ - :[{\"start\":3677,\"end\":3683}],\"entity\":\"Europe\"},\"ORG/JSON\":{\"position\"\ - :[{\"start\":760,\"end\":764}],\"entity\":\"JSON\"},\"ORG/Nuclia\":{\"position\"\ - :[{\"start\":955,\"end\":961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"\ - end\":5507}],\"entity\":\"Nuclia\"},\"PRODUCT/OpenAI\":{\"position\":[{\"\ - start\":2279,\"end\":2285}],\"entity\":\"OpenAI\"},\"LOC/Asia\":{\"position\"\ - :[{\"start\":531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"\ - end\":3720},{\"start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}],\"\ - entity\":\"Asia\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\"\ - :{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs >\ - \ develop > python sdk > 05 search\",\"extracted\":{\"text\":{\"text\":\"\ - docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"fields\"\ - :{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":0.5840893983840942,\"score_type\":\"VECTOR\",\"order\":13,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"6e8250e6b5264156988657a221fd5e94\":{\"id\"\ - :\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\"\ - ,\"title\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md)\ - \ / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\\ - n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\ - \\_key\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\\ - n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\\ - n\\n***\\n\\n### type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\"\ - ,\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"\ - 2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > namespaces >\ - \ Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - :{\"score\":0.5846757292747498,\"score_type\":\"VECTOR\",\"order\":12,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\\ - n\\n# Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\\ - n\\n> `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\\ - n\\n***\\n\\n### driver?\\n\\n> `optional` **driver**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\\ - n\\n***\\n\\n### input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\\ - n\\n***\\n\\n### max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\\ - n\\n***\\n\\n### output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\\ - n\\n#### default\\\\_max?\\n\\n> `optional` **default\\\\_max**: `number`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\\ - n\\n***\\n\\n### prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface:\ - \ ModelConfig \\n Properties \\n assume_role? \\n \\n optional assume_role:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592\ - \ \\n \\n driver? \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n \\\ - n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588\ - \ \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"\ - key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\"\ - :480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"\ - 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:\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\ - \",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\"\ - ,\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\\ - n\\n# Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\\ - n\\n> `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\\ - n\\n***\\n\\n### effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\\ - n Interface: ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional\ - \ budget_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n \\n optional effort: NumericReasoningEffort \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"\ - key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\"\ - :329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\"\ - ,\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\"\ - :\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - :{\"score\":0.589895486831665,\"score_type\":\"VECTOR\",\"order\":9,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface:\ - \ ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\"\ - ,\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs > develop\ - \ > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2026-06-09T08:08:04.176136\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Retrieval Agents Orchestrator\\\ - n\\nInteract with Nuclia's Retrieval Agents Orchestrator to have intelligent\ - \ conversations over several knowledge sources with persistent session management\ - \ and real-time streaming responses.\\n\\n## Prerequisites\\n\\nInstall the\ - \ Nuclia SDK:\\n\\n```sh\\npip install nuclia\\n```\\n\\nEnsure you have:\\\ - n- A valid Nuclia authentication token (see [Authentication](02-auth.md))\\\ - n- Access to a configured Retrieval Agent\\n\\n## Overview\\n\\nThe nuclia.py\ - \ library provides several ways to interact with your Retrieval Agents Orchestrators:\\\ - n\\n- **Interactive CLI**: A rich, user-friendly terminal interface (recommended)\\\ - n- **Standard CLI**: Direct access to raw websocket messages for debugging\\\ - n- **Session Management**: Create and manage persistent conversation sessions\\\ - n- **Programmatic API**: Python SDK for building custom applications\\n\\\ - n\\n## Listing Available Agents\\n\\nDiscover what Retrieval Agents Orchestrators\ - \ you have access to.\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents list\\n \ - \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\\ - n\\n agents = NucliaAgents()\\n all_agents = agents.list()\\n\\n for agent\ - \ in all_agents:\\n print(f\\\"Agent: {agent.title} ({agent.id})\\\"\ - )\\n print(f\\\" Slug: {agent.slug}\\\")\\n print(f\\\" Zone:\ - \ {agent.zone}\\\")\\n ```\\n\\n### Getting a Specific Agent\\n\\n- CLI:\\\ - n\\n ```sh\\n nuclia agents get --account=\\\"my-account\\\" --id=\\\"agent-uuid\\\ - \" --zone=\\\"europe-1\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents\ - \ import NucliaAgents\\n\\n agents = NucliaAgents()\\n agent_details = agents.get(\\\ - n account=\\\"my-account\\\",\\n id=\\\"agent-uuid\\\",\\n \ - \ zone=\\\"europe-1\\\"\\n )\\n print(agent_details)\\n ```\\n\\n### Setting\ - \ a Default Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents default [AGENT_SLUG\ - \ or AGENT_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents\ - \ import NucliaAgents\\n\\n agents = NucliaAgents()\\n agents.default(\\\ - \"my-agent\\\")\\n ```\\n\\nThis sets the default agent for all subsequent\ - \ operations.\\n\\n## Interactive CLI (Recommended)\\n\\nThe interactive CLI\ - \ provides a beautiful, real-time interface for conversing with your Retrieval\ - \ Agents Orchestrator.\\n\\n### Starting the Interactive CLI\\n\\n- CLI:\\\ - n\\n ```sh\\n nuclia agent cli interact\\n ```\\n\\n- SDK:\\n\\n ```python\\\ - n from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\\ - n agent.cli.interact()\\n ```\\n\\nThis launches an interactive terminal\ - \ session where you can:\\n- Ask questions and see streaming responses\\n-\ - \ View processing steps in real-time\\n- Manage conversation sessions\\n-\ - \ See retrieved context and citations\\n\\n### Interactive CLI Commands\\\ - n\\nThe CLI supports several commands (prefix with `/`):\\n\\n| Command |\ - \ Description |\\n|---------|-------------|\\n| `/help` | Show available commands\ - \ |\\n| `/new_session` | Create a new persistent session |\\n| `/list_sessions`\ - \ | List all your sessions |\\n| `/change_session` | Switch to a different\ - \ session, use 'ephemeral' for a temporary session |\\n| `/clear` | Clear\ - \ the screen |\\n| `/exit` | Exit the CLI |\\n\\nPlease note that all commands\ - \ related to sessions require a Retrieval Agent Orchestrator with the option\ - \ **Agent with memory** enabled during creation.\\n\\n## Session Management\\\ - n\\nSessions allow you to maintain conversation context across multiple interactions.\\\ - n\\n> This feature will only be available if you checked **Agent with memory**\ - \ during the creation of your Retrieval Agents Orchestrator.\\n\\n### Creating\ - \ a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent session new --name=\\\ - \"My Research Session\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent\ - \ import NucliaAgent\\n\\n agent = NucliaAgent()\\n session_uuid = agent.session.new(\\\ - \"My Research Session\\\")\\n print(f\\\"Created session: {session_uuid}\\\ - \")\\n ```\\n\\n### Listing Sessions\\n\\n- CLI:\\n\\n ```sh\\n nuclia\ - \ agent session list\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent\ - \ import NucliaAgent\\n\\n agent = NucliaAgent()\\n sessions = agent.session.list()\\\ - n for session in sessions.resources:\\n print(f\\\"{session.title}:\ - \ {session.id}\\\")\\n ```\\n\\n### Getting a Session\\n\\n- CLI:\\n\\n \ - \ ```sh\\n nuclia agent session get --session_uuid=[SESSION_UUID]\\n ```\\\ - n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\\ - n\\n agent = NucliaAgent()\\n session = agent.session.get(session_uuid)\\\ - n print(f\\\"Session: {session.title}\\\")\\n print(f\\\"Created: {session.created}\\\ - \")\\n ```\\n\\n### Deleting a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia\ - \ agent session delete --session_uuid=[SESSION_UUID]\\n ```\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n agent\ - \ = NucliaAgent()\\n agent.session.delete(session_uuid)\\n ```\\n\\n## Interaction\\\ - n\\nAside from the interactive CLI, you can interact with your Retrieval Agents\ - \ Orchestrator with the simple CLI or programmatically using the SDK.\\n\\\ - n### Basic Interaction\\n\\n\\n**CLI:**\\n```bash\\nnuclia agent interact\ - \ \\\"What is Eric known for?\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom nuclia.sdk.agent\ - \ import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate over streaming\ - \ responses\\nfor response in agent.interact(\\n question=\\\"What is Eric\ - \ known for?\\\"\\n):\\n if response.operation == \\\"ANSWER\\\" and response.answer:\\\ - n print(response.answer)\\n elif response.step:\\n print(f\\\ - \"Processing: {response.step.module}\\\")\\n```\\n\\nNot supplying a `session_uuid`\ - \ when calling `interact` will use an ephemeral session by default. To maintain\ - \ context, provide a persistent session UUID.\\n\\n### Using Persistent Sessions\\\ - n\\n**CLI:**\\n```bash\\nnuclia agent sessions new \\\"Customer Support Chat\\\ - \"\\n# Note the session UUID returned\\nnuclia agent interact \\\"What are\ - \ your business hours?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\nnuclia agent\ - \ interact \\\"Are you open on weekends?\\\" --session_uuid=\\\"SESSION_UUID\\\ - \"\\n```\\n\\n**SDK:**\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\\ - n\\nagent = NucliaAgent()\\n\\n# Create a session\\nsession_uuid = agent.session.new(\\\ - \"Customer Support Chat\\\")\\n\\n# Have a conversation with context\\nfor\ - \ response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\ - \"What are your business hours?\\\"\\n):\\n if response.answer:\\n \ - \ print(response.answer)\\n\\n# Follow-up question maintains context\\\ - nfor response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\ - \"Are you open on weekends?\\\"\\n):\\n if response.answer:\\n print(response.answer)\\\ - n```\\n\\n## Understanding Response Types\\n\\nWhen interacting with an agent,\ - \ you receive a stream of `AragAnswer` objects with different operations:\\\ - n\\n| Operation | Description |\\n|-----------|-------------|\\n| `START`\ - \ | Interaction has begun |\\n| `ANSWER` | Processing step or partial answer\ - \ |\\n| `DONE` | Interaction complete |\\n| `ERROR` | An error occurred |\\\ - n| `AGENT_REQUEST` | Agent needs user feedback |\\n\\n### Response Attributes\\\ - n\\nEach response may contain:\\n\\n- **`step`**: Information about the current\ - \ processing step\\n - `module`: The module being executed (e.g., \\\"rephrase\\\ - \", \\\"basic_ask\\\", \\\"remi\\\")\\n - `title`: Display title for the\ - \ step\\n - `value`: Result of the step\\n - `reason`: Explanation for the\ - \ step\\n - `timeit`: Time taken in seconds\\n - `input_nuclia_tokens`/`output_nuclia_tokens`:\ - \ Token usage\\n\\n- **`context`**: Retrieved context from the knowledge base\\\ - n - `chunks`: List of retrieved text chunks with sources\\n - `summary`:\ - \ Summary of the context or partial answer\\n\\n- **`answer`**: The final\ - \ answer text (Markdown formatted)\\n\\n- **`generated_text`**: Intermediate\ - \ generated text\\n\\n- **`possible_answer`**: Alternative answer being considered\\\ - n\\n- **`exception`**: Error details if something went wrong\\n\\n### Processing\ - \ Responses\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom\ - \ nuclia_models.agent.interaction import AnswerOperation\\n\\nagent = NucliaAgent()\\\ - n\\nfor response in agent.interact(question=\\\"Tell me about AI\\\"):\\n\ - \ if response.operation == AnswerOperation.START:\\n print(\\\"\ - Starting...\\\")\\n \\n elif response.step:\\n print(f\\\"Step:\ - \ {response.step.module} ({response.step.timeit:.2f}s)\\\")\\n \\n elif\ - \ response.context:\\n print(f\\\"Retrieved {len(response.context.chunks)}\ - \ chunks\\\")\\n for chunk in response.context.chunks:\\n \ - \ print(f\\\" - {chunk.title}: {chunk.text[:100]}...\\\")\\n \\n \ - \ elif response.answer:\\n print(f\\\"\\\\nFinal Answer:\\\\n{response.answer}\\\ - \")\\n \\n elif response.operation == AnswerOperation.DONE:\\n \ - \ print(\\\"Complete!\\\")\\n \\n elif response.operation == AnswerOperation.ERROR:\\\ - n print(f\\\"Error: {response.exception.detail if response.exception\ - \ else 'Unknown'}\\\")\\n```\\n\\n## Standard CLI for Raw Messages\\n\\nFor\ - \ debugging or advanced use cases, you can access raw websocket messages programmatically:\\\ - n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\\ - n\\n# Iterate over all messages\\nfor message in agent.interact(\\n question=\\\ - \"What is RAO?\\\"\\n):\\n # message is an AragAnswer object with all raw\ - \ data\\n print(f\\\"Operation: {message.operation}\\\")\\n print(f\\\ - \"Raw message: {message.model_dump_json(indent=2)}\\\")\\n```\\n\\nThis gives\ - \ you direct access to all websocket message data for debugging or custom\ - \ processing.\\n\\n## Advanced Features\\n\\n### Agent Feedback Requests\\\ - n\\nAgents can request additional input from users during processing:\\n\\\ - n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom nuclia_models.agent.interaction\ - \ import AnswerOperation\\n\\nagent = NucliaAgent()\\ngenerator = agent.interact(question=\\\ - \"Help me with X\\\")\\n\\nfor response in generator:\\n if response.operation\ - \ == AnswerOperation.AGENT_REQUEST:\\n # Agent is requesting user input\\\ - n user_input = input(f\\\"Agent asks: {response.feedback.question}\\\ - \\n> \\\")\\n # Send response back\\n generator.send(user_input)\\\ - n elif response.answer:\\n print(response.answer)\\n```\\n\\n###\ - \ Error Handling\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\\ - nfrom nuclia.exceptions import RaoAPIException\\n\\nagent = NucliaAgent()\\\ - n\\ntry:\\n for response in agent.interact(question=\\\"Hello?\\\"):\\\ - n if response.exception:\\n print(f\\\"Agent error: {response.exception.detail}\\\ - \")\\n elif response.answer:\\n print(response.answer)\\\ - nexcept RaoAPIException as e:\\n print(f\\\"API error: {e.detail}\\\")\\\ - nexcept Exception as e:\\n print(f\\\"Unexpected error: {e}\\\")\\n```\\\ - n\\n### Passing Custom Headers to MCP\\n\\nIf your Retrieval Agents Orchestrator\ - \ requires custom headers for MCP Agents, you can pass them as follows:\\\ - n\\n**CLI:**\\n```bash\\nnuclia agent interact \\\"What is AI?\\\" --headers\ - \ '{\\\"X-Custom-Header\\\": \\\"value\\\"}'\\n```\\n\\n**SDK:**\\n```python\\\ - nfrom nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\nfor\ - \ response in agent.interact(\\n question=\\\"What is AI?\\\",\\n headers={\\\ - \"X-Custom-Header\\\": \\\"value\\\"}\\n):\\n if response.answer:\\n \ - \ print(response.answer)\\n```\\n\\nPlease ensure that the 'Allowed Headers'\ - \ configuration in your MCP agent includes any custom headers you wish to\ - \ use.\\n\\n## Best Practices\\n\\n1. **Use Sessions for Context**: Create\ - \ sessions when you need multi-turn conversations with context retention\\\ - n2. **Use Ephemeral Sessions for One-offs**: Don't supply a session UUID for\ - \ using agents in a stateless manner.\\n3. **Stream for UX**: Process responses\ - \ as they arrive for better user experience\\n4. **Handle All Operations**:\ - \ Check for different operation types (START, ANSWER, DONE, ERROR) when processing\ - \ responses\\n5. **Clean Up Sessions**: Delete sessions when done to avoid\ - \ clutter\\n6. **Use Interactive CLI**: For manual testing and exploration,\ - \ the interactive CLI provides the best experience\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"bbaaf8cccd2b664ba4f7daf47d1f2bf4\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Retrieval Agents\ - \ Orchestrator \\n Interact with Nuclia's Retrieval Agents Orchestrator to\ - \ have intelligent conversations over several knowledge sources with persistent\ - \ session management and real-time streaming responses. \\n Prerequisites\ - \ \\n Install the Nuclia SDK: \\n sh \\n pip install nuclia \\n Ensure you\ - \ have: \\n - A valid Nuclia authentication token (see Authentication) \\\ - n - Access to a configured Retrieval Agent \\n Overview \\n The nuclia.py\ - \ library provides several ways to interact with your Retrieval Agents Orchestrators:\ - \ \\n \\n Interactive CLI: A rich, user-friendly terminal interface (recommended)\ - \ \\n Standard CLI: Direct access to raw websocket messages for debugging\ - \ \\n Session Management: Create and manage persistent conversation sessions\ - \ \\n Programmatic API: Python SDK for building custom applications \\n \\\ - n Listing Available Agents \\n Discover what Retrieval Agents Orchestrators\ - \ you have access to. \\n \\n CLI: \\n \\n sh \\n nuclia agents list \\n \\\ - n SDK: \\n \\n ```python \\n from nuclia.sdk.agents import NucliaAgents \\\ - n agents = NucliaAgents() \\n all_agents = agents.list() \\n for agent in\ - \ all_agents: \\n print(f Agent: {agent.title} ({agent.id}) ) \\n print(f\ - \ Slug: {agent.slug} ) \\n print(f Zone: {agent.zone} ) \\n ``` \\n Getting\ - \ a Specific Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents get --account=\ - \ my-account --id= agent-uuid --zone= europe-1 \\n \\n SDK: \\n \\n ```python\ - \ \\n from nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents()\ - \ \\n agent_details = agents.get( \\n account= my-account , \\n id= agent-uuid\ - \ , \\n zone= europe-1 \\n ) \\n print(agent_details) \\n ``` \\n Setting\ - \ a Default Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents default [AGENT_SLUG\ - \ or AGENT_UUID] \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agents\ - \ import NucliaAgents \\n agents = NucliaAgents() \\n agents.default( my-agent\ - \ ) \\n ``` \\n This sets the default agent for all subsequent operations.\ - \ \\n Interactive CLI (Recommended) \\n The interactive CLI provides a beautiful,\ - \ real-time interface for conversing with your Retrieval Agents Orchestrator.\ - \ \\n Starting the Interactive CLI \\n \\n CLI: \\n \\n sh \\n nuclia agent\ - \ cli interact \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import\ - \ NucliaAgent \\n agent = NucliaAgent() \\n agent.cli.interact() \\n ``` \\\ - n This launches an interactive terminal session where you can: \\n - Ask questions\ - \ and see streaming responses \\n - View processing steps in real-time \\\ - n - Manage conversation sessions \\n - See retrieved context and citations\ - \ \\n Interactive CLI Commands \\n The CLI supports several commands (prefix\ - \ with /): \\n | Command | Description | \\n |---------|-------------| \\\ - n | /help | Show available commands | \\n | /new_session | Create a new persistent\ - \ session | \\n | /list_sessions | List all your sessions | \\n | /change_session\ - \ | Switch to a different session, use 'ephemeral' for a temporary session\ - \ | \\n | /clear | Clear the screen | \\n | /exit | Exit the CLI | \\n Please\ - \ note that all commands related to sessions require a Retrieval Agent Orchestrator\ - \ with the option Agent with memory enabled during creation. \\n Session Management\ - \ \\n Sessions allow you to maintain conversation context across multiple\ - \ interactions. \\n \\n This feature will only be available if you checked\ - \ Agent with memory during the creation of your Retrieval Agents Orchestrator.\ - \ \\n \\n Creating a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session\ - \ new --name= My Research Session \\n \\n SDK: \\n \\n ```python \\n from\ - \ nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n session_uuid\ - \ = agent.session.new( My Research Session ) \\n print(f Created session:\ - \ {session_uuid} ) \\n ``` \\n Listing Sessions \\n \\n CLI: \\n \\n sh \\\ - n nuclia agent session list \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent\ - \ import NucliaAgent \\n agent = NucliaAgent() \\n sessions = agent.session.list()\ - \ \\n for session in sessions.resources: \\n print(f {session.title}: {session.id}\ - \ ) \\n ``` \\n Getting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent\ - \ session get --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python\ - \ \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\\ - n session = agent.session.get(session_uuid) \\n print(f Session: {session.title}\ - \ ) \\n print(f Created: {session.created} ) \\n ``` \\n Deleting a Session\ - \ \\n \\n CLI: \\n \\n sh \\n nuclia agent session delete --session_uuid=[SESSION_UUID]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import NucliaAgent\ - \ \\n agent = NucliaAgent() \\n agent.session.delete(session_uuid) \\n ```\ - \ \\n Interaction \\n Aside from the interactive CLI, you can interact with\ - \ your Retrieval Agents Orchestrator with the simple CLI or programmatically\ - \ using the SDK. \\n Basic Interaction \\n CLI: \\n bash \\n nuclia agent\ - \ interact What is Eric known for? \\n SDK: \\n ```python \\n from nuclia.sdk.agent\ - \ import NucliaAgent \\n agent = NucliaAgent() \\n Iterate over streaming\ - \ responses \\n for response in agent.interact( \\n question= What is Eric\ - \ known for? \\n ): \\n if response.operation == ANSWER and response.answer:\ - \ \\n print(response.answer) \\n elif response.step: \\n print(f Processing:\ - \ {response.step.module} ) \\n ``` \\n Not supplying a session_uuid when calling\ - \ interact will use an ephemeral session by default. To maintain context,\ - \ provide a persistent session UUID. \\n Using Persistent Sessions \\n CLI:\ - \ \\n ```bash \\n nuclia agent sessions new Customer Support Chat \\n Note\ - \ the session UUID returned \\n nuclia agent interact What are your business\ - \ hours? --session_uuid= SESSION_UUID \\n nuclia agent interact Are you open\ - \ on weekends? --session_uuid= SESSION_UUID \\n ``` \\n SDK: \\n ```python\ - \ \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\\ - n Create a session \\n session_uuid = agent.session.new( Customer Support\ - \ Chat ) \\n Have a conversation with context \\n for response in agent.interact(\ - \ \\n session_uuid=session_uuid, \\n question= What are your business hours?\ - \ \\n ): \\n if response.answer: \\n print(response.answer) \\n Follow-up\ - \ question maintains context \\n for response in agent.interact( \\n session_uuid=session_uuid,\ - \ \\n question= Are you open on weekends? \\n ): \\n if response.answer: \\\ - n print(response.answer) \\n ``` \\n Understanding Response Types \\n When\ - \ interacting with an agent, you receive a stream of AragAnswer objects with\ - \ different operations: \\n | Operation | Description | \\n |-----------|-------------|\ - \ \\n | START | Interaction has begun | \\n | ANSWER | Processing step or\ - \ partial answer | \\n | DONE | Interaction complete | \\n | ERROR | An error\ - \ occurred | \\n | AGENT_REQUEST | Agent needs user feedback | \\n Response\ - \ Attributes \\n Each response may contain: \\n \\n step: Information about\ - \ the current processing step \\n module: The module being executed (e.g.,\ - \ rephrase , basic_ask , remi ) \\n title: Display title for the step \\n\ - \ value: Result of the step \\n reason: Explanation for the step \\n timeit:\ - \ Time taken in seconds \\n \\n input_nuclia_tokens/output_nuclia_tokens:\ - \ Token usage \\n \\n \\n context: Retrieved context from the knowledge base\ - \ \\n \\n chunks: List of retrieved text chunks with sources \\n \\n summary:\ - \ Summary of the context or partial answer \\n \\n \\n answer: The final answer\ - \ text (Markdown formatted) \\n \\n \\n generated_text: Intermediate generated\ - \ text \\n \\n \\n possible_answer: Alternative answer being considered \\\ - n \\n \\n exception: Error details if something went wrong \\n \\n \\n Processing\ - \ Responses \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\\ - n from nuclia_models.agent.interaction import AnswerOperation \\n agent =\ - \ NucliaAgent() \\n for response in agent.interact(question= Tell me about\ - \ AI ): \\n if response.operation == AnswerOperation.START: \\n print( Starting...\ - \ ) \\n elif response.step: \\n print(f Step: {response.step.module} ({response.step.timeit:.2f}s)\ - \ ) \\n \\n elif response.context: \\n print(f Retrieved {len(response.context.chunks)}\ - \ chunks ) \\n for chunk in response.context.chunks: \\n print(f - {chunk.title}:\ - \ {chunk.text[:100]}... ) \\n \\n elif response.answer: \\n print(f \\\\nFinal\ - \ Answer:\\\\n{response.answer} ) \\n \\n elif response.operation == AnswerOperation.DONE:\ - \ \\n print( Complete! ) \\n \\n elif response.operation == AnswerOperation.ERROR:\ - \ \\n print(f Error: {response.exception.detail if response.exception else\ - \ 'Unknown'} ) \\n \\n ``` \\n Standard CLI for Raw Messages \\n For debugging\ - \ or advanced use cases, you can access raw websocket messages programmatically:\ - \ \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent()\ - \ \\n Iterate over all messages \\n for message in agent.interact( \\n question=\ - \ What is RAO? \\n ): \\n # message is an AragAnswer object with all raw data\ - \ \\n print(f Operation: {message.operation} ) \\n print(f Raw message: {message.model_dump_json(indent=2)}\ - \ ) \\n ``` \\n This gives you direct access to all websocket message data\ - \ for debugging or custom processing. \\n Advanced Features \\n Agent Feedback\ - \ Requests \\n Agents can request additional input from users during processing:\ - \ \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction\ - \ import AnswerOperation \\n agent = NucliaAgent() \\n generator = agent.interact(question=\ - \ Help me with X ) \\n for response in generator: \\n if response.operation\ - \ == AnswerOperation.AGENT_REQUEST: \\n # Agent is requesting user input \\\ - n user_input = input(f Agent asks: {response.feedback.question}\\\\n> ) \\\ - n # Send response back \\n generator.send(user_input) \\n elif response.answer:\ - \ \\n print(response.answer) \\n ``` \\n Error Handling \\n ```python \\n\ - \ from nuclia.sdk.agent import NucliaAgent \\n from nuclia.exceptions import\ - \ RaoAPIException \\n agent = NucliaAgent() \\n try: \\n for response in agent.interact(question=\ - \ Hello? ): \\n if response.exception: \\n print(f Agent error: {response.exception.detail}\ - \ ) \\n elif response.answer: \\n print(response.answer) \\n except RaoAPIException\ - \ as e: \\n print(f API error: {e.detail} ) \\n except Exception as e: \\\ - n print(f Unexpected error: {e} ) \\n ``` \\n Passing Custom Headers to MCP\ - \ \\n If your Retrieval Agents Orchestrator requires custom headers for MCP\ - \ Agents, you can pass them as follows: \\n CLI: \\n bash \\n nuclia agent\ - \ interact What is AI? --headers '{ X-Custom-Header : value }' \\n SDK: \\\ - n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent()\ - \ \\n for response in agent.interact( \\n question= What is AI? , \\n headers={\ - \ X-Custom-Header : value } \\n ): \\n if response.answer: \\n print(response.answer)\ - \ \\n ``` \\n Please ensure that the 'Allowed Headers' configuration in your\ - \ MCP agent includes any custom headers you wish to use. \\n Best Practices\ - \ \\n \\n Use Sessions for Context: Create sessions when you need multi-turn\ - \ conversations with context retention \\n Use Ephemeral Sessions for One-offs:\ - \ Don't supply a session UUID for using agents in a stateless manner. \\n\ - \ Stream for UX: Process responses as they arrive for better user experience\ - \ \\n Handle All Operations: Check for different operation types (START, ANSWER,\ - 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:{\"position\":[{\"start\":4531,\"end\":4535},{\"start\":4722,\"end\":4726}],\"\ - entity\":\"Eric\"},\"ORG/NucliaAgent\":{\"position\":[{\"start\":2102,\"end\"\ - :2113},{\"start\":3611,\"end\":3622},{\"start\":3633,\"end\":3644},{\"start\"\ - :4226,\"end\":4237},{\"start\":4597,\"end\":4608},{\"start\":4619,\"end\"\ - :4630},{\"start\":5395,\"end\":5406},{\"start\":5417,\"end\":5428},{\"start\"\ - :7120,\"end\":7131},{\"start\":7204,\"end\":7215},{\"start\":8088,\"end\"\ - :8099},{\"start\":8110,\"end\":8121},{\"start\":8633,\"end\":8644},{\"start\"\ - :9145,\"end\":9156},{\"start\":9215,\"end\":9226},{\"start\":9828,\"end\"\ - :9839},{\"start\":9850,\"end\":9861}],\"entity\":\"NucliaAgent\"},\"ORG/MCP\"\ - :{\"position\":[{\"start\":10085,\"end\":10088}],\"entity\":\"MCP\"},\"PRODUCT/Retrieval\ - \ Agents Orchestrator\":{\"position\":[{\"start\":1942,\"end\":1971},{\"start\"\ - :3171,\"end\":3200},{\"start\":4380,\"end\":4409}],\"entity\":\"Retrieval\ - \ Agents Orchestrator\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\"\ - :\"followed by\",\"metadata\":{\"paragraph_id\":\"44d05174f1954331b62f5e4026f2b01a/t/page/5050-5651\"\ - ,\"source_start\":5300,\"source_end\":5308,\"to_start\":5395,\"to_end\":5406},\"\ - from\":{\"value\":\"weekends\",\"type\":\"entity\",\"group\":\"DATE\"},\"\ - to\":{\"value\":\"NucliaAgent\",\"type\":\"entity\",\"group\":\"ORG\"}},{\"\ - relation\":\"OTHER\",\"label\":\"has part\",\"metadata\":{\"paragraph_id\"\ - :\"44d05174f1954331b62f5e4026f2b01a/t/page/9507-9990\",\"source_start\":9828,\"\ - source_end\":9839,\"to_start\":9850,\"to_end\":9861},\"from\":{\"value\":\"\ - NucliaAgent\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\":{\"value\":\"\ - NucliaAgent\",\"type\":\"entity\",\"group\":\"ORG\"}}],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > develop > python sdk > 14 rao\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 14 rao\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668\"\ - :{\"score\":0.5873297452926636,\"score_type\":\"VECTOR\",\"order\":11,\"text\"\ - :\" \\n step: Information about the current processing step \\n module: The\ - \ module being executed (e.g., rephrase , basic_ask , remi ) \\n title: Display\ - \ title for the step \\n value: Result of the step \\n reason: Explanation\ - \ for the step \\n timeit: Time taken in seconds \\n \\n input_nuclia_tokens/output_nuclia_tokens:\ - \ Token usage \\n \\n \\n\",\"id\":\"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":19,\"\ - start\":6346,\"end\":6668,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\"\ - :\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > Consumption\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\"\ - ,\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\\ - n\\n# Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\ - \\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\\ - n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface:\ - \ Consumption \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens:\ - \ TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"\ - 2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Consumption\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\"\ - :0.5943073630332947,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption\ - \ \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\"\ - ,\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\\ - n\\n# Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\\ - n\\n***\\n\\n### line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\\ - n\\n***\\n\\n### text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\\ - n\\n***\\n\\n### width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\\ - n\\n***\\n\\n### x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\\ - n\\n***\\n\\n### y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface:\ - \ PageToken \\n Properties \\n height \\n \\n height: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 \\n \\n\ - \ line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250\ - \ \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248\ - \ \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246\ - \ \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - 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\ \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\"\ - ,\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs > rag\ - \ > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. It must not be used in production. \\n preferMarkdown:\ - \ require the generative answer to be formatted in Markdown. \\n openNewTab:\ - \ open the link in a new tab when clicking on the result. \\n orFilterLogic:\ - \ use the OR logic for filters instead of the default AND logic. \\n noChatHistory:\ - \ the previous questions and answers in the chat mode will not be passed as\ - \ context when generating a new answer. \\n showHidden: display hidden resources\ - \ in the search results. \\n showAttachedImages: display images attached to\ - \ the matching paragraphs in the search results. \\n \\n Other parameters\ - \ \\n \\n backend: the URL of the backend to use. Useful if you use your own\ - \ proxy to access the Agentic RAG API. \\n zone: the zone to use. \\n knowledgebox:\ - \ the Knowledge Box id. \\n placeholder: the text displayed in the search\ - \ bar when it is empty. \\n lang: the language of the widget. Currently supported:\ - \ ca, fr, en, es. Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. It must use {context} and {question} variables.\ - \ \\n system_prompt: the system prompt to use for the generative model. \\\ - n rephrase_prompt: the prompt to use when optimizing the user query to get\ - \ the best search results. \\n generativemodel: the generative model to use\ - \ for the answer generation. \\n rag_strategies: the RAG strategies to apply\ - \ to the retrieved paragraphs. \\n rag_images_strategies: the RAG strategies\ - \ to apply to the retrieved images. \\n not_enough_data_message: the message\ - \ to display when there is not enough data to generate an answer. \\n ask_to_resource:\ - \ the resource ID to use as context for the generative model. \\n max_tokens:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question). \\n max_output_tokens:\ - \ the maximum number of tokens to generate. \\n max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n vectorset: the embedding model to use for the semantic search.\ - \ \\n chat_placeholder: the placeholder to display in the chat input. \\n\ - \ audit_metadata: custom metatada to add in API calls for auditing purposes.\ - \ \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\"\ - :{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":213,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":213,\"key\":\"\"}]},{\"start\":213,\"end\":467,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":214,\"end\":402,\"key\":\"\"},{\"start\":402,\"end\":467,\"key\"\ - :\"\"}]},{\"start\":467,\"end\":847,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":468,\"end\"\ - 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:4479}],\"entity\":\"CSS\"}},\"relations\":[],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\":{\"\ - text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.7143178582191467,\"score_type\":\"VECTOR\",\"order\":0,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\"\ - ,\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs > ingestion\ - \ > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: rate-limiting\\\ - ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate limits\ - \ are an essential aspect of the Agentic RAG platform, ensuring fair usage\ - \ and optimal performance for all users interacting with Agentic RAG APIs.\ - \ This document outlines the rate limits enforced by Agentic RAG and provides\ - \ guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\\ - n\\nAgentic RAG can apply two types of limits to its APIs:\\n\\n- **Regular\ - \ API rate limits**: By default, the sum of all authenticated requests in\ - \ a Agentic RAG account cannot exceed 2400 requests per minute. Note that\ - \ this limit can be customized on a per-account basis. Please contact [Agentic\ - \ RAG's support team](mailto:support@nuclia.com) if you need an increase.\\\ - n\\n- **Ingestion back pressure limits**: Agentic RAG implements a back-pressure\ - \ mechanism to manage ingestion pipeline overload. This mainly affects endpoints\ - \ for uploading data and creating or updating resources.\\n\\n## Handling\ - \ Rate-Limited Responses\\n\\nAgentic RAG adheres to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4)\ - \ and will return a response with a `429` status codes when the limits are\ - \ exceeded.\\n\\nThe official Agentic RAG API clients already have built-in\ - \ mechanisms for retrying requests when rate limits are encountered:\\n\\\ - n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- [Nuclia JavaScript\ - \ client](/docs/develop/js-sdk/)\\n\\nHowever, if you are interacting directly\ - \ with the API, we recommend using an [exponential backoff retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff)\ - \ when limits are reached.\\n\\nWhen ingestion back pressure rate limits are\ - \ hit, the response will include a `try_after` key with an estimated UTC time\ - \ for retrying the request. You can use this value for retry logic as an alternative\ - \ to the exponential backoff strategy.\\n\\n## Example 1: Regular API rate\ - \ limits\\n\\nHere's an example of how to implement an exponential backoff\ - \ retry strategy in Python:\\n\\n```python\\nimport time\\nimport requests\\\ - n\\ndef make_request_with_exponential_backoff(url, headers, max_retries=5):\\\ - n retries = 0\\n while retries < max_retries:\\n response = requests.get(url,\ - \ headers=headers)\\n if response.status_code == 200:\\n \ - \ return response.json()\\n elif response.status_code == 429:\\n \ - \ wait_time = 2 ** retries # Exponential backoff: 2^retries\\n\ - \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\ - \")\\n time.sleep(wait_time)\\n retries += 1\\n \ - \ else:\\n response.raise_for_status()\\n raise Exception(\\\ - \"Max retries exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\ - \"\\nheaders = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\\ - ndata = make_request_with_exponential_backoff(url, headers)\\nprint(data)\\\ - n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's an example\ - \ of how to use the try_after key from the response to manage rate limits:\\\ - n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport requests\\\ - n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\\ - n retries = 0\\n while retries < max_retries:\\n response = requests.get(url,\ - \ headers=headers)\\n response_body = response.json()\\n if\ - \ response.status_code == 200:\\n return response.json()\\n \ - \ elif response.status_code == 429 and \\\"try_after\\\" in response_body:\\\ - n try_after = response_body[\\\"try_after\\\"]\\n retry_time\ - \ = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n \ - \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n \ - \ print(\\n f\\\"Rate limit exceeded. Retrying at {retry_time}\ - \ (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\\ - n retries += 1\\n else:\\n response.raise_for_status()\\\ - n raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\\ - nurl = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\ - \"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url,\ - \ headers)\\nprint(data)\\n```\\n\\nThese examples demonstrate how to handle\ - \ rate limits effectively, ensuring that your application respects the limits\ - \ and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\"\ - ,\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\"\ - :{\"text\":\" \\n id: rate-limiting \\n title: Manage rate limiting \\n \\\ - n Manage rate limiting \\n Rate limits are an essential aspect of the Agentic\ - \ RAG platform, ensuring fair usage and optimal performance for all users\ - \ interacting with Agentic RAG APIs. This document outlines the rate limits\ - \ enforced by Agentic RAG and provides guidelines for handling rate-limited\ - \ responses effectively. \\n Introduction \\n Agentic RAG can apply two types\ - \ of limits to its APIs: \\n \\n \\n Regular API rate limits: By default,\ - \ the sum of all authenticated requests in a Agentic RAG account cannot exceed\ - \ 2400 requests per minute. Note that this limit can be customized on a per-account\ - \ basis. Please contact Agentic RAG's support team if you need an increase.\ - \ \\n \\n \\n Ingestion back pressure limits: Agentic RAG implements a back-pressure\ - \ mechanism to manage ingestion pipeline overload. This mainly affects endpoints\ - \ for uploading data and creating or updating resources. \\n \\n \\n Handling\ - \ Rate-Limited Responses \\n Agentic RAG adheres to the HTTP standard and\ - \ will return a response with a 429 status codes when the limits are exceeded.\ - \ \\n The official Agentic RAG API clients already have built-in mechanisms\ - \ for retrying requests when rate limits are encountered: \\n \\n Nuclia Python\ - \ client \\n Nuclia JavaScript client \\n \\n However, if you are interacting\ - \ directly with the API, we recommend using an exponential backoff retry strategy\ - \ when limits are reached. \\n When ingestion back pressure rate limits are\ - \ hit, the response will include a try_after key with an estimated UTC time\ - \ for retrying the request. You can use this value for retry logic as an alternative\ - \ to the exponential backoff strategy. \\n Example 1: Regular API rate limits\ - \ \\n Here's an example of how to implement an exponential backoff retry strategy\ - \ in Python: \\n ```python \\n import time \\n import requests \\n def make_request_with_exponential_backoff(url,\ - \ headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries:\ - \ \\n response = requests.get(url, headers=headers) \\n if response.status_code\ - \ == 200: \\n return response.json() \\n elif response.status_code == 429:\ - \ \\n wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate\ - \ limit exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time)\ - \ \\n retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception(\ - \ Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint\ - \ \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url,\ - \ headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure\ - \ limits \\n Here's an example of how to use the try_after key from the response\ - \ to manage rate limits: \\n ```python \\n import time \\n from datetime import\ - \ datetime \\n import requests \\n def make_request_with_try_after_info(url,\ - \ headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries:\ - \ \\n response = requests.get(url, headers=headers) \\n response_body = response.json()\ - \ \\n if response.status_code == 200: \\n return response.json() \\n elif\ - \ response.status_code == 429 and try_after in response_body: \\n try_after\ - \ = response_body[ try_after ] \\n retry_time = datetime.strptime(try_after,\ - \ %Y-%m-%dT%H:%M:%S.%fZ ) \\n wait_time = (retry_time - datetime.utcnow()).total_seconds()\ - \ \\n print( \\n f Rate limit exceeded. Retrying at {retry_time} (in {wait_time}\ - \ seconds)... \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\\ - n response.raise_for_status() \\n raise Exception( Max retries exceeded )\ - \ \\n Example usage \\n url = https://your-endpoint \\n headers = { Authorization\ - \ : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url,\ - \ headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle\ - \ rate limits effectively, ensuring that your application respects the limits\ - \ and retries appropriately.\",\"split_text\":{},\"deleted_splits\":[]},\"\ - metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\"\ - :373,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3,\"end\":236,\"key\":\"\"},{\"start\":236,\"\ - end\":373,\"key\":\"\"}]},{\"start\":373,\"end\":721,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - 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data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\\ - n\\n# Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\\ - n\\n## Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\ - \\_json\\\\_schema**: `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\\ - n\\n***\\n\\n### audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**:\ - \ `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean` \\\\\ - | `\\\"none\\\"` \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\\ - nIt will return the text blocks that have been effectively used to build each\ - \ section of the answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\\ - n\\n***\\n\\n### debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\\ - n\\n***\\n\\n### extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**:\ - \ `string`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\\ - n\\n***\\n\\n### extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\ - \\_context\\\\_images**: `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\\ - n\\n***\\n\\n### ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\\ - n\\n#### Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\\ - n\\n***\\n\\n### features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\\ - n\\n***\\n\\n### field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\\ - _type\\\\_filter**: [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\\ - n\\n***\\n\\n### fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\\ - n\\n***\\n\\n### filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**:\ - \ [`FilterExpression`](FilterExpression.md)\\n\\n#### Inherited from\\n\\\ - n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\\ - n\\n***\\n\\n### filters?\\n\\n> `optional` **filters**: `string`[] \\\\|\ - \ [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\\ - n\\n***\\n\\n### highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\\ - n\\n***\\n\\n### keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**:\ - \ `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\ - \ \\\\| `object`\\n\\nDefines the maximum number of tokens that the model\ - \ will take as context.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\\ - n\\n***\\n\\n### min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number`\ - \ \\\\| [`MinScore`](MinScore.md)\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\\ - n\\n***\\n\\n### prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\\ - n\\n***\\n\\n### query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\\ - n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\\ - _type\\n\\n> **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\\ - n\\n***\\n\\n### rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\\ - _images\\\\_strategies**: [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\\ - n\\n***\\n\\n### rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**:\ - \ [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\\ - _creation\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\ - \\_creation\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\ - \\_modification\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\ - \\_modification\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\\ - n\\n***\\n\\n### rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\\ - n\\n***\\n\\n### rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\\ - n\\n***\\n\\n### reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\\ - n\\n***\\n\\n### resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**:\ - \ `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\\ - n\\n***\\n\\n### search\\\\_configuration?\\n\\n> `optional` **search\\\\\ - _configuration**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\\ - n\\n***\\n\\n### security?\\n\\n> `optional` **security**: `object`\\n\\n####\ - \ groups\\n\\n> **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\\ - n\\n***\\n\\n### show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\\ - n\\n***\\n\\n### show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\\ - n\\n***\\n\\n### show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\\ - n\\n***\\n\\n### synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\\ - n\\n***\\n\\n### top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\\ - n\\n***\\n\\n### vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface:\ - \ ChatOptions \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\\ - n answer_json_schema? \\n \\n optional answer_json_schema: object \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 \\n\ - \ \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index\ - \ Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\\ - n \\n citation_threshold? \\n \\n optional citation_threshold: number \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\\\| none \\\\|\ - \ default \\\\| llm_footnotes \\n \\n It will return the text blocks that\ - \ have been effectively used to build each section of the answer. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 \\n \\n debug?\ - \ \\n \\n optional debug: boolean \\n \\n Inherited from \\n BaseSearchOptions.debug\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 \\\ - n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images?\ - \ \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118\ - \ \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[]\ - \ \\n \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\\ - n features? \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117\ - \ \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields?\ - \ \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\\ - n \\n filter_expression? \\n \\n optional filter_expression: FilterExpression\ - \ \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters?\ - \ \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63\ - \ \\n \\n generative_model? \\n \\n optional generative_model: string \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108\ - \ \\n \\n highlight? \\n \\n optional highlight: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76\ - \ \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\\ - | Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\\ - n max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines\ - \ the maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from\ - \ \\n BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n\ - \ \\n prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image?\ - \ \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded:\ - \ string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies?\ - \ \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies?\ - \ \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106\ - \ \\n \\n range_creation_end? \\n \\n optional range_creation_end: string\ - \ \\n \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start?\ - \ \\n \\n optional range_creation_start: string \\n \\n Inherited from \\\ - n BaseSearchOptions.range_creation_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67\ - \ \\n \\n range_modification_end? \\n \\n optional range_modification_end:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\\ - n \\n range_modification_start? \\n \\n optional range_modification_start:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_start\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 \\\ - n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited\ - \ from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase?\ - \ \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\\ - n \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from\ - \ \\n BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83\ - \ \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\\ - n \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration?\ - \ \\n \\n optional search_configuration: string \\n \\n Inherited from \\\ - n BaseSearchOptions.search_configuration \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86\ - \ \\n \\n security? \\n \\n optional security: object \\n \\n groups \\n \\\ - n groups: string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\\ - n show? \\n \\n optional show: ResourceProperties[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.show \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71\ - \ \\n \\n show_consumption? \\n \\n optional show_consumption: boolean \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.show_hidden \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80\ - \ \\n \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k?\ - \ \\n \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\\ - n \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from\ - \ \\n BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\"\ - :[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"\ - key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\"\ - :499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"\ - end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"\ - 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:\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"\ - key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"\ - end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"\ - end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"\ - key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"\ - end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"\ - start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"\ - key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"\ - end\":3929,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3650,\"end\":3795,\"key\":\"\"},{\"start\":3795,\"\ - end\":3929,\"key\":\"\"}]},{\"start\":3929,\"end\":4317,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3932,\"end\":4123,\"key\":\"\"},{\"start\":4123,\"end\":4317,\"\ - key\":\"\"}]},{\"start\":4317,\"end\":4520,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4320,\"\ - end\":4520,\"key\":\"\"}]},{\"start\":4520,\"end\":4884,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4523,\"end\":4717,\"key\":\"\"},{\"start\":4717,\"end\":4884,\"\ - key\":\"\"}]},{\"start\":4884,\"end\":5167,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4887,\"\ - end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5167,\"key\":\"\"}]},{\"\ - start\":5167,\"end\":5525,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5170,\"end\":5339,\"\ - key\":\"\"},{\"start\":5339,\"end\":5525,\"key\":\"\"}]},{\"start\":5525,\"\ - end\":5897,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5528,\"end\":5711,\"key\":\"\"},{\"start\":5711,\"\ - end\":5897,\"key\":\"\"}]},{\"start\":5897,\"end\":6194,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":5898,\"end\":6071,\"key\":\"\"},{\"start\":6071,\"end\":6194,\"\ - key\":\"\"}]},{\"start\":6194,\"end\":6478,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6197,\"\ - end\":6366,\"key\":\"\"},{\"start\":6366,\"end\":6478,\"key\":\"\"}]},{\"\ - start\":6478,\"end\":6778,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":6481,\"end\":6635,\"\ - key\":\"\"},{\"start\":6635,\"end\":6778,\"key\":\"\"}]}],\"ner\":{\"boolean\"\ - :\"PERSON\",\"112 min\":\"TIME\"},\"entities\":{\"processor\":{\"entities\"\ - :[{\"text\":\"boolean\",\"label\":\"PERSON\",\"positions\":[{\"start\":647,\"\ - end\":654}]},{\"text\":\"112 min\",\"label\":\"TIME\",\"positions\":[{\"start\"\ - :2991,\"end\":3002}]}]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:09.547197Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:09.110083Z\",\"last_extract\":\"\ - 2026-07-14T12:51:05.901654Z\",\"last_processing_start\":\"2026-07-14T12:51:05.827255Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"TIME/112 min\":{\"\ - position\":[{\"start\":2991,\"end\":3002}],\"entity\":\"112 min\"},\"PERSON/boolean\"\ - :{\"position\":[{\"start\":647,\"end\":654}],\"entity\":\"boolean\"}},\"relations\"\ - :[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\"\ - :[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces\ - \ > ChatOptions\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"\ - score\":0.6752755641937256,\"score_type\":\"VECTOR\",\"order\":1,\"text\"\ - :\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum\ - \ number of tokens that the model will take as context. \\n Defined in \\\ - n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"\ - end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\"\ - ,\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\"\ - :\"docs > rag > advanced > openai api compatible models\",\"summary\":\"\"\ - ,\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: openai-api-compatible-models\\\ - ntitle: Connect to any OpenAI API compatible LLM\\n---\\n\\n# Connect to any\ - \ OpenAI API compatible LLM\\n\\nAgentic RAG allows you to connect to any\ - \ OpenAI API compatible LLM. This means that you can use any LLM that has\ - \ an API compatible with the OpenAI API which has become a standard in the\ - \ industry.\\n\\nMany of the options for self-hosted LLMs, open-source LLMs\ - \ hosted by cloud providers or commercial LLMs are compatible with the OpenAI\ - \ API. This means that you can use them with Agentic RAG without any modifications.\\\ - n\\n## Configuration\\n\\nTo modify your knowledge box configuration you can\ - \ do so in three manners, through the API, the Nuclia CLI / SDK or the Agentic\ - \ RAG dashboard.\\n\\nThe Agentic RAG dashboard offers the most user-friendly\ - \ way to modify the configuration of your knowledge box and we will use it\ - \ in this example.\\n\\nWe will be setting up a connection to the **Phi 4\ - \ Reasoning Plus** model, hosted by **OpenRouter** which offers a wide range\ - \ of open-source and commercial models compatible with the OpenAI API. We\ - \ can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free),\ - \ the API parameters are located under the **API** tab.\\n\\n1. **Open the\ - \ AI Models page** \\n In the left sidebar under **Advanced**, click **AI\ - \ Models**.\\n2. **Select \u201COpenAI API Compatible Model\u201D** \\n \ - \ From the models list, choose **OpenAI API Compatible Model**.\\n3. **Enable\ - \ custom Key** \\n Toggle the option for using you own `OpenAI API Compatible\ - \ Key` if it is not already enabled.\\n4. **Fill in the configuration parameters**\\\ - n\\n - **API Key**:\\n - Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key.\\n - Example: We will set this to our OpenRouter API key.\\\ - n - **API URL**:\\n - Description: The URL of the API endpoint for your\ - \ LLM. This may be shared between multiple models.\\n - Example: For OpenRouter,\ - \ it is the same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\\ - n - Description: The name of the model you want to use, it needs to exactly\ - \ match the name of the model in the API.\\n - Example: For Phi 4 Reasoning\ - \ Plus in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\\ - n - **Maximum supported input tokens**:\\n - Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only.\\n - Example: For Phi 4 Reasoning Plus, the total context\ - \ size is `32768` tokens, as we want to leave room for the output, we will\ - \ set the maximum supported input tokens as `32768 - 1024 = 31744`.\\n -\ - \ **Maximum supported output tokens**:\\n - Description: The maximum number\ - \ of tokens that the model can generate as output. Again, we should keep in\ - \ mind that this value summed to the **Maximum supported input tokens** should\ - \ not exceed the total context size supported by the model.\\n - Example:\ - \ For Phi 4 Reasoning Plus, the maximum output tokens is specified at `32768`,\ - \ but we already reserved `31744` for the input tokens, so we will set this\ - \ to `32768 - 31744 = 1024`.\\n - **Model Features**:\\n - Description:\ - \ Under this section you will find multiple toggles related to features supported\ - \ by the model, these vary from model to model, but most often the default\ - \ values are well suited to most use cases. The most relevant toggle is for\ - \ `Image Support` which allows you to use images as input for the model.\\\ - n - Example: Image input is not supported by Phi 4 Reasoning Plus, so\ - \ we will leave it disabled.\\n\\n5. **Save** \\n Click **Save changes**.\\\ - n\\n6. **Test your model** \\n Run a sample query in Agentic RAG or via\ - \ API/CLI. Adjust your prompt templates and token settings as needed.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ openai-api-compatible-models \\n title: Connect to any OpenAI API compatible\ - \ LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic RAG allows\ - \ you to connect to any OpenAI API compatible LLM. This means that you can\ - \ use any LLM that has an API compatible with the OpenAI API which has become\ - \ a standard in the industry. \\n Many of the options for self-hosted LLMs,\ - \ open-source LLMs hosted by cloud providers or commercial LLMs are compatible\ - \ with the OpenAI API. This means that you can use them with Agentic RAG without\ - \ any modifications. \\n Configuration \\n To modify your knowledge box configuration\ - \ you can do so in three manners, through the API, the Nuclia CLI / SDK or\ - \ the Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most\ - \ user-friendly way to modify the configuration of your knowledge box and\ - \ we will use it in this example. \\n We will be setting up a connection to\ - \ the Phi 4 Reasoning Plus model, hosted by OpenRouter which offers a wide\ - \ range of open-source and commercial models compatible with the OpenAI API.\ - \ We can see more information about this specific model here, the API parameters\ - \ are located under the API tab. \\n \\n Open the AI Models page \\n In the\ - \ left sidebar under Advanced, click AI Models. \\n Select OpenAI API Compatible\ - \ Model \\n From the models list, choose OpenAI API Compatible Model. \\n\ - \ Enable custom Key \\n Toggle the option for using you own OpenAI API Compatible\ - \ Key if it is not already enabled. \\n \\n Fill in the configuration parameters\ - \ \\n \\n \\n API Key: \\n \\n Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key. \\n Example: We will set this to our OpenRouter API key. \\\ - n \\n \\n API URL: \\n Description: The URL of the API endpoint for your LLM.\ - \ This may be shared between multiple models. \\n Example: For OpenRouter,\ - \ it is the same for all models: https://openrouter.ai/api/v1 \\n \\n \\n\ - \ Model: \\n Description: The name of the model you want to use, it needs\ - \ to exactly match the name of the model in the API. \\n Example: For Phi\ - \ 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free.\ - \ \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n Example: For Phi 4 Reasoning Plus, the total context size\ - \ is 32768 tokens, as we want to leave room for the output, we will set the\ - \ maximum supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum\ - \ supported output tokens: \\n Description: The maximum number of tokens that\ - \ the model can generate as output. Again, we should keep in mind that this\ - \ value summed to the Maximum supported input tokens should not exceed the\ - \ total context size supported by the model. \\n Example: For Phi 4 Reasoning\ - \ Plus, the maximum output tokens is specified at 32768, but we already reserved\ - \ 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024.\ - \ \\n \\n \\n \\n Model Features: \\n \\n Description: Under this section\ - \ you will find multiple toggles related to features supported by the model,\ - \ these vary from model to model, but most often the default values are well\ - \ suited to most use cases. The most relevant toggle is for Image Support\ - \ which allows you to use images as input for the model. \\n Example: Image\ - \ input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled.\ - \ \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model\ - \ \\n Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt\ - \ templates and token settings as needed. \\n \\n \",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"https://openrouter.ai/api/v1\"\ - ],\"paragraphs\":[{\"start\":0,\"end\":330,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"\ - end\":201,\"key\":\"\"},{\"start\":201,\"end\":330,\"key\":\"\"}]},{\"start\"\ - :330,\"end\":549,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":331,\"end\":471,\"key\"\ - :\"\"},{\"start\":471,\"end\":549,\"key\":\"\"}]},{\"start\":549,\"end\":858,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":550,\"end\":711,\"key\":\"\"},{\"start\":711,\"\ - end\":858,\"key\":\"\"}]},{\"start\":858,\"end\":1156,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":859,\"end\":1045,\"key\":\"\"},{\"start\":1045,\"end\":1156,\"key\"\ - 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in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n***\\n\\n### BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"\ - bytes_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n***\\n\\n### CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"\ - chars_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n***\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\\ - _PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### Defined 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libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": " Use the max_tokens parameter - on the /ask endpoint to set hard limits on: \n - Context size: Limits the amount - of retrieved information sent to the LLM \n - Answer length: Limits the length - of the generated response \n Important Considerations \n Context Limitations: - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326": " \n Large context: Results - from using RAG strategies like Full resource or Neighbouring paragraphs , or - from using the extra_context parameter \n Long questions: More detailed or complex - questions require more input tokens \n Long prompts: Extensive system prompts - increase the input token count \n Detailed answers: Comprehensive responses - require more output tokens \n Images in context: When using multimodal models, - images included in the retrieved context significantly increase token consumption - \n \n How to Limit and Control Token Consumption \n Strategy 1: Optimize Your - Parameters \n The first approach to reducing token consumption is to fine-tune - your request parameters: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": " \n optional max_tokens: - number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: - string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n", - "8b3e0ef630a346d1b591143309db87ec/t/page/0-281": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / NucliaTokensMetric \n Interface: NucliaTokensMetric \n Extends - \n \n UsageMetric \n \n Properties \n details \n \n details: NucliaTokensDetails[] - \n \n Overrides \n UsageMetric.details \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:185 - \n \n name \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n ```python - \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, - Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My - question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "6e8250e6b5264156988657a221fd5e94/t/page/0-397": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / Ask / ConsumptionAskResponseItem \n Interface: ConsumptionAskResponseItem - \n Properties \n customer_key_tokens \n \n customer_key_tokens: TokenConsumption - \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:127 \n \n - normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined in - \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: - string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \n \n - output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": " \n optional driver: string - \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 \n \n input_tokens - \n \n input_tokens: object \n \n max \n \n max: number \n \n min? \n \n optional - min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / ReasoningConfig \n Interface: ReasoningConfig \n Properties - \n budget_tokens? \n \n optional budget_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 - \n \n effort? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\n", - "44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668": " \n step: Information - about the current processing step \n module: The module being executed (e.g., - rephrase , basic_ask , remi ) \n title: Display title for the step \n value: - Result of the step \n reason: Explanation for the step \n timeit: Time taken - in seconds \n \n input_nuclia_tokens/output_nuclia_tokens: Token usage \n \n - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\n", - "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / Consumption \n Interface: Consumption \n Properties \n customer_key_tokens - \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 - \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\n", - "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / PageToken \n Interface: PageToken \n Properties \n height - \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \n \n text \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "dd41482018924facb5dbb87a7d53f122/t/page/3310-3757": " ) \n time.sleep(wait_time) - \n retries += 1 \n else: \n response.raise_for_status() \n raise Exception( - Max retries exceeded ) \n Example usage \n url = https://your-endpoint \n headers - = { Authorization : Bearer YOUR_ACCESS_TOKEN } \n data = make_request_with_try_after_info(url, - headers) \n print(data) \n ``` \n These examples demonstrate how to handle rate - limits effectively, ensuring that your application respects the limits and retries - appropriately.\n\n\nLink: https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\n", - "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": " \n optional max_tokens: - number \\| object \n \n Defines the maximum number of tokens that the model - will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: The maximum - number of tokens that the model can generate as output. Again, we should keep - in mind that this value summed to the Maximum supported input tokens should - not exceed the total context size supported by the model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": " \n \n Maximum supported - input tokens: \n Description: The maximum number of tokens that the model can - accept as input. Be mindful that this takes into account the tokens used in - the prompt, query and context. Also take note that some models may provide their - context window as the total between input and output tokens, while others may - provide it as the input tokens only. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n", - "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": "@nuclia/core \u2022 Docs - \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration Members - \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 - \n \n BYTES_PROCESSED \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:197 \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: - chars_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n MEDIA_FILES_PROCESSED \n \n MEDIA_FILES_PROCESSED: media_files_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n \n MEDIA_SECONDS_PROCESSED - \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 - \n \n NUCLIA_TOKENS \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:207 \n \n PAGES_PROCESSED \n \n - PAGES_PROCESSED: pages_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 - \n \n PARAGRAPHS_PROCESSED \n \n PARAGRAPHS_PROCESSED: paragraphs_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 \n \n PRE_PROCESSING_TIME - \n \n PRE_PROCESSING_TIME: pre_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 - \n \n RESOURCES_PROCESSED \n \n RESOURCES_PROCESSED: resources_processed \n - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n SEARCHES_PERFORMED - \n \n SEARCHES_PERFORMED: searches_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n SLOW_PROCESSING_TIME \n \n SLOW_PROCESSING_TIME: slow_processing_time - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 \n \n SUGGESTIONS_PERFORMED - \n \n SUGGESTIONS_PERFORMED: suggestions_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 - \n \n TRAIN_SECONDS \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\n"}}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '11266' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/rerank - response: - body: - string: '{"context_scores":{"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.7902066707611084,"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.7264255881309509,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.38219907879829407,"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575":0.32071492075920105,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.3004375994205475,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.28101080656051636,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.28002533316612244,"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043":0.2792384624481201,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.18683576583862305,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.15882562100887299,"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833":0.01829933375120163,"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224":0.011687257327139378,"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757":0.010944577865302563,"4194605cdfbd414f8fa762630d555bc1/t/page/0-358":0.009268013760447502,"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349":0.004117917735129595,"8b3e0ef630a346d1b591143309db87ec/t/page/0-281":0.003930085338652134,"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299":0.003524340922012925,"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668":0.003027835162356496,"6e8250e6b5264156988657a221fd5e94/t/page/0-397":0.002934951800853014,"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326":0.0023504828568547964}}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '1432' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:05:52 GMT - nuclia-learning-model: - - bge-reranker-v2-m3 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '152' - x-nuclia-trace-id: - - e612f0a63adbf11c1373e280184c219f - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\n\u00bfCu\u00e1l - es el enlace a la documentaci\u00f3n oficial sobre el par\u00e1metro `max_tokens`?\n\n\n\nContext:\n\n**block-AA**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\nTags: - /k/text\n``` max_tokens: the maximum number of input tokens to put in the final - context (including the prompt, the retrieved results and the user question). - \n max_output_tokens: the maximum number of tokens to generate. \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n ```\n\n\n---\"\n\n\n**block-AB**\n\n## - Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\nTags: /k/text\n``` Use - the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context - size: Limits the amount of retrieved information sent to the LLM \n - Answer - length: Limits the length of the generated response \n Important Considerations - \n Context Limitations: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n - ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\nTags: - /k/text\n``` \n optional max_tokens: number \\| object \n \n Defines the maximum - number of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n - ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: - /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum - number of tokens that the model can accept as input. Be mindful that this takes - into account the tokens used in the prompt, query and context. Also take note - that some models may provide their context window as the total between input - and output tokens, while others may provide it as the input tokens only. \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n - ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\nTags: - /k/text\n``` \n optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\nTags: - /k/text\n``` \n SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search - import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( - \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n - ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\nTags: - /k/text\n``` \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\nTags: - /k/text\n``` Description: The maximum number of tokens that the model can generate - as output. Again, we should keep in mind that this value summed to the Maximum - supported input tokens should not exceed the total context size supported by - the model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n - ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\nTags: - /k/text\n``` \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 - \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n - \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n - ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/480-703\nTags: - /k/text\n``` \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 - \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n - ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": false, "citation_threshold": - null, "generative_model": "chatgpt-azure-4o-mini", "max_tokens": 8192, "query_context_images": - {}, "prefer_markdown": null, "json_schema": {"title": "validate_or_answer", - "description": "Validate or answer", "parameters": {"type": "object", "properties": - {"reason": {"type": "string", "description": "Reasoning for the answer or validation"}, - "answer": {"type": "string", "description": "Partial or complete answer to the - user query from the information in the context."}, "missing_info_query": {"type": - "string", "description": "Query needed to retrieve the missing information in - case the context is not enough to answer the question. If the context does not - answer the question at all, just return the original question."}, "useful": - {"type": "string", "description": "Is the context useful to answer the question?", - "enum": ["yes", "no"]}, "citations": {"type": "array", "items": {"type": "string", - "description": "Block ID cited in the answer, e.g. block-AB"}, "description": - "List of block IDs cited in the answer, if any"}}, "required": ["reason", "answer", - "missing_info_query", "useful", "citations"]}}, "format_prompt": false, "rerank_context": - false, "tools": [], "tool_choice": {"type": "required"}, "reasoning": false, - "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '8939' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-origin: - - RAO - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat - response: - body: - string: '{"chunk":{"type":"object","object":{"reason":"The context provides - multiple links related to the `max_tokens` parameter, but does not specify - a single official documentation link for it. The links provided are for different - aspects of the `max_tokens` parameter, but none are explicitly labeled as - the official documentation for `max_tokens`. Therefore, the context is partially - useful but does not fully answer the question.","answer":"The context includes - several links related to the `max_tokens` parameter. Here are some of them:\n1. - [Link to advanced widget features](https://docs.rag.progress.cloud/docs/rag/advanced/widget/features)\n2. - [Link to advanced consumption](https://docs.rag.progress.cloud/docs/rag/advanced/consumption)\n3. - [Link to JS SDK interfaces](https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions)\n4. - [Link to OpenAI API compatible models](https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models)","missing_info_query":"What - is the official documentation link specifically for the `max_tokens` parameter?","useful":"yes","citations":["block-AA","block-AB","block-AC","block-AD"]}}} - - {"chunk":{"type":"status","code":"0"}} - - {"chunk":{"type":"meta","input_tokens":61,"output_tokens":30,"timings":{"generative":3.7919263209996643},"input_nuclia_tokens":0.061,"output_nuclia_tokens":0.03}} - - {"chunk":{"normalized_tokens":{"input":0.0606,"output":0.02988,"image":0.0},"customer_key_tokens":{"input":0.0,"output":0.0,"image":0.0},"type":"consumption"}} - - ' - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Transfer-Encoding: - - chunked - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/x-ndjson - date: - - Wed, 15 Jul 2026 07:05:52 GMT - nuclia-learning-id: - - c69b8d508cca44689772a76fd18cb02e - nuclia-learning-model: - - chatgpt-azure-4o-mini - via: - - 1.1 google - x-envoy-upstream-service-time: - - '3797' - x-nuclia-trace-id: - - 13c9cba4f3ae844714e8905f4de570fc - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "summarize", "system": "You - are a helpful AI assistant. Your role is to provide accurate, clear, and well-structured - answers based strictly on the information provided to you.\nKey principles:\n- - Answer only using the information in the provided context\n- Do not use external - knowledge, assumptions, or prior experience\n- Maintain a professional and informative - tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge - this clearly\n\nAlways follow any additional instructions provided about format, - style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": - {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## - Question\nExplica c\u00f3mo utilizar el par\u00e1metro `max_tokens` en espa\u00f1ol, - y proporciona un enlace a la documentaci\u00f3n oficial.\n\n## Provided Context\n[START - OF CONTEXT]\n## Retrieval on nuclia-docs Knowledge Box\n\n# Explica c\u00f3mo - utilizar el par\u00e1metro `max_tokens` en espa\u00f1ol, y proporciona un enlace - a la documentaci\u00f3n oficial.\n\n El par\u00e1metro `max_tokens` se utiliza - en el endpoint /ask para establecer l\u00edmites en: 1. El tama\u00f1o del contexto: - limita la cantidad de informaci\u00f3n recuperada enviada al modelo de lenguaje. - 2. La longitud de la respuesta generada: limita la longitud de la respuesta - generada. El valor de `max_tokens` define el n\u00famero m\u00e1ximo de tokens - de entrada que se incluir\u00e1n en el contexto final, incluyendo el aviso, - los resultados recuperados y la pregunta del usuario. Tambi\u00e9n se puede - definir un n\u00famero m\u00e1ximo de tokens a generar con `max_output_tokens`.\n\n## - Retrieval on nuclia-docs Knowledge Box\n\n# \u00bfCu\u00e1l es el enlace a la - documentaci\u00f3n oficial sobre el par\u00e1metro `max_tokens`?\n\n The context - includes several links related to the `max_tokens` parameter. Here are some - of them:\n1. [Link to advanced widget features](https://docs.rag.progress.cloud/docs/rag/advanced/widget/features)\n2. - [Link to advanced consumption](https://docs.rag.progress.cloud/docs/rag/advanced/consumption)\n3. - [Link to JS SDK interfaces](https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions)\n4. - [Link to OpenAI API compatible models](https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models)\n[END - OF CONTEXT]\n\n## Answering Guidelines\n- Carefully read all context; it may - be lengthy or detailed\n- Do not omit or overlook any relevant information\n- - If the context is incomplete or insufficient, state: \"Not enough data to answer - this.\"\n- Read carefully any extra instructions below if provided and use them - to answer\n\nNow provide your answer to the question: Explica c\u00f3mo utilizar - el par\u00e1metro `max_tokens` en espa\u00f1ol, y proporciona un enlace a la - documentaci\u00f3n oficial."}, "citations": null, "citation_threshold": null, - "generative_model": "chatgpt-azure-4o-mini", "max_tokens": 5000, "query_context_images": - {}, "prefer_markdown": null, "json_schema": null, "format_prompt": false, "rerank_context": - false, "tools": [], "tool_choice": {"type": "auto"}, "reasoning": false, "seed": - null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '3065' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-client-ident: - - default - x-message: - - 2c0bd763788348ec99dcf3d51774421a - x-origin: - - RAO - x-session: - - default_default_session - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat - response: - body: - string: "{\"chunk\":{\"type\":\"text\",\"text\":\"El\"}}\n{\"chunk\":{\"type\"\ - :\"text\",\"text\":\" par\xE1\"}}\n{\"chunk\":{\"type\":\"text\",\"text\"\ - :\"metro\"}}\n{\"chunk\":{\"type\":\"text\",\"text\":\" `\"}}\n{\"chunk\"\ - 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Allowlist these if you restrict inbound traffic to your infrastructure. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n :::note \\n The current list of IP addresses is also available in machine-readable + format: JSON | YAML. These lists may change over time, so we recommend periodically + fetching and applying updates to your firewall rules to ensure uninterrupted + service. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: public-ips \\n title: Public IP Addresses \\n \\n If your firewall + restricts network traffic, you may need to allowlist the following IP addresses. + They are grouped by region and split into two categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + ::: \\n Europe (GCP) \\n | Inbound | Outbound | \\n | ------------------ | + --------------------- | \\n | 34.111.1.187/32 | 35.204.65.155/32 | \\n | 136.68.112.29/32 + | 34.91.38.151/32 | \\n | | 35.204.139.129/32 | \\n | | 35.204.108.221/32 + | \\n Australia (AWS) \\n | Inbound | Outbound | \\n | ------------------- + | --------------------- | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n + | 3.25.20.9/32 | 3.24.144.83/32 | \\n | 52.64.202.159/32 | 3.24.81.206/32 + | \\n Europe (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 63.182.151.234/32 | 63.179.23.139/32 | \\n | + 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32 | 3.66.123.24/32 + | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | ------------------- | + --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\n | 16.164.50.185/32 + | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32 | \\n United + States (AWS) \\n | Inbound | Outbound | \\n | -------------------- | -------------------- + | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\n | 18.119.145.185/32 | 18.225.104.91/32 + | \\n | 3.21.239.244/32 | 16.59.115.61/32 | \\n Private connectivity (AWS + PrivateLink) \\n For AWS-hosted regions, it is possible to expose access privately + through an AWS endpoint service (AWS PrivateLink) instead of over the public + internet, allowing connections to be established from your VPC without traversing + public IP addresses. This option is not available through self-service configuration. + Please reach out to your account manager for details.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Inbound \u2014 the IP addresses that Progress Agentic RAG receives requests + on. Allowlist these if you restrict outbound traffic from your infrastructure + and need to reach our services (e.g., calling our APIs). \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > management > security > 5 public ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs + > agentic > how to > agentic retrieval\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: agentic-retrieval \\n title: Agentic retrieval with the Smart agent + \\n \\n Agentic retrieval with the Smart agent \\n The Smart agent is a powerful + tool that enhances the capabilities of traditional retrieval-augmented generation + (RAG) systems. Regular RAG's fixed search-then-generate process is limiting + for complex questions that need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Different models can be chosen for different tasks: \\n \\n Context validation + happens when the Prune context option is enabled (recommended), we recommend + using a fast model - planning or the execution model (depending on the planning + mode you have selected): we recommend using a more powerful model. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n Note: the rephrasing model is only used in more complex workflow, it can + be ignored in the present case. \\n Finally, add a Summarize agent in the + Generation step to generate a final answer from the retrieved information. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + The Smart Agent plans the answer: picks the most appropriate sources, splits + the question into sub-questions, evaluates relevancy, and iterates autonomously + until the information is sufficient. \\n Basic usage \\n To set up a Smart + Agent, declare all the sources you want to use (Knowledge Boxes, Perplexity, + etc.) in the Sources section of the left menu. Then, create a new workflow + in the Workflows section and add a Smart Agent in the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Chat mode \\n If you want to use the Smart Agent in a chat interface, you + need the following: \\n \\n Enable the Session history option in the Smart + Agent configuration. This will allow the Smart Agent to take into account + the previous conversation when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Then connect your sources as Registered Agents to the Smart Agent. It is very + important that you provide an extensive description of each registered agent, + so that the Smart Agent can understand what each source is about and when + to use it. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n reactive: The Smart Agent will decide what to do first, and will plan + the next steps based on the information it retrieves. It is expected to be + faster. \\n plan_execute: The Smart Agent will plan all the steps in advance, + and will execute them. It will be slower but more accurate when processing + complex questions. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Also select the proper function for each registered agent, depending on the + type of source (MCP agents will not need one, the functions are provided dynamically + by the MCP server). \\n In the Smart Agent configuration, you can select the + planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + In the Summarize Agent, enable the Conversational mode. This will allow the + Summarize Agent to generate a final answer that will not repeat the information + already provided in the previous conversation, and will be more natural for + a chat interface. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Custom frontend \\n You can also create a custom frontend for your Retrieval + Agent. This allows you to have full control over the user interface and user + experience. \\n You can directly implement the API calls to your Retrieval + Agent in your frontend code (see the Websocket API section for more details), + or you can use the JavaScript SDK provided by Agentic to simplify the integration. + \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + MCP \\n If you want to integrate your Retrieval Agent with a broader AI-based + system (for example Claude Cowork or Copilot), it is reachable throught MCP. + \\n The MCP endpoint is visible on the agent home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + \\n id: deploy \\n title: Deploy \\n \\n Deploy \\n Your Agentic Retrieval + Agent can be deployed in different ways, depending on your needs and the environment + you are working in. \\n Ready-to-use widget \\n The easiest way to deploy + your Retrieval Agent is to use the ready-to-use widget. You can embed it in + your website or application, and it will provide a chat user interface for + interacting with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\":\"page\",\"text\":\" + Go to the Widgets section in the left menu, and click on Create widget to + create a new widget. 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\ object \\n \\n groups \\n \\n groups: string[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:166\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:165\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":1,\"start\":225,\"end\":477},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs\ - \ \\n \\n @nuclia/core / SuggestOptions \\n Interface: SuggestOptions \\n\ - \ Properties \\n highlight? \\n \\n optional highlight: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:164 \\n \\n security?\ - \ \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":225},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"f539dfaa013a45d6a0e319bc7288ff07\"\ - ,\"field_type\":\"a\",\"field\":\"title\",\"text\":\"docs > develop > js sdk\ - \ > interfaces > SuggestOptions\",\"labels\":[],\"position\":{\"page_number\"\ - :0,\"index\":0,\"start\":0,\"end\":53},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"af442e9be6484c44a4a1dbc396e38dca\",\"field_type\":\"a\",\"\ - field\":\"title\",\"text\":\"docs > develop > js sdk > interfaces > KBKVSchemas\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":50},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"af442e9be6484c44a4a1dbc396e38dca\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs\ - \ \\n \\n @nuclia/core / KBKVSchemas \\n Interface: KBKVSchemas \\n Properties\ - \ \\n schemas \\n \\n schemas: object \\n \\n Index Signature \\n [name: string]:\ - \ KVSchema \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:602\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":238},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"701e9133181a4002bc373eaa1254daf8\"\ - 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:\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"a\",\"field\":\"title\"\ - ,\"text\":\"docs > develop > js sdk > interfaces > KVSchema\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":47},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core\ - \ / KVSchema \\n Interface: KVSchema \\n Properties \\n description? \\n \\\ - n optional description: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:597\ - \ \\n \\n fields \\n \\n fields: KVSchemaField[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:598\ - \ \\n \\n name \\n \\n\",\"labels\":[],\"position\":{\"page_number\":0,\"\ - index\":0,\"start\":0,\"end\":330},\"fuzzy_result\":false},{\"score\":0.0,\"\ - rid\":\"e88f4784ecb144bf87be0b7e543cb7ef\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"text\":\" name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:596\"\ - 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This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers 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length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.6482165455818176,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.5993967652320862,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"8b3e0ef630a346d1b591143309db87ec\":{\"id\":\"8b3e0ef630a346d1b591143309db87ec\",\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensMetric-md\",\"title\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"la\",\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:48.423622\",\"modified\":\"2026-07-14T12:51:37.835367\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensMetric\\n\\n# + Interface: NucliaTokensMetric\\n\\n## Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"UsageMetric.name\"],\"paragraphs\":[{\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":281,\"key\":\"\"}]},{\"start\":281,\"end\":511,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":282,\"end\":511,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:39.809701Z\",\"last_understanding\":\"2026-07-14T12:51:39.578340Z\",\"last_extract\":\"2026-07-14T12:51:39.320533Z\",\"last_processing_start\":\"2026-07-14T12:51:39.300652Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\":{\"score\":0.5960935354232788,\"score_type\":\"VECTOR\",\"order\":13,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n\",\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\",\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"docs + > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\",\"modified\":\"2026-07-14T12:51:02.450318\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\n\\n# + Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\n\\n> + `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\n\\n***\\n\\n### + llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\n\\n***\\n\\n### + manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**: `object`\\n\\n#### + splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:539](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L539)\\n\\n***\\n\\n### + max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\n\\n***\\n\\n### + name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d24d884112df2f67b327ac6d6302ddf1\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\n Interface: SplitStrategy + \\n Properties \\n custom_split? \\n \\n optional custom_split: CustomSplitStrategy + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n + llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:538 \\n \\n manual_split? \\n \\n + optional manual_split: object \\n \\n splitter \\n \\n splitter: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:539 \\n \\n max_paragraph? + \\n \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 + \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\":234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\":\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:04.701538Z\",\"last_understanding\":\"2026-07-14T12:51:04.225754Z\",\"last_extract\":\"2026-07-14T12:51:03.819024Z\",\"last_processing_start\":\"2026-07-14T12:51:03.798124Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\":{\"score\":0.5957005620002747,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\" + \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 + \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\",\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md) + / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.599697470664978,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.6202770471572876,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.594592273235321,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\",\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\n\\n# + Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\n\\n> + `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\n\\n***\\n\\n### + effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n \\n optional effort: NumericReasoningEffort \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\":329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\",\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\":\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\":{\"score\":0.6052000522613525,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\",\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs + > develop > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2026-06-09T08:08:04.176136\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Retrieval Agents Orchestrator\\n\\nInteract with Nuclia's Retrieval Agents + Orchestrator to have intelligent conversations over several knowledge sources + with persistent session management and real-time streaming responses.\\n\\n## + Prerequisites\\n\\nInstall the Nuclia SDK:\\n\\n```sh\\npip install nuclia\\n```\\n\\nEnsure + you have:\\n- A valid Nuclia authentication token (see [Authentication](02-auth.md))\\n- + Access to a configured Retrieval Agent\\n\\n## Overview\\n\\nThe nuclia.py + library provides several ways to interact with your Retrieval Agents Orchestrators:\\n\\n- + **Interactive CLI**: A rich, user-friendly terminal interface (recommended)\\n- + **Standard CLI**: Direct access to raw websocket messages for debugging\\n- + **Session Management**: Create and manage persistent conversation sessions\\n- + **Programmatic API**: Python SDK for building custom applications\\n\\n\\n## + Listing Available Agents\\n\\nDiscover what Retrieval Agents Orchestrators + you have access to.\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents list\\n ```\\n\\n- + SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n all_agents = agents.list()\\n\\n for agent + in all_agents:\\n print(f\\\"Agent: {agent.title} ({agent.id})\\\")\\n + \ print(f\\\" Slug: {agent.slug}\\\")\\n print(f\\\" Zone: {agent.zone}\\\")\\n + \ ```\\n\\n### Getting a Specific Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents get --account=\\\"my-account\\\" --id=\\\"agent-uuid\\\" --zone=\\\"europe-1\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n agent_details = agents.get(\\n account=\\\"my-account\\\",\\n + \ id=\\\"agent-uuid\\\",\\n zone=\\\"europe-1\\\"\\n )\\n print(agent_details)\\n + \ ```\\n\\n### Setting a Default Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents default [AGENT_SLUG or AGENT_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agents import NucliaAgents\\n\\n agents = NucliaAgents()\\n + \ agents.default(\\\"my-agent\\\")\\n ```\\n\\nThis sets the default agent + for all subsequent operations.\\n\\n## Interactive CLI (Recommended)\\n\\nThe + interactive CLI provides a beautiful, real-time interface for conversing with + your Retrieval Agents Orchestrator.\\n\\n### Starting the Interactive CLI\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent cli interact\\n ```\\n\\n- SDK:\\n\\n + \ ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ agent.cli.interact()\\n ```\\n\\nThis launches an interactive terminal + session where you can:\\n- Ask questions and see streaming responses\\n- View + processing steps in real-time\\n- Manage conversation sessions\\n- See retrieved + context and citations\\n\\n### Interactive CLI Commands\\n\\nThe CLI supports + several commands (prefix with `/`):\\n\\n| Command | Description |\\n|---------|-------------|\\n| + `/help` | Show available commands |\\n| `/new_session` | Create a new persistent + session |\\n| `/list_sessions` | List all your sessions |\\n| `/change_session` + | Switch to a different session, use 'ephemeral' for a temporary session |\\n| + `/clear` | Clear the screen |\\n| `/exit` | Exit the CLI |\\n\\nPlease note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option **Agent with memory** enabled during creation.\\n\\n## Session + Management\\n\\nSessions allow you to maintain conversation context across + multiple interactions.\\n\\n> This feature will only be available if you checked + **Agent with memory** during the creation of your Retrieval Agents Orchestrator.\\n\\n### + Creating a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent session new --name=\\\"My + Research Session\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n session_uuid = agent.session.new(\\\"My + Research Session\\\")\\n print(f\\\"Created session: {session_uuid}\\\")\\n + \ ```\\n\\n### Listing Sessions\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session list\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n sessions = agent.session.list()\\n + \ for session in sessions.resources:\\n print(f\\\"{session.title}: {session.id}\\\")\\n + \ ```\\n\\n### Getting a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session get --session_uuid=[SESSION_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ session = agent.session.get(session_uuid)\\n print(f\\\"Session: {session.title}\\\")\\n + \ print(f\\\"Created: {session.created}\\\")\\n ```\\n\\n### Deleting a Session\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent session delete --session_uuid=[SESSION_UUID]\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n + \ agent = NucliaAgent()\\n agent.session.delete(session_uuid)\\n ```\\n\\n## + Interaction\\n\\nAside from the interactive CLI, you can interact with your + Retrieval Agents Orchestrator with the simple CLI or programmatically using + the SDK.\\n\\n### Basic Interaction\\n\\n\\n**CLI:**\\n```bash\\nnuclia agent + interact \\\"What is Eric known for?\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over streaming responses\\nfor response in agent.interact(\\n question=\\\"What + is Eric known for?\\\"\\n):\\n if response.operation == \\\"ANSWER\\\" + and response.answer:\\n print(response.answer)\\n elif response.step:\\n + \ print(f\\\"Processing: {response.step.module}\\\")\\n```\\n\\nNot + supplying a `session_uuid` when calling `interact` will use an ephemeral session + by default. To maintain context, provide a persistent session UUID.\\n\\n### + Using Persistent Sessions\\n\\n**CLI:**\\n```bash\\nnuclia agent sessions + new \\\"Customer Support Chat\\\"\\n# Note the session UUID returned\\nnuclia + agent interact \\\"What are your business hours?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\nnuclia + agent interact \\\"Are you open on weekends?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Create + a session\\nsession_uuid = agent.session.new(\\\"Customer Support Chat\\\")\\n\\n# + Have a conversation with context\\nfor response in agent.interact(\\n session_uuid=session_uuid,\\n + \ question=\\\"What are your business hours?\\\"\\n):\\n if response.answer:\\n + \ print(response.answer)\\n\\n# Follow-up question maintains context\\nfor + response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\"Are + you open on weekends?\\\"\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\n## + Understanding Response Types\\n\\nWhen interacting with an agent, you receive + a stream of `AragAnswer` objects with different operations:\\n\\n| Operation + | Description |\\n|-----------|-------------|\\n| `START` | Interaction has + begun |\\n| `ANSWER` | Processing step or partial answer |\\n| `DONE` | Interaction + complete |\\n| `ERROR` | An error occurred |\\n| `AGENT_REQUEST` | Agent needs + user feedback |\\n\\n### Response Attributes\\n\\nEach response may contain:\\n\\n- + **`step`**: Information about the current processing step\\n - `module`: + The module being executed (e.g., \\\"rephrase\\\", \\\"basic_ask\\\", \\\"remi\\\")\\n + \ - `title`: Display title for the step\\n - `value`: Result of the step\\n + \ - `reason`: Explanation for the step\\n - `timeit`: Time taken in seconds\\n + \ - `input_nuclia_tokens`/`output_nuclia_tokens`: Token usage\\n\\n- **`context`**: + Retrieved context from the knowledge base\\n - `chunks`: List of retrieved + text chunks with sources\\n - `summary`: Summary of the context or partial + answer\\n\\n- **`answer`**: The final answer text (Markdown formatted)\\n\\n- + **`generated_text`**: Intermediate generated text\\n\\n- **`possible_answer`**: + Alternative answer being considered\\n\\n- **`exception`**: Error details + if something went wrong\\n\\n### Processing Responses\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\nfrom nuclia_models.agent.interaction + import AnswerOperation\\n\\nagent = NucliaAgent()\\n\\nfor response in agent.interact(question=\\\"Tell + me about AI\\\"):\\n if response.operation == AnswerOperation.START:\\n + \ print(\\\"Starting...\\\")\\n \\n elif response.step:\\n print(f\\\"Step: + {response.step.module} ({response.step.timeit:.2f}s)\\\")\\n \\n elif + response.context:\\n print(f\\\"Retrieved {len(response.context.chunks)} + chunks\\\")\\n for chunk in response.context.chunks:\\n print(f\\\" + \ - {chunk.title}: {chunk.text[:100]}...\\\")\\n \\n elif response.answer:\\n + \ print(f\\\"\\\\nFinal Answer:\\\\n{response.answer}\\\")\\n \\n + \ elif response.operation == AnswerOperation.DONE:\\n print(\\\"Complete!\\\")\\n + \ \\n elif response.operation == AnswerOperation.ERROR:\\n print(f\\\"Error: + {response.exception.detail if response.exception else 'Unknown'}\\\")\\n```\\n\\n## + Standard CLI for Raw Messages\\n\\nFor debugging or advanced use cases, you + can access raw websocket messages programmatically:\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over all messages\\nfor message in agent.interact(\\n question=\\\"What + is RAO?\\\"\\n):\\n # message is an AragAnswer object with all raw data\\n + \ print(f\\\"Operation: {message.operation}\\\")\\n print(f\\\"Raw message: + {message.model_dump_json(indent=2)}\\\")\\n```\\n\\nThis gives you direct + access to all websocket message data for debugging or custom processing.\\n\\n## + Advanced Features\\n\\n### Agent Feedback Requests\\n\\nAgents can request + additional input from users during processing:\\n\\n```python\\nfrom nuclia.sdk.agent + import NucliaAgent\\nfrom nuclia_models.agent.interaction import AnswerOperation\\n\\nagent + = NucliaAgent()\\ngenerator = agent.interact(question=\\\"Help me with X\\\")\\n\\nfor + response in generator:\\n if response.operation == AnswerOperation.AGENT_REQUEST:\\n + \ # Agent is requesting user input\\n user_input = input(f\\\"Agent + asks: {response.feedback.question}\\\\n> \\\")\\n # Send response back\\n + \ generator.send(user_input)\\n elif response.answer:\\n print(response.answer)\\n```\\n\\n### + Error Handling\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom + nuclia.exceptions import RaoAPIException\\n\\nagent = NucliaAgent()\\n\\ntry:\\n + \ for response in agent.interact(question=\\\"Hello?\\\"):\\n if + response.exception:\\n print(f\\\"Agent error: {response.exception.detail}\\\")\\n + \ elif response.answer:\\n print(response.answer)\\nexcept + RaoAPIException as e:\\n print(f\\\"API error: {e.detail}\\\")\\nexcept + Exception as e:\\n print(f\\\"Unexpected error: {e}\\\")\\n```\\n\\n### + Passing Custom Headers to MCP\\n\\nIf your Retrieval Agents Orchestrator requires + custom headers for MCP Agents, you can pass them as follows:\\n\\n**CLI:**\\n```bash\\nnuclia + agent interact \\\"What is AI?\\\" --headers '{\\\"X-Custom-Header\\\": \\\"value\\\"}'\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\nfor response + in agent.interact(\\n question=\\\"What is AI?\\\",\\n headers={\\\"X-Custom-Header\\\": + \\\"value\\\"}\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\nPlease + ensure that the 'Allowed Headers' configuration in your MCP agent includes + any custom headers you wish to use.\\n\\n## Best Practices\\n\\n1. **Use Sessions + for Context**: Create sessions when you need multi-turn conversations with + context retention\\n2. **Use Ephemeral Sessions for One-offs**: Don't supply + a session UUID for using agents in a stateless manner.\\n3. **Stream for UX**: + Process responses as they arrive for better user experience\\n4. **Handle + All Operations**: Check for different operation types (START, ANSWER, DONE, + ERROR) when processing responses\\n5. **Clean Up Sessions**: Delete sessions + when done to avoid clutter\\n6. **Use Interactive CLI**: For manual testing + and exploration, the interactive CLI provides the best experience\\n\",\"format\":\"MARKDOWN\",\"md5\":\"bbaaf8cccd2b664ba4f7daf47d1f2bf4\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Retrieval + Agents Orchestrator \\n Interact with Nuclia's Retrieval Agents Orchestrator + to have intelligent conversations over several knowledge sources with persistent + session management and real-time streaming responses. \\n Prerequisites \\n + Install the Nuclia SDK: \\n sh \\n pip install nuclia \\n Ensure you have: + \\n - A valid Nuclia authentication token (see Authentication) \\n - Access + to a configured Retrieval Agent \\n Overview \\n The nuclia.py library provides + several ways to interact with your Retrieval Agents Orchestrators: \\n \\n + Interactive CLI: A rich, user-friendly terminal interface (recommended) \\n + Standard CLI: Direct access to raw websocket messages for debugging \\n Session + Management: Create and manage persistent conversation sessions \\n Programmatic + API: Python SDK for building custom applications \\n \\n Listing Available + Agents \\n Discover what Retrieval Agents Orchestrators you have access to. + \\n \\n CLI: \\n \\n sh \\n nuclia agents list \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() + \\n all_agents = agents.list() \\n for agent in all_agents: \\n print(f Agent: + {agent.title} ({agent.id}) ) \\n print(f Slug: {agent.slug} ) \\n print(f + Zone: {agent.zone} ) \\n ``` \\n Getting a Specific Agent \\n \\n CLI: \\n + \\n sh \\n nuclia agents get --account= my-account --id= agent-uuid --zone= + europe-1 \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agents import + NucliaAgents \\n agents = NucliaAgents() \\n agent_details = agents.get( \\n + account= my-account , \\n id= agent-uuid , \\n zone= europe-1 \\n ) \\n print(agent_details) + \\n ``` \\n Setting a Default Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents + default [AGENT_SLUG or AGENT_UUID] \\n \\n SDK: \\n \\n ```python \\n from + nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() \\n agents.default( + my-agent ) \\n ``` \\n This sets the default agent for all subsequent operations. + \\n Interactive CLI (Recommended) \\n The interactive CLI provides a beautiful, + real-time interface for conversing with your Retrieval Agents Orchestrator. + \\n Starting the Interactive CLI \\n \\n CLI: \\n \\n sh \\n nuclia agent + cli interact \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n agent.cli.interact() \\n ``` \\n + This launches an interactive terminal session where you can: \\n - Ask questions + and see streaming responses \\n - View processing steps in real-time \\n - + Manage conversation sessions \\n - See retrieved context and citations \\n + Interactive CLI Commands \\n The CLI supports several commands (prefix with + /): \\n | Command | Description | \\n |---------|-------------| \\n | /help + | Show available commands | \\n | /new_session | Create a new persistent session + | \\n | /list_sessions | List all your sessions | \\n | /change_session | + Switch to a different session, use 'ephemeral' for a temporary session | \\n + | /clear | Clear the screen | \\n | /exit | Exit the CLI | \\n Please note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option Agent with memory enabled during creation. \\n Session Management + \\n Sessions allow you to maintain conversation context across multiple interactions. + \\n \\n This feature will only be available if you checked Agent with memory + during the creation of your Retrieval Agents Orchestrator. \\n \\n Creating + a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session new --name= My + Research Session \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session_uuid = agent.session.new( + My Research Session ) \\n print(f Created session: {session_uuid} ) \\n ``` + \\n Listing Sessions \\n \\n CLI: \\n \\n sh \\n nuclia agent session list + \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import NucliaAgent + \\n agent = NucliaAgent() \\n sessions = agent.session.list() \\n for session + in sessions.resources: \\n print(f {session.title}: {session.id} ) \\n ``` + \\n Getting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session get + --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session = agent.session.get(session_uuid) + \\n print(f Session: {session.title} ) \\n print(f Created: {session.created} + ) \\n ``` \\n Deleting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent + session delete --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n + agent.session.delete(session_uuid) \\n ``` \\n Interaction \\n Aside from + the interactive CLI, you can interact with your Retrieval Agents Orchestrator + with the simple CLI or programmatically using the SDK. \\n Basic Interaction + \\n CLI: \\n bash \\n nuclia agent interact What is Eric known for? \\n SDK: + \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() + \\n Iterate over streaming responses \\n for response in agent.interact( \\n + question= What is Eric known for? \\n ): \\n if response.operation == ANSWER + and response.answer: \\n print(response.answer) \\n elif response.step: \\n + print(f Processing: {response.step.module} ) \\n ``` \\n Not supplying a session_uuid + when calling interact will use an ephemeral session by default. To maintain + context, provide a persistent session UUID. \\n Using Persistent Sessions + \\n CLI: \\n ```bash \\n nuclia agent sessions new Customer Support Chat \\n + Note the session UUID returned \\n nuclia agent interact What are your business + hours? --session_uuid= SESSION_UUID \\n nuclia agent interact Are you open + on weekends? --session_uuid= SESSION_UUID \\n ``` \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n Create + a session \\n session_uuid = agent.session.new( Customer Support Chat ) \\n + Have a conversation with context \\n for response in agent.interact( \\n session_uuid=session_uuid, + \\n question= What are your business hours? \\n ): \\n if response.answer: + \\n print(response.answer) \\n Follow-up question maintains context \\n for + response in agent.interact( \\n session_uuid=session_uuid, \\n question= Are + you open on weekends? \\n ): \\n if response.answer: \\n print(response.answer) + \\n ``` \\n Understanding Response Types \\n When interacting with an agent, + you receive a stream of AragAnswer objects with different operations: \\n + | Operation | Description | \\n |-----------|-------------| \\n | START | + Interaction has begun | \\n | ANSWER | Processing step or partial answer | + \\n | DONE | Interaction complete | \\n | ERROR | An error occurred | \\n + | AGENT_REQUEST | Agent needs user feedback | \\n Response Attributes \\n + Each response may contain: \\n \\n step: Information about the current processing + step \\n module: The module being executed (e.g., rephrase , basic_ask , remi + ) \\n title: Display title for the step \\n value: Result of the step \\n + reason: Explanation for the step \\n timeit: Time taken in seconds \\n \\n + input_nuclia_tokens/output_nuclia_tokens: Token usage \\n \\n \\n context: + Retrieved context from the knowledge base \\n \\n chunks: List of retrieved + text chunks with sources \\n \\n summary: Summary of the context or partial + answer \\n \\n \\n answer: The final answer text (Markdown formatted) \\n + \\n \\n generated_text: Intermediate generated text \\n \\n \\n possible_answer: + Alternative answer being considered \\n \\n \\n exception: Error details if + something went wrong \\n \\n \\n Processing Responses \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n for response in agent.interact(question= + Tell me about AI ): \\n if response.operation == AnswerOperation.START: \\n + print( Starting... ) \\n elif response.step: \\n print(f Step: {response.step.module} + ({response.step.timeit:.2f}s) ) \\n \\n elif response.context: \\n print(f + Retrieved {len(response.context.chunks)} chunks ) \\n for chunk in response.context.chunks: + \\n print(f - {chunk.title}: {chunk.text[:100]}... ) \\n \\n elif response.answer: + \\n print(f \\\\nFinal Answer:\\\\n{response.answer} ) \\n \\n elif response.operation + == AnswerOperation.DONE: \\n print( Complete! ) \\n \\n elif response.operation + == AnswerOperation.ERROR: \\n print(f Error: {response.exception.detail if + response.exception else 'Unknown'} ) \\n \\n ``` \\n Standard CLI for Raw + Messages \\n For debugging or advanced use cases, you can access raw websocket + messages programmatically: \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n Iterate over all messages \\n for + message in agent.interact( \\n question= What is RAO? \\n ): \\n # message + is an AragAnswer object with all raw data \\n print(f Operation: {message.operation} + ) \\n print(f Raw message: {message.model_dump_json(indent=2)} ) \\n ``` \\n + This gives you direct access to all websocket message data for debugging or + custom processing. \\n Advanced Features \\n Agent Feedback Requests \\n Agents + can request additional input from users during processing: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n generator = agent.interact(question= + Help me with X ) \\n for response in generator: \\n if response.operation + == AnswerOperation.AGENT_REQUEST: \\n # Agent is requesting user input \\n + user_input = input(f Agent asks: {response.feedback.question}\\\\n> ) \\n + # Send response back \\n generator.send(user_input) \\n elif response.answer: + \\n print(response.answer) \\n ``` \\n Error Handling \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia.exceptions import RaoAPIException + \\n agent = NucliaAgent() \\n try: \\n for response in agent.interact(question= + Hello? ): \\n if response.exception: \\n print(f Agent error: {response.exception.detail} + ) \\n elif response.answer: \\n print(response.answer) \\n except RaoAPIException + as e: \\n print(f API error: {e.detail} ) \\n except Exception as e: \\n print(f + Unexpected error: {e} ) \\n ``` \\n Passing Custom Headers to MCP \\n If your + Retrieval Agents Orchestrator requires custom headers for MCP Agents, you + can pass them as follows: \\n CLI: \\n bash \\n nuclia agent interact What + is AI? --headers '{ X-Custom-Header : value }' \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n for + response in agent.interact( \\n question= What is AI? , \\n headers={ X-Custom-Header + : value } \\n ): \\n if response.answer: \\n print(response.answer) \\n ``` + \\n Please ensure that the 'Allowed Headers' configuration in your MCP agent + includes any custom headers you wish to use. \\n Best Practices \\n \\n Use + Sessions for Context: Create sessions when you need multi-turn conversations + with context retention \\n Use Ephemeral Sessions for One-offs: Don't supply + a session UUID for using agents in a stateless manner. \\n Stream for UX: + Process responses as they arrive for better user experience \\n Handle All + Operations: Check for different operation types (START, ANSWER, DONE, ERROR) + when processing responses \\n Clean Up Sessions: Delete sessions when done + to avoid clutter \\n Use Interactive CLI: For manual testing and exploration, + the interactive CLI provides the best experience \\n 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\\n timeit: Time taken in seconds \\n \\n input_nuclia_tokens/output_nuclia_tokens: + Token usage \\n \\n \\n\",\"id\":\"44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":19,\"start\":6346,\"end\":6668,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.6083368062973022,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\n\\n___\\n\\n### + TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\"train_seconds\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:13:38.314222Z\",\"last_understanding\":\"2026-06-09T08:13:35.850668Z\",\"last_extract\":\"2026-06-09T08:13:35.182287Z\",\"last_processing_start\":\"2026-06-09T08:13:35.144379Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enums > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enums > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.5916038155555725,\"score_type\":\"VECTOR\",\"order\":17,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\",\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.5957225561141968,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the + Knowledge Box\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > rag > advanced > widget > features\",\"extracted\":{\"text\":{\"text\":\"docs + > rag > advanced > widget > features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.7151519060134888,\"score_type\":\"VECTOR\",\"order\":0,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate + limits are an essential aspect of the Agentic RAG platform, ensuring fair + usage and optimal performance for all users interacting with Agentic RAG APIs. + This document outlines the rate limits enforced by Agentic RAG and provides + guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic + RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: + By default, the sum of all authenticated requests in a Agentic RAG account + cannot exceed 2400 requests per minute. Note that this limit can be customized + on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) + if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic + RAG implements a back-pressure mechanism to manage ingestion pipeline overload. + This mainly affects endpoints for uploading data and creating or updating + resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres + to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) + and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe + official Agentic RAG API clients already have built-in mechanisms for retrying + requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- + [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are + interacting directly with the API, we recommend using an [exponential backoff + retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits + are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response + will include a `try_after` key with an estimated UTC time for retrying the + request. You can use this value for retry logic as an alternative to the exponential + backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an + example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport + time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, + headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n + \ response = requests.get(url, headers=headers)\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n + \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n + \ time.sleep(wait_time)\\n retries += 1\\n else:\\n + \ response.raise_for_status()\\n raise Exception(\\\"Max retries + exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders + = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, + headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's + an example of how to use the try_after key from the response to manage rate + limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport + requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n + \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, + headers=headers)\\n response_body = response.json()\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n + \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n + \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n + \ print(\\n f\\\"Rate limit exceeded. Retrying at + {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n + \ retries += 1\\n else:\\n response.raise_for_status()\\n + \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl + = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer + YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese + examples demonstrate how to handle rate limits effectively, ensuring that + your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate + limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, + ensuring fair usage and optimal performance for all users interacting with + Agentic RAG APIs. This document outlines the rate limits enforced by Agentic + RAG and provides guidelines for handling rate-limited responses effectively. + \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: + \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated + requests in a Agentic RAG account cannot exceed 2400 requests per minute. + Note that this limit can be customized on a per-account basis. Please contact + Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion + back pressure limits: Agentic RAG implements a back-pressure mechanism to + manage ingestion pipeline overload. This mainly affects endpoints for uploading + data and creating or updating resources. \\n \\n \\n Handling Rate-Limited + Responses \\n Agentic RAG adheres to the HTTP standard and will return a response + with a 429 status codes when the limits are exceeded. \\n The official Agentic + RAG API clients already have built-in mechanisms for retrying requests when + rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript + client \\n \\n However, if you are interacting directly with the API, we recommend + using an exponential backoff retry strategy when limits are reached. \\n When + ingestion back pressure rate limits are hit, the response will include a try_after + key with an estimated UTC time for retrying the request. You can use this + value for retry logic as an alternative to the exponential backoff strategy. + \\n Example 1: Regular API rate limits \\n Here's an example of how to implement + an exponential backoff retry strategy in Python: \\n ```python \\n import + time \\n import requests \\n def make_request_with_exponential_backoff(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n if response.status_code + == 200: \\n return response.json() \\n elif response.status_code == 429: \\n + wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit + exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n + retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( + Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n + headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n ```python \\n import time \\n from datetime import + datetime \\n import requests \\n def make_request_with_try_after_info(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n response_body = response.json() + \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code + == 429 and try_after in response_body: \\n try_after = response_body[ try_after + ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n + wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n + f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... + \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries 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+ RAG\"},\"LAW/HTTP\":{\"position\":[{\"start\":998,\"end\":1002}],\"entity\":\"HTTP\"},\"PRODUCT/Max\":{\"position\":[{\"start\":3409,\"end\":3412}],\"entity\":\"Max\"},\"TIME/seconds\":{\"position\":[{\"start\":2199,\"end\":2206}],\"entity\":\"seconds\"},\"PRODUCT/Python\":{\"position\":[{\"start\":1232,\"end\":1238},{\"start\":1773,\"end\":1779}],\"entity\":\"Python\"},\"ORG/Agentic + RAG's\":{\"position\":[{\"start\":668,\"end\":681}],\"entity\":\"Agentic RAG's\"},\"LAW/Handling + Rate-Limited Responses Agentic RAG\":{\"position\":[{\"start\":937,\"end\":982}],\"entity\":\"Handling + Rate-Limited Responses Agentic RAG\"},\"TIME/UTC\":{\"position\":[{\"start\":1526,\"end\":1529}],\"entity\":\"UTC\"},\"PRODUCT/Agentic + RAG API\":{\"position\":[{\"start\":1108,\"end\":1123}],\"entity\":\"Agentic + RAG API\"},\"ORG/Nuclia\":{\"position\":[{\"start\":1225,\"end\":1231},{\"start\":1248,\"end\":1254}],\"entity\":\"Nuclia\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"operating + system\",\"metadata\":{\"paragraph_id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1222-1656\",\"source_start\":1248,\"source_end\":1254,\"to_start\":1232,\"to_end\":1238},\"from\":{\"value\":\"Nuclia\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\":{\"value\":\"Python\",\"type\":\"entity\",\"group\":\"PRODUCT\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > ingestion > how to > rate limiting\",\"extracted\":{\"text\":{\"text\":\"docs + > ingestion > how to > rate limiting\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.590923011302948,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" + ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1b2a9e67b9f14a0cb81efaa05b8793b8\":{\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8\",\"slug\":\"docs-develop-js-sdk-interfaces-AugmentedField-md\",\"title\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:00:05.937520\",\"modified\":\"2026-07-14T12:49:32.163110\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AugmentedField\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / AugmentedField\\n\\n# + Interface: AugmentedField\\n\\n## Properties\\n\\n### applied\\\\_data\\\\_augmentation\\n\\n> + **applied\\\\_data\\\\_augmentation**: [`AppliedDataAugmentation`](AppliedDataAugmentation.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:571](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L571)\\n\\n***\\n\\n### + input\\\\_nuclia\\\\_tokens\\n\\n> **input\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:572](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L572)\\n\\n***\\n\\n### + metadata\\n\\n> **metadata**: [`FieldMetadata`](FieldMetadata.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:570](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L570)\\n\\n***\\n\\n### + output\\\\_nuclia\\\\_tokens\\n\\n> **output\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:573](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L573)\\n\\n***\\n\\n### + time\\n\\n> **time**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:574](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L574)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"1e8e95b886439059ae4ec717c9bcf283\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n time: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:574\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":641,\"key\":\"\"}]},{\"start\":641,\"end\":729,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":642,\"end\":729,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:33.585675Z\",\"last_understanding\":\"2026-07-14T12:49:33.340252Z\",\"last_extract\":\"2026-07-14T12:49:32.941746Z\",\"last_processing_start\":\"2026-07-14T12:49:32.917812Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > AugmentedField\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\":{\"score\":0.5881041884422302,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n\",\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n 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ChatOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\":{\"score\":0.6769328117370605,\"score_type\":\"VECTOR\",\"order\":1,\"text\":\" + \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum number + of tokens that the model will take as context. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 + \\n \\n min_score? \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible + LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG + allows you to connect to any OpenAI API compatible LLM. This means that you + can use any LLM that has an API compatible with the OpenAI API which has become + a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, + open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box + configuration you can do so in three manners, through the API, the Nuclia + CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers + the most user-friendly way to modify the configuration of your knowledge box + and we will use it in this example.\\n\\nWe will be setting up a connection + to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers + a wide range of open-source and commercial models compatible with the OpenAI + API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), + the API parameters are located under the **API** tab.\\n\\n1. **Open the AI + Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. + **Select \u201COpenAI API Compatible Model\u201D** \\n From the models + list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n + \ Toggle the option for using you own `OpenAI API Compatible Key` if it is + not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - + **API Key**:\\n - Description: The API key for your LLM. This is the key + that you would use as an authorization header in the API. You may leave this + blank if the endpoint you are connecting to does not require an API key.\\n + \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n + \ - Description: The URL of the API endpoint for your LLM. This may be + shared between multiple models.\\n - Example: For OpenRouter, it is the + same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - + Description: The name of the model you want to use, it needs to exactly match + the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus + in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - + **Maximum supported input tokens**:\\n - Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only.\\n - + Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, + as we want to leave room for the output, we will set the maximum supported + input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output + tokens**:\\n - Description: The maximum number of tokens that the model + can generate as output. Again, we should keep in mind that this value summed + to the **Maximum supported input tokens** should not exceed the total context + size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the + maximum output tokens is specified at `32768`, but we already reserved `31744` + for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - + **Model Features**:\\n - Description: Under this section you will find + multiple toggles related to features supported by the model, these vary from + model to model, but most often the default values are well suited to most + use cases. The most relevant toggle is for `Image Support` which allows you + to use images as input for the model.\\n - Example: Image input is not + supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** + \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample + query in Agentic RAG or via API/CLI. Adjust your prompt templates and token + settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API + compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic + RAG allows you to connect to any OpenAI API compatible LLM. This means that + you can use any LLM that has an API compatible with the OpenAI API which has + become a standard in the industry. \\n Many of the options for self-hosted + LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications. \\n Configuration \\n To modify your knowledge box configuration + you can do so in three manners, through the API, the Nuclia CLI / SDK or the + Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly + way to modify the configuration of your knowledge box and we will use it in + this example. \\n We will be setting up a connection to the Phi 4 Reasoning + Plus model, hosted by OpenRouter which offers a wide range of open-source + and commercial models compatible with the OpenAI API. We can see more information + about this specific model here, the API parameters are located under the API + tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, + click AI Models. \\n Select OpenAI API Compatible Model \\n From the models + list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle + the option for using you own OpenAI API Compatible Key if it is not already + enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: + \\n \\n Description: The API key for your LLM. This is the key that you would + use as an authorization header in the API. You may leave this blank if the + endpoint you are connecting to does not require an API key. \\n Example: We + will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: + The URL of the API endpoint for your LLM. This may be shared between multiple + models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 + \\n \\n \\n Model: \\n Description: The name of the model you want to use, + it needs to exactly match the name of the model in the API. \\n Example: For + Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. + \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n Example: + For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want + to leave room for the output, we will set the maximum supported input tokens + as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified + at 32768, but we already reserved 31744 for the input tokens, so we will set + this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: + Under this section you will find multiple toggles related to features supported + by the model, these vary from model to model, but most often the default values + are well suited to most use cases. The most relevant toggle is for Image Support + which allows you to use images as input for the model. \\n Example: Image + input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. + \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n + Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt templates + and token settings as needed. \\n \\n 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\\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.63338303565979,\"score_type\":\"VECTOR\",\"order\":5,\"text\":\" + \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs + > develop > js sdk > enumerations > 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AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: 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\\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n 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libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}}},\"relations\":{\"entities\":{}},\"query\":\"Explica + el uso del par\xE1metro `max_tokens` y enlaza a la documentaci\xF3n 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headers: Alt-Svc: - h3=":443"; ma=2592000 Content-Length: - - '16898' + - '207667' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 15 Jul 2026 07:57:16 GMT + - Wed, 15 Jul 2026 08:11:07 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '56' + - '360' x-nuclia-trace-id: - - 8f7e80506099561b202eaee782c0ab56 + - d72ecd4c779ba2045b3d590634eb0307 status: code: 200 message: OK - request: - body: '{"user_id": "arag-ask", "texts": ["Expl\u00edcame c\u00f3mo usar el par\u00e1metro - `max_tokens` en respuestas, en espa\u00f1ol, y proporciona un enlace a la documentaci\u00f3n - oficial."]}' + body: '{"query": "Esboniwch sut i ddefnyddio''r paramedr `max_tokens` a darparwch + ddolen i''r ddogfennaeth swyddogol.", "filters": [], "show": ["basic", "origin", + "extra", "extracted", "values", "relations"], "extracted": ["text", "metadata", + "file", "link"], "security": {"groups": []}, "features": ["keyword"], "reranker": + "noop", "keyword_filters": ["max_tokens", "par\u00e1metro"]}' headers: Accept: - '*/*' @@ -940,7 +2294,7 @@ interactions: Connection: - keep-alive Content-Length: - - '164' + - '347' Host: - europe-1.nuclia.cloud User-Agent: @@ -950,36 +2304,50 @@ interactions: x-stf-serviceaccount: - DUMMY method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/predict/run-agents-text + uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find response: body: - string: '{"results":[{"input_nuclia_tokens":0.0,"output_nuclia_tokens":0.0,"time":0.0001270771026611328,"payloads":[]}]}' + string: '{"resources":{},"relations":{"entities":{}},"query":"Esboniwch sut + i ddefnyddio''r paramedr `max_tokens` a darparwch ddolen i''r ddogfennaeth + swyddogol.","rephrased_query":null,"total":0,"page_number":0,"page_size":20,"next_page":false,"shards":["306cbabb-72a5-417c-827f-7874e205c858"],"min_score":{"semantic":0.0,"bm25":0.0},"best_matches":[]}' headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Content-Length: - - '111' + - '342' access-control-expose-headers: - X-NUCLIA-TRACE-ID content-type: - application/json date: - - Wed, 15 Jul 2026 07:57:17 GMT + - Wed, 15 Jul 2026 08:11:08 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '18' + - '26' x-nuclia-trace-id: - - d361830863f9e4b3539d34c9abcc2232 + - fea262869465674fddc8eb7244aeefe4 status: code: 200 message: OK - request: - body: '{"query": "Esboniwch sut i ddefnyddio''r paramedr max_tokens mewn ymatebion - a darparwch ddolen i''r ddogfennaeth swyddogol.", "filters": [], "show": ["basic", - "origin", "extra", "extracted", "values", "relations"], "extracted": ["text", - "metadata", "file", "link"], "security": {"groups": []}, "features": ["keyword"], - "reranker": "noop", "keyword_filters": ["max_tokens", "par\u00e1metro"]}' + body: '{"data": ["4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340", "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221", + "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977", "8b3e0ef630a346d1b591143309db87ec/t/page/0-281", + "42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696", "6e8250e6b5264156988657a221fd5e94/t/page/0-397", + "43004f553e534ffe9c9e735856bd9b23/t/page/480-703", "43004f553e534ffe9c9e735856bd9b23/t/page/212-480", + "0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224", "44d05174f1954331b62f5e4026f2b01a/t/page/6346-6668", + "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349", "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139", + "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299", "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412", + "dd41482018924facb5dbb87a7d53f122/t/page/3310-3757", "1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641", + "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011", "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043", + "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575", "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833"], + "hydration": {"resource": {"title": true, "summary": false, "origin": false, + "security": false}, "field": {"text": {"value": false, "extracted_text": false}, + "file": {"value": false, "extracted_text": false}, "link": {"value": false, + "extracted_text": false}, "conversation": {"value": false}, "generic": {"value": + false, "extracted_text": false}}, "paragraph": {"text": false, "image": {"source_image": + false}, "table": {"table_page_preview": true}, "page": {"page_with_visual": + false}, "related": null}}}' headers: Accept: - '*/*' @@ -988,6361 +2356,60 @@ interactions: Connection: - keep-alive Content-Length: - - '360' - Host: - - europe-1.nuclia.cloud - User-Agent: - - nucliadb-sdk/6.13.1.post6414 - content-type: - - application/json - x-stf-serviceaccount: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find - response: - body: - string: "{\"resources\":{\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\"\ - ,\"modified\":\"2026-06-02T15:40:39.731007\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\\ - n\\n# Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\\ - n\\n## Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\ - \\_json\\\\_schema**: `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\\ - n\\n***\\n\\n### audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**:\ - \ `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean` \\\\\ - | `\\\"none\\\"` \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\\ - nIt will return the text blocks that have been effectively used to build each\ - \ section of the answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\\ - n\\n***\\n\\n### debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\\ - n\\n***\\n\\n### extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**:\ - \ `string`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\\ - n\\n***\\n\\n### extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\ - \\_context\\\\_images**: `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\\ - n\\n***\\n\\n### ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\\ - n\\n#### Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\\ - n\\n***\\n\\n### features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\\ - n\\n***\\n\\n### field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\\ - _type\\\\_filter**: [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\\ - n\\n***\\n\\n### fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\\ - n\\n***\\n\\n### filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**:\ - \ [`FilterExpression`](FilterExpression.md)\\n\\n#### Inherited from\\n\\\ - n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\\ - n\\n***\\n\\n### filters?\\n\\n> `optional` **filters**: `string`[] \\\\|\ - \ [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\\ - n\\n***\\n\\n### highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\\ - n\\n***\\n\\n### keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**:\ - \ `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\ - \ \\\\| `object`\\n\\nDefines the maximum number of tokens that the model\ - \ will take as context.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\\ - n\\n***\\n\\n### min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number`\ - \ \\\\| [`MinScore`](MinScore.md)\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\\ - n\\n***\\n\\n### prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\\ - n\\n***\\n\\n### query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\\ - n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\\ - _type\\n\\n> **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\\ - n\\n***\\n\\n### rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\\ - _images\\\\_strategies**: [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\\ - n\\n***\\n\\n### rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**:\ - \ [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\\ - _creation\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\ - \\_creation\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\ - \\_modification\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\ - \\_modification\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\\ - n\\n***\\n\\n### rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\\ - n\\n***\\n\\n### rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\\ - n\\n***\\n\\n### reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\\ - n\\n***\\n\\n### resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**:\ - \ `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\\ - n\\n***\\n\\n### search\\\\_configuration?\\n\\n> `optional` **search\\\\\ - _configuration**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\\ - n\\n***\\n\\n### security?\\n\\n> `optional` **security**: `object`\\n\\n####\ - \ groups\\n\\n> **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\\ - n\\n***\\n\\n### show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\\ - n\\n***\\n\\n### show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\\ - n\\n***\\n\\n### show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\\ - n\\n***\\n\\n### synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\\ - n\\n***\\n\\n### top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\\ - n\\n***\\n\\n### vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"6d5a917c8b0119f502754c488a87228e\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface:\ - \ ChatOptions \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\\ - n answer_json_schema? \\n \\n optional answer_json_schema: object \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 \\n\ - \ \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index\ - \ Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\\ - n \\n citation_threshold? \\n \\n optional citation_threshold: number \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\\\| none \\\\|\ - \ default \\\\| llm_footnotes \\n \\n It will return the text blocks that\ - \ have been effectively used to build each section of the answer. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 \\n \\n debug?\ - \ \\n \\n optional debug: boolean \\n \\n Inherited from \\n BaseSearchOptions.debug\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 \\\ - n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images?\ - \ \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118\ - \ \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[]\ - \ \\n \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\\ - n features? \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117\ - \ \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields?\ - \ \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\\ - n \\n filter_expression? \\n \\n optional filter_expression: FilterExpression\ - \ \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters?\ - \ \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63\ - \ \\n \\n generative_model? \\n \\n optional generative_model: string \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108\ - \ \\n \\n highlight? \\n \\n optional highlight: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76\ - \ \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\\ - | Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\\ - n max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines\ - \ the maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from\ - \ \\n BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n\ - \ \\n prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image?\ - \ \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded:\ - \ string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies?\ - \ \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies?\ - \ \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106\ - \ \\n \\n range_creation_end? \\n \\n optional range_creation_end: string\ - \ \\n \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start?\ - \ \\n \\n optional range_creation_start: string \\n \\n Inherited from \\\ - n BaseSearchOptions.range_creation_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67\ - \ \\n \\n range_modification_end? \\n \\n optional range_modification_end:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\\ - n \\n range_modification_start? \\n \\n optional range_modification_start:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_start\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 \\\ - n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited\ - \ from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase?\ - \ \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\\ - n \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from\ - \ \\n BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83\ - \ \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\\ - n \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration?\ - \ \\n \\n optional search_configuration: string \\n \\n Inherited from \\\ - n BaseSearchOptions.search_configuration \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86\ - \ \\n \\n security? \\n \\n optional security: object \\n \\n groups \\n \\\ - n groups: string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\\ - n show? \\n \\n optional show: ResourceProperties[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.show \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71\ - \ \\n \\n show_consumption? \\n \\n optional show_consumption: boolean \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.show_hidden \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80\ - \ \\n \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k?\ - \ \\n \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\\ - n \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from\ - \ \\n BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\"\ - :[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"\ - key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\"\ - :499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"\ - end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"\ - end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\"\ - :\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"\ - end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\"\ - :1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"\ - key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"\ - end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"\ - start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"\ - key\":\"\"},{\"start\":2330,\"end\":2450,\"key\":\"\"}]},{\"start\":2450,\"\ - end\":2808,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2453,\"end\":2622,\"key\":\"\"},{\"start\":2622,\"\ - end\":2808,\"key\":\"\"}]},{\"start\":2808,\"end\":3011,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2811,\"end\":2929,\"key\":\"\"},{\"start\":2929,\"end\":3011,\"\ - key\":\"\"}]},{\"start\":3011,\"end\":3311,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3014,\"\ - end\":3194,\"key\":\"\"},{\"start\":3194,\"end\":3311,\"key\":\"\"}]},{\"\ - start\":3311,\"end\":3647,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3440,\"\ - key\":\"\"},{\"start\":3440,\"end\":3647,\"key\":\"\"}]},{\"start\":3647,\"\ - 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}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\"\ - :{\"score\":10.046268463134766,\"score_type\":\"BM25\",\"order\":3,\"text\"\ - :\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum\ - \ number of tokens that the model will take as context. \\n Defined in \\\ - n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"end\":3011,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - 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models\\ntitle:\ - \ Semantic models\\n---\\n\\n| **Model Name** | **Size** | **Threshold**\ - \ | **Max Tokens** | **Matryoshka** | **Multilingual** | **External** |\\\ - n| --------------------------- | -------- | ------------- | --------------\ - \ | -------------- | ---------------- | ------------ |\\n| `en-2024-04-24`\ - \ | 768 | 0.47 | 2048 | No \ - \ | No | No |\\n| `multilingual-2023-08-16` | 1024\ - \ | 0.7 | 512 | No | Yes \ - \ | No |\\n| `multilingual-2024-05-06` | 1024 | 0.4 \ - \ | 2048 | No | Yes | No \ - \ |\\n| `Open AI small` | 1536 | 0.5 | 8192 \ - \ | Yes | No | No |\\n| `Open AI\ - \ large` | 3072 | 0.5 | 8192 | Yes \ - \ | No | Yes |\\n| `Google multilingual Gecko`\ - \ | 768 | 0.55 | 3072 | Yes | Yes \ - \ | Yes |\\n| `Hugging Face` | N/A | N/A\ - \ | N/A | N/A | N/A | Yes \ - \ |\\n\\n# Embeddings models\\n\\nEmbeddings are like fingerprints for\ - \ words or data. They help computers understand the similarities and differences\ - \ between them, making it easier for machines to perform tasks like understanding\ - \ language or recognizing patterns.\\n\\nIn a Retrieval-Augmented Generation\ - \ (RAG) system, the quality of the embeddings you choose directly impacts\ - \ how effectively the system retrieves and understands information. The embedding\ - \ model you select determines how well your system can find relevant data,\ - \ interpret user queries, and generate accurate responses. Choosing the right\ - \ model ensures that your Knowledge Box can deliver precise and contextually\ - \ relevant information, which is crucial for maintaining high-quality user\ - \ interactions and decision-making processes.\\n\\nThe choice of embedding\ - \ model depends on the languages that will be used in both resources and queries.\ - \ If you plan to use only English, a monolingual English model would be the\ - \ ideal choice. For multilingual applications, it\u2019s important to consider\ - \ the specific languages involved. Most multilingual embeddings support high-resource\ - \ languages\u2014widespread languages like English, Chinese, Spanish, French,\ - \ and Japanese. However, if your use case involves low-resource languages\u2014\ - those that are less common and have fewer resources, such as Basque, Welsh,\ - \ or Irish\u2014you\u2019ll need to choose your embedding model more carefully\ - \ to ensure adequate support.\\n\\n# Agentic RAG's semantic models\\n\\nThese\ - \ embedding models run 100% on Agentic RAG's infrastructure. This option ensures\ - \ that all processes remain within Agentic RAG's secure and controlled environment,\ - \ optimizing efficiency and security.\\n\\n## en-2024-04-24\\n\\nOur most\ - \ up to date English model. Suitable for use cases in which both your queries\ - \ and resources are only in English.\\n\\n## multilingual-2024-05-06\\n\\\ - nOur most up to date multilingual model providing strong support for both\ - \ high-resource and many low-resource languages.\\nOptimized for widely spoken\ - \ languages.\\n\\n## multilingual-2023-08-16\\n\\nOur best model for low resource\ - \ and asian languages.\\n\\n# Trusted External Partner Models\\n\\nThese embedding\ - \ models run on the infrastructure of our trusted partners. This allows you\ - \ to leverage the expertise and technological capabilities of otherleaders\ - \ in the field of artificial intelligence.\\n\\n## Google's Gecko\\n\\nGoogle's\ - \ gecko multilingual embeddings.\\nTheir use may result in additional costs,\ - \ contact our sales department if you want to find out more.\\n\\n## OpenAI\ - \ 3 small\\n\\nMultilingual embeddings provided by openai, the smallest and\ - \ fastest they offer.\\nMore information [here](https://platform.openai.com/docs/guides/embeddings)\\\ - nTheir use may result in additional costs, contact our sales department if\ - \ you want to find out more.\\n\\n## OpenAI 3 large\\n\\nMultilingual embeddings\ - \ provided by openai, bigger but with more precision than their small ones.\\\ - nMore information [here](https://platform.openai.com/docs/guides/embeddings)\\\ - nTheir use may result in additional costs, contact our sales department if\ - \ you want to find out more.\\n\\n## Hugging Face\\n\\nWe allow you to use\ - \ any embedding model from Hugging Face to which you have access.\\nTo configure\ - \ the model, you will need to provide the following information:\\n\\n###\ - \ Hugging Face Model Configuration\\n\\n- **Hugging Face Endpoint URL** \\\ - n The URL of the Hugging Face embedding model endpoint. This is a required\ - \ field and must point to the specific model you wish to use.\\n\\n- **Hugging\ - \ Face API Key** \\n Your API Key, which grants access to the desired Hugging\ - \ Face endpoint. This is also a required field.\\n\\n- **Embedding Vector\ - \ Size** \\n The size of the embeddings generated by the model.\\n\\n###\ - \ Advanced Configuration\\n\\n- **Matryoshka Dimensions** \\n A list of\ - \ matryoshka dimensions to use for the embeddings, listed in descending order.\\\ - n If you are unsure of what to enter here, it is safe to leave this field\ - \ empty.\\n\\n- **Similarity Function** \\n The similarity function determines\ - \ how the embeddings are compared. You can choose between DOT or COSINE.\\\ - n If you are uncertain, leave this set to DOT.\\n\\n- **Semantic Threshold**\ - \ \\n The threshold value for semantic similarity. This setting determines\ - \ the sensitivity of similarity comparisons.\\n Adjust this value based on\ - \ your specific use case or leave it at the default setting (e.g., 0.5).\\\ - n\\n- **Passage Prompt** \\n The prompt to use for passage embedding. Only\ - \ set this value if your embedding model requires a specific passage prompt.\\\ - n\\n- **Query Prompt** \\n The prompt to use for query embedding. Only set\ - \ this value if your embedding model requires a specific query prompt.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"0c425d43435eefbb56efebb8f21ff584\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ models \\n title: Semantic models \\n \\n | Model Name | Size | Threshold\ - \ | Max Tokens | Matryoshka | Multilingual | External | \\n | ---------------------------\ - \ | -------- | ------------- | -------------- | -------------- | ----------------\ - \ | ------------ | \\n | en-2024-04-24 | 768 | 0.47 | 2048 | No | No | No\ - \ | \\n | multilingual-2023-08-16 | 1024 | 0.7 | 512 | No | Yes | No | \\\ - n | multilingual-2024-05-06 | 1024 | 0.4 | 2048 | No | Yes | No | \\n | Open\ - \ AI small | 1536 | 0.5 | 8192 | Yes | No | No | \\n | Open AI large | 3072\ - \ | 0.5 | 8192 | Yes | No | Yes | \\n | Google multilingual Gecko | 768 |\ - \ 0.55 | 3072 | Yes | Yes | Yes | \\n | Hugging Face | N/A | N/A | N/A | N/A\ - \ | N/A | Yes | \\n Embeddings models \\n Embeddings are like fingerprints\ - \ for words or data. They help computers understand the similarities and differences\ - \ between them, making it easier for machines to perform tasks like understanding\ - \ language or recognizing patterns. \\n In a Retrieval-Augmented Generation\ - \ (RAG) system, the quality of the embeddings you choose directly impacts\ - \ how effectively the system retrieves and understands information. The embedding\ - \ model you select determines how well your system can find relevant data,\ - \ interpret user queries, and generate accurate responses. Choosing the right\ - \ model ensures that your Knowledge Box can deliver precise and contextually\ - \ relevant information, which is crucial for maintaining high-quality user\ - \ interactions and decision-making processes. \\n The choice of embedding\ - \ model depends on the languages that will be used in both resources and queries.\ - \ If you plan to use only English, a monolingual English model would be the\ - \ ideal choice. For multilingual applications, it's important to consider\ - \ the specific languages involved. Most multilingual embeddings support high-resource\ - \ languages\u2014widespread languages like English, Chinese, Spanish, French,\ - \ and Japanese. However, if your use case involves low-resource languages\u2014\ - those that are less common and have fewer resources, such as Basque, Welsh,\ - \ or Irish\u2014you'll need to choose your embedding model more carefully\ - \ to ensure adequate support. \\n Agentic RAG's semantic models \\n These\ - \ embedding models run 100% on Agentic RAG's infrastructure. This option ensures\ - \ that all processes remain within Agentic RAG's secure and controlled environment,\ - \ optimizing efficiency and security. \\n en-2024-04-24 \\n Our most up to\ - \ date English model. Suitable for use cases in which both your queries and\ - \ resources are only in English. \\n multilingual-2024-05-06 \\n Our most\ - \ up to date multilingual model providing strong support for both high-resource\ - \ and many low-resource languages. \\n Optimized for widely spoken languages.\ - \ \\n multilingual-2023-08-16 \\n Our best model for low resource and asian\ - \ languages. \\n Trusted External Partner Models \\n These embedding models\ - \ run on the infrastructure of our trusted partners. This allows you to leverage\ - \ the expertise and technological capabilities of otherleaders in the field\ - \ of artificial intelligence. \\n Google's Gecko \\n Google's gecko multilingual\ - \ embeddings. \\n Their use may result in additional costs, contact our sales\ - \ department if you want to find out more. \\n OpenAI 3 small \\n Multilingual\ - \ embeddings provided by openai, the smallest and fastest they offer. \\n\ - \ More information here \\n Their use may result in additional costs, contact\ - \ our sales department if you want to find out more. \\n OpenAI 3 large \\\ - n Multilingual embeddings provided by openai, bigger but with more precision\ - \ than their small ones. \\n More information here \\n Their use may result\ - \ in additional costs, contact our sales department if you want to find out\ - \ more. \\n Hugging Face \\n We allow you to use any embedding model from\ - \ Hugging Face to which you have access. \\n To configure the model, you will\ - \ need to provide the following information: \\n Hugging Face Model Configuration\ - \ \\n \\n \\n Hugging Face Endpoint URL \\n The URL of the Hugging Face embedding\ - \ model endpoint. 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:1602},{\"start\":1618,\"end\":1625},{\"start\":1837,\"end\":1844},{\"start\"\ - :2390,\"end\":2397},{\"start\":2481,\"end\":2488}]},{\"text\":\"Chinese\"\ - ,\"label\":\"LANGUAGE\",\"positions\":[{\"start\":1846,\"end\":1853}]},{\"\ - text\":\"Spanish\",\"label\":\"LANGUAGE\",\"positions\":[{\"start\":1855,\"\ - end\":1862}]},{\"text\":\"French\",\"label\":\"LANGUAGE\",\"positions\":[{\"\ - start\":1864,\"end\":1870}]},{\"text\":\"Japanese\",\"label\":\"LANGUAGE\"\ - ,\"positions\":[{\"start\":1876,\"end\":1884}]},{\"text\":\"Basque\",\"label\"\ - :\"LANGUAGE\",\"positions\":[{\"start\":2005,\"end\":2011}]},{\"text\":\"\ - Welsh\",\"label\":\"LANGUAGE\",\"positions\":[{\"start\":2013,\"end\":2018}]},{\"\ - text\":\"Irish\",\"label\":\"NORP\",\"positions\":[{\"start\":2023,\"end\"\ - :2028}]},{\"text\":\"Agentic RAG's\",\"label\":\"ORG\",\"positions\":[{\"\ - start\":2117,\"end\":2130},{\"start\":2184,\"end\":2197},{\"start\":2267,\"\ - end\":2280}]},{\"text\":\"2024-05-06\",\"label\":\"DATE\",\"positions\":[{\"\ - start\":2505,\"end\":2515}]},{\"text\":\"2023-08-16\",\"label\":\"DATE\",\"\ - positions\":[{\"start\":2694,\"end\":2704}]},{\"text\":\"asian\",\"label\"\ - :\"NORP\",\"positions\":[{\"start\":2743,\"end\":2748}]},{\"text\":\"Google\"\ - ,\"label\":\"ORG\",\"positions\":[{\"start\":3002,\"end\":3008},{\"start\"\ - :3019,\"end\":3025}]},{\"text\":\"Gecko\",\"label\":\"PRODUCT\",\"positions\"\ - :[{\"start\":3011,\"end\":3016}]},{\"text\":\"OpenAI\",\"label\":\"ORG\",\"\ - positions\":[{\"start\":3164,\"end\":3170}]},{\"text\":\"openai\",\"label\"\ - :\"ORG\",\"positions\":[{\"start\":3217,\"end\":3223},{\"start\":3444,\"end\"\ - :3450}]},{\"text\":\"Hugging Face API Key Your API Key\",\"label\":\"PRODUCT\"\ - ,\"positions\":[{\"start\":4020,\"end\":4055}]},{\"text\":\"Hugging Face\"\ - ,\"label\":\"PRODUCT\",\"positions\":[{\"start\":4092,\"end\":4104}]},{\"\ - text\":\"DOT\",\"label\":\"LAW\",\"positions\":[{\"start\":4573,\"end\":4576},{\"\ - start\":4630,\"end\":4633}]},{\"text\":\"COSINE\",\"label\":\"LAW\",\"positions\"\ - :[{\"start\":4580,\"end\":4586}]}]}},\"classifications\":[],\"last_index\"\ - :\"2025-08-27T15:34:26.486786Z\",\"last_understanding\":\"2025-08-27T15:34:25.897261Z\"\ - ,\"last_extract\":\"2025-08-27T15:34:19.118682Z\",\"last_processing_start\"\ - :\"2025-08-27T15:34:19.022852Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{\"ORG/Agentic RAG's\":{\"position\":[{\"start\":2117,\"end\":2130},{\"start\"\ - :2184,\"end\":2197},{\"start\":2267,\"end\":2280}],\"entity\":\"Agentic RAG's\"\ - },\"LAW/COSINE\":{\"position\":[{\"start\":4580,\"end\":4586}],\"entity\"\ - :\"COSINE\"},\"PRODUCT/Hugging Face API Key \\n Your API Key\":{\"position\"\ - :[{\"start\":4020,\"end\":4055}],\"entity\":\"Hugging Face API Key Your API\ - \ Key\"},\"ORG/Google\":{\"position\":[{\"start\":3002,\"end\":3008},{\"start\"\ - :3019,\"end\":3025}],\"entity\":\"Google\"},\"DATE/2023-08-16\":{\"position\"\ - :[{\"start\":2694,\"end\":2704}],\"entity\":\"2023-08-16\"},\"PRODUCT/Knowledge\ - \ Box\":{\"position\":[{\"start\":1297,\"end\":1310}],\"entity\":\"Knowledge\ - \ Box\"},\"DATE/2024-04-24\":{\"position\":[{\"start\":265,\"end\":275},{\"\ - start\":2357,\"end\":2367}],\"entity\":\"2024-04-24\"},\"PRODUCT/Hugging Face\"\ - :{\"position\":[{\"start\":4092,\"end\":4104}],\"entity\":\"Hugging Face\"\ - },\"LANGUAGE/Chinese\":{\"position\":[{\"start\":1846,\"end\":1853}],\"entity\"\ - :\"Chinese\"},\"ORG/openai\":{\"position\":[{\"start\":3217,\"end\":3223},{\"\ - start\":3444,\"end\":3450}],\"entity\":\"openai\"},\"PRODUCT/Gecko\":{\"position\"\ - :[{\"start\":3011,\"end\":3016}],\"entity\":\"Gecko\"},\"LAW/DOT\":{\"position\"\ - :[{\"start\":4573,\"end\":4576},{\"start\":4630,\"end\":4633}],\"entity\"\ - :\"DOT\"},\"NORP/Irish\":{\"position\":[{\"start\":2023,\"end\":2028}],\"\ - entity\":\"Irish\"},\"DATE/2024-05-06\":{\"position\":[{\"start\":2505,\"\ - end\":2515}],\"entity\":\"2024-05-06\"},\"LANGUAGE/Japanese\":{\"position\"\ - :[{\"start\":1876,\"end\":1884}],\"entity\":\"Japanese\"},\"LANGUAGE/Welsh\"\ - :{\"position\":[{\"start\":2013,\"end\":2018}],\"entity\":\"Welsh\"},\"LANGUAGE/French\"\ - :{\"position\":[{\"start\":1864,\"end\":1870}],\"entity\":\"French\"},\"LANGUAGE/Spanish\"\ - :{\"position\":[{\"start\":1855,\"end\":1862}],\"entity\":\"Spanish\"},\"\ - ORG/OpenAI\":{\"position\":[{\"start\":3164,\"end\":3170}],\"entity\":\"OpenAI\"\ - },\"LANGUAGE/English\":{\"position\":[{\"start\":1595,\"end\":1602},{\"start\"\ - :1618,\"end\":1625},{\"start\":1837,\"end\":1844},{\"start\":2390,\"end\"\ - :2397},{\"start\":2481,\"end\":2488}],\"entity\":\"English\"},\"PERSON/Max\ - \ Tokens\":{\"position\":[{\"start\":77,\"end\":87}],\"entity\":\"Max Tokens\"\ - },\"NORP/asian\":{\"position\":[{\"start\":2743,\"end\":2748}],\"entity\"\ - :\"asian\"},\"LANGUAGE/Basque\":{\"position\":[{\"start\":2005,\"end\":2011}],\"\ - entity\":\"Basque\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"\ - has part\",\"metadata\":{\"paragraph_id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\"\ - ,\"source_start\":77,\"source_end\":87,\"to_start\":265,\"to_end\":275},\"\ - from\":{\"value\":\"Max Tokens\",\"type\":\"entity\",\"group\":\"PERSON\"\ - },\"to\":{\"value\":\"2024-04-24\",\"type\":\"entity\",\"group\":\"DATE\"\ - }},{\"relation\":\"OTHER\",\"label\":\"has part\",\"metadata\":{\"paragraph_id\"\ - :\"4be29234ea764ce7889f78e60fe8ec78/t/page/1466-2116\",\"source_start\":1846,\"\ - source_end\":1853,\"to_start\":1876,\"to_end\":1884},\"from\":{\"value\":\"\ - Chinese\",\"type\":\"entity\",\"group\":\"LANGUAGE\"},\"to\":{\"value\":\"\ - Japanese\",\"type\":\"entity\",\"group\":\"LANGUAGE\"}},{\"relation\":\"OTHER\"\ - ,\"label\":\"has part\",\"metadata\":{\"paragraph_id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/1466-2116\"\ - ,\"source_start\":1855,\"source_end\":1862,\"to_start\":1876,\"to_end\":1884},\"\ - from\":{\"value\":\"Spanish\",\"type\":\"entity\",\"group\":\"LANGUAGE\"},\"\ - to\":{\"value\":\"Japanese\",\"type\":\"entity\",\"group\":\"LANGUAGE\"}},{\"\ - relation\":\"OTHER\",\"label\":\"has part\",\"metadata\":{\"paragraph_id\"\ - :\"4be29234ea764ce7889f78e60fe8ec78/t/page/1466-2116\",\"source_start\":2005,\"\ - source_end\":2011,\"to_start\":2023,\"to_end\":2028},\"from\":{\"value\":\"\ - Basque\",\"type\":\"entity\",\"group\":\"LANGUAGE\"},\"to\":{\"value\":\"\ - Irish\",\"type\":\"entity\",\"group\":\"NORP\"}},{\"relation\":\"OTHER\",\"\ - label\":\"language of work or name\",\"metadata\":{\"paragraph_id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/2353-2639\"\ - ,\"source_start\":2357,\"source_end\":2367,\"to_start\":2390,\"to_end\":2397},\"\ - from\":{\"value\":\"2024-04-24\",\"type\":\"entity\",\"group\":\"DATE\"},\"\ - to\":{\"value\":\"English\",\"type\":\"entity\",\"group\":\"LANGUAGE\"}},{\"\ - relation\":\"OTHER\",\"label\":\"main subject\",\"metadata\":{\"paragraph_id\"\ - :\"4be29234ea764ce7889f78e60fe8ec78/t/page/2639-3001\",\"source_start\":2694,\"\ - source_end\":2704,\"to_start\":2743,\"to_end\":2748},\"from\":{\"value\":\"\ - 2023-08-16\",\"type\":\"entity\",\"group\":\"DATE\"},\"to\":{\"value\":\"\ - asian\",\"type\":\"entity\",\"group\":\"NORP\"}},{\"relation\":\"OTHER\",\"\ - label\":\"developer\",\"metadata\":{\"paragraph_id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/3001-3263\"\ - ,\"source_start\":3019,\"source_end\":3025,\"to_start\":3011,\"to_end\":3016},\"\ - from\":{\"value\":\"Google\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\"\ - :{\"value\":\"Gecko\",\"type\":\"entity\",\"group\":\"PRODUCT\"}}],\"mime_type\"\ - :\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > models\",\"extracted\":{\"\ - text\":{\"text\":\"docs > rag > advanced > models\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\"\ - :{\"score\":5.611546039581299,\"score_type\":\"BM25\",\"order\":14,\"text\"\ - :\" \\n id: models \\n title: Semantic models \\n \\n | Model Name | Size\ - \ | Threshold | Max Tokens | Matryoshka | Multilingual | External | \\n |\ - \ --------------------------- | -------- | ------------- | --------------\ - \ | -------------- | ---------------- | ------------ | \\n | en-2024-04-24\ - \ | 768 | 0.47 | 2048 | No | No | No | \\n | multilingual-2023-08-16 | 1024\ - \ | 0.7 | 512 | No | Yes | No | \\n | multilingual-2024-05-06 | 1024 | 0.4\ - \ | 2048 | No | Yes | No | \\n | Open AI small | 1536 | 0.5 | 8192 | Yes |\ - \ No | No | \\n | Open AI large | 3072 | 0.5 | 8192 | Yes | No | Yes | \\\ - n | Google multilingual Gecko | 768 | 0.55 | 3072 | Yes | Yes | Yes | \\n\ - \ | Hugging Face | N/A | N/A | N/A | N/A | N/A | Yes | \\n Embeddings models\ - \ \\n Embeddings are like fingerprints for words or data. They help computers\ - \ understand the similarities and differences between them, making it easier\ - \ for machines to perform tasks like understanding language or recognizing\ - \ patterns. \\n\",\"id\":\"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"\ - end\":935,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - \ : My search , filters : [ /icon/application/pdf , /classification.labels/region/Asia\ - \ ]}) \\n search.ask(query={ query : My search , top_k : 5}) \\n Graph queries\ - \ \\n The Python SDK allows graph queries supported by the /graph endpoint.\ - \ Although \\n a bit cumbersome, the knowledge graph can be queried as in\ - \ this example: \\n \\n CLI: \\n \\n bash \\n nuclia kb search graph --query='{\ - \ query : { prop : path , source : { value : Rust }, destination : { value\ - \ : Python }}}' \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\\ - n search = sdk.NucliaSearch() \\n search.graph( \\n query={ \\n query : {\ - \ \\n prop : path , \\n source : { value : Rust }, \\n destination : { value\ - \ : Python } \\n } \\n } \\n ) \\n For more information about graph querying,\ - \ please refer to Nuclia's graph \\n doc or the API reference\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":305,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":200,\"\ - key\":\"\"},{\"start\":200,\"end\":305,\"key\":\"\"}]},{\"start\":305,\"end\"\ - :550,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":308,\"end\":362,\"key\":\"\"},{\"start\":362,\"\ - end\":550,\"key\":\"\"}]},{\"start\":550,\"end\":774,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":551,\"end\":774,\"key\":\"\"}]},{\"start\":774,\"end\":1091,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":775,\"end\":1091,\"key\":\"\"}]},{\"start\":1091,\"end\":1361,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1094,\"end\":1361,\"key\":\"\"}]},{\"start\"\ - :1361,\"end\":1790,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1362,\"end\":1630,\"key\"\ - :\"\"},{\"start\":1630,\"end\":1728,\"key\":\"\"},{\"start\":1728,\"end\"\ - :1790,\"key\":\"\"}]},{\"start\":1790,\"end\":2276,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":1793,\"end\":2276,\"key\":\"\"}]},{\"start\":2276,\"end\":2505,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2279,\"end\":2318,\"key\":\"\"},{\"start\":2318,\"\ - end\":2378,\"key\":\"\"},{\"start\":2378,\"end\":2505,\"key\":\"\"}]},{\"\ - start\":2505,\"end\":2709,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2506,\"end\":2610,\"\ - key\":\"\"},{\"start\":2610,\"end\":2709,\"key\":\"\"}]},{\"start\":2709,\"\ - end\":2991,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2710,\"end\":2835,\"key\":\"\"},{\"start\":2835,\"\ - end\":2918,\"key\":\"\"},{\"start\":2918,\"end\":2991,\"key\":\"\"}]},{\"\ - start\":2991,\"end\":3313,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2992,\"end\":3054,\"\ - key\":\"\"},{\"start\":3054,\"end\":3117,\"key\":\"\"},{\"start\":3117,\"\ - end\":3188,\"key\":\"\"},{\"start\":3188,\"end\":3313,\"key\":\"\"}]},{\"\ - start\":3313,\"end\":3889,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3314,\"end\":3889,\"\ - key\":\"\"}]},{\"start\":3889,\"end\":4202,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3892,\"\ - end\":4202,\"key\":\"\"}]},{\"start\":4202,\"end\":4464,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4203,\"end\":4377,\"key\":\"\"},{\"start\":4377,\"end\":4464,\"\ - key\":\"\"}]},{\"start\":4464,\"end\":4689,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4467,\"\ - end\":4689,\"key\":\"\"}]},{\"start\":4689,\"end\":5095,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4690,\"end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5095,\"\ - key\":\"\"}]},{\"start\":5095,\"end\":5543,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5098,\"\ - end\":5543,\"key\":\"\"}]}],\"ner\":{\"JSON\":\"ORG\",\"Nuclia\":\"ORG\",\"\ - Europe\":\"LOC\",\"Asia\":\"LOC\",\"OpenAI\":\"PRODUCT\"},\"entities\":{\"\ - processor\":{\"entities\":[{\"text\":\"Asia\",\"label\":\"LOC\",\"positions\"\ - :[{\"start\":531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"\ - end\":3720},{\"start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}]},{\"\ - text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":760,\"end\":764}]},{\"\ - text\":\"Nuclia\",\"label\":\"ORG\",\"positions\":[{\"start\":955,\"end\"\ - :961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"end\":5507}]},{\"text\"\ - :\"OpenAI\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":2279,\"end\":2285}]},{\"\ - text\":\"Europe\",\"label\":\"LOC\",\"positions\":[{\"start\":3677,\"end\"\ - :3683}]}]}},\"classifications\":[],\"last_index\":\"2025-09-18T11:03:29.417038Z\"\ - ,\"last_understanding\":\"2025-09-18T11:03:28.773187Z\",\"last_extract\":\"\ - 2025-09-18T11:03:24.225246Z\",\"last_processing_start\":\"2025-09-18T11:01:44.762993Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/JSON\":{\"position\"\ - :[{\"start\":760,\"end\":764}],\"entity\":\"JSON\"},\"ORG/Nuclia\":{\"position\"\ - :[{\"start\":955,\"end\":961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"\ - end\":5507}],\"entity\":\"Nuclia\"},\"PRODUCT/OpenAI\":{\"position\":[{\"\ - start\":2279,\"end\":2285}],\"entity\":\"OpenAI\"},\"LOC/Asia\":{\"position\"\ - :[{\"start\":531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"\ - end\":3720},{\"start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}],\"\ - entity\":\"Asia\"},\"LOC/Europe\":{\"position\":[{\"start\":3677,\"end\":3683}],\"\ - entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"\ - split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"\ - value\":\"docs > develop > python sdk > 05 search\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\"\ - :{\"score\":13.133426666259766,\"score_type\":\"BM25\",\"order\":1,\"text\"\ - :\" ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n\",\"id\"\ - :\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[],\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":10.241169929504395,\"score_type\":\"BM25\",\"order\":2,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":1790,\"\ - end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/4202-4464\":{\"score\":7.43405294418335,\"\ - score_type\":\"BM25\",\"order\":10,\"text\":\" Complex queries \\n The Python\ - \ SDK allows to use all the options supported by the /find and /ask endpoints,\ - \ \\n but not all of the options can be passed as specific parameter. \\n\ - \ In these cases, you can just pass your query as a dictionnary in the query\ - \ parameter. \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/4202-4464\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":13,\"start\":4202,\"\ - end\":4464,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/3889-4202\":{\"score\":5.7538838386535645,\"\ - score_type\":\"BM25\",\"order\":12,\"text\":\" \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search ask --query= My question --rag_strategies='[{ name : hierarchy\ - \ }]' \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search =\ - \ sdk.NucliaSearch() \\n search.ask(query= My question , rag_strategies=[{\ - \ name : hierarchy }]) \\n See the RAG strategies documentation for more information.\ - \ \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/3889-4202\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":12,\"start\":3889,\"end\":4202,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\"\ - ,\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs > rag > advanced\ - \ > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-02T22:04:59.702465\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: consumption\\\ - ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\"\ - ;\\nimport TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\\ - n\\nAgentic RAG is a license and consumption-based service. This means that\ - \ you pay for the computational resources you consume. The consumption is\ - \ measured in **Agentic RAG tokens**.\\nAll public 3rd-party LLMs base their\ - \ pricing on the number of tokens consumed. In the LLM world, a token is around\ - \ 4-5 characters on average, which might fit an entire word or be split into\ - \ parts. The number of tokens is proportional to the amount of text, measured\ - \ in chunks of 4-5 characters. It closely relates to words but not entirely.\ - \ The longer a sentence is, the more tokens it will consume to read or to\ - \ generate it.\\nSince all these 3rd-party LLMs have different pricing, Agentic\ - \ RAG tokens serve to normalize the cost across all of them.\\n\\n## How Tokens\ - \ Are Consumed in RAG\\n\\nWhen a user asks a question to your Knowledge Box,\ - \ the token consumption process follows these steps:\\n\\n1. **Question Processing**:\ - \ The system finds the most relevant paragraphs to answer the question\\n2.\ - \ **Context Assembly**: These paragraphs are used as context when calling\ - \ the LLM model\\n3. **Prompt Creation**: Agentic RAG assembles the **prompt**,\ - \ **context**, and **question** into a single string\\n4. **LLM Processing**:\ - \ This complete string is sent to the LLM, corresponding to a certain number\ - \ of **input tokens**\\n5. **Answer Generation**: The LLM generates the answer,\ - \ which corresponds to a certain number of **output tokens**\\n\\n**Total\ - \ consumption** = Input tokens + Output tokens + Image tokens\\n\\n### Factors\ - \ That Impact Token Consumption\\n\\nToken consumption is directly affected\ - \ by:\\n\\n- **Large context**: Results from using RAG strategies like \\\"\ - Full resource\\\" or \\\"Neighbouring paragraphs\\\", or from using the `extra_context`\ - \ parameter\\n- **Long questions**: More detailed or complex questions require\ - \ more input tokens\\n- **Long prompts**: Extensive system prompts increase\ - \ the input token count\\n- **Detailed answers**: Comprehensive responses\ - \ require more output tokens\\n- **Images in context**: When using multimodal\ - \ models, images included in the retrieved context significantly increase\ - \ token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\\ - n### Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing\ - \ token consumption is to fine-tune your request parameters:\\n\\n- **Optimize\ - \ prompts**: Ensure your prompts are concise and focused, avoiding unnecessary\ - \ verbosity\\n- **Control resource scope**: When using the \\\"Full resource\\\ - \" strategy, use the `count` attribute to limit the number of resources returned\\\ - n- **Tune neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy,\ - \ optimize the `before` and `after` attributes to balance context quality\ - \ with token efficiency\\n- **Manage summary length**: When using the \\\"\ - Hierarchical\\\" strategy, ensure that resource summaries are appropriately\ - \ sized\\n- **Choose efficient models**: Select LLMs that offer better token\ - \ efficiency (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT\ - \ 4o)\\n\\n### Strategy 2: Set Hard Limits\\n\\nYou can implement safeguards\ - \ against excessive token consumption:\\n\\nUse the `max_tokens` parameter\ - \ on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post)\ - \ to set hard limits on:\\n- **Context size**: Limits the amount of retrieved\ - \ information sent to the LLM\\n- **Answer length**: Limits the length of\ - \ the generated response\\n\\n#### Important Considerations\\n\\n**Context\ - \ Limitations**:\\n- Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with\\n- Balance between\ - \ cost control and answer quality\\n\\n**Answer Length Limitations**:\\n-\ - \ The LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way\\n- **Recommended approach**: Include length requirements\ - \ in your prompt (e.g., \\\"Please answer in less than 200 words\\\") rather\ - \ than relying solely on hard limits\\n- This allows the LLM to naturally\ - \ conclude its response within the desired length\\n\\n## How to Monitor Token\ - \ Consumption\\n\\n### Understanding Token Consumption Data\\n\\nYou can receive\ - \ detailed token consumption information from the following endpoints that\ - \ utilize LLM models: `ask`, `chat`, `remi`, `query`, `sentence`, `summarize`,\ - \ `tokens`, and `rerank`.\\n\\n:::note\\nThe `rephrase` endpoint currently\ - \ does not support token consumption monitoring.\\n:::\\n\\nTo receive token\ - \ consumption data, you must include the following header in your request:\\\ - n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption data is provided\ - \ in different formats depending on the response type:\\n- **Streaming responses**\ - \ (`application/x-ndjson`): Token consumption appears as a separate JSON chunk\ - \ with type \\\"consumption\\\"\\n- **Standard responses** (`application/json`):\ - \ Token consumption is included in a \\\"consumption\\\" field within the\ - \ main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": {\\\ - n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n\\n\\n### Understanding Token Types\\n\\n**Normalized\ - \ Tokens** (`normalized_tokens`):\\n- These represent the number of Agentic\ - \ RAG tokens consumed and that you will be billed for\\n- Values are normalized\ - \ across different LLM providers for consistent billing\\n- Include separate\ - \ counts for:\\n - `input`: Tokens used for the prompt, context, and question\\\ - n - `output`: Tokens used for the generated response\\n - `image`: Tokens\ - \ used for image processing (when applicable)\\n\\n**Customer Key Tokens**\ - \ (`customer_key_tokens`):\\n- These represent tokens consumed when using\ - \ your own LLM API keys\\n- These tokens are **not billed** by Agentic RAG\ - \ since you're using your own API keys\\n- Values are also normalized for\ - \ comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id: consumption\ - \ \\n title: Token consumption \\n \\n import Tabs from @theme/Tabs ; \\n\ - \ import TabItem from @theme/TabItem ; \\n Token consumption \\n Agentic RAG\ - \ is a license and consumption-based service. This means that you pay for\ - \ the computational resources you consume. The consumption is measured in\ - \ Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on\ - \ the number of tokens consumed. In the LLM world, a token is around 4-5 characters\ - \ on average, which might fit an entire word or be split into parts. The number\ - \ of tokens is proportional to the amount of text, measured in chunks of 4-5\ - \ characters. It closely relates to words but not entirely. The longer a sentence\ - \ is, the more tokens it will consume to read or to generate it. \\n Since\ - \ all these 3rd-party LLMs have different pricing, Agentic RAG tokens serve\ - \ to normalize the cost across all of them. \\n How Tokens Are Consumed in\ - \ RAG \\n When a user asks a question to your Knowledge Box, the token consumption\ - \ process follows these steps: \\n \\n Question Processing: The system finds\ - \ the most relevant paragraphs to answer the question \\n Context Assembly:\ - \ These paragraphs are used as context when calling the LLM model \\n Prompt\ - \ Creation: Agentic RAG assembles the prompt, context, and question into a\ - \ single string \\n LLM Processing: This complete string is sent to the LLM,\ - \ corresponding to a certain number of input tokens \\n Answer Generation:\ - \ The LLM generates the answer, which corresponds to a certain number of output\ - \ tokens \\n \\n Total consumption = Input tokens + Output tokens + Image\ - \ tokens \\n Factors That Impact Token Consumption \\n Token consumption is\ - \ directly affected by: \\n \\n Large context: Results from using RAG strategies\ - \ like Full resource or Neighbouring paragraphs , or from using the extra_context\ - \ parameter \\n Long questions: More detailed or complex questions require\ - \ more input tokens \\n Long prompts: Extensive system prompts increase the\ - \ input token count \\n Detailed answers: Comprehensive responses require\ - \ more output tokens \\n Images in context: When using multimodal models,\ - \ images included in the retrieved context significantly increase token consumption\ - \ \\n \\n How to Limit and Control Token Consumption \\n Strategy 1: Optimize\ - \ Your Parameters \\n The first approach to reducing token consumption is\ - \ to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure your\ - \ prompts are concise and focused, avoiding unnecessary verbosity \\n Control\ - \ resource scope: When using the Full resource strategy, use the count attribute\ - \ to limit the number of resources returned \\n Tune neighboring context:\ - \ For the Neighbouring paragraphs strategy, optimize the before and after\ - \ attributes to balance context quality with token efficiency \\n Manage summary\ - \ length: When using the Hierarchical strategy, ensure that resource summaries\ - \ are appropriately sized \\n Choose efficient models: Select LLMs that offer\ - \ better token efficiency (typically, ChatGPT 4o-mini is more cost-effective\ - \ than ChatGPT 4o) \\n \\n Strategy 2: Set Hard Limits \\n You can implement\ - \ safeguards against excessive token consumption: \\n Use the max_tokens parameter\ - \ on the /ask endpoint to set hard limits on: \\n - Context size: Limits the\ - \ amount of retrieved information sent to the LLM \\n - Answer length: Limits\ - \ the length of the generated response \\n Important Considerations \\n Context\ - \ Limitations: \\n - Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with \\n - Balance between\ - \ cost control and answer quality \\n Answer Length Limitations: \\n - The\ - \ LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way \\n - Recommended approach: Include length requirements\ - \ in your prompt (e.g., Please answer in less than 200 words ) rather than\ - \ relying solely on hard limits \\n - This allows the LLM to naturally conclude\ - \ its response within the desired length \\n How to Monitor Token Consumption\ - \ \\n Understanding Token Consumption Data \\n You can receive detailed token\ - \ consumption information from the following endpoints that utilize LLM models:\ - \ ask, chat, remi, query, sentence, summarize, tokens, and rerank. \\n :::note\ - \ \\n The rephrase endpoint currently does not support token consumption monitoring.\ - \ \\n ::: \\n To receive token consumption data, you must include the following\ - \ header in your request: \\n X-SHOW-CONSUMPTION: true \\n The token consumption\ - \ data is provided in different formats depending on the response type: \\\ - n - Streaming responses (application/x-ndjson): Token consumption appears\ - \ as a separate JSON chunk with type consumption \\n - Standard responses\ - \ (application/json): Token consumption is included in a consumption field\ - \ within the main response \\n Token Consumption Response Format \\n \\n \\\ - n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens :\ - \ { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\\ - n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens):\ - \ \\n - These represent the number of Agentic RAG tokens consumed and that\ - \ you will be billed for \\n - Values are normalized across different LLM\ - \ providers for consistent billing \\n - Include separate counts for: \\n\ - \ - input: Tokens used for the prompt, context, and question \\n - output:\ - \ Tokens used for the generated response \\n - image: Tokens used for image\ - \ processing (when applicable) \\n Customer Key Tokens (customer_key_tokens):\ - \ \\n - These represent tokens consumed when using your own LLM API keys \\\ - n - These tokens are not billed by Agentic RAG since you're using your own\ - \ API keys \\n - Values are also normalized for comparison purposes across\ - \ different providers\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\"\ - :{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":319,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":198,\"key\":\"\"},{\"start\":198,\"end\":267,\"key\"\ - :\"\"},{\"start\":267,\"end\":319,\"key\":\"\"}]},{\"start\":319,\"end\":747,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - 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:[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3076,\"end\":3340,\"key\":\"\"}]},{\"start\":3340,\"end\":4136,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3341,\"end\":4136,\"key\":\"\"}]},{\"start\"\ - :4136,\"end\":4446,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":4137,\"end\":4228,\"key\"\ - :\"\"},{\"start\":4228,\"end\":4446,\"key\":\"\"}]},{\"start\":4446,\"end\"\ - :4728,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":4447,\"end\":4728,\"key\":\"\"}]},{\"start\"\ - :4728,\"end\":4932,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":4729,\"end\":4932,\"key\"\ - :\"\"}]},{\"start\":4932,\"end\":5189,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4933,\"\ - end\":5189,\"key\":\"\"}]},{\"start\":5189,\"end\":5396,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":5190,\"end\":5396,\"key\":\"\"}]},{\"start\":5396,\"end\":5615,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5397,\"end\":5615,\"key\":\"\"}]},{\"start\"\ - :5615,\"end\":5849,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":5616,\"end\":5849,\"key\"\ - :\"\"}]}],\"ner\":{\"Agentic RAG\":\"PRODUCT\",\"LLM\":\"ORG\",\"Knowledge\ - \ Box\":\"PRODUCT\",\"json\":\"PERSON\"},\"entities\":{\"processor\":{\"entities\"\ - :[{\"text\":\"Agentic RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\"\ - :298,\"end\":309},{\"start\":5222,\"end\":5233},{\"start\":5718,\"end\":5729}]},{\"\ - text\":\"LLM\",\"label\":\"ORG\",\"positions\":[{\"start\":406,\"end\":409},{\"\ - start\":4058,\"end\":4061},{\"start\":5325,\"end\":5328}]},{\"text\":\"Knowledge\ - \ Box\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":941,\"end\":954}]},{\"\ - text\":\"json\",\"label\":\"PERSON\",\"positions\":[{\"start\":4729,\"end\"\ - :4733}]}]}},\"classifications\":[],\"last_index\":\"2026-06-02T22:05:14.995116Z\"\ - ,\"last_understanding\":\"2026-06-02T22:05:12.925927Z\",\"last_extract\":\"\ - 2026-06-02T22:05:10.952860Z\",\"last_processing_start\":\"2026-06-02T22:05:10.857810Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic RAG\"\ - :{\"position\":[{\"start\":298,\"end\":309},{\"start\":5222,\"end\":5233},{\"\ - start\":5718,\"end\":5729}],\"entity\":\"Agentic RAG\"},\"ORG/LLM\":{\"position\"\ - :[{\"start\":406,\"end\":409},{\"start\":4058,\"end\":4061},{\"start\":5325,\"\ - end\":5328}],\"entity\":\"LLM\"},\"PRODUCT/Knowledge Box\":{\"position\":[{\"\ - start\":941,\"end\":954}],\"entity\":\"Knowledge Box\"},\"PERSON/json\":{\"\ - position\":[{\"start\":4729,\"end\":4733}],\"entity\":\"json\"}},\"relations\"\ - :[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\"\ - :[]}}}},\"generics\":{\"title\":{\"value\":\"docs > rag > advanced > consumption.\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > rag > advanced > consumption.\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - :{\"score\":16.384029388427734,\"score_type\":\"BM25\",\"order\":0,\"text\"\ - :\" Use the max_tokens parameter on the /ask endpoint to set hard limits on:\ - \ \\n - Context size: Limits the amount of retrieved information sent to the\ - \ LLM \\n - Answer length: Limits the length of the generated response \\\ - n Important Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"\ - end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\":{\"score\":6.36094331741333,\"\ - score_type\":\"BM25\",\"order\":11,\"text\":\" \\n Large context: Results\ - \ from using RAG strategies like Full resource or Neighbouring paragraphs\ - \ , or from using the extra_context parameter \\n Long questions: More detailed\ - \ or complex questions require more input tokens \\n Long prompts: Extensive\ - \ system prompts increase the input token count \\n Detailed answers: Comprehensive\ - \ responses require more output tokens \\n Images in context: When using multimodal\ - \ models, images included in the retrieved context significantly increase\ - \ token consumption \\n \\n How to Limit and Control Token Consumption \\\ - n Strategy 1: Optimize Your Parameters \\n The first approach to reducing\ - \ token consumption is to fine-tune your request parameters: \\n\",\"id\"\ - :\"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326\",\"labels\":[],\"position\"\ - :{\"page_number\":0,\"index\":4,\"start\":1650,\"end\":2326,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932\"\ - :{\"score\":5.62393856048584,\"score_type\":\"BM25\",\"order\":13,\"text\"\ - :\" json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens\ - \ : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":10,\"start\":4728,\"\ - end\":4932,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/5189-5396\":{\"score\":5.359618663787842,\"\ - score_type\":\"BM25\",\"order\":16,\"text\":\" - These represent the number\ - \ of Agentic RAG tokens consumed and that you will be billed for \\n - Values\ - \ are normalized across different LLM providers for consistent billing \\\ - n - Include separate counts for: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/5189-5396\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":12,\"start\":5189,\"\ - end\":5396,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/5396-5615\":{\"score\":5.359618663787842,\"\ - score_type\":\"BM25\",\"order\":17,\"text\":\" - input: Tokens used for the\ - \ prompt, context, and question \\n - output: Tokens used for the generated\ - \ response \\n - image: Tokens used for image processing (when applicable)\ - \ \\n Customer Key Tokens (customer_key_tokens): \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/5396-5615\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":13,\"start\":5396,\"\ - end\":5615,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189\":{\"score\":5.359618663787842,\"\ - score_type\":\"BM25\",\"order\":18,\"text\":\" json \\n { \\n consumption\ - \ : { \\n normalized_tokens : { \\n input : 13, \\n output : 34, \\n image\ - \ : 0.0 \\n }, \\n customer_key_tokens : { \\n input : 0.0, \\n output : 0.0,\ - \ \\n image : 0.0 \\n } \\n } \\n } \\n \\n \\n Understanding Token Types\ - \ \\n Normalized Tokens (normalized_tokens): \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":11,\"start\":4932,\"\ - end\":5189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\"\ - ,\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs > rag\ - \ > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2025-10-27T14:35:08.325400\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. It must not be used in production. \\n preferMarkdown:\ - \ require the generative answer to be formatted in Markdown. \\n openNewTab:\ - \ open the link in a new tab when clicking on the result. \\n orFilterLogic:\ - \ use the OR logic for filters instead of the default AND logic. \\n noChatHistory:\ - \ the previous questions and answers in the chat mode will not be passed as\ - \ context when generating a new answer. \\n showHidden: display hidden resources\ - \ in the search results. \\n showAttachedImages: display images attached to\ - \ the matching paragraphs in the search results. \\n \\n Other parameters\ - \ \\n \\n backend: the URL of the backend to use. Useful if you use your own\ - \ proxy to access the Agentic RAG API. \\n zone: the zone to use. \\n knowledgebox:\ - \ the Knowledge Box id. \\n placeholder: the text displayed in the search\ - \ bar when it is empty. \\n lang: the language of the widget. Currently supported:\ - \ ca, fr, en, es. Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. It must use {context} and {question} variables.\ - \ \\n system_prompt: the system prompt to use for the generative model. \\\ - n rephrase_prompt: the prompt to use when optimizing the user query to get\ - \ the best search results. \\n generativemodel: the generative model to use\ - \ for the answer generation. \\n rag_strategies: the RAG strategies to apply\ - \ to the retrieved paragraphs. \\n rag_images_strategies: the RAG strategies\ - \ to apply to the retrieved images. \\n not_enough_data_message: the message\ - \ to display when there is not enough data to generate an answer. \\n ask_to_resource:\ - \ the resource ID to use as context for the generative model. \\n max_tokens:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question). \\n max_output_tokens:\ - \ the maximum number of tokens to generate. \\n max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n vectorset: the embedding model to use for the semantic search.\ - \ \\n chat_placeholder: the placeholder to display in the chat input. \\n\ - \ audit_metadata: custom metatada to add in API calls for auditing purposes.\ - \ \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\"\ - :{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":213,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":213,\"key\":\"\"}]},{\"start\":213,\"end\":467,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":214,\"end\":402,\"key\":\"\"},{\"start\":402,\"end\":467,\"key\"\ - :\"\"}]},{\"start\":467,\"end\":847,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":468,\"end\"\ - :631,\"key\":\"\"},{\"start\":631,\"end\":735,\"key\":\"\"},{\"start\":735,\"\ - end\":847,\"key\":\"\"}]},{\"start\":847,\"end\":1049,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":850,\"end\":941,\"key\":\"\"},{\"start\":941,\"end\":1049,\"key\"\ - :\"\"}]},{\"start\":1049,\"end\":1299,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1052,\"\ - end\":1179,\"key\":\"\"},{\"start\":1179,\"end\":1299,\"key\":\"\"}]},{\"\ - start\":1299,\"end\":1551,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1300,\"end\":1426,\"\ - key\":\"\"},{\"start\":1426,\"end\":1551,\"key\":\"\"}]},{\"start\":1551,\"\ - end\":1809,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1552,\"end\":1675,\"key\":\"\"},{\"start\":1675,\"\ - end\":1809,\"key\":\"\"}]},{\"start\":1809,\"end\":2156,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1810,\"end\":1894,\"key\":\"\"},{\"start\":1894,\"end\":2008,\"\ - key\":\"\"},{\"start\":2008,\"end\":2156,\"key\":\"\"}]},{\"start\":2156,\"\ - end\":2410,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2157,\"end\":2246,\"key\":\"\"},{\"start\":2246,\"\ - end\":2325,\"key\":\"\"},{\"start\":2325,\"end\":2410,\"key\":\"\"}]},{\"\ - start\":2410,\"end\":2681,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2411,\"end\":2498,\"\ - key\":\"\"},{\"start\":2498,\"end\":2609,\"key\":\"\"},{\"start\":2609,\"\ - end\":2681,\"key\":\"\"}]},{\"start\":2681,\"end\":2980,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2682,\"end\":2739,\"key\":\"\"},{\"start\":2739,\"end\":2834,\"\ - key\":\"\"},{\"start\":2834,\"end\":2944,\"key\":\"\"},{\"start\":2944,\"\ - end\":2980,\"key\":\"\"}]},{\"start\":2980,\"end\":3207,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2981,\"end\":3058,\"key\":\"\"},{\"start\":3058,\"end\":3128,\"\ - key\":\"\"},{\"start\":3128,\"end\":3207,\"key\":\"\"}]},{\"start\":3207,\"\ - end\":3494,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3208,\"end\":3337,\"key\":\"\"},{\"start\":3337,\"\ - end\":3399,\"key\":\"\"},{\"start\":3399,\"end\":3494,\"key\":\"\"}]},{\"\ - start\":3494,\"end\":3756,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3497,\"end\":3558,\"\ - key\":\"\"},{\"start\":3558,\"end\":3624,\"key\":\"\"},{\"start\":3624,\"\ - end\":3649,\"key\":\"\"},{\"start\":3649,\"end\":3687,\"key\":\"\"},{\"start\"\ - :3687,\"end\":3756,\"key\":\"\"}]},{\"start\":3756,\"end\":3971,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3757,\"end\":3791,\"key\":\"\"},{\"start\":3791,\"end\":3828,\"\ - key\":\"\"},{\"start\":3828,\"end\":3843,\"key\":\"\"},{\"start\":3843,\"\ - end\":3871,\"key\":\"\"},{\"start\":3871,\"end\":3971,\"key\":\"\"}]},{\"\ - start\":3971,\"end\":4191,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3972,\"end\":3999,\"\ - key\":\"\"},{\"start\":3999,\"end\":4052,\"key\":\"\"},{\"start\":4052,\"\ - end\":4121,\"key\":\"\"},{\"start\":4121,\"end\":4191,\"key\":\"\"}]},{\"\ - start\":4191,\"end\":4450,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4192,\"end\":4248,\"\ - key\":\"\"},{\"start\":4248,\"end\":4367,\"key\":\"\"},{\"start\":4367,\"\ - end\":4450,\"key\":\"\"}]},{\"start\":4450,\"end\":4694,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4451,\"end\":4525,\"key\":\"\"},{\"start\":4525,\"end\":4577,\"\ - key\":\"\"},{\"start\":4577,\"end\":4627,\"key\":\"\"},{\"start\":4627,\"\ - end\":4694,\"key\":\"\"}]},{\"start\":4694,\"end\":4943,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4695,\"end\":4795,\"key\":\"\"},{\"start\":4795,\"end\":4869,\"\ - key\":\"\"},{\"start\":4869,\"end\":4943,\"key\":\"\"}]},{\"start\":4943,\"\ - end\":5203,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":4944,\"end\":5022,\"key\":\"\"},{\"start\":5022,\"\ - end\":5125,\"key\":\"\"},{\"start\":5125,\"end\":5203,\"key\":\"\"}]},{\"\ - start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5204,\"end\":5350,\"\ - key\":\"\"},{\"start\":5350,\"end\":5412,\"key\":\"\"}]},{\"start\":5412,\"\ - end\":5678,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5413,\"end\":5526,\"key\":\"\"},{\"start\":5526,\"\ - end\":5593,\"key\":\"\"},{\"start\":5593,\"end\":5678,\"key\":\"\"}]},{\"\ - start\":5678,\"end\":5886,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5679,\"end\":5744,\"\ - key\":\"\"},{\"start\":5744,\"end\":5810,\"key\":\"\"},{\"start\":5810,\"\ - end\":5886,\"key\":\"\"}]}],\"ner\":{\"JSON\":\"ORG\",\"Agentic RAG\":\"PRODUCT\"\ - ,\"Markdown\":\"ORG\",\"the Knowledge Box\":\"ORG\",\"NucliaDB\":\"ORG\",\"\ - CSS\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"\ - Agentic RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"\ - start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\"\ - :[{\"start\":3046,\"end\":3054}]},{\"text\":\"the Knowledge Box\",\"label\"\ - :\"ORG\",\"positions\":[{\"start\":4031,\"end\":4048}]},{\"text\":\"NucliaDB\"\ - ,\"label\":\"ORG\",\"positions\":[{\"start\":4100,\"end\":4108}]},{\"text\"\ - :\"CSS\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":4476,\"end\":4479}]},{\"\ - text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"\ - start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2025-10-27T14:35:15.052135Z\"\ - ,\"last_understanding\":\"2025-10-27T14:35:14.504231Z\",\"last_extract\":\"\ - 2025-10-27T14:35:09.724520Z\",\"last_processing_start\":\"2025-10-27T14:35:09.621790Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic RAG\"\ - :{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"\ - entity\":\"Agentic RAG\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"\ - end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\"\ - :5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"\ - ORG/the Knowledge Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\"\ - :\"the Knowledge Box\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"\ - end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\"\ - :3046,\"end\":3054}],\"entity\":\"Markdown\"}},\"relations\":[],\"mime_type\"\ - :\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\"\ - :{\"text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":9.94736385345459,\"score_type\":\"BM25\",\"order\":4,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[],\"position\"\ - :{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207\"\ - :{\"score\":8.573065757751465,\"score_type\":\"BM25\",\"order\":8,\"text\"\ - :\" preferMarkdown: require the generative answer to be formatted in Markdown.\ - \ \\n openNewTab: open the link in a new tab when clicking on the result.\ - \ \\n orFilterLogic: use the OR logic for filters instead of the default AND\ - \ logic. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":11,\"start\":2980,\"\ - end\":3207,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\":{\"score\":7.8066325187683105,\"\ - score_type\":\"BM25\",\"order\":9,\"text\":\" max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":21,\"start\":5412,\"\ - end\":5678,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551\":{\"score\":5.430427074432373,\"\ - score_type\":\"BM25\",\"order\":15,\"text\":\" navigateToFile: open the file\ - \ in the browser when clicking on the result (by default, the file is displayed\ - \ in the viewer). \\n navigateToLink: open the link in the browser when clicking\ - \ on the result (by default, the link is displayed in the viewer). \\n\",\"\ - id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551\",\"labels\":[],\"\ - position\":{\"page_number\":0,\"index\":5,\"start\":1299,\"end\":1551,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299\"\ - :{\"score\":5.174862861633301,\"score_type\":\"BM25\",\"order\":19,\"text\"\ - :\" \\n Web component: \\n html \\n \\n The features parameter \\n The features\ - \ parameter allows you to customize the behavior of the widget. It is a comma-separated\ - \ list of features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":1049,\"\ - end\":1299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\"\ - ,\"modified\":\"2026-06-02T15:39:28.012340\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\\ - n\\n# Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\\ - _history?\\n\\n> `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\\ - n\\n***\\n\\n### context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\\ - n\\n***\\n\\n### format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\\ - n\\n***\\n\\n### json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\\ - n\\n***\\n\\n### query\\\\_context?\\n\\n> `optional` **query\\\\_context**:\ - \ `string`[] \\\\| `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\ - \\_context\\\\_images**: `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**:\ - \ `string`\\n\\n#### content\\\\_type\\n\\n> **content\\\\_type**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\\ - _context\\\\_order**: `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`:\ - \ `string`\\\\]: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\\ - n\\n***\\n\\n### rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\\ - n\\n***\\n\\n### retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\\ - n\\n***\\n\\n### system?\\n\\n> `optional` **system**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\\ - n\\n***\\n\\n### truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\\ - n\\n***\\n\\n### user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\\ - n\\n#### prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"3207e2a9db5e75240b8535b73d1e3324\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions\ - \ \\n Interface: PredictAnswerOptions \\n Properties \\n chat_history? \\\ - n \\n optional chat_history: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242\ - \ \\n \\n citation_threshold? \\n \\n optional citation_threshold: number\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 \\n \\n context? \\\ - n \\n optional context: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243\ - \ \\n \\n format_prompt? \\n \\n optional format_prompt: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 \\n \\n generative_model?\ - \ \\n \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n \\n optional query_context: string[] \\\\| object \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\\ - n \\n query_context_images? \\n \\n optional query_context_images: object\ - \ \\n \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\\ - n content_type: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252\ - \ \\n \\n query_context_order? \\n \\n optional query_context_order: object\ - \ \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context?\ - \ \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259\ - \ \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\\ - n \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241\ - \ \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt?\ - \ \\n \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - 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:\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"\ - key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"\ - classifications\":[],\"last_index\":\"2026-06-02T15:40:25.331027Z\",\"last_understanding\"\ - :\"2026-06-02T15:40:22.840031Z\",\"last_extract\":\"2026-06-02T15:40:21.280327Z\"\ - ,\"last_processing_start\":\"2026-06-02T15:40:21.225072Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > interfaces\ - \ > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"\ - score\":9.572464942932129,\"score_type\":\"BM25\",\"order\":5,\"text\":\"\ - \ \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"\ - 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":9.572464942932129,\"\ - score_type\":\"BM25\",\"order\":6,\"text\":\" \\n optional max_tokens: number\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"\ - 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body: '{"query": "Explain how to use the `max_tokens` parameter in Spanish and - provide a link to the official documentation.", "filters": [], "min_score": - {"semantic": 0.4}, "show": ["basic", "origin", "extra", "extracted", "values", - "relations"], "extracted": ["text", "metadata", "file", "link"], "vectorset": - "multilingual-2024-05-06", "security": {"groups": []}, "features": ["semantic"], - "reranker": "noop"}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '378' - Host: - - europe-1.nuclia.cloud - User-Agent: - - nucliadb-sdk/6.13.1.post6414 - content-type: - - application/json - x-stf-serviceaccount: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find - response: - body: - string: "{\"resources\":{\"666b3a9d01f74323b6ca6c9939834bec\":{\"id\":\"666b3a9d01f74323b6ca6c9939834bec\"\ - ,\"slug\":\"docs-develop-js-sdk-enums-StatsType-md\",\"title\":\"docs > develop\ - \ > js sdk > enums > StatsType\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\",\"sv\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:51:41.005040\",\"modified\":\"2025-04-15T14:51:41.005050\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/enums/StatsType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / StatsType\\n\\n# Enumeration: StatsType\\n\\\ - n**`Deprecated`**\\n\\n## Table of contents\\n\\n### Enumeration Members\\\ - n\\n- [AI\\\\_TOKENS\\\\_USED](StatsType.md#ai_tokens_used)\\n- [BYTES](StatsType.md#bytes)\\\ - n- [CHARS](StatsType.md#chars)\\n- [DOCS\\\\_NO\\\\_MEDIA](StatsType.md#docs_no_media)\\\ - n- [MEDIA\\\\_SECONDS](StatsType.md#media_seconds)\\n- [PAGES](StatsType.md#pages)\\\ - n- [PROCESSING\\\\_TIME](StatsType.md#processing_time)\\n- [RESOURCES](StatsType.md#resources)\\\ - n- [SEARCHES](StatsType.md#searches)\\n- [SUGGESTIONS](StatsType.md#suggestions)\\\ - n- [TRAIN\\\\_SECONDS](StatsType.md#train_seconds)\\n\\n## Enumeration Members\\\ - n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\ - \"ai_tokens_used\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n___\\n\\n### BYTES\\n\\n\u2022 **BYTES** = ``\\\"bytes\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L200)\\\ - n\\n___\\n\\n### CHARS\\n\\n\u2022 **CHARS** = ``\\\"chars\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n___\\n\\n### DOCS\\\\_NO\\\\_MEDIA\\n\\n\u2022 **DOCS\\\\_NO\\\\_MEDIA**\ - \ = ``\\\"docs_no_media\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L205)\\\ - n\\n___\\n\\n### MEDIA\\\\_SECONDS\\n\\n\u2022 **MEDIA\\\\_SECONDS** = ``\\\ - \"media_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L199)\\\ - n\\n___\\n\\n### PAGES\\n\\n\u2022 **PAGES** = ``\\\"pages\\\"``\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L202)\\\ - n\\n___\\n\\n### PROCESSING\\\\_TIME\\n\\n\u2022 **PROCESSING\\\\_TIME** =\ - \ ``\\\"processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L196)\\\ - n\\n___\\n\\n### RESOURCES\\n\\n\u2022 **RESOURCES** = ``\\\"resources\\\"\ - ``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L201)\\\ - n\\n___\\n\\n### SEARCHES\\n\\n\u2022 **SEARCHES** = ``\\\"searches\\\"``\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n___\\n\\n### SUGGESTIONS\\n\\n\u2022 **SUGGESTIONS** = ``\\\"suggestions\\\ - \"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L204)\\\ - n\\n___\\n\\n### TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\ - \"train_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/1544a468/libs/sdk-core/src/lib/db/db.models.ts#L203)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"13077c5570e4437fb58c1c5ab75d310b\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / StatsType \\n Enumeration: StatsType \\\ - n Deprecated \\n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED\ - \ \\n BYTES \\n CHARS \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME\ - \ \\n RESOURCES \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration\ - \ Members \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\\ - n \u2022 BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200\ - \ \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\\ - n \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199\ - \ \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202\ - \ \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES\ - \ \\n \u2022 RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197\ - \ \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":1386,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":1386,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:52:23.251743Z\"\ - ,\"last_understanding\":\"2025-04-15T14:51:53.147129Z\",\"last_extract\":\"\ - 2025-04-15T14:51:52.462436Z\",\"last_processing_start\":\"2025-04-15T14:51:52.416502Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > enums > StatsType\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > enums > StatsType\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\"\ - :{\"score\":0.5312874913215637,\"score_type\":\"VECTOR\",\"order\":8,\"text\"\ - :\"@nuclia/core / Exports / StatsType \\n Enumeration: StatsType \\n Deprecated\ - \ \\n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\\ - n BYTES \\n CHARS \\n DOCS_NO_MEDIA \\n MEDIA_SECONDS \\n PAGES \\n PROCESSING_TIME\ - \ \\n RESOURCES \\n SEARCHES \\n SUGGESTIONS \\n TRAIN_SECONDS \\n \\n Enumeration\ - \ Members \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES \\\ - n \u2022 BYTES = bytes \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200\ - \ \\n \\n CHARS \\n \u2022 CHARS = chars \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n DOCS_NO_MEDIA \\n \u2022 DOCS_NO_MEDIA = docs_no_media \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n MEDIA_SECONDS \\\ - n \u2022 MEDIA_SECONDS = media_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199\ - \ \\n \\n PAGES \\n \u2022 PAGES = pages \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202\ - \ \\n \\n PROCESSING_TIME \\n \u2022 PROCESSING_TIME = processing_time \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n RESOURCES\ - \ \\n \u2022 RESOURCES = resources \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n SEARCHES \\n \u2022 SEARCHES = searches \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197\ - \ \\n \\n SUGGESTIONS \\n \u2022 SUGGESTIONS = suggestions \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:204 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\"\ - ,\"id\":\"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :1386,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\"\ - ,\"modified\":\"2026-06-02T15:43:14.212428\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\\ - n\\n# Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\\ - n\\n> `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\\ - n\\n***\\n\\n### llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\\ - n\\n***\\n\\n### manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**:\ - \ `object`\\n\\n#### splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\\ - n\\n***\\n\\n### max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\\ - n\\n***\\n\\n### name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:534](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L534)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"7074857dc3154de0e287bf1a4a31f240\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\\ - n Interface: SplitStrategy \\n Properties \\n custom_split? \\n \\n optional\ - \ custom_split: CustomSplitStrategy \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536\ - \ \\n \\n llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n manual_split?\ - \ \\n \\n optional manual_split: object \\n \\n splitter \\n \\n splitter:\ - \ string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:538\ - \ \\n \\n max_paragraph? \\n \\n optional max_paragraph: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 \\n \\n name? \\n \\\ - n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\"\ - :234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\"\ - :\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"\ - end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:44:43.409066Z\"\ - ,\"last_understanding\":\"2026-06-02T15:44:17.926743Z\",\"last_extract\":\"\ - 2026-06-02T15:44:17.298950Z\",\"last_processing_start\":\"2026-06-02T15:44:17.276971Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\"\ - :{\"score\":0.5252838134765625,\"score_type\":\"VECTOR\",\"order\":11,\"text\"\ - :\" \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\ - \ \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\"\ - ,\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\"\ - :696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 03c141d8a4704db794df47287f7a9e64\":{\"id\":\"03c141d8a4704db794df47287f7a9e64\"\ - ,\"slug\":\"docs-develop-js-sdk-classes-KnowledgeBox-md\",\"title\":\"docs\ - \ > develop > js sdk > classes > KnowledgeBox\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:27.481326\"\ - ,\"modified\":\"2026-06-02T15:38:18.347887\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/classes/KnowledgeBox\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / KnowledgeBox\\\ - n\\n# Class: KnowledgeBox\\n\\nProvides access to all the Knowledge Box contents\ - \ and services in read mode.\\n\\nFor any Knowledge Box operation that requires\ - \ write access, you need to use `WritableKnowledgeBox` instead.\\n\\n## Extends\\\ - n\\n- [`IKnowledgeBox`](../interfaces/IKnowledgeBox.md)\\n\\n## Extended by\\\ - n\\n- [`WritableKnowledgeBox`](WritableKnowledgeBox.md)\\n\\n## Implements\\\ - n\\n- [`IKnowledgeBox`](../interfaces/IKnowledgeBox.md)\\n\\n## Constructors\\\ - n\\n### new KnowledgeBox()\\n\\n> **new KnowledgeBox**(`nuclia`, `account`,\ - \ `data`): [`KnowledgeBox`](KnowledgeBox.md)\\n\\n#### Parameters\\n\\n\u2022\ - \ **nuclia**: [`INuclia`](../interfaces/INuclia.md)\\n\\n\u2022 **account**:\ - \ `string`\\n\\n\u2022 **data**: [`IKnowledgeBoxBase`](../interfaces/IKnowledgeBoxBase.md)\ - \ \\\\| [`IKnowledgeBoxStandalone`](../interfaces/IKnowledgeBoxStandalone.md)\\\ - n\\n#### Returns\\n\\n[`KnowledgeBox`](KnowledgeBox.md)\\n\\n#### Inherited\ - \ from\\n\\n`IKnowledgeBox.constructor`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:148](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L148)\\\ - n\\n## Properties\\n\\n### accountId\\n\\n> **accountId**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:98](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L98)\\\ - n\\n***\\n\\n### allowed\\\\_ip\\\\_addresses?\\n\\n> `optional` **allowed\\\ - \\_ip\\\\_addresses**: `null` \\\\| `string`[]\\n\\n#### Implementation of\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_ip_addresses`](../interfaces/IKnowledgeBox.md#allowed_ip_addresses)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_ip_addresses`](../interfaces/IKnowledgeBox.md#allowed_ip_addresses)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:38](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L38)\\\ - n\\n***\\n\\n### allowed\\\\_origins?\\n\\n> `optional` **allowed\\\\_origins**:\ - \ `null` \\\\| `string`[]\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_origins`](../interfaces/IKnowledgeBox.md#allowed_origins)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`allowed_origins`](../interfaces/IKnowledgeBox.md#allowed_origins)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:37](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L37)\\\ - n\\n***\\n\\n### created?\\n\\n> `optional` **created**: `string`\\n\\n####\ - \ Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`created`](../interfaces/IKnowledgeBox.md#created)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`created`](../interfaces/IKnowledgeBox.md#created)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:36](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L36)\\\ - n\\n***\\n\\n### description?\\n\\n> `optional` **description**: `string`\\\ - n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`description`](../interfaces/IKnowledgeBox.md#description)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`description`](../interfaces/IKnowledgeBox.md#description)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:34](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L34)\\\ - n\\n***\\n\\n### enforce\\\\_security?\\n\\n> `optional` **enforce\\\\_security**:\ - \ `boolean`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`enforce_security`](../interfaces/IKnowledgeBox.md#enforce_security)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`enforce_security`](../interfaces/IKnowledgeBox.md#enforce_security)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:42](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L42)\\\ - n\\n***\\n\\n### external\\\\_index\\\\_provider?\\n\\n> `optional` **external\\\ - \\_index\\\\_provider**: `string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`external_index_provider`](../interfaces/IKnowledgeBox.md#external_index_provider)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`external_index_provider`](../interfaces/IKnowledgeBox.md#external_index_provider)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:43](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L43)\\\ - n\\n***\\n\\n### hidden\\\\_resources\\\\_enabled?\\n\\n> `optional` **hidden\\\ - \\_resources\\\\_enabled**: `boolean`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_enabled`](../interfaces/IKnowledgeBox.md#hidden_resources_enabled)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_enabled`](../interfaces/IKnowledgeBox.md#hidden_resources_enabled)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:40](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L40)\\\ - n\\n***\\n\\n### hidden\\\\_resources\\\\_hide\\\\_on\\\\_creation?\\n\\n>\ - \ `optional` **hidden\\\\_resources\\\\_hide\\\\_on\\\\_creation**: `boolean`\\\ - n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_hide_on_creation`](../interfaces/IKnowledgeBox.md#hidden_resources_hide_on_creation)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`hidden_resources_hide_on_creation`](../interfaces/IKnowledgeBox.md#hidden_resources_hide_on_creation)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:41](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L41)\\\ - n\\n***\\n\\n### id\\n\\n> **id**: `string`\\n\\n#### Implementation of\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`id`](../interfaces/IKnowledgeBox.md#id)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`id`](../interfaces/IKnowledgeBox.md#id)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:30](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L30)\\\ - n\\n***\\n\\n### nuclia\\n\\n> `protected` **nuclia**: [`INuclia`](../interfaces/INuclia.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:99](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L99)\\\ - n\\n***\\n\\n### search\\\\_configs?\\n\\n> `optional` **search\\\\_configs**:\ - \ `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `any`\\\ - n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search_configs`](../interfaces/IKnowledgeBox.md#search_configs)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search_configs`](../interfaces/IKnowledgeBox.md#search_configs)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:39](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L39)\\\ - n\\n***\\n\\n### slug\\n\\n> **slug**: `string`\\n\\n#### Implementation of\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`slug`](../interfaces/IKnowledgeBox.md#slug)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`slug`](../interfaces/IKnowledgeBox.md#slug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:31](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L31)\\\ - n\\n***\\n\\n### state?\\n\\n> `optional` **state**: [`KBStates`](../type-aliases/KBStates.md)\\\ - n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`state`](../interfaces/IKnowledgeBox.md#state)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`state`](../interfaces/IKnowledgeBox.md#state)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:33](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L33)\\\ - n\\n***\\n\\n### title\\n\\n> **title**: `string`\\n\\n#### Implementation\ - \ of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`title`](../interfaces/IKnowledgeBox.md#title)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`title`](../interfaces/IKnowledgeBox.md#title)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:32](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L32)\\\ - n\\n***\\n\\n### zone\\n\\n> **zone**: `string`\\n\\n#### Implementation of\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`zone`](../interfaces/IKnowledgeBox.md#zone)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`zone`](../interfaces/IKnowledgeBox.md#zone)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:35](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L35)\\\ - n\\n## Accessors\\n\\n### fullpath\\n\\n> `get` **fullpath**(): `string`\\\ - n\\nThe Knowledge Box fullpath on the regional API.\\n\\nExample: `https://europe-1.rag.progress.cloud/api/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0`\\\ - n\\n#### Returns\\n\\n`string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`fullpath`](../interfaces/IKnowledgeBox.md#fullpath)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`fullpath`](../interfaces/IKnowledgeBox.md#fullpath)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:144](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L144)\\\ - n\\n***\\n\\n### path\\n\\n> `get` **path**(): `string`\\n\\nThe Knowledge\ - \ Box path on the regional API.\\n\\nExample: `/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0`\\\ - n\\n#### Returns\\n\\n`string`\\n\\n#### Implementation of\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`path`](../interfaces/IKnowledgeBox.md#path)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`path`](../interfaces/IKnowledgeBox.md#path)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:135](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L135)\\\ - n\\n## Methods\\n\\n### ask()\\n\\n#### ask(query, context, features, options)\\\ - n\\n> **ask**(`query`, `context`?, `features`?, `options`?): `Observable`\\\ - \\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\ - \\>\\n\\nRetrieves a generative answer for the given query.\\n\\nThe generative\ - \ answer is a text that is generated chunk by chunk by the language model.\\\ - nIt is retrieved through a readable HTTP stream, so the `chat()` method returns\ - \ an `Observable`\\nemitting a value each time a new chunk is available.\\\ - nThe `incomplete` attribute of the emitted value indicates if the asnwer is\ - \ complete or not.\\n\\nExample:\\n```ts\\n nuclia.knowledgeBox\\n .ask('where\ - \ does the Little Prince live')\\n .pipe(filter((answer) => !answer.incomplete))\\\ - n .subscribe((answer) => {\\n console.log(answer.text);\\n });\\n\ - \ ```\\n\\n##### Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022 **context?**:\ - \ [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n\u2022\ - \ **features?**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n\u2022 **options?**: [`ChatOptions`](../interfaces/ChatOptions.md)\\n\\\ - n##### Returns\\n\\n`Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n#####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`ask`](../interfaces/IKnowledgeBox.md#ask)\\\ - n\\n##### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:368](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L368)\\\ - n\\n#### ask(query, context, features, options, callback)\\n\\n> **ask**(`query`,\ - \ `context`?, `features`?, `options`?, `callback`?): `Observable`\\\\<`null`\\\ - \\>\\n\\n##### Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022 **context?**:\ - \ [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n\u2022\ - \ **features?**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n\u2022 **options?**: [`ChatOptions`](../interfaces/ChatOptions.md)\\n\\\ - n\u2022 **callback?**\\n\\n##### Returns\\n\\n`Observable`\\\\<`null`\\\\\ - >\\n\\n##### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`ask`](../interfaces/IKnowledgeBox.md#ask)\\\ - n\\n##### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:374](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L374)\\\ - n\\n***\\n\\n### catalog()\\n\\n> **catalog**(`query`, `options`?): `Observable`\\\ - \\<[`Results`](../namespaces/Search/interfaces/Results.md) \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\ - \\>\\n\\n#### Parameters\\n\\n\u2022 **query**: `string` \\\\| [`CatalogQuery`](../interfaces/CatalogQuery.md)\\\ - n\\n\u2022 **options?**: [`CatalogOptions`](../interfaces/CatalogOptions.md)\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`catalog`](../interfaces/IKnowledgeBox.md#catalog)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:646](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L646)\\\ - n\\n***\\n\\n### counters()\\n\\n> **counters**(): `Observable`\\\\<[`Counters`](../interfaces/Counters.md)\\\ - \\>\\n\\nReturns totals for each kind of contents stored in the Knowledge\ - \ Box (resources, fields, paragraphs, vectors)\\n\\n#### Returns\\n\\n`Observable`\\\ - \\<[`Counters`](../interfaces/Counters.md)\\\\>\\n\\n#### Inherited from\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`counters`](../interfaces/IKnowledgeBox.md#counters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:671](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L671)\\\ - n\\n***\\n\\n### createAgenticRAGPipeline()\\n\\n> **createAgenticRAGPipeline**(`steps`):\ - \ [`Pipeline`](../namespaces/Agentic/classes/Pipeline.md)\\n\\n#### Parameters\\\ - n\\n\u2022 **steps**: [`Steps`](../namespaces/Agentic/type-aliases/Steps.md)\\\ - n\\n#### Returns\\n\\n[`Pipeline`](../namespaces/Agentic/classes/Pipeline.md)\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`createAgenticRAGPipeline`](../interfaces/IKnowledgeBox.md#createagenticragpipeline)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:346](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L346)\\\ - n\\n***\\n\\n### feedback()\\n\\n> **feedback**(`answerId`, `good`, `feedback`,\ - \ `text_block_id`?): `Observable`\\\\<`void`\\\\>\\n\\n#### Parameters\\n\\\ - n\u2022 **answerId**: `string`\\n\\n\u2022 **good**: `boolean`\\n\\n\u2022\ - \ **feedback**: `string` = `''`\\n\\n\u2022 **text\\\\_block\\\\_id?**: `string`\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<`void`\\\\>\\n\\n#### Inherited from\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`feedback`](../interfaces/IKnowledgeBox.md#feedback)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:660](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L660)\\\ - n\\n***\\n\\n### find()\\n\\n> **find**(`query`, `features`, `options`?):\ - \ `Observable`\\\\<[`FindResults`](../namespaces/Search/interfaces/FindResults.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms\ - \ a find operation in the Knowledge Box, which is the recommended way to search\ - \ for results.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .find('where\ - \ does the Little Prince live')\\n .subscribe((searchResult) => {\\n \ - \ console.log('search result', searchResult);\\n });\\n ```\\n\\n#### Parameters\\\ - n\\n\u2022 **query**: `string`\\n\\n\u2022 **features**: [`Features`](../namespaces/Search/enumerations/Features.md)[]\ - \ = `[]`\\n\\n\u2022 **options?**: [`SearchOptions`](../interfaces/SearchOptions.md)\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`FindResults`](../namespaces/Search/interfaces/FindResults.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`find`](../interfaces/IKnowledgeBox.md#find)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:408](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L408)\\\ - n\\n***\\n\\n### generate()\\n\\n> **generate**(`question`, `context`): `Observable`\\\ - \\<`object`\\\\>\\n\\nPerforms a question answering operation based on a given\ - \ context.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .generate('Who\ - \ is Eric from Toronto?', [\\n 'Eric is a taxi driver',\\n 'Eric was\ - \ born in France',\\n 'Eric lives in Toronto',\\n ]))\\n .subscribe(({\ - \ answer }) => {\\n console.log('answer', answer);\\n });\\n ```\\n\\\ - n#### Parameters\\n\\n\u2022 **question**: `string`\\n\\n\u2022 **context**:\ - \ `string`[] = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\\>\\\ - n\\n##### answer\\n\\n> **answer**: `string`\\n\\n##### cannotAnswer\\n\\\ - n> **cannotAnswer**: `boolean`\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generate`](../interfaces/IKnowledgeBox.md#generate)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:530](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L530)\\\ - n\\n***\\n\\n### generateJSON()\\n\\n> **generateJSON**(`question`, `json_schema`,\ - \ `context`): `Observable`\\\\<`object`\\\\>\\n\\nPerforms a question answering\ - \ operation using a JSON schema.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\\ - n .generateJSON(\\n 'Who is Eric from Toronto?',\\n {\\n name:\ - \ 'info',\\n parameters: {\\n properties: {\\n location:\ - \ {\\n title: 'Location',\\n description: 'The location\ - \ of the person',\\n type: 'string',\\n },\\n \ - \ name: {\\n title: 'Name',\\n description: 'The\ - \ name of the person',\\n type: 'string',\\n },\\n \ - \ },\\n required: ['name', 'location'],\\n },\\n \ - \ },\\n [\\n 'Eric is a taxi driver',\\n 'Eric was born in\ - \ France',\\n 'Eric lives in Toronto',\\n ],\\n )).subscribe((answer)\ - \ => {\\n console.log('location', answer.answer.location);\\n });\\\ - n ```\\n\\n#### Parameters\\n\\n\u2022 **question**: `string`\\n\\n\u2022\ - \ **json\\\\_schema**: `object`\\n\\n\u2022 **context**: `string`[] = `[]`\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\n##### answer\\n\\\ - n> **answer**: `object`\\n\\n##### success\\n\\n> **success**: `boolean`\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generateJSON`](../interfaces/IKnowledgeBox.md#generatejson)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:574](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L574)\\\ - n\\n***\\n\\n### generateRandomQuestionAboutResource()\\n\\n> **generateRandomQuestionAboutResource**(`resource`):\ - \ `Observable`\\\\<`string`\\\\>\\n\\nGenerates a random question about the\ - \ given resource.\\nIt picks an entities relation from the extracted metadata\ - \ and generates a question about it.\\nIt returns an empty string if no question\ - \ can be generated.\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .getResource('09a94719a6444c5a9689394f6ed9baf6',\ - \ [ResourceProperties.EXTRACTED], [ExtractedDataTypes.METADATA])\\n .pipe(\\\ - n switchMap((resource) => knowledgeBox.generateRandomQuestionAboutResource(resource)),\\\ - n )\\n .subscribe((question) => {\\n console.log('question', question);\\\ - n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **resource**: [`Resource`](Resource.md)\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### Inherited from\\\ - n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`generateRandomQuestionAboutResource`](../interfaces/IKnowledgeBox.md#generaterandomquestionaboutresource)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:620](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L620)\\\ - n\\n***\\n\\n### getConfiguration()\\n\\n> **getConfiguration**(): `Observable`\\\ - \\<`object`\\\\>\\n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\\>\\n\\\ - n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getConfiguration`](../interfaces/IKnowledgeBox.md#getconfiguration)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:738](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L738)\\\ - n\\n***\\n\\n### getEntities()\\n\\n> **getEntities**(): `Observable`\\\\\ - <[`Entities`](../interfaces/Entities.md)\\\\>\\n\\nReturns all the NER families\ - \ defined in the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Entities`](../interfaces/Entities.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getEntities`](../interfaces/IKnowledgeBox.md#getentities)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:167](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L167)\\\ - n\\n***\\n\\n### getEntitiesGroup()\\n\\n> **getEntitiesGroup**(`groupId`):\ - \ `Observable`\\\\<[`EntitiesGroup`](../interfaces/EntitiesGroup.md)\\\\>\\\ - n\\nReturns the NER family with the given id.\\n\\n#### Parameters\\n\\n\u2022\ - \ **groupId**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`EntitiesGroup`](../interfaces/EntitiesGroup.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getEntitiesGroup`](../interfaces/IKnowledgeBox.md#getentitiesgroup)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:172](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L172)\\\ - n\\n***\\n\\n### getExtractStrategies()\\n\\n> **getExtractStrategies**():\ - \ `Observable`\\\\<[`ExtractStrategies`](../type-aliases/ExtractStrategies.md)\\\ - \\>\\n\\n#### Returns\\n\\n`Observable`\\\\<[`ExtractStrategies`](../type-aliases/ExtractStrategies.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getExtractStrategies`](../interfaces/IKnowledgeBox.md#getextractstrategies)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:914](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L914)\\\ - n\\n***\\n\\n### getFacets()\\n\\n> **getFacets**(`facets`): `Observable`\\\ - \\<[`FacetsResult`](../namespaces/Search/interfaces/FacetsResult.md)\\\\>\\\ - n\\nGet the total amount of matches in the Knowledge Box for specific criteria\ - \ (facets) passed in argument\\n\\n#### Parameters\\n\\n\u2022 **facets**:\ - \ `string`[]\\n\\nList of facets to request\\n\\n#### Returns\\n\\n`Observable`\\\ - \\<[`FacetsResult`](../namespaces/Search/interfaces/FacetsResult.md)\\\\>\\\ - n\\nAn observable containing an object where each key is a string and maps\ - \ to an object containing values and their corresponding counts.\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFacets`](../interfaces/IKnowledgeBox.md#getfacets)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:199](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L199)\\\ - n\\n***\\n\\n### getFullResource()\\n\\n> **getFullResource**(`uuid`): `Observable`\\\ - \\<[`Resource`](Resource.md)\\\\>\\n\\nRetrieves a resource from the Knowledge\ - \ Box with all its attached metadata and content.\\n\\n#### Parameters\\n\\\ - n\u2022 **uuid**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFullResource`](../interfaces/IKnowledgeBox.md#getfullresource)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:258](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L258)\\\ - n\\n***\\n\\n### getFullResourceBySlug()\\n\\n> **getFullResourceBySlug**(`slug`):\ - \ `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\n#### Parameters\\n\\\ - n\u2022 **slug**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getFullResourceBySlug`](../interfaces/IKnowledgeBox.md#getfullresourcebyslug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:290](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L290)\\\ - n\\n***\\n\\n### getGenerativeProviders()\\n\\n> **getGenerativeProviders**():\ - \ `Observable`\\\\<[`GenerativeProviders`](../interfaces/GenerativeProviders.md)\\\ - \\>\\n\\n#### Returns\\n\\n`Observable`\\\\<[`GenerativeProviders`](../interfaces/GenerativeProviders.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getGenerativeProviders`](../interfaces/IKnowledgeBox.md#getgenerativeproviders)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:748](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L748)\\\ - n\\n***\\n\\n### getInvites()\\n\\n> **getInvites**(): `Observable`\\\\<[`KbInvite`](../interfaces/KbInvite.md)[]\\\ - \\>\\n\\n#### Returns\\n\\n`Observable`\\\\<[`KbInvite`](../interfaces/KbInvite.md)[]\\\ - \\>\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:763](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L763)\\\ - n\\n***\\n\\n### getKVSchema()\\n\\n> **getKVSchema**(`name`): `Observable`\\\ - \\<[`KVSchema`](../interfaces/KVSchema.md)\\\\>\\n\\nReturns the KV schema\ - \ with the given name.\\n\\n#### Parameters\\n\\n\u2022 **name**: `string`\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`KVSchema`](../interfaces/KVSchema.md)\\\ - \\>\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:190](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L190)\\\ - n\\n***\\n\\n### getKVSchemas()\\n\\n> **getKVSchemas**(): `Observable`\\\\\ - <[`KBKVSchemas`](../interfaces/KBKVSchemas.md)\\\\>\\n\\nReturns all the KV\ - \ schemas defined in the Knowledge Box.\\n\\n#### Returns\\n\\n`Observable`\\\ - \\<[`KBKVSchemas`](../interfaces/KBKVSchemas.md)\\\\>\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:185](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L185)\\\ - n\\n***\\n\\n### getLabels()\\n\\n> **getLabels**(): `Observable`\\\\<[`LabelSets`](../interfaces/LabelSets.md)\\\ - \\>\\n\\nReturns all the labels defined in the Knowledge Box.\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<[`LabelSets`](../interfaces/LabelSets.md)\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getLabels`](../interfaces/IKnowledgeBox.md#getlabels)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:177](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L177)\\\ - n\\n***\\n\\n### getLearningSchema()\\n\\n> **getLearningSchema**(): `Observable`\\\ - \\<[`LearningConfigurations`](../interfaces/LearningConfigurations.md)\\\\\ - >\\n\\n#### Returns\\n\\n`Observable`\\\\<[`LearningConfigurations`](../interfaces/LearningConfigurations.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getLearningSchema`](../interfaces/IKnowledgeBox.md#getlearningschema)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:742](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L742)\\\ - n\\n***\\n\\n### getResource()\\n\\n> **getResource**(`uuid`, `show`, `extracted`):\ - \ `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\n\\nRetrieves a resource\ - \ from the Knowledge Box.\\n\\n- `show` defines which properties are returned.\ - \ Default retrieves only the basic metadata.\\n- `extracted` defines which\ - \ extracted data are returned.\\n It is ignored if `ResourceProperties.EXTRACTED`\ - \ is not in the returned properties.\\n Default is an empty array.\\n\\\ - nExample:\\n ```ts\\n nuclia.db\\n .getKnowledgeBox()\\n .pipe(switchMap((knowledgeBox)\ - \ => knowledgeBox.getResource('09a94719a6444c5a9689394f6ed9baf6')))\\n .subscribe((resource)\ - \ => {\\n console.log('resource', resource);\\n });\\n ```\\n\\n####\ - \ Parameters\\n\\n\u2022 **uuid**: `string`\\n\\n\u2022 **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\ - \ = `...`\\n\\n\u2022 **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\ - \ = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getResource`](../interfaces/IKnowledgeBox.md#getresource)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L247)\\\ - n\\n***\\n\\n### getResourceBySlug()\\n\\n> **getResourceBySlug**(`slug`,\ - \ `show`, `extracted`): `Observable`\\\\<[`Resource`](Resource.md)\\\\>\\\ - n\\n#### Parameters\\n\\n\u2022 **slug**: `string`\\n\\n\u2022 **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\ - \ = `...`\\n\\n\u2022 **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\ - \ = `[]`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`Resource`](Resource.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getResourceBySlug`](../interfaces/IKnowledgeBox.md#getresourcebyslug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:282](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L282)\\\ - n\\n***\\n\\n### getResourceFromData()\\n\\n> **getResourceFromData**(`data`):\ - \ [`Resource`](Resource.md)\\n\\n#### Parameters\\n\\n\u2022 **data**: [`IResource`](../interfaces/IResource.md)\\\ - n\\n#### Returns\\n\\n[`Resource`](Resource.md)\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.ts:342](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L342)\\\ - n\\n***\\n\\n### getSearchConfig()\\n\\n> **getSearchConfig**(`id`): `Observable`\\\ - \\<[`SearchConfig`](../type-aliases/SearchConfig.md)\\\\>\\n\\n#### Parameters\\\ - n\\n\u2022 **id**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<[`SearchConfig`](../type-aliases/SearchConfig.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSearchConfig`](../interfaces/IKnowledgeBox.md#getsearchconfig)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:922](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L922)\\\ - n\\n***\\n\\n### getSearchConfigs()\\n\\n> **getSearchConfigs**(): `Observable`\\\ - \\<[`SearchConfigs`](../type-aliases/SearchConfigs.md)\\\\>\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<[`SearchConfigs`](../type-aliases/SearchConfigs.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSearchConfigs`](../interfaces/IKnowledgeBox.md#getsearchconfigs)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:926](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L926)\\\ - n\\n***\\n\\n### getSplitStrategies()\\n\\n> **getSplitStrategies**(): `Observable`\\\ - \\<[`SplitStrategies`](../type-aliases/SplitStrategies.md)\\\\>\\n\\n####\ - \ Returns\\n\\n`Observable`\\\\<[`SplitStrategies`](../type-aliases/SplitStrategies.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getSplitStrategies`](../interfaces/IKnowledgeBox.md#getsplitstrategies)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:918](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L918)\\\ - n\\n***\\n\\n### getTempToken()\\n\\n> **getTempToken**(`payload`?, `ignoreExpiration`?):\ - \ `Observable`\\\\<`string`\\\\>\\n\\nReturns an ephemeral token.\\nRequires\ - \ account id and zone to be set in the Nuclia options (except when working\ - \ with a local NucliaDB instance).\\n\\nThis is useful when displaying a clickable\ - \ link to a file in a private Knowledge Box\\n(the token will authorize the\ - \ request even though there are no authentication headers).\\n\\nExample:\\\ - n ```ts\\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`;\\\ - n ```\\n\\n#### Parameters\\n\\n\u2022 **payload?**: `any`\\n\\nOptional payload\ - \ to provide extra data for the token generation\\n\\n\u2022 **ignoreExpiration?**:\ - \ `boolean` = `false`\\n\\nOptional By default, a temp token is valid for\ - \ 5min and the same token returned if this method is called several times\ - \ during this time.\\nPassing ignoreExpiration flag to true will ignore this\ - \ expiration delay and will always return a new token.\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<`string`\\\\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getTempToken`](../interfaces/IKnowledgeBox.md#gettemptoken)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:707](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L707)\\\ - n\\n***\\n\\n### getUsers()\\n\\n> **getUsers**(): `Observable`\\\\<[`FullKbUser`](../interfaces/FullKbUser.md)[]\\\ - \\>\\n\\n#### Returns\\n\\n`Observable`\\\\<[`FullKbUser`](../interfaces/FullKbUser.md)[]\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`getUsers`](../interfaces/IKnowledgeBox.md#getusers)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:754](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L754)\\\ - n\\n***\\n\\n### listenToAllNotifications()\\n\\n> **listenToAllNotifications**():\ - \ `Observable`\\\\<[`NotificationMessage`](../interfaces/NotificationMessage.md)[]\\\ - \\>\\n\\nStart listening to all the notifications sent by the Knowledge Box.\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`NotificationMessage`](../interfaces/NotificationMessage.md)[]\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToAllNotifications`](../interfaces/IKnowledgeBox.md#listentoallnotifications)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:775](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L775)\\\ - n\\n***\\n\\n### listenToProcessingNotifications()\\n\\n> **listenToProcessingNotifications**():\ - \ `Observable`\\\\<[`ResourceBaseNotification`](../interfaces/ResourceBaseNotification.md)[]\\\ - \\>\\n\\nStart listening to the Knowledge Box notifications, and returns the\ - \ list of notifications for resources which have processing completed (either\ - \ successfully or not).\\nNotifications are sent anytime processing is completed,\ - \ and processing is done anytime the resource is created or modified (like\ - \ when a summary is added to the resource for example).\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<[`ResourceBaseNotification`](../interfaces/ResourceBaseNotification.md)[]\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToProcessingNotifications`](../interfaces/IKnowledgeBox.md#listentoprocessingnotifications)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:854](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L854)\\\ - n\\n***\\n\\n### listenToResourceOperationNotifications()\\n\\n> **listenToResourceOperationNotifications**():\ - \ `Observable`\\\\<[`ResourceOperationNotification`](../interfaces/ResourceOperationNotification.md)[]\\\ - \\>\\n\\nStart listening to the Knowledge Box notifications, and returns the\ - \ list of notifications corresponding to the different operations affecting\ - \ the resources:\\ncreated, modified, deleted.\\nThe notification contains\ - \ the resource id, title, a timestamp and a flag indicating if the operation\ - \ was successful or not.\\n\\n#### Returns\\n\\n`Observable`\\\\<[`ResourceOperationNotification`](../interfaces/ResourceOperationNotification.md)[]\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listenToResourceOperationNotifications`](../interfaces/IKnowledgeBox.md#listentoresourceoperationnotifications)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:799](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L799)\\\ - n\\n***\\n\\n### listResources()\\n\\n> **listResources**(`page`?, `size`?):\ - \ `Observable`\\\\<[`ResourceList`](../interfaces/ResourceList.md)\\\\>\\\ - n\\nLists all the resources stored in the Knowledge Box.\\n\\n#### Parameters\\\ - n\\n\u2022 **page?**: `number`\\n\\n\u2022 **size?**: `number`\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<[`ResourceList`](../interfaces/ResourceList.md)\\\\>\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`listResources`](../interfaces/IKnowledgeBox.md#listresources)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:676](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L676)\\\ - n\\n***\\n\\n### predictAnswer()\\n\\n> **predictAnswer**(`question`, `options`?,\ - \ `synchronous`?): `Observable`\\\\<[`Answer`](../namespaces/Ask/interfaces/Answer.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms\ - \ a question answering operation\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\\ - n .predictAnswer('Who is Eric from Toronto?'))\\n .subscribe((answer)\ - \ => {\\n if (answer.type !== 'error') {\\n console.log('answer',\ - \ answer.text);\\n }\\n });\\n ```\\n\\n#### Parameters\\n\\n\u2022\ - \ **question**: `string`\\n\\n\u2022 **options?**: [`PredictAnswerOptions`](../interfaces/PredictAnswerOptions.md)\\\ - n\\n\u2022 **synchronous?**: `boolean` = `true`\\n\\n#### Returns\\n\\n`Observable`\\\ - \\<[`Answer`](../namespaces/Ask/interfaces/Answer.md) \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`predictAnswer`](../interfaces/IKnowledgeBox.md#predictanswer)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:506](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L506)\\\ - n\\n***\\n\\n### processingStatus()\\n\\n> **processingStatus**(`cursor`?,\ - \ `scheduled`?, `limit`?): `Observable`\\\\<`object`\\\\>\\n\\n#### Parameters\\\ - n\\n\u2022 **cursor?**: `string`\\n\\n\u2022 **scheduled?**: `boolean`\\n\\\ - n\u2022 **limit?**: `number`\\n\\n#### Returns\\n\\n`Observable`\\\\<`object`\\\ - \\>\\n\\n##### cursor\\n\\n> **cursor**: `string`\\n\\n##### results\\n\\\ - n> **results**: [`ProcessingStatus`](../interfaces/ProcessingStatus.md)[]\\\ - n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`processingStatus`](../interfaces/IKnowledgeBox.md#processingstatus)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:896](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L896)\\\ - n\\n***\\n\\n### rephrase()\\n\\n> **rephrase**(`question`, `user_context`?,\ - \ `rephrase_prompt`?): `Observable`\\\\<`string`\\\\>\\n\\nPerforms a question\ - \ rephrasing operation.\\nIt returns a rephrased question that can be used\ - \ as input for the `generate()` method.\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\\ - n .rephrase('Eric lives Toronto')\\n .subscribe((rephrased) => {\\n console.log('rephrased',\ - \ rephrased); // Where does Eric live?\\n });\\n ```\\n\\n#### Parameters\\\ - n\\n\u2022 **question**: `string`\\n\\n\u2022 **user\\\\_context?**: `string`[]\\\ - n\\n\u2022 **rephrase\\\\_prompt?**: `string`\\n\\n#### Returns\\n\\n`Observable`\\\ - \\<`string`\\\\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`rephrase`](../interfaces/IKnowledgeBox.md#rephrase)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:598](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L598)\\\ - n\\n***\\n\\n### search()\\n\\n> **search**(`query`, `features`, `options`?):\ - \ `Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nPerforms\ - \ a search operation in the knowledge box.\\n\\nIt is similar to `find()`\ - \ but the results are not nested.\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\\ - n .search('where does the Little Prince live', [Search.Features.KEYWORD])\\\ - n .subscribe((searchResult) => {\\n console.log('search result', searchResult);\\\ - n });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **query**: `string`\\n\\n\u2022\ - \ **features**: [`Features`](../namespaces/Search/enumerations/Features.md)[]\ - \ = `[]`\\n\\n\u2022 **options?**: [`SearchOptions`](../interfaces/SearchOptions.md)\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`Results`](../namespaces/Search/interfaces/Results.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`search`](../interfaces/IKnowledgeBox.md#search)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:430](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L430)\\\ - n\\n***\\n\\n### stopListeningToNotifications()\\n\\n> **stopListeningToNotifications**():\ - \ `void`\\n\\nStop listening the notifications sent by the Knowledge Box.\\\ - n\\n#### Returns\\n\\n`void`\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`stopListeningToNotifications`](../interfaces/IKnowledgeBox.md#stoplisteningtonotifications)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:786](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L786)\\\ - n\\n***\\n\\n### suggest()\\n\\n> **suggest**(`query`, `inTitleOnly`, `features`,\ - \ `options`?): `Observable`\\\\<[`Suggestions`](../namespaces/Search/interfaces/Suggestions.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\nSuggests\ - \ paragraphs based on the given query.\\n\\n#### Parameters\\n\\n\u2022 **query**:\ - \ `string`\\n\\n\u2022 **inTitleOnly**: `boolean` = `false`\\n\\n\u2022 **features**:\ - \ [`SuggestionFeatures`](../namespaces/Search/enumerations/SuggestionFeatures.md)[]\ - \ = `[]`\\n\\n\u2022 **options?**: [`SuggestOptions`](../interfaces/SuggestOptions.md)\\\ - n\\n#### Returns\\n\\n`Observable`\\\\<[`Suggestions`](../namespaces/Search/interfaces/Suggestions.md)\ - \ \\\\| [`IErrorResponse`](../interfaces/IErrorResponse.md)\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`suggest`](../interfaces/IKnowledgeBox.md#suggest)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:651](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L651)\\\ - n\\n***\\n\\n### summarize()\\n\\n> **summarize**(`ressourceIds`, `user_prompt`?,\ - \ `generative_model`?): `Observable`\\\\<`string`\\\\>\\n\\nSummarize resources.\\\ - n\\nIt reads the resources text content and return a global summary about\ - \ them and one summery per resource.\\n\\nThe optional `user_prompt` parameter\ - \ allows you to specify a text that will be used to generate the summary,\\\ - nand must use the `{text}` placeholder to indicate where the resource text\ - \ should be inserted\\n(example: 'Make a one-line summary of the following\ - \ text: {text}').\\n\\nExample:\\n ```ts\\n nuclia.knowledgeBox\\n .summarize(['09a94719a6444c5a9689394f6ed9baf6'])\\\ - n .subscribe((summary) => {\\n console.log('Summary', summary);\\n \ - \ });\\n ```\\n\\n#### Parameters\\n\\n\u2022 **ressourceIds**: `string`[]\\\ - n\\n\u2022 **user\\\\_prompt?**: `string`\\n\\n\u2022 **generative\\\\_model?**:\ - \ `string`\\n\\n#### Returns\\n\\n`Observable`\\\\<`string`\\\\>\\n\\n####\ - \ Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`summarize`](../interfaces/IKnowledgeBox.md#summarize)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:456](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L456)\\\ - n\\n***\\n\\n### tokens()\\n\\n> **tokens**(`text`): `Observable`\\\\<[`SentenceToken`](../interfaces/SentenceToken.md)[]\\\ - \\>\\n\\nPerforms a tokenization of the given text.\\n\\nExample:\\n ```ts\\\ - n nuclia.knowledgeBox\\n .tokens('Does James Joyce live in Dublin?')\\n\ - \ .subscribe((tokens) => {\\n console.log('tokens', tokens);\\n });\\\ - n ```\\n\\n#### Parameters\\n\\n\u2022 **text**: `string`\\n\\n#### Returns\\\ - n\\n`Observable`\\\\<[`SentenceToken`](../interfaces/SentenceToken.md)[]\\\ - \\>\\n\\n#### Inherited from\\n\\n[`IKnowledgeBox`](../interfaces/IKnowledgeBox.md).[`tokens`](../interfaces/IKnowledgeBox.md#tokens)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.ts:486](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.ts#L486)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"61889effe1181f7a4b4bac909424adb7\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / KnowledgeBox \\n\ - \ Class: KnowledgeBox \\n Provides access to all the Knowledge Box contents\ - \ and services in read mode. \\n For any Knowledge Box operation that requires\ - \ write access, you need to use WritableKnowledgeBox instead. \\n Extends\ - \ \\n \\n IKnowledgeBox \\n \\n Extended by \\n \\n WritableKnowledgeBox \\\ - n \\n Implements \\n \\n IKnowledgeBox \\n \\n Constructors \\n new KnowledgeBox()\ - \ \\n \\n new KnowledgeBox(nuclia, account, data): KnowledgeBox \\n \\n Parameters\ - \ \\n \u2022 nuclia: INuclia \\n \u2022 account: string \\n \u2022 data: IKnowledgeBoxBase\ - \ \\\\| IKnowledgeBoxStandalone \\n Returns \\n KnowledgeBox \\n Inherited\ - \ from \\n IKnowledgeBox.constructor \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:148\ - \ \\n Properties \\n accountId \\n \\n accountId: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.ts:98 \\n \\n allowed_ip_addresses? \\\ - n \\n optional allowed_ip_addresses: null \\\\| string[] \\n \\n Implementation\ - \ of \\n IKnowledgeBox.allowed_ip_addresses \\n Inherited from \\n IKnowledgeBox.allowed_ip_addresses\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:38 \\n \\n allowed_origins?\ - \ \\n \\n optional allowed_origins: null \\\\| string[] \\n \\n Implementation\ - \ of \\n IKnowledgeBox.allowed_origins \\n Inherited from \\n IKnowledgeBox.allowed_origins\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:37 \\n \\n created?\ - \ \\n \\n optional created: string \\n \\n Implementation of \\n IKnowledgeBox.created\ - \ \\n Inherited from \\n IKnowledgeBox.created \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:36\ - \ \\n \\n description? \\n \\n optional description: string \\n \\n Implementation\ - \ of \\n IKnowledgeBox.description \\n Inherited from \\n IKnowledgeBox.description\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:34 \\n \\n enforce_security?\ - \ \\n \\n optional enforce_security: boolean \\n \\n Implementation of \\\ - n IKnowledgeBox.enforce_security \\n Inherited from \\n IKnowledgeBox.enforce_security\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:42 \\n \\n external_index_provider?\ - \ \\n \\n optional external_index_provider: string \\n \\n Implementation\ - \ of \\n IKnowledgeBox.external_index_provider \\n Inherited from \\n IKnowledgeBox.external_index_provider\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:43 \\n \\n hidden_resources_enabled?\ - \ \\n \\n optional hidden_resources_enabled: boolean \\n \\n Implementation\ - \ of \\n IKnowledgeBox.hidden_resources_enabled \\n Inherited from \\n IKnowledgeBox.hidden_resources_enabled\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:40 \\n \\n hidden_resources_hide_on_creation?\ - \ \\n \\n optional hidden_resources_hide_on_creation: boolean \\n \\n Implementation\ - \ of \\n IKnowledgeBox.hidden_resources_hide_on_creation \\n Inherited from\ - \ \\n IKnowledgeBox.hidden_resources_hide_on_creation \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:41\ - \ \\n \\n id \\n \\n id: string \\n \\n Implementation of \\n IKnowledgeBox.id\ - \ \\n Inherited from \\n IKnowledgeBox.id \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:30\ - \ \\n \\n nuclia \\n \\n protected nuclia: INuclia \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.ts:99 \\n \\n search_configs? \\n \\n optional\ - \ search_configs: object \\n \\n Index Signature \\n [key: string]: any \\\ - n Implementation of \\n IKnowledgeBox.search_configs \\n Inherited from \\\ - n IKnowledgeBox.search_configs \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:39\ - \ \\n \\n slug \\n \\n slug: string \\n \\n Implementation of \\n IKnowledgeBox.slug\ - \ \\n Inherited from \\n IKnowledgeBox.slug \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:31\ - \ \\n \\n state? \\n \\n optional state: KBStates \\n \\n Implementation of\ - \ \\n IKnowledgeBox.state \\n Inherited from \\n IKnowledgeBox.state \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:33 \\n \\n title \\n \\\ - n title: string \\n \\n Implementation of \\n IKnowledgeBox.title \\n Inherited\ - \ from \\n IKnowledgeBox.title \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:32\ - \ \\n \\n zone \\n \\n zone: string \\n \\n Implementation of \\n IKnowledgeBox.zone\ - \ \\n Inherited from \\n IKnowledgeBox.zone \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:35\ - \ \\n Accessors \\n fullpath \\n \\n get fullpath(): string \\n \\n The Knowledge\ - \ Box fullpath on the regional API. \\n Example: https://europe-1.rag.progress.cloud/api/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0\ - \ \\n Returns \\n string \\n Implementation of \\n IKnowledgeBox.fullpath\ - \ \\n Inherited from \\n IKnowledgeBox.fullpath \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:144\ - \ \\n \\n path \\n \\n get path(): string \\n \\n The Knowledge Box path on\ - \ the regional API. \\n Example: /v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0\ - \ \\n Returns \\n string \\n Implementation of \\n IKnowledgeBox.path \\n\ - \ Inherited from \\n IKnowledgeBox.path \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:135\ - \ \\n Methods \\n ask() \\n ask(query, context, features, options) \\n \\\ - n ask(query, context?, features?, options?): Observable\\\\ \\n \\n Retrieves\ - \ a generative answer for the given query. \\n The generative answer is a\ - \ text that is generated chunk by chunk by the language model. \\n It is retrieved\ - \ through a readable HTTP stream, so the chat() method returns an Observable\ - \ \\n emitting a value each time a new chunk is available. \\n The incomplete\ - \ attribute of the emitted value indicates if the asnwer is complete or not.\ - \ \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .ask('where does the Little\ - \ Prince live') \\n .pipe(filter((answer) => !answer.incomplete)) \\n .subscribe((answer)\ - \ => { \\n console.log(answer.text); \\n }); \\n Parameters \\n \u2022 query:\ - \ string \\n \u2022 context?: ContextEntry[] \\n \u2022 features?: Features[]\ - \ \\n \u2022 options?: ChatOptions \\n Returns \\n Observable\\\\ \\n Inherited\ - \ from \\n IKnowledgeBox.ask \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:368\ - \ \\n ask(query, context, features, options, callback) \\n \\n ask(query,\ - \ context?, features?, options?, callback?): Observable\\\\ \\n \\n Parameters\ - \ \\n \u2022 query: string \\n \u2022 context?: ContextEntry[] \\n \u2022\ - \ features?: Features[] \\n \u2022 options?: ChatOptions \\n \u2022 callback?\ - \ \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.ask\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:374 \\n \\n catalog()\ - \ \\n \\n catalog(query, options?): Observable\\\\ \\n \\n Parameters \\n\ - \ \u2022 query: string \\\\| CatalogQuery \\n \u2022 options?: CatalogOptions\ - \ \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.catalog\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:646 \\n \\n counters()\ - \ \\n \\n counters(): Observable\\\\ \\n \\n Returns totals for each kind\ - \ of contents stored in the Knowledge Box (resources, fields, paragraphs,\ - \ vectors) \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.counters\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:671 \\n \\n createAgenticRAGPipeline()\ - \ \\n \\n createAgenticRAGPipeline(steps): Pipeline \\n \\n Parameters \\\ - n \u2022 steps: Steps \\n Returns \\n Pipeline \\n Inherited from \\n IKnowledgeBox.createAgenticRAGPipeline\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:346 \\n \\n feedback()\ - \ \\n \\n feedback(answerId, good, feedback, text_block_id?): Observable\\\ - \\ \\n \\n Parameters \\n \u2022 answerId: string \\n \u2022 good: boolean\ - \ \\n \u2022 feedback: string = '' \\n \u2022 text_block_id?: string \\n Returns\ - \ \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.feedback \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.ts:660 \\n \\n find() \\n \\n find(query,\ - \ features, options?): Observable\\\\ \\n \\n Performs a find operation in\ - \ the Knowledge Box, which is the recommended way to search for results. \\\ - n Example: \\n ts \\n nuclia.knowledgeBox \\n .find('where does the Little\ - \ Prince live') \\n .subscribe((searchResult) => { \\n console.log('search\ - \ result', searchResult); \\n }); \\n Parameters \\n \u2022 query: string\ - \ \\n \u2022 features: Features[] = [] \\n \u2022 options?: SearchOptions\ - \ \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.find\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:408 \\n \\n generate()\ - \ \\n \\n generate(question, context): Observable\\\\ \\n \\n Performs a question\ - \ answering operation based on a given context. \\n Example: \\n ts \\n nuclia.knowledgeBox\ - \ \\n .generate('Who is Eric from Toronto?', [ \\n 'Eric is a taxi driver',\ - \ \\n 'Eric was born in France', \\n 'Eric lives in Toronto', \\n ])) \\n\ - \ .subscribe(({ answer }) => { \\n console.log('answer', answer); \\n });\ - \ \\n Parameters \\n \u2022 question: string \\n \u2022 context: string[]\ - \ = [] \\n Returns \\n Observable\\\\ \\n answer \\n \\n answer: string \\\ - n \\n cannotAnswer \\n \\n cannotAnswer: boolean \\n \\n Inherited from \\\ - n IKnowledgeBox.generate \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:530\ - \ \\n \\n generateJSON() \\n \\n generateJSON(question, json_schema, context):\ - \ Observable\\\\ \\n \\n Performs a question answering operation using a JSON\ - \ schema. \\n Example: \\n ts \\n nuclia.knowledgeBox \\n .generateJSON( \\\ - n 'Who is Eric from Toronto?', \\n { \\n name: 'info', \\n parameters: { \\\ - n properties: { \\n location: { \\n title: 'Location', \\n description: 'The\ - \ location of the person', \\n type: 'string', \\n }, \\n name: { \\n title:\ - \ 'Name', \\n description: 'The name of the person', \\n type: 'string', \\\ - n }, \\n }, \\n required: ['name', 'location'], \\n }, \\n }, \\n [ \\n 'Eric\ - \ is a taxi driver', \\n 'Eric was born in France', \\n 'Eric lives in Toronto',\ - \ \\n ], \\n )).subscribe((answer) => { \\n console.log('location', answer.answer.location);\ - \ \\n }); \\n Parameters \\n \u2022 question: string \\n \u2022 json_schema:\ - \ object \\n \u2022 context: string[] = [] \\n Returns \\n Observable\\\\\ - \ \\n answer \\n \\n answer: object \\n \\n success \\n \\n success: boolean\ - \ \\n \\n Inherited from \\n IKnowledgeBox.generateJSON \\n Defined in \\\ - n libs/sdk-core/src/lib/db/kb/kb.ts:574 \\n \\n generateRandomQuestionAboutResource()\ - \ \\n \\n generateRandomQuestionAboutResource(resource): Observable\\\\ \\\ - n \\n Generates a random question about the given resource. \\n It picks an\ - \ entities relation from the extracted metadata and generates a question about\ - \ it. \\n It returns an empty string if no question can be generated. \\n\ - \ Example: \\n ts \\n nuclia.knowledgeBox \\n .getResource('09a94719a6444c5a9689394f6ed9baf6',\ - \ [ResourceProperties.EXTRACTED], [ExtractedDataTypes.METADATA]) \\n .pipe(\ - \ \\n switchMap((resource) => knowledgeBox.generateRandomQuestionAboutResource(resource)),\ - \ \\n ) \\n .subscribe((question) => { \\n console.log('question', question);\ - \ \\n }); \\n Parameters \\n \u2022 resource: Resource \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.generateRandomQuestionAboutResource\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:620 \\n \\n getConfiguration()\ - \ \\n \\n getConfiguration(): Observable\\\\ \\n \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.getConfiguration \\n Defined in \\\ - n libs/sdk-core/src/lib/db/kb/kb.ts:738 \\n \\n getEntities() \\n \\n getEntities():\ - \ Observable\\\\ \\n \\n Returns all the NER families defined in the Knowledge\ - \ Box. \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getEntities\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:167 \\n \\n getEntitiesGroup()\ - \ \\n \\n getEntitiesGroup(groupId): Observable\\\\ \\n \\n Returns the NER\ - \ family with the given id. \\n Parameters \\n \u2022 groupId: string \\n\ - \ Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getEntitiesGroup\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:172 \\n \\n getExtractStrategies()\ - \ \\n \\n getExtractStrategies(): Observable\\\\ \\n \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.getExtractStrategies \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.ts:914 \\n \\n getFacets() \\n \\n getFacets(facets):\ - \ Observable\\\\ \\n \\n Get the total amount of matches in the Knowledge\ - \ Box for specific criteria (facets) passed in argument \\n Parameters \\\ - n \u2022 facets: string[] \\n List of facets to request \\n Returns \\n Observable\\\ - \\ \\n An observable containing an object where each key is a string and maps\ - \ to an object containing values and their corresponding counts. \\n Inherited\ - \ from \\n IKnowledgeBox.getFacets \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:199\ - \ \\n \\n getFullResource() \\n \\n getFullResource(uuid): Observable\\\\\ - \ \\n \\n Retrieves a resource from the Knowledge Box with all its attached\ - \ metadata and content. \\n Parameters \\n \u2022 uuid: string \\n Returns\ - \ \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getFullResource\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:258 \\n \\n getFullResourceBySlug()\ - \ \\n \\n getFullResourceBySlug(slug): Observable\\\\ \\n \\n Parameters \\\ - n \u2022 slug: string \\n Returns \\n Observable\\\\ \\n Inherited from \\\ - n IKnowledgeBox.getFullResourceBySlug \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:290\ - \ \\n \\n getGenerativeProviders() \\n \\n getGenerativeProviders(): Observable\\\ - \\ \\n \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getGenerativeProviders\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:748 \\n \\n getInvites()\ - \ \\n \\n getInvites(): Observable\\\\ \\n \\n Returns \\n Observable\\\\\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:763 \\n \\n getKVSchema()\ - \ \\n \\n getKVSchema(name): Observable\\\\ \\n \\n Returns the KV schema\ - \ with the given name. \\n Parameters \\n \u2022 name: string \\n Returns\ - \ \\n Observable\\\\ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:190\ - \ \\n \\n getKVSchemas() \\n \\n getKVSchemas(): Observable\\\\ \\n \\n Returns\ - \ all the KV schemas defined in the Knowledge Box. \\n Returns \\n Observable\\\ - \\ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:185 \\n \\n getLabels()\ - \ \\n \\n getLabels(): Observable\\\\ \\n \\n Returns all the labels defined\ - \ in the Knowledge Box. \\n Returns \\n Observable\\\\ \\n Inherited from\ - \ \\n IKnowledgeBox.getLabels \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:177\ - \ \\n \\n getLearningSchema() \\n \\n getLearningSchema(): Observable\\\\\ - \ \\n \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getLearningSchema\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:742 \\n \\n getResource()\ - \ \\n \\n getResource(uuid, show, extracted): Observable\\\\ \\n \\n Retrieves\ - \ a resource from the Knowledge Box. \\n \\n show defines which properties\ - \ are returned. Default retrieves only the basic metadata. \\n extracted defines\ - \ which extracted data are returned. \\n It is ignored if ResourceProperties.EXTRACTED\ - \ is not in the returned properties. \\n Default is an empty array. \\n \\\ - n Example: \\n ts \\n nuclia.db \\n .getKnowledgeBox() \\n .pipe(switchMap((knowledgeBox)\ - \ => knowledgeBox.getResource('09a94719a6444c5a9689394f6ed9baf6'))) \\n .subscribe((resource)\ - \ => { \\n console.log('resource', resource); \\n }); \\n Parameters \\n \u2022\ - \ uuid: string \\n \u2022 show: ResourceProperties[] = ... \\n \u2022 extracted:\ - \ ExtractedDataTypes[] = [] \\n Returns \\n Observable\\\\ \\n Inherited from\ - \ \\n IKnowledgeBox.getResource \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:247\ - \ \\n \\n getResourceBySlug() \\n \\n getResourceBySlug(slug, show, extracted):\ - \ Observable\\\\ \\n \\n Parameters \\n \u2022 slug: string \\n \u2022 show:\ - \ ResourceProperties[] = ... \\n \u2022 extracted: ExtractedDataTypes[] =\ - \ [] \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getResourceBySlug\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:282 \\n \\n getResourceFromData()\ - \ \\n \\n getResourceFromData(data): Resource \\n \\n Parameters \\n \u2022\ - \ data: IResource \\n Returns \\n Resource \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:342\ - \ \\n \\n getSearchConfig() \\n \\n getSearchConfig(id): Observable\\\\ \\\ - n \\n Parameters \\n \u2022 id: string \\n Returns \\n Observable\\\\ \\n\ - \ Inherited from \\n IKnowledgeBox.getSearchConfig \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:922\ - \ \\n \\n getSearchConfigs() \\n \\n getSearchConfigs(): Observable\\\\ \\\ - n \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getSearchConfigs\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:926 \\n \\n getSplitStrategies()\ - \ \\n \\n getSplitStrategies(): Observable\\\\ \\n \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.getSplitStrategies \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.ts:918 \\n \\n getTempToken() \\n \\\ - n getTempToken(payload?, ignoreExpiration?): Observable\\\\ \\n \\n Returns\ - \ an ephemeral token. \\n Requires account id and zone to be set in the Nuclia\ - \ options (except when working with a local NucliaDB instance). \\n This is\ - \ useful when displaying a clickable link to a file in a private Knowledge\ - \ Box \\n (the token will authorize the request even though there are no authentication\ - \ headers). \\n Example: \\n ts \\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`;\ - \ \\n Parameters \\n \u2022 payload?: any \\n Optional payload to provide\ - \ extra data for the token generation \\n \u2022 ignoreExpiration?: boolean\ - \ = false \\n Optional By default, a temp token is valid for 5min and the\ - \ same token returned if this method is called several times during this time.\ - \ \\n Passing ignoreExpiration flag to true will ignore this expiration delay\ - \ and will always return a new token. \\n Returns \\n Observable\\\\ \\n Inherited\ - \ from \\n IKnowledgeBox.getTempToken \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:707\ - \ \\n \\n getUsers() \\n \\n getUsers(): Observable\\\\ \\n \\n Returns \\\ - n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.getUsers \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.ts:754 \\n \\n listenToAllNotifications()\ - \ \\n \\n listenToAllNotifications(): Observable\\\\ \\n \\n Start listening\ - \ to all the notifications sent by the Knowledge Box. \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.listenToAllNotifications \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.ts:775 \\n \\n listenToProcessingNotifications()\ - \ \\n \\n listenToProcessingNotifications(): Observable\\\\ \\n \\n Start\ - \ listening to the Knowledge Box notifications, and returns the list of notifications\ - \ for resources which have processing completed (either successfully or not).\ - \ \\n Notifications are sent anytime processing is completed, and processing\ - \ is done anytime the resource is created or modified (like when a summary\ - \ is added to the resource for example). \\n Returns \\n Observable\\\\ \\\ - n Inherited from \\n IKnowledgeBox.listenToProcessingNotifications \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.ts:854 \\n \\n listenToResourceOperationNotifications()\ - \ \\n \\n listenToResourceOperationNotifications(): Observable\\\\ \\n \\\ - n Start listening to the Knowledge Box notifications, and returns the list\ - \ of notifications corresponding to the different operations affecting the\ - \ resources: \\n created, modified, deleted. \\n The notification contains\ - \ the resource id, title, a timestamp and a flag indicating if the operation\ - \ was successful or not. \\n Returns \\n Observable\\\\ \\n Inherited from\ - \ \\n IKnowledgeBox.listenToResourceOperationNotifications \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.ts:799 \\n \\n listResources() \\n \\\ - n listResources(page?, size?): Observable\\\\ \\n \\n Lists all the resources\ - \ stored in the Knowledge Box. \\n Parameters \\n \u2022 page?: number \\\ - n \u2022 size?: number \\n Returns \\n Observable\\\\ \\n Inherited from \\\ - n IKnowledgeBox.listResources \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:676\ - \ \\n \\n predictAnswer() \\n \\n predictAnswer(question, options?, synchronous?):\ - \ Observable\\\\ \\n \\n Performs a question answering operation \\n Example:\ - \ \\n ts \\n nuclia.knowledgeBox \\n .predictAnswer('Who is Eric from Toronto?'))\ - \ \\n .subscribe((answer) => { \\n if (answer.type !== 'error') { \\n console.log('answer',\ - \ answer.text); \\n } \\n }); \\n Parameters \\n \u2022 question: string \\\ - n \u2022 options?: PredictAnswerOptions \\n \u2022 synchronous?: boolean =\ - \ true \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.predictAnswer\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:506 \\n \\n processingStatus()\ - \ \\n \\n processingStatus(cursor?, scheduled?, limit?): Observable\\\\ \\\ - n \\n Parameters \\n \u2022 cursor?: string \\n \u2022 scheduled?: boolean\ - \ \\n \u2022 limit?: number \\n Returns \\n Observable\\\\ \\n cursor \\n\ - \ \\n cursor: string \\n \\n results \\n \\n results: ProcessingStatus[] \\\ - n \\n Inherited from \\n IKnowledgeBox.processingStatus \\n Defined in \\\ - n libs/sdk-core/src/lib/db/kb/kb.ts:896 \\n \\n rephrase() \\n \\n rephrase(question,\ - \ user_context?, rephrase_prompt?): Observable\\\\ \\n \\n Performs a question\ - \ rephrasing operation. \\n It returns a rephrased question that can be used\ - \ as input for the generate() method. \\n Example: \\n ts \\n nuclia.knowledgeBox\ - \ \\n .rephrase('Eric lives Toronto') \\n .subscribe((rephrased) => { \\n\ - \ console.log('rephrased', rephrased); // Where does Eric live? \\n }); \\\ - n Parameters \\n \u2022 question: string \\n \u2022 user_context?: string[]\ - \ \\n \u2022 rephrase_prompt?: string \\n Returns \\n Observable\\\\ \\n Inherited\ - \ from \\n IKnowledgeBox.rephrase \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:598\ - \ \\n \\n search() \\n \\n search(query, features, options?): Observable\\\ - \\ \\n \\n Performs a search operation in the knowledge box. \\n It is similar\ - \ to find() but the results are not nested. \\n Example: \\n ts \\n nuclia.knowledgeBox\ - \ \\n .search('where does the Little Prince live', [Search.Features.KEYWORD])\ - \ \\n .subscribe((searchResult) => { \\n console.log('search result', searchResult);\ - \ \\n }); \\n Parameters \\n \u2022 query: string \\n \u2022 features: Features[]\ - \ = [] \\n \u2022 options?: SearchOptions \\n Returns \\n Observable\\\\ \\\ - n Inherited from \\n IKnowledgeBox.search \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:430\ - \ \\n \\n stopListeningToNotifications() \\n \\n stopListeningToNotifications():\ - \ void \\n \\n Stop listening the notifications sent by the Knowledge Box.\ - \ \\n Returns \\n void \\n Inherited from \\n IKnowledgeBox.stopListeningToNotifications\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:786 \\n \\n suggest()\ - \ \\n \\n suggest(query, inTitleOnly, features, options?): Observable\\\\\ - \ \\n \\n Suggests paragraphs based on the given query. \\n Parameters \\\ - n \u2022 query: string \\n \u2022 inTitleOnly: boolean = false \\n \u2022\ - \ features: SuggestionFeatures[] = [] \\n \u2022 options?: SuggestOptions\ - \ \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.suggest\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:651 \\n \\n summarize()\ - \ \\n \\n summarize(ressourceIds, user_prompt?, generative_model?): Observable\\\ - \\ \\n \\n Summarize resources. \\n It reads the resources text content and\ - \ return a global summary about them and one summery per resource. \\n The\ - \ optional user_prompt parameter allows you to specify a text that will be\ - \ used to generate the summary, \\n and must use the {text} placeholder to\ - \ indicate where the resource text should be inserted \\n (example: 'Make\ - \ a one-line summary of the following text: {text}'). \\n Example: \\n ts\ - \ \\n nuclia.knowledgeBox \\n .summarize(['09a94719a6444c5a9689394f6ed9baf6'])\ - \ \\n .subscribe((summary) => { \\n console.log('Summary', summary); \\n });\ - \ \\n Parameters \\n \u2022 ressourceIds: string[] \\n \u2022 user_prompt?:\ - \ string \\n \u2022 generative_model?: string \\n Returns \\n Observable\\\ - \\ \\n Inherited from \\n IKnowledgeBox.summarize \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:456\ - \ \\n \\n tokens() \\n \\n tokens(text): Observable\\\\ \\n \\n Performs a\ - \ tokenization of the given text. \\n Example: \\n ts \\n nuclia.knowledgeBox\ - \ \\n .tokens('Does James Joyce live in Dublin?') \\n .subscribe((tokens)\ - \ => { \\n console.log('tokens', tokens); \\n }); \\n Parameters \\n \u2022\ - \ text: string \\n Returns \\n Observable\\\\ \\n Inherited from \\n IKnowledgeBox.tokens\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.ts:486\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"https://europe-1.rag.progress.cloud/api/v1/kb/3cce4a71-9cb9-4fda-beee-8a1512616bf0\"\ - ,\"IKnowledgeBox.feedback\",\"IKnowledgeBox.search\",\"IKnowledgeBox.zone\"\ - ,\"IKnowledgeBox.id\"],\"paragraphs\":[{\"start\":0,\"end\":262,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":0,\"end\":155,\"key\":\"\"},{\"start\":155,\"end\":262,\"key\"\ - :\"\"}]},{\"start\":262,\"end\":820,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":263,\"end\"\ - :820,\"key\":\"\"}]},{\"start\":820,\"end\":1065,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":823,\"\ - end\":1065,\"key\":\"\"}]},{\"start\":1065,\"end\":1287,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1068,\"end\":1287,\"key\":\"\"}]},{\"start\":1287,\"end\":1688,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1290,\"end\":1482,\"key\":\"\"},{\"start\":1482,\"\ - end\":1688,\"key\":\"\"}]},{\"start\":1688,\"end\":1920,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1691,\"end\":1920,\"key\":\"\"}]},{\"start\":1920,\"end\":2173,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1923,\"end\":2173,\"key\":\"\"}]},{\"start\"\ - :2173,\"end\":2439,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":2176,\"end\":2439,\"key\"\ - :\"\"}]},{\"start\":2439,\"end\":2702,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2442,\"\ - end\":2702,\"key\":\"\"}]},{\"start\":2702,\"end\":2966,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2703,\"end\":2966,\"key\":\"\"}]},{\"start\":2966,\"end\":3212,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":2969,\"end\":3212,\"key\":\"\"}]},{\"start\"\ - :3212,\"end\":3561,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":3213,\"end\":3381,\"key\"\ - :\"\"},{\"start\":3381,\"end\":3561,\"key\":\"\"}]},{\"start\":3561,\"end\"\ - :3987,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3562,\"end\":3987,\"key\":\"\"}]},{\"start\"\ - :3987,\"end\":4251,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":3988,\"end\":4251,\"key\"\ - :\"\"}]},{\"start\":4251,\"end\":4699,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4252,\"\ - end\":4321,\"key\":\"\"},{\"start\":4321,\"end\":4699,\"key\":\"\"}]},{\"\ - start\":4699,\"end\":4937,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":4700,\"end\":4790,\"\ - key\":\"\"},{\"start\":4790,\"end\":4937,\"key\":\"\"}]},{\"start\":4937,\"\ - end\":5214,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":4938,\"end\":5030,\"key\":\"\"},{\"start\":5030,\"\ - end\":5069,\"key\":\"\"},{\"start\":5069,\"end\":5113,\"key\":\"\"},{\"start\"\ - :5113,\"end\":5138,\"key\":\"\"},{\"start\":5138,\"end\":5161,\"key\":\"\"\ - },{\"start\":5161,\"end\":5214,\"key\":\"\"}]},{\"start\":5214,\"end\":5695,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":5215,\"end\":5262,\"key\":\"\"},{\"start\":5262,\"\ - end\":5292,\"key\":\"\"},{\"start\":5292,\"end\":5317,\"key\":\"\"},{\"start\"\ - :5317,\"end\":5611,\"key\":\"\"},{\"start\":5611,\"end\":5641,\"key\":\"\"\ - },{\"start\":5641,\"end\":5666,\"key\":\"\"},{\"start\":5666,\"end\":5695,\"\ - key\":\"\"}]},{\"start\":5695,\"end\":7051,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5696,\"\ - end\":5925,\"key\":\"\"},{\"start\":5925,\"end\":6758,\"key\":\"\"},{\"start\"\ - :6758,\"end\":7051,\"key\":\"\"}]},{\"start\":7051,\"end\":7553,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":7052,\"end\":7091,\"key\":\"\"},{\"start\":7091,\"end\":7136,\"\ - key\":\"\"},{\"start\":7136,\"end\":7291,\"key\":\"\"},{\"start\":7291,\"\ - end\":7553,\"key\":\"\"}]},{\"start\":7553,\"end\":7787,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":7554,\"end\":7593,\"key\":\"\"},{\"start\":7593,\"end\":7725,\"\ - key\":\"\"},{\"start\":7725,\"end\":7787,\"key\":\"\"}]},{\"start\":7787,\"\ - end\":8192,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":7788,\"end\":8192,\"key\":\"\"}]},{\"start\"\ - :8192,\"end\":8745,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":8193,\"end\":8232,\"key\"\ - :\"\"},{\"start\":8232,\"end\":8669,\"key\":\"\"},{\"start\":8669,\"end\"\ - :8745,\"key\":\"\"}]},{\"start\":8745,\"end\":9185,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":8746,\"end\":9185,\"key\":\"\"}]},{\"start\":9185,\"end\":9660,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":9186,\"end\":9281,\"key\":\"\"},{\"start\":9281,\"\ - end\":9343,\"key\":\"\"},{\"start\":9343,\"end\":9382,\"key\":\"\"},{\"start\"\ - :9382,\"end\":9496,\"key\":\"\"},{\"start\":9496,\"end\":9596,\"key\":\"\"\ - },{\"start\":9596,\"end\":9660,\"key\":\"\"}]},{\"start\":9660,\"end\":10148,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":9661,\"end\":10148,\"key\":\"\"}]},{\"start\"\ - :10148,\"end\":10382,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":10149,\"end\":10382,\"key\"\ - 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:\"residence\",\"metadata\":{\"paragraph_id\":\"03c141d8a4704db794df47287f7a9e64/t/page/17901-18804\"\ - ,\"source_start\":17952,\"source_end\":17956,\"to_start\":17962,\"to_end\"\ - :17969},\"from\":{\"value\":\"Eric\",\"type\":\"entity\",\"group\":\"PERSON\"\ - },\"to\":{\"value\":\"Toronto\",\"type\":\"entity\",\"group\":\"GPE\"}},{\"\ - relation\":\"OTHER\",\"label\":\"residence\",\"metadata\":{\"paragraph_id\"\ - :\"03c141d8a4704db794df47287f7a9e64/t/page/18804-19057\",\"source_start\"\ - :18941,\"source_end\":18945,\"to_start\":18952,\"to_end\":18959},\"from\"\ - :{\"value\":\"Eric\",\"type\":\"entity\",\"group\":\"PERSON\"},\"to\":{\"\ - value\":\"Toronto\",\"type\":\"entity\",\"group\":\"GPE\"}},{\"relation\"\ - :\"OTHER\",\"label\":\"residence\",\"metadata\":{\"paragraph_id\":\"03c141d8a4704db794df47287f7a9e64/t/page/21461-21757\"\ - ,\"source_start\":21514,\"source_end\":21525,\"to_start\":21534,\"to_end\"\ - :21540},\"from\":{\"value\":\"James Joyce\",\"type\":\"entity\",\"group\"\ - :\"PERSON\"},\"to\":{\"value\":\"Dublin\",\"type\":\"entity\",\"group\":\"\ - GPE\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\"\ - :[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop > js sdk > classes\ - \ > KnowledgeBox\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > classes > KnowledgeBox\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"03c141d8a4704db794df47287f7a9e64/t/page/15230-15632\"\ - :{\"score\":0.5163909792900085,\"score_type\":\"VECTOR\",\"order\":18,\"text\"\ - :\" Example: \\n ts \\n const downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`;\ - \ \\n Parameters \\n \u2022 payload?: any \\n Optional payload to provide\ - \ extra data for the token generation \\n \u2022 ignoreExpiration?: boolean\ - \ = false \\n Optional By default, a temp token is valid for 5min and the\ - \ same token returned if this method is called several times during this time.\ - \ \\n\",\"id\":\"03c141d8a4704db794df47287f7a9e64/t/page/15230-15632\",\"\ - labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":37,\"start\"\ - :15230,\"end\":15632,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\"\ - :\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\"\ - ,\"title\":\"docs > rag > advanced > openai api compatible models\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2025-08-27T15:33:06.862963\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: openai-api-compatible-models\\\ - ntitle: Connect to any OpenAI API compatible LLM\\n---\\n\\n# Connect to any\ - \ OpenAI API compatible LLM\\n\\nAgentic RAG allows you to connect to any\ - \ OpenAI API compatible LLM. This means that you can use any LLM that has\ - \ an API compatible with the OpenAI API which has become a standard in the\ - \ industry.\\n\\nMany of the options for self-hosted LLMs, open-source LLMs\ - \ hosted by cloud providers or commercial LLMs are compatible with the OpenAI\ - \ API. This means that you can use them with Agentic RAG without any modifications.\\\ - n\\n## Configuration\\n\\nTo modify your knowledge box configuration you can\ - \ do so in three manners, through the API, the Nuclia CLI / SDK or the Agentic\ - \ RAG dashboard.\\n\\nThe Agentic RAG dashboard offers the most user-friendly\ - \ way to modify the configuration of your knowledge box and we will use it\ - \ in this example.\\n\\nWe will be setting up a connection to the **Phi 4\ - \ Reasoning Plus** model, hosted by **OpenRouter** which offers a wide range\ - \ of open-source and commercial models compatible with the OpenAI API. We\ - \ can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free),\ - \ the API parameters are located under the **API** tab.\\n\\n1. **Open the\ - \ AI Models page** \\n In the left sidebar under **Advanced**, click **AI\ - \ Models**.\\n2. **Select \u201COpenAI API Compatible Model\u201D** \\n \ - \ From the models list, choose **OpenAI API Compatible Model**.\\n3. **Enable\ - \ custom Key** \\n Toggle the option for using you own `OpenAI API Compatible\ - \ Key` if it is not already enabled.\\n4. **Fill in the configuration parameters**\\\ - n\\n - **API Key**:\\n - Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key.\\n - Example: We will set this to our OpenRouter API key.\\\ - n - **API URL**:\\n - Description: The URL of the API endpoint for your\ - \ LLM. This may be shared between multiple models.\\n - Example: For OpenRouter,\ - \ it is the same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\\ - n - Description: The name of the model you want to use, it needs to exactly\ - \ match the name of the model in the API.\\n - Example: For Phi 4 Reasoning\ - \ Plus in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\\ - n - **Maximum supported input tokens**:\\n - Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only.\\n - Example: For Phi 4 Reasoning Plus, the total context\ - \ size is `32768` tokens, as we want to leave room for the output, we will\ - \ set the maximum supported input tokens as `32768 - 1024 = 31744`.\\n -\ - \ **Maximum supported output tokens**:\\n - Description: The maximum number\ - \ of tokens that the model can generate as output. Again, we should keep in\ - \ mind that this value summed to the **Maximum supported input tokens** should\ - \ not exceed the total context size supported by the model.\\n - Example:\ - \ For Phi 4 Reasoning Plus, the maximum output tokens is specified at `32768`,\ - \ but we already reserved `31744` for the input tokens, so we will set this\ - \ to `32768 - 31744 = 1024`.\\n - **Model Features**:\\n - Description:\ - \ Under this section you will find multiple toggles related to features supported\ - \ by the model, these vary from model to model, but most often the default\ - \ values are well suited to most use cases. The most relevant toggle is for\ - \ `Image Support` which allows you to use images as input for the model.\\\ - n - Example: Image input is not supported by Phi 4 Reasoning Plus, so\ - \ we will leave it disabled.\\n\\n5. **Save** \\n Click **Save changes**.\\\ - n\\n6. **Test your model** \\n Run a sample query in Agentic RAG or via\ - \ API/CLI. Adjust your prompt templates and token settings as needed.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ openai-api-compatible-models \\n title: Connect to any OpenAI API compatible\ - \ LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic RAG allows\ - \ you to connect to any OpenAI API compatible LLM. This means that you can\ - \ use any LLM that has an API compatible with the OpenAI API which has become\ - \ a standard in the industry. \\n Many of the options for self-hosted LLMs,\ - \ open-source LLMs hosted by cloud providers or commercial LLMs are compatible\ - \ with the OpenAI API. This means that you can use them with Agentic RAG without\ - \ any modifications. \\n Configuration \\n To modify your knowledge box configuration\ - \ you can do so in three manners, through the API, the Nuclia CLI / SDK or\ - \ the Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most\ - \ user-friendly way to modify the configuration of your knowledge box and\ - \ we will use it in this example. \\n We will be setting up a connection to\ - \ the Phi 4 Reasoning Plus model, hosted by OpenRouter which offers a wide\ - \ range of open-source and commercial models compatible with the OpenAI API.\ - \ We can see more information about this specific model here, the API parameters\ - \ are located under the API tab. \\n \\n Open the AI Models page \\n In the\ - \ left sidebar under Advanced, click AI Models. \\n Select OpenAI API Compatible\ - \ Model \\n From the models list, choose OpenAI API Compatible Model. \\n\ - \ Enable custom Key \\n Toggle the option for using you own OpenAI API Compatible\ - \ Key if it is not already enabled. \\n \\n Fill in the configuration parameters\ - \ \\n \\n \\n API Key: \\n \\n Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key. \\n Example: We will set this to our OpenRouter API key. \\\ - n \\n \\n API URL: \\n Description: The URL of the API endpoint for your LLM.\ - \ This may be shared between multiple models. \\n Example: For OpenRouter,\ - \ it is the same for all models: https://openrouter.ai/api/v1 \\n \\n \\n\ - \ Model: \\n Description: The name of the model you want to use, it needs\ - \ to exactly match the name of the model in the API. \\n Example: For Phi\ - \ 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free.\ - \ \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n Example: For Phi 4 Reasoning Plus, the total context size\ - \ is 32768 tokens, as we want to leave room for the output, we will set the\ - \ maximum supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum\ - \ supported output tokens: \\n Description: The maximum number of tokens that\ - \ the model can generate as output. Again, we should keep in mind that this\ - \ value summed to the Maximum supported input tokens should not exceed the\ - \ total context size supported by the model. \\n Example: For Phi 4 Reasoning\ - \ Plus, the maximum output tokens is specified at 32768, but we already reserved\ - \ 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024.\ - \ \\n \\n \\n \\n Model Features: \\n \\n Description: Under this section\ - \ you will find multiple toggles related to features supported by the model,\ - \ these vary from model to model, but most often the default values are well\ - \ suited to most use cases. The most relevant toggle is for Image Support\ - \ which allows you to use images as input for the model. \\n Example: Image\ - \ input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled.\ - \ \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model\ - \ \\n Run a sample query in Agentic RAG or via API/CLI. 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Again, we should keep in mind that this value summed to the Maximum\ - \ supported input tokens should not exceed the total context size supported\ - \ by the model. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"\ - start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - :{\"score\":0.5799813270568848,\"score_type\":\"VECTOR\",\"order\":3,\"text\"\ - :\" \\n \\n Maximum supported input tokens: \\n Description: The maximum number\ - \ of tokens that the model can accept as input. Be mindful that this takes\ - \ into account the tokens used in the prompt, query and context. Also take\ - \ note that some models may provide their context window as the total between\ - \ input and output tokens, while others may provide it as the input tokens\ - \ only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - :{\"score\":0.5293720960617065,\"score_type\":\"VECTOR\",\"order\":10,\"text\"\ - :\" Example: For Phi 4 Reasoning Plus, the total context size is 32768 tokens,\ - \ as we want to leave room for the output, we will set the maximum supported\ - \ input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output\ - \ tokens: \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\"\ - :2575,\"end\":2804,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/3043-3250\"\ - :{\"score\":0.5172687768936157,\"score_type\":\"VECTOR\",\"order\":17,\"text\"\ - :\" Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified\ - \ at 32768, but we already reserved 31744 for the input tokens, so we will\ - \ set this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n\"\ - ,\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/3043-3250\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":11,\"start\":3043,\"\ - end\":3250,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\"\ - ,\"modified\":\"2026-06-02T15:41:38.655253\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\\ - n\\n# Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\\ - n\\n***\\n\\n### line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\\ - n\\n***\\n\\n### text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\\ - n\\n***\\n\\n### width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\\ - n\\n***\\n\\n### x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\\ - n\\n***\\n\\n### y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"b174ec97138cd1778c102918672d3f07\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface:\ - \ PageToken \\n Properties \\n height \\n \\n height: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 \\n \\n\ - \ line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250\ - \ \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248\ - \ \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246\ - \ \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"\ - processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:48.900917Z\"\ - ,\"last_understanding\":\"2026-06-02T15:43:43.037157Z\",\"last_extract\":\"\ - 2026-06-02T15:43:42.655587Z\",\"last_processing_start\":\"2026-06-02T15:43:42.629857Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ PageToken\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\"\ - :0.531770646572113,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\\ - n Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249\ - \ \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2025-09-18T11:01:18.918196\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - \ : My search , filters : [ /icon/application/pdf , /classification.labels/region/Asia\ - \ ]}) \\n search.ask(query={ query : My search , top_k : 5}) \\n Graph queries\ - \ \\n The Python SDK allows graph queries supported by the /graph endpoint.\ - \ Although \\n a bit cumbersome, the knowledge graph can be queried as in\ - \ this example: \\n \\n CLI: \\n \\n bash \\n nuclia kb search graph --query='{\ - \ query : { prop : path , source : { value : Rust }, destination : { value\ - \ : Python }}}' \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\\ - n search = sdk.NucliaSearch() \\n search.graph( \\n query={ \\n query : {\ - \ \\n prop : path , \\n source : { value : Rust }, \\n destination : { value\ - \ : Python } \\n } \\n } \\n ) \\n For more information about graph querying,\ - \ please refer to Nuclia's graph \\n doc or the API reference\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":305,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":200,\"\ - key\":\"\"},{\"start\":200,\"end\":305,\"key\":\"\"}]},{\"start\":305,\"end\"\ - :550,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":308,\"end\":362,\"key\":\"\"},{\"start\":362,\"\ - end\":550,\"key\":\"\"}]},{\"start\":550,\"end\":774,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":551,\"end\":774,\"key\":\"\"}]},{\"start\":774,\"end\":1091,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":775,\"end\":1091,\"key\":\"\"}]},{\"start\":1091,\"end\":1361,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1094,\"end\":1361,\"key\":\"\"}]},{\"start\"\ - :1361,\"end\":1790,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1362,\"end\":1630,\"key\"\ - 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end\":4689,\"key\":\"\"}]},{\"start\":4689,\"end\":5095,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":4690,\"end\":5010,\"key\":\"\"},{\"start\":5010,\"end\":5095,\"\ - key\":\"\"}]},{\"start\":5095,\"end\":5543,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":5098,\"\ - end\":5543,\"key\":\"\"}]}],\"ner\":{\"JSON\":\"ORG\",\"Nuclia\":\"ORG\",\"\ - Europe\":\"LOC\",\"Asia\":\"LOC\",\"OpenAI\":\"PRODUCT\"},\"entities\":{\"\ - processor\":{\"entities\":[{\"text\":\"Asia\",\"label\":\"LOC\",\"positions\"\ - :[{\"start\":531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"\ - end\":3720},{\"start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}]},{\"\ - text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":760,\"end\":764}]},{\"\ - text\":\"Nuclia\",\"label\":\"ORG\",\"positions\":[{\"start\":955,\"end\"\ - :961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"end\":5507}]},{\"text\"\ - :\"OpenAI\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":2279,\"end\":2285}]},{\"\ - text\":\"Europe\",\"label\":\"LOC\",\"positions\":[{\"start\":3677,\"end\"\ - :3683}]}]}},\"classifications\":[],\"last_index\":\"2025-09-18T11:03:29.417038Z\"\ - ,\"last_understanding\":\"2025-09-18T11:03:28.773187Z\",\"last_extract\":\"\ - 2025-09-18T11:03:24.225246Z\",\"last_processing_start\":\"2025-09-18T11:01:44.762993Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/JSON\":{\"position\"\ - :[{\"start\":760,\"end\":764}],\"entity\":\"JSON\"},\"ORG/Nuclia\":{\"position\"\ - :[{\"start\":955,\"end\":961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"\ - end\":5507}],\"entity\":\"Nuclia\"},\"PRODUCT/OpenAI\":{\"position\":[{\"\ - start\":2279,\"end\":2285}],\"entity\":\"OpenAI\"},\"LOC/Asia\":{\"position\"\ - :[{\"start\":531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"\ - end\":3720},{\"start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}],\"\ - entity\":\"Asia\"},\"LOC/Europe\":{\"position\":[{\"start\":3677,\"end\":3683}],\"\ - entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"\ - split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"\ - value\":\"docs > develop > python sdk > 05 search\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":0.5417318344116211,\"score_type\":\"VECTOR\",\"order\":5,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\"\ - :{\"score\":0.5198746919631958,\"score_type\":\"VECTOR\",\"order\":15,\"text\"\ - :\" ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n\",\"id\"\ - :\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":8,\"start\":2505,\"end\":2709,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - d73154bff6b84efb8fd2f780cb325820\":{\"id\":\"d73154bff6b84efb8fd2f780cb325820\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-GraphStrategy-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > GraphStrategy\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:42.740271\"\ - ,\"modified\":\"2026-06-02T15:40:24.653084\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/GraphStrategy\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / GraphStrategy\\\ - n\\n# Interface: GraphStrategy\\n\\n## Properties\\n\\n### ~~agentic\\\\_graph\\\ - \\_only?~~\\n\\n> `optional` **agentic\\\\_graph\\\\_only**: `boolean`\\n\\\ - n#### Deprecated\\n\\nUse exclude_processor_relations\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:396](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L396)\\\ - n\\n***\\n\\n### exclude\\\\_processor\\\\_relations?\\n\\n> `optional` **exclude\\\ - \\_processor\\\\_relations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:397](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L397)\\\ - n\\n***\\n\\n### hops\\n\\n> **hops**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:391](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L391)\\\ - n\\n***\\n\\n### name\\n\\n> **name**: [`GRAPH`](../enumerations/RagStrategyName.md#graph)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:390](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L390)\\\ - n\\n***\\n\\n### query\\\\_entity\\\\_detection?\\n\\n> `optional` **query\\\ - \\_entity\\\\_detection**: `\\\"predict\\\"` \\\\| `\\\"suggest\\\"`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:400](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L400)\\\ - n\\n***\\n\\n### relation\\\\_ranking?\\n\\n> `optional` **relation\\\\_ranking**:\ - \ `\\\"generative\\\"` \\\\| `\\\"reranker\\\"`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:398](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L398)\\\ - n\\n***\\n\\n### relation\\\\_text\\\\_as\\\\_paragraphs?\\n\\n> `optional`\ - \ **relation\\\\_text\\\\_as\\\\_paragraphs**: `boolean`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:399](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L399)\\\ - n\\n***\\n\\n### top\\\\_k\\n\\n> **top\\\\_k**: `number`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:392](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L392)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"27a5993f7e7cbea2db8dfa40e8e080a9\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / GraphStrategy \\\ - n Interface: GraphStrategy \\n Properties \\n ~~agentic_graph_only?~~ \\n\ - \ \\n optional agentic_graph_only: boolean \\n \\n Deprecated \\n Use exclude_processor_relations\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:396 \\n \\n\ - \ exclude_processor_relations? \\n \\n optional exclude_processor_relations:\ - \ boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:397\ - \ \\n \\n hops \\n \\n hops: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:391\ - \ \\n \\n name \\n \\n name: GRAPH \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:390\ - \ \\n \\n query_entity_detection? \\n \\n optional query_entity_detection:\ - \ predict \\\\| suggest \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:400\ - \ \\n \\n relation_ranking? \\n \\n optional relation_ranking: generative\ - \ \\\\| reranker \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:398\ - \ \\n \\n relation_text_as_paragraphs? \\n \\n optional relation_text_as_paragraphs:\ - \ boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:399\ - \ \\n \\n top_k \\n \\n top_k: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:392\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":303,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":116,\"key\":\"\"},{\"start\":116,\"end\":303,\"key\":\"\"}]},{\"start\"\ - :303,\"end\":514,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":306,\"end\":514,\"key\"\ - :\"\"}]},{\"start\":514,\"end\":758,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":515,\"end\"\ - :621,\"key\":\"\"},{\"start\":621,\"end\":758,\"key\":\"\"}]},{\"start\":758,\"\ - end\":1031,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":761,\"end\":910,\"key\":\"\"},{\"start\":910,\"\ - end\":1031,\"key\":\"\"}]},{\"start\":1031,\"end\":1108,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1032,\"end\":1108,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"\ - processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:41:56.445021Z\"\ - ,\"last_understanding\":\"2026-06-02T15:41:55.943903Z\",\"last_extract\":\"\ - 2026-06-02T15:41:55.317924Z\",\"last_processing_start\":\"2026-06-02T15:41:55.292975Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ GraphStrategy\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > GraphStrategy\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108\"\ - :{\"score\":0.5245689153671265,\"score_type\":\"VECTOR\",\"order\":12,\"text\"\ - :\" top_k: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:392\"\ - ,\"id\":\"d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":1031,\"end\"\ - :1108,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 8b3e0ef630a346d1b591143309db87ec\":{\"id\":\"8b3e0ef630a346d1b591143309db87ec\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-NucliaTokensMetric-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > NucliaTokensMetric\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"la\",\"en\"],\"status\":\"PROCESSED\"\ - },\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:48:48.423622\"\ - ,\"modified\":\"2026-06-02T15:41:27.179307\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / NucliaTokensMetric\\\ - n\\n# Interface: NucliaTokensMetric\\n\\n## Extends\\n\\n- [`UsageMetric`](UsageMetric.md)\\\ - n\\n## Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\\ - n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L185)\\\ - n\\n***\\n\\n### name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\\ - n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L183)\\\ - n\\n***\\n\\n### value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\\ - n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L184)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"364d53482d534a8d35dafdae78bb6482\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric\ - \ \\n Interface: NucliaTokensMetric \\n Extends \\n \\n UsageMetric \\n \\\ - n Properties \\n details \\n \\n details: NucliaTokensDetails[] \\n \\n Overrides\ - \ \\n UsageMetric.details \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185\ - \ \\n \\n name \\n \\n name: nuclia_tokens \\n \\n Overrides \\n UsageMetric.name\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n value\ - \ \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:184\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"UsageMetric.name\"\ - ],\"paragraphs\":[{\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":281,\"key\":\"\"}]},{\"start\":281,\"end\":511,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":282,\"end\":511,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:38.829539Z\"\ - ,\"last_understanding\":\"2026-06-02T15:43:35.837370Z\",\"last_extract\":\"\ - 2026-06-02T15:43:35.557254Z\",\"last_processing_start\":\"2026-06-02T15:43:35.535837Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ NucliaTokensMetric\",\"extracted\":{\"text\":{\"text\":\"docs > develop\ - \ > js sdk > interfaces > NucliaTokensMetric\"}}}}},\"security\":null,\"fields\"\ - :{\"/t/page\":{\"paragraphs\":{\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\"\ - :{\"score\":0.5204078555107117,\"score_type\":\"VECTOR\",\"order\":14,\"text\"\ - :\" name: nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n value \\n \\n value:\ - \ number \\n \\n Overrides \\n UsageMetric.value \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184\"\ - ,\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/281-511\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":281,\"end\"\ - :511,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\"\ - ,\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs > rag\ - \ > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2025-10-27T14:35:08.325400\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. It must not be used in production. \\n preferMarkdown:\ - \ require the generative answer to be formatted in Markdown. \\n openNewTab:\ - \ open the link in a new tab when clicking on the result. \\n orFilterLogic:\ - \ use the OR logic for filters instead of the default AND logic. \\n noChatHistory:\ - \ the previous questions and answers in the chat mode will not be passed as\ - \ context when generating a new answer. \\n showHidden: display hidden resources\ - \ in the search results. \\n showAttachedImages: display images attached to\ - \ the matching paragraphs in the search results. \\n \\n Other parameters\ - \ \\n \\n backend: the URL of the backend to use. Useful if you use your own\ - \ proxy to access the Agentic RAG API. \\n zone: the zone to use. \\n knowledgebox:\ - \ the Knowledge Box id. \\n placeholder: the text displayed in the search\ - \ bar when it is empty. \\n lang: the language of the widget. Currently supported:\ - \ ca, fr, en, es. Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. It must use {context} and {question} variables.\ - \ \\n system_prompt: the system prompt to use for the generative model. \\\ - n rephrase_prompt: the prompt to use when optimizing the user query to get\ - \ the best search results. \\n generativemodel: the generative model to use\ - \ for the answer generation. \\n rag_strategies: the RAG strategies to apply\ - \ to the retrieved paragraphs. \\n rag_images_strategies: the RAG strategies\ - \ to apply to the retrieved images. \\n not_enough_data_message: the message\ - \ to display when there is not enough data to generate an answer. \\n ask_to_resource:\ - \ the resource ID to use as context for the generative model. \\n max_tokens:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question). \\n max_output_tokens:\ - \ the maximum number of tokens to generate. \\n max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20). \\n query_prepend: the hard-coded text to prepend to the user query.\ - \ \\n json_schema: the JSON schema to use to get a JSON answer from the generative\ - \ model. \\n vectorset: the embedding model to use for the semantic search.\ - \ \\n chat_placeholder: the placeholder to display in the chat input. \\n\ - \ audit_metadata: custom metatada to add in API calls for auditing purposes.\ - \ \\n \",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\"\ - :{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":213,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":213,\"key\":\"\"}]},{\"start\":213,\"end\":467,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":214,\"end\":402,\"key\":\"\"},{\"start\":402,\"end\":467,\"key\"\ - 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,\"Markdown\":\"ORG\",\"the Knowledge Box\":\"ORG\",\"NucliaDB\":\"ORG\",\"\ - CSS\":\"PRODUCT\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"\ - Agentic RAG\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":61,\"end\":72},{\"\ - start\":105,\"end\":116}]},{\"text\":\"Markdown\",\"label\":\"ORG\",\"positions\"\ - :[{\"start\":3046,\"end\":3054}]},{\"text\":\"the Knowledge Box\",\"label\"\ - :\"ORG\",\"positions\":[{\"start\":4031,\"end\":4048}]},{\"text\":\"NucliaDB\"\ - ,\"label\":\"ORG\",\"positions\":[{\"start\":4100,\"end\":4108}]},{\"text\"\ - :\"CSS\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":4476,\"end\":4479}]},{\"\ - text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"\ - start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2025-10-27T14:35:15.052135Z\"\ - ,\"last_understanding\":\"2025-10-27T14:35:14.504231Z\",\"last_extract\":\"\ - 2025-10-27T14:35:09.724520Z\",\"last_processing_start\":\"2025-10-27T14:35:09.621790Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"PRODUCT/Agentic RAG\"\ - :{\"position\":[{\"start\":61,\"end\":72},{\"start\":105,\"end\":116}],\"\ - entity\":\"Agentic RAG\"},\"ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"\ - end\":4108}],\"entity\":\"NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\"\ - :5610,\"end\":5614},{\"start\":5638,\"end\":5642}],\"entity\":\"JSON\"},\"\ - ORG/the Knowledge Box\":{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\"\ - :\"the Knowledge Box\"},\"PRODUCT/CSS\":{\"position\":[{\"start\":4476,\"\ - end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\":{\"position\":[{\"start\"\ - :3046,\"end\":3054}],\"entity\":\"Markdown\"}},\"relations\":[],\"mime_type\"\ - :\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\"\ - :{\"text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.6339551210403442,\"score_type\":\"VECTOR\",\"order\":0,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 012cf265c54845449d61ab04e0127e8a\":{\"id\":\"012cf265c54845449d61ab04e0127e8a\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-AuthTokens-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > AuthTokens\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:44.958530\"\ - ,\"modified\":\"2026-06-02T15:40:34.154729\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AuthTokens\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / AuthTokens\\\ - n\\n# Interface: AuthTokens\\n\\n## Properties\\n\\n### access\\\\_token\\\ - n\\n> **access\\\\_token**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/auth/auth.models.ts:2](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/auth/auth.models.ts#L2)\\\ - n\\n***\\n\\n### id\\\\_token?\\n\\n> `optional` **id\\\\_token**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/auth/auth.models.ts:4](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/auth/auth.models.ts#L4)\\\ - n\\n***\\n\\n### refresh\\\\_token\\n\\n> **refresh\\\\_token**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/auth/auth.models.ts:3](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/auth/auth.models.ts#L3)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d75b05dcecfac218e29daade9cb6587c\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / AuthTokens \\n Interface:\ - \ AuthTokens \\n Properties \\n access_token \\n \\n access_token: string\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/auth/auth.models.ts:2 \\n \\\ - n id_token? \\n \\n optional id_token: string \\n \\n Defined in \\n libs/sdk-core/src/lib/auth/auth.models.ts:4\ - \ \\n \\n refresh_token \\n \\n refresh_token: string \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/auth/auth.models.ts:3\",\"split_text\":{},\"deleted_splits\"\ - :[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"\ - end\":203,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":0,\"end\":203,\"key\":\"\"}]},{\"start\":203,\"\ - end\":398,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":206,\"end\":398,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-06-02T15:42:21.620213Z\",\"last_understanding\":\"2026-06-02T15:42:21.084919Z\"\ - ,\"last_extract\":\"2026-06-02T15:42:20.618234Z\",\"last_processing_start\"\ - :\"2026-06-02T15:42:20.600057Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > AuthTokens\",\"extracted\":{\"text\":{\"text\":\"\ - docs > develop > js sdk > interfaces > AuthTokens\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"012cf265c54845449d61ab04e0127e8a/t/page/203-398\"\ - :{\"score\":0.522169828414917,\"score_type\":\"VECTOR\",\"order\":13,\"text\"\ - :\" \\n optional id_token: string \\n \\n Defined in \\n libs/sdk-core/src/lib/auth/auth.models.ts:4\ - \ \\n \\n refresh_token \\n \\n refresh_token: string \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/auth/auth.models.ts:3\",\"id\":\"012cf265c54845449d61ab04e0127e8a/t/page/203-398\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\"\ - :203,\"end\":398,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\"\ - :\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ModelConfig\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-06-02T15:43:13.632521\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\\ - n\\n# Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\\ - n\\n> `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\\ - n\\n***\\n\\n### driver?\\n\\n> `optional` **driver**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\\ - n\\n***\\n\\n### input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:586](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L586)\\\ - n\\n***\\n\\n### max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\\ - n\\n***\\n\\n### output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\\ - n\\n#### default\\\\_max?\\n\\n> `optional` **default\\\\_max**: `number`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\\ - n\\n***\\n\\n### prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"34d1ff190433791d9bec5987606d5826\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface:\ - \ ModelConfig \\n Properties \\n assume_role? \\n \\n optional assume_role:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n driver? \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:586 \\n \\n max_images? \\n \\\ - n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587\ - \ \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"\ - key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\"\ - :480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"\ - end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\ - \"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\"\ - :806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-06-02T15:44:43.530490Z\",\"last_understanding\":\"2026-06-02T15:44:18.042002Z\"\ - ,\"last_extract\":\"2026-06-02T15:44:17.294916Z\",\"last_processing_start\"\ - :\"2026-06-02T15:44:17.270585Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - :{\"score\":0.5973739624023438,\"score_type\":\"VECTOR\",\"order\":1,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\"\ - :480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.5759848356246948,\"score_type\":\"VECTOR\",\"order\":4,\"text\"\ - :\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:586 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\"\ - ,\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs > develop\ - \ > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\"\ - ,\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2025-04-15T14:51:41.582119\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.nuclia.dev/docs/develop/js-sdk/enums/UsageType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md)\ - \ / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\\ - n## Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\ - \\_USED](UsageType.md#ai_tokens_used)\\n- [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\\ - n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- [MEDIA\\\\_FILES\\\ - \\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\ - \\_PROCESSED](UsageType.md#media_seconds_processed)\\n- [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\\ - n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- [PARAGRAPHS\\\\\ - _PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\\ - _TIME](UsageType.md#pre_processing_time)\\n- [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\\ - n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- [SLOW\\\\\ - _PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\ - \\_PERFORMED](UsageType.md#suggestions_performed)\\n- [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\\ - n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\ - \\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\\ - n\\n___\\n\\n### BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** =\ - \ ``\\\"bytes_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\\ - n\\n___\\n\\n### CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** =\ - \ ``\\\"chars_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\\ - n\\n___\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\ - \\_PROCESSED** = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\\ - n\\n___\\n\\n### MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\ - \\_PROCESSED** = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\\ - n\\n___\\n\\n### NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\ - \"nuclia_tokens_billed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\\ - n\\n___\\n\\n### PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** =\ - \ ``\\\"pages_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\\ - n\\n___\\n\\n### PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED**\ - \ = ``\\\"paragraphs_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\\ - n\\n___\\n\\n### PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\ - \\_TIME** = ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\\ - n\\n___\\n\\n### RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED**\ - \ = ``\\\"resources_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\\ - n\\n___\\n\\n### SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED**\ - \ = ``\\\"searches_performed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\\ - n\\n___\\n\\n### SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\ - \\_TIME** = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\\ - n\\n___\\n\\n### SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED**\ - \ = ``\\\"suggestions_performed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:189](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L189)\\\ - n\\n___\\n\\n### TRAIN\\\\_SECONDS\\n\\n\u2022 **TRAIN\\\\_SECONDS** = ``\\\ - \"train_seconds\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:187](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L187)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"232462bafe6a7eb30c1df7131403005e\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core / Exports / UsageType \\n Enumeration: UsageType \\\ - n Table of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED\ - \ \\n CHARS_PROCESSED \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED\ - \ \\n NUCLIA_TOKENS \\n PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME\ - \ \\n RESOURCES_PROCESSED \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME\ - \ \\n SUGGESTIONS_PERFORMED \\n TRAIN_SECONDS \\n \\n Enumeration Members\ - \ \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:190 \\n \\n BYTES_PROCESSED\ - \ \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:181\ - \ \\n \\n CHARS_PROCESSED \\n \u2022 CHARS_PROCESSED = chars_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 \\n \\n MEDIA_FILES_PROCESSED\ - \ \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED\ - \ \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\\ - n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191\ - \ \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED\ - \ \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\\ - n \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178\ - \ \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED\ - \ \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED\ - \ \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":2139,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2025-04-15T14:52:20.175640Z\"\ - ,\"last_understanding\":\"2025-04-15T14:51:54.134128Z\",\"last_extract\":\"\ - 2025-04-15T14:51:53.503631Z\",\"last_processing_start\":\"2025-04-15T14:51:53.450076Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > enums > UsageType\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > enums > UsageType\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\"\ - :{\"score\":0.5296503305435181,\"score_type\":\"VECTOR\",\"order\":9,\"text\"\ - :\"@nuclia/core / Exports / UsageType \\n Enumeration: UsageType \\n Table\ - \ of contents \\n Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED\ - \ \\n CHARS_PROCESSED \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED\ - \ \\n NUCLIA_TOKENS \\n PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME\ - \ \\n RESOURCES_PROCESSED \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME\ - \ \\n SUGGESTIONS_PERFORMED \\n TRAIN_SECONDS \\n \\n Enumeration Members\ - \ \\n AI_TOKENS_USED \\n \u2022 AI_TOKENS_USED = ai_tokens_used \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:190 \\n \\n BYTES_PROCESSED\ - \ \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:181\ - \ \\n \\n CHARS_PROCESSED \\n \u2022 CHARS_PROCESSED = chars_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 \\n \\n MEDIA_FILES_PROCESSED\ - \ \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED\ - \ \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\\ - n \u2022 NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191\ - \ \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED\ - \ \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\\ - n \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178\ - \ \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED\ - \ \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED\ - \ \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n\ - \ \u2022 TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\"\ - ,\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"\ - /k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\"\ - :2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4194605cdfbd414f8fa762630d555bc1\":{\"id\":\"4194605cdfbd414f8fa762630d555bc1\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PageStructure-md\",\"title\":\"\ - docs > develop > js sdk > interfaces > PageStructure\",\"summary\":\"\",\"\ - icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"\ - language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:01.758771\"\ - ,\"modified\":\"2026-06-02T15:41:37.990484\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageStructure\\\ - n\\n# Interface: PageStructure\\n\\n## Properties\\n\\n### page\\n\\n> **page**:\ - \ `object`\\n\\n#### height\\n\\n> **height**: `number`\\n\\n#### width\\\ - n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:238](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L238)\\\ - n\\n***\\n\\n### tokens\\n\\n> **tokens**: [`PageToken`](PageToken.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:242](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/resource/resource.models.ts#L242)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d64df098b6c544fe4fcdfd0b8d22a271\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageStructure \\\ - n Interface: PageStructure \\n Properties \\n page \\n \\n page: object \\\ - n \\n height \\n \\n height: number \\n \\n width \\n \\n width: number \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238\ - \ \\n \\n tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":358,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":358,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-06-02T15:43:47.288115Z\"\ - ,\"last_understanding\":\"2026-06-02T15:43:41.818455Z\",\"last_extract\":\"\ - 2026-06-02T15:43:41.624860Z\",\"last_processing_start\":\"2026-06-02T15:43:41.606570Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ PageStructure\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js\ - \ sdk > interfaces > PageStructure\"}}}}},\"security\":null,\"fields\":{\"\ - /t/page\":{\"paragraphs\":{\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\"\ - :{\"score\":0.5178142786026001,\"score_type\":\"VECTOR\",\"order\":16,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageStructure \\n Interface:\ - \ PageStructure \\n Properties \\n page \\n \\n page: object \\n \\n height\ - \ \\n \\n height: number \\n \\n width \\n \\n width: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 \\n \\n\ - \ tokens \\n \\n tokens: PageToken[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\"\ - ,\"id\":\"4194605cdfbd414f8fa762630d555bc1/t/page/0-358\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":358,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\"\ - ,\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs\ - \ > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\"\ - ,\"modified\":\"2026-06-02T15:38:04.014988\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\\ - n\\n# Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\ - \\_USED\\n\\n> **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n***\\n\\n### BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"\ - bytes_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n***\\n\\n### CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"\ - chars_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1e43b7ed1446e3c0e98b96653673062f734a3f2/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n***\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\\ - _PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### Defined 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- headers: - Alt-Svc: - - h3=":443"; ma=2592000 - Content-Length: - - '11274' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:57:19 GMT - via: - - 1.1 google - x-envoy-upstream-service-time: - - '161' - x-nuclia-trace-id: - - 5b8e7f062f63645fbc27ec978d104e4a - status: - code: 200 - message: OK -- request: - body: '{"question": "Explica c\u00f3mo usar el par\u00e1metro `max_tokens` en - espa\u00f1ol y proporciona un enlace a la documentaci\u00f3n oficial.", "user_id": - "arag-ask-rerank", "context": {"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386": - "@nuclia/core / Exports / StatsType \n Enumeration: StatsType \n Deprecated - \n Table of contents \n Enumeration Members \n \n AI_TOKENS_USED \n BYTES \n - CHARS \n DOCS_NO_MEDIA \n MEDIA_SECONDS \n PAGES \n PROCESSING_TIME \n RESOURCES - \n SEARCHES \n SUGGESTIONS \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED - \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 - \n \n BYTES \n \u2022 BYTES = bytes \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 - \n \n CHARS \n \u2022 CHARS = chars \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n DOCS_NO_MEDIA \n \u2022 DOCS_NO_MEDIA = docs_no_media \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:205 \n \n MEDIA_SECONDS \n \u2022 MEDIA_SECONDS - = media_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 \n - \n PAGES \n \u2022 PAGES = pages \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 - \n \n PROCESSING_TIME \n \u2022 PROCESSING_TIME = processing_time \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n RESOURCES \n \u2022 RESOURCES - = resources \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 \n \n - SEARCHES \n \u2022 SEARCHES = searches \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:197 - \n \n SUGGESTIONS \n \u2022 SUGGESTIONS = suggestions \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n TRAIN_SECONDS \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:203\n\n\nLink: https://docs.nuclia.dev/docs/develop/js-sdk/enums/StatsType\n", - "42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696": " \n optional max_paragraph: - number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:535 \n \n - name? \n \n optional name: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:534\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\n", - "03c141d8a4704db794df47287f7a9e64/t/page/15230-15632": " Example: \n ts \n const - downloadLink = `${nuclia.rest.getFullpath(filePath)}?eph-token=${nuclia.knowledgeBox.getTempToken()}`; - \n Parameters \n \u2022 payload?: any \n Optional payload to provide extra data - for the token generation \n \u2022 ignoreExpiration?: boolean = false \n Optional - By default, a temp token is valid for 5min and the same token returned if this - method is called several times during this time. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/classes/KnowledgeBox\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: The maximum - number of tokens that the model can generate as output. Again, we should keep - in mind that this value summed to the Maximum supported input tokens should - not exceed the total context size supported by the model. \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": " \n \n Maximum supported - input tokens: \n Description: The maximum number of tokens that the model can - accept as input. Be mindful that this takes into account the tokens used in - the prompt, query and context. Also take note that some models may provide their - context window as the total between input and output tokens, while others may - provide it as the input tokens only. \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804": " Example: For Phi 4 Reasoning - Plus, the total context size is 32768 tokens, as we want to leave room for the - output, we will set the maximum supported input tokens as 32768 - 1024 = 31744. - \n \n \n Maximum supported output tokens: \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/3043-3250": " Example: For Phi 4 Reasoning - Plus, the maximum output tokens is specified at 32768, but we already reserved - 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024. \n - \n \n \n Model Features: \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n", - "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / PageToken \n Interface: PageToken \n Properties \n height - \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \n \n text \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n ```python - \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, - Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My - question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709": " ::: \n :::warning \n - Enabling reasoning can use additional tokens, which may increase your usage - costs. \n You may need to increase max_tokens to give the LLM enough room to - reason and generate an answer. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108": " top_k: number \n \n Defined - in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:392\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/GraphStrategy\n", - "8b3e0ef630a346d1b591143309db87ec/t/page/281-511": " name: nuclia_tokens \n - \n Overrides \n UsageMetric.name \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:183 - \n \n value \n \n value: number \n \n Overrides \n UsageMetric.value \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:184\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/NucliaTokensMetric\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "012cf265c54845449d61ab04e0127e8a/t/page/203-398": " \n optional id_token: string - \n \n Defined in \n libs/sdk-core/src/lib/auth/auth.models.ts:4 \n \n refresh_token - \n \n refresh_token: string \n \n Defined in \n libs/sdk-core/src/lib/auth/auth.models.ts:3\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AuthTokens\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: - string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 \n \n - output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": " \n optional driver: string - \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:589 \n \n input_tokens - \n \n input_tokens: object \n \n max \n \n max: number \n \n min? \n \n optional - min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:586 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139": "@nuclia/core / Exports / - UsageType \n Enumeration: UsageType \n Table of contents \n Enumeration Members - \n \n AI_TOKENS_USED \n BYTES_PROCESSED \n CHARS_PROCESSED \n MEDIA_FILES_PROCESSED - \n MEDIA_SECONDS_PROCESSED \n NUCLIA_TOKENS \n PAGES_PROCESSED \n PARAGRAPHS_PROCESSED - \n PRE_PROCESSING_TIME \n RESOURCES_PROCESSED \n SEARCHES_PERFORMED \n SLOW_PROCESSING_TIME - \n SUGGESTIONS_PERFORMED \n TRAIN_SECONDS \n \n Enumeration Members \n AI_TOKENS_USED - \n \u2022 AI_TOKENS_USED = ai_tokens_used \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:190 - \n \n BYTES_PROCESSED \n \u2022 BYTES_PROCESSED = bytes_processed \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:181 \n \n CHARS_PROCESSED \n \u2022 - CHARS_PROCESSED = chars_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:182 - \n \n MEDIA_FILES_PROCESSED \n \u2022 MEDIA_FILES_PROCESSED = media_files_processed - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:184 \n \n MEDIA_SECONDS_PROCESSED - \n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \n Defined in \n - libs/sdk-core/src/lib/db/db.models.ts:183 \n \n NUCLIA_TOKENS \n \u2022 NUCLIA_TOKENS - = nuclia_tokens_billed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:191 - \n \n PAGES_PROCESSED \n \u2022 PAGES_PROCESSED = pages_processed \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:185 \n \n PARAGRAPHS_PROCESSED \n - \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:186 - \n \n PRE_PROCESSING_TIME \n \u2022 PRE_PROCESSING_TIME = pre_processing_time - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:178 \n \n RESOURCES_PROCESSED - \n \u2022 RESOURCES_PROCESSED = resources_processed \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:180 - \n \n SEARCHES_PERFORMED \n \u2022 SEARCHES_PERFORMED = searches_performed \n - Defined in \n libs/sdk-core/src/lib/db/db.models.ts:188 \n \n SLOW_PROCESSING_TIME - \n \u2022 SLOW_PROCESSING_TIME = slow_processing_time \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:179 - \n \n SUGGESTIONS_PERFORMED \n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:189 \n \n TRAIN_SECONDS - \n \u2022 TRAIN_SECONDS = train_seconds \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:187\n\n\nLink: - https://docs.nuclia.dev/docs/develop/js-sdk/enums/UsageType\n", "4194605cdfbd414f8fa762630d555bc1/t/page/0-358": - "@nuclia/core \u2022 Docs \n \n @nuclia/core / PageStructure \n Interface: PageStructure - \n Properties \n page \n \n page: object \n \n height \n \n height: number \n - \n width \n \n width: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:238 - \n \n tokens \n \n tokens: PageToken[] \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:242\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageStructure\n", - "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": "@nuclia/core \u2022 Docs - \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration Members - \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 - \n \n BYTES_PROCESSED \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:197 \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: - chars_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n MEDIA_FILES_PROCESSED \n \n MEDIA_FILES_PROCESSED: media_files_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n \n MEDIA_SECONDS_PROCESSED - \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 - \n \n NUCLIA_TOKENS \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:207 \n \n PAGES_PROCESSED \n \n - PAGES_PROCESSED: pages_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 - \n \n PARAGRAPHS_PROCESSED \n \n PARAGRAPHS_PROCESSED: paragraphs_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 \n \n PRE_PROCESSING_TIME - \n \n PRE_PROCESSING_TIME: pre_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 - \n \n RESOURCES_PROCESSED \n \n RESOURCES_PROCESSED: resources_processed \n - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n SEARCHES_PERFORMED - \n \n SEARCHES_PERFORMED: searches_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n SLOW_PROCESSING_TIME \n \n SLOW_PROCESSING_TIME: slow_processing_time - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 \n \n SUGGESTIONS_PERFORMED - \n \n SUGGESTIONS_PERFORMED: suggestions_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 - \n \n TRAIN_SECONDS \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\n", - "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": " \n optional max_tokens: - number \\| object \n \n Defines the maximum number of tokens that the model - will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n", - "e8525e64c5b44982b958d32cf6090613/t/page/2450-2808": " \n optional highlight: - boolean \n \n Inherited from \n BaseSearchOptions.highlight \n Defined in \n - libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? \n - \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n BaseSearchOptions.keyword_filters - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 \n \n max_tokens? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n", - "4be29234ea764ce7889f78e60fe8ec78/t/page/0-935": " \n id: models \n title: Semantic - models \n \n | Model Name | Size | Threshold | Max Tokens | Matryoshka | Multilingual - | External | \n | --------------------------- | -------- | ------------- | -------------- - | -------------- | ---------------- | ------------ | \n | en-2024-04-24 | 768 - | 0.47 | 2048 | No | No | No | \n | multilingual-2023-08-16 | 1024 | 0.7 | 512 - | No | Yes | No | \n | multilingual-2024-05-06 | 1024 | 0.4 | 2048 | No | Yes - | No | \n | Open AI small | 1536 | 0.5 | 8192 | Yes | No | No | \n | Open AI - large | 3072 | 0.5 | 8192 | Yes | No | Yes | \n | Google multilingual Gecko - | 768 | 0.55 | 3072 | Yes | Yes | Yes | \n | Hugging Face | N/A | N/A | N/A - | N/A | N/A | Yes | \n Embeddings models \n Embeddings are like fingerprints - for words or data. They help computers understand the similarities and differences - between them, making it easier for machines to perform tasks like understanding - language or recognizing patterns. \n\n\n\nLink: https://docs.nuclia.dev/docs/rag/advanced/models\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/4202-4464": " Complex queries \n The - Python SDK allows to use all the options supported by the /find and /ask endpoints, - \n but not all of the options can be passed as specific parameter. \n In these - cases, you can just pass your query as a dictionnary in the query parameter. - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/3889-4202": " \n CLI: \n \n bash \n - nuclia kb search ask --query= My question --rag_strategies=''[{ name : hierarchy - }]'' \n \n SDK: \n \n python \n from nuclia import sdk \n search = sdk.NucliaSearch() - \n search.ask(query= My question , rag_strategies=[{ name : hierarchy }]) \n - See the RAG strategies documentation for more information. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": " Use the max_tokens parameter - on the /ask endpoint to set hard limits on: \n - Context size: Limits the amount - of retrieved information sent to the LLM \n - Answer length: Limits the length - of the generated response \n Important Considerations \n Context Limitations: - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326": " \n Large context: Results - from using RAG strategies like Full resource or Neighbouring paragraphs , or - from using the extra_context parameter \n Long questions: More detailed or complex - questions require more input tokens \n Long prompts: Extensive system prompts - increase the input token count \n Detailed answers: Comprehensive responses - require more output tokens \n Images in context: When using multimodal models, - images included in the retrieved context significantly increase token consumption - \n \n How to Limit and Control Token Consumption \n Strategy 1: Optimize Your - Parameters \n The first approach to reducing token consumption is to fine-tune - your request parameters: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932": " json \n { \n item : { - \n type : consumption , \n normalized_tokens : { \n input : 13, \n output : - 34, \n image : 0.0 \n }, \n customer_key_tokens : { \n input : 0.0, \n output - : 0.0, \n image : 0.0 \n } \n } \n } \n \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/5189-5396": " - These represent the - number of Agentic RAG tokens consumed and that you will be billed for \n - Values - are normalized across different LLM providers for consistent billing \n - Include - separate counts for: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/5396-5615": " - input: Tokens used - for the prompt, context, and question \n - output: Tokens used for the generated - response \n - image: Tokens used for image processing (when applicable) \n Customer - Key Tokens (customer_key_tokens): \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189": " json \n { \n consumption - : { \n normalized_tokens : { \n input : 13, \n output : 34, \n image : 0.0 \n - }, \n customer_key_tokens : { \n input : 0.0, \n output : 0.0, \n image : 0.0 - \n } \n } \n } \n \n \n Understanding Token Types \n Normalized Tokens (normalized_tokens): - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207": " preferMarkdown: require - the generative answer to be formatted in Markdown. \n openNewTab: open the link - in a new tab when clicking on the result. \n orFilterLogic: use the OR logic - for filters instead of the default AND logic. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678": " max_paragraphs: the maximum - number of paragraphs to pass in the context to the generative model (default: - 20). \n query_prepend: the hard-coded text to prepend to the user query. \n - json_schema: the JSON schema to use to get a JSON answer from the generative - model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551": " navigateToFile: open - the file in the browser when clicking on the result (by default, the file is - displayed in the viewer). \n navigateToLink: open the link in the browser when - clicking on the result (by default, the link is displayed in the viewer). \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", "9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299": - " \n Web component: \n html \n \n The features parameter \n The features parameter - allows you to customize the behavior of the widget. It is a comma-separated - list of features among the following: \n \n filter: display a filter dropdown - in the search bar. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: - string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": " \n optional max_tokens: - number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n"}}' - headers: - Accept: - - '*/*' - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '20642' - Content-Type: - - application/json - Host: - - europe-1.nuclia.cloud - User-Agent: - - nuclia.py/4.9.25 - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.nuclia.cloud/api/v1/predict/rerank - response: - body: - string: '{"context_scores":{"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340":0.5153759717941284,"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412":0.5089483261108398,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276":0.19132767617702484,"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011":0.16424818336963654,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221":0.14116297662258148,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709":0.12347127497196198,"e8525e64c5b44982b958d32cf6090613/t/page/2450-2808":0.1200256198644638,"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977":0.09859886020421982,"43004f553e534ffe9c9e735856bd9b23/t/page/212-480":0.08525123447179794,"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575":0.08254930377006531,"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043":0.07821886986494064,"43004f553e534ffe9c9e735856bd9b23/t/page/480-703":0.038901977241039276,"89cc367c149e4f6eab0e06a743d1edba/t/page/3043-3250":0.03861093148589134,"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804":0.025130946189165115,"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139":0.024703869596123695,"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833":0.017442485317587852,"666b3a9d01f74323b6ca6c9939834bec/t/page/0-1386":0.013480102643370628,"4be29234ea764ce7889f78e60fe8ec78/t/page/0-935":0.008813166059553623,"8b3e0ef630a346d1b591143309db87ec/t/page/281-511":0.004591683857142925,"03c141d8a4704db794df47287f7a9e64/t/page/15230-15632":0.0029464049730449915,"4194605cdfbd414f8fa762630d555bc1/t/page/0-358":0.0028671587351709604,"4039d76b0fff4962900836ab3fdec9f7/t/page/5396-5615":0.0026522597763687372,"4039d76b0fff4962900836ab3fdec9f7/t/page/4728-4932":0.0024438865948468447,"012cf265c54845449d61ab04e0127e8a/t/page/203-398":0.00214894930832088,"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696":0.0021074723917990923,"4039d76b0fff4962900836ab3fdec9f7/t/page/1650-2326":0.0017007224960252643,"4039d76b0fff4962900836ab3fdec9f7/t/page/4932-5189":0.0015247863484546542,"d73154bff6b84efb8fd2f780cb325820/t/page/1031-1108":0.0012160416226834059,"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299":0.0010484692174941301,"4039d76b0fff4962900836ab3fdec9f7/t/page/5189-5396":0.0009584648651070893,"9f7036a7a6694700b72d52eb58a8326c/t/page/5412-5678":0.0006238938658498228,"9f7036a7a6694700b72d52eb58a8326c/t/page/1299-1551":0.00021995337738189846,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/4202-4464":0.00019260287808720022,"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/3889-4202":0.0001837845629779622,"9f7036a7a6694700b72d52eb58a8326c/t/page/1049-1299":0.00017400544311385602,"9f7036a7a6694700b72d52eb58a8326c/t/page/2980-3207":0.00015720345254521817}}' - headers: - Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '2620' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 03 Jun 2026 07:41:09 GMT - nuclia-learning-model: - - bge-reranker-v2-m3 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '193' - x-nuclia-trace-id: - - 3bc0326100e426c670cb6665155c47a5 - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\nExplica - c\u00f3mo usar el par\u00e1metro `max_tokens` en espa\u00f1ol y proporciona - un enlace a la documentaci\u00f3n oficial.\n\n\n\nContext:\n\n**block-AA**\n\n## - Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\n``` Use the max_tokens - parameter on the /ask endpoint to set hard limits on: \n - Context size: Limits - the amount of retrieved information sent to the LLM \n - Answer length: Limits - the length of the generated response \n Important Considerations \n Context - Limitations: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n - ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\n``` max_tokens: - the maximum number of input tokens to put in the final context (including the - prompt, the retrieved results and the user question). \n max_output_tokens: - the maximum number of tokens to generate. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n - ```\n\n\n---\"\n\n\n**block-AC**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\n``` \n - SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search - import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( - \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n - ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\n``` \n - optional max_tokens: number \\| object \n \n Defines the maximum number of tokens - that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n - ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\n``` \n - optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/2505-2709\n``` ::: - \n :::warning \n Enabling reasoning can use additional tokens, which may increase - your usage costs. \n You may need to increase max_tokens to give the LLM enough - room to reason and generate an answer. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n - ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\n``` \n - optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? - \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n - BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n - ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\n``` \n - optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\nTags: - /k/text\n``` \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:589 - \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n - \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:586 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n - ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: - /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum - number of tokens that the model can accept as input. Be mindful that this takes - into account the tokens used in the prompt, query and context. Also take note - that some models may provide their context window as the total between input - and output tokens, while others may provide it as the input tokens only. \n\n\n\nLink: - https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n ```\n\n\n---\"\n\n\n**block-AK**\n\n## - Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\nTags: /k/text\n``` Description: - The maximum number of tokens that the model can generate as output. Again, we - should keep in mind that this value summed to the Maximum supported input tokens - should not exceed the total context size supported by the model. \n\n\n\nLink: - https://docs.nuclia.dev/docs/rag/advanced/openai-api-compatible-models\n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": false, "citation_threshold": null, "generative_model": - "chatgpt-azure-4o-mini", "max_tokens": 2000, "query_context_images": {}, "prefer_markdown": - null, "json_schema": {"title": "validate_or_answer", "description": "Validate - or answer", "parameters": {"type": "object", "properties": {"reason": {"type": - "string", "description": "Reasoning for the answer or validation"}, "answer": - {"type": "string", "description": "Partial or complete answer to the user query - from the information in the context."}, "missing_info_query": {"type": "string", - "description": "Query needed to retrieve the missing information in case the - context is not enough to answer the question. 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Your role is to provide accurate, clear, and well-structured - answers based strictly on the information provided to you.\nKey principles:\n- - Answer only using the information in the provided context\n- Do not use external - knowledge, assumptions, or prior experience\n- Maintain a professional and informative - tone\n- Be concise yet thorough\n- If information is insufficient, acknowledge - this clearly\n\nAlways follow any additional instructions provided about format, - style, or domain-specific behavior.", "chat_history": [], "context": [], "query_context": - {}, "query_context_order": {}, "truncate": true, "user_prompt": {"prompt": "\n## - Question\nExplica c\u00f3mo usar el par\u00e1metro `max_tokens` en espa\u00f1ol - y proporciona un enlace a la documentaci\u00f3n oficial.\n\n## Provided Context\n[START - OF CONTEXT]\n## Retrieval on nuclia-docs Knowledge Box\n\n# Explica c\u00f3mo - usar el par\u00e1metro `max_tokens` en espa\u00f1ol y proporciona un enlace - a la documentaci\u00f3n oficial.\n\n El par\u00e1metro `max_tokens` se utiliza - en el endpoint /ask para establecer l\u00edmites en el tama\u00f1o del contexto - y en la longitud de la respuesta generada. El `max_tokens` define el n\u00famero - m\u00e1ximo de tokens de entrada que se pueden incluir en el contexto final, - incluyendo el prompt, los resultados recuperados y la pregunta del usuario. - Tambi\u00e9n se puede usar para limitar la cantidad de tokens que se generar\u00e1n - como respuesta. Para m\u00e1s informaci\u00f3n, puedes consultar la documentaci\u00f3n - oficial en el siguiente enlace: https://docs.rag.progress.cloud/docs/rag/advanced/consumption.\n[END - OF CONTEXT]\n\n## Answering Guidelines\n- Carefully read all context; it may - be lengthy or detailed\n- Do not omit or overlook any relevant information\n- - If the context is incomplete or insufficient, state: \"Not enough data to answer - this.\"\n- Read carefully any extra instructions below if provided and use them - to answer\n\nNow provide your answer to the question: Explica c\u00f3mo usar - el par\u00e1metro `max_tokens` en espa\u00f1ol y proporciona un enlace a la - documentaci\u00f3n oficial."}, "citations": null, "citation_threshold": null, - "generative_model": "chatgpt-azure-4o-mini", "max_tokens": 5000, "query_context_images": - {}, "prefer_markdown": null, "json_schema": null, "format_prompt": false, "rerank_context": - false, "tools": [], "tool_choice": {"type": "auto"}, "reasoning": false, "seed": - 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Please\ - \ reach out to your account manager for details.\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"\ - 007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n Inbound \u2014 the IP addresses that Progress Agentic RAG\ - \ receives requests on. 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This will allow the Summarize Agent to generate\ - \ a final answer that will not repeat the information already provided in\ - \ the previous conversation, and will be more natural for a chat interface.\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" Custom frontend \\n You can also create\ - \ a custom frontend for your Retrieval Agent. 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\ systems (e.g., webhooks, sync agents, or other integrations). Allowlist\ - \ these if you restrict inbound traffic to your infrastructure. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":418,\"end\":652},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n :::note \\n The current list of IP addresses\ - \ is also available in machine-readable format: JSON | YAML. These lists may\ - \ change over time, so we recommend periodically fetching and applying updates\ - \ to your firewall rules to ensure uninterrupted service. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":3,\"start\":652,\"end\":907},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n id: public-ips \\n title: Public IP Addresses\ - \ \\n \\n If your firewall restricts network traffic, you may need to allowlist\ - \ the following IP addresses. They are grouped by region and split into two\ - \ categories: \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"\ - position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":208},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" ::: \\n Europe (GCP) \\n | Inbound |\ - \ Outbound | \\n | ------------------ | --------------------- | \\n | 34.111.1.187/32\ - \ | 35.204.65.155/32 | \\n | 136.68.112.29/32 | 34.91.38.151/32 | \\n | |\ - \ 35.204.139.129/32 | \\n | | 35.204.108.221/32 | \\n Australia (AWS) \\n\ - \ | Inbound | Outbound | \\n | ------------------- | ---------------------\ - \ | \\n | 54.253.224.44/32 | 15.135.151.213/32 | \\n | 3.25.20.9/32 | 3.24.144.83/32\ - \ | \\n | 52.64.202.159/32 | 3.24.81.206/32 | \\n Europe (AWS) \\n | Inbound\ - \ | Outbound | \\n | ------------------- | --------------------- | \\n | 63.182.151.234/32\ - \ | 63.179.23.139/32 | \\n | 35.159.36.135/32 | 3.78.13.149/32 | \\n | 52.57.40.91/32\ - \ | 3.66.123.24/32 | \\n Israel (AWS) \\n | Inbound | Outbound | \\n | -------------------\ - \ | --------------------- | \\n | 51.84.176.167/32 | 51.17.150.97/32 | \\\ - n | 16.164.50.185/32 | 51.84.112.203/32 | \\n | 16.164.85.69/32 | 51.17.212.226/32\ - \ | \\n United States (AWS) \\n | Inbound | Outbound | \\n | --------------------\ - \ | -------------------- | \\n | 18.225.228.199/32 | 3.137.28.168/32 | \\\ - n | 18.119.145.185/32 | 18.225.104.91/32 | \\n | 3.21.239.244/32 | 16.59.115.61/32\ - \ | \\n Private connectivity (AWS PrivateLink) \\n For AWS-hosted regions,\ - \ it is possible to expose access privately through an AWS endpoint service\ - \ (AWS PrivateLink) instead of over the public internet, allowing connections\ - \ to be established from your VPC without traversing public IP addresses.\ - \ This option is not available through self-service configuration. Please\ - \ reach out to your account manager for details.\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":907,\"end\":2402},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"\ - 007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" \\n Inbound \u2014 the IP addresses that Progress Agentic RAG\ - \ receives requests on. Allowlist these if you restrict outbound traffic from\ - \ your infrastructure and need to reach our services (e.g., calling our APIs).\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":1,\"start\":208,\"end\":418},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\"\ - :\"a\",\"field\":\"title\",\"text\":\"docs > management > security > 5 public\ - \ ips\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - a\",\"field\":\"title\",\"text\":\"docs > agentic > how to > agentic retrieval\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":0,\"start\":0,\"end\":43},\"fuzzy_result\":false},{\"\ - score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"\ - t\",\"field\":\"page\",\"text\":\" \\n id: agentic-retrieval \\n title: Agentic\ - \ retrieval with the Smart agent \\n \\n Agentic retrieval with the Smart\ - \ agent \\n The Smart agent is a powerful tool that enhances the capabilities\ - \ of traditional retrieval-augmented generation (RAG) systems. Regular RAG's\ - \ fixed search-then-generate process is limiting for complex questions that\ - \ need intermediary steps and reasoning. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":0,\"\ - start\":0,\"end\":373},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n Different models\ - \ can be chosen for different tasks: \\n \\n Context validation happens when\ - \ the Prune context option is enabled (recommended), we recommend using a\ - \ fast model - planning or the execution model (depending on the planning\ - \ mode you have selected): we recommend using a more powerful model. \\n\"\ - ,\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"\ - page_number\":0,\"index\":5,\"start\":1640,\"end\":1943},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" \\n Note: the rephrasing model is only\ - \ used in more complex workflow, it can be ignored in the present case. \\\ - n Finally, add a Summarize agent in the Generation step to generate a final\ - \ answer from the retrieved information. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":6,\"\ - start\":1943,\"end\":2167},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" The Smart Agent plans the answer: picks the most appropriate\ - \ sources, splits the question into sub-questions, evaluates relevancy, and\ - \ iterates autonomously until the information is sufficient. \\n Basic usage\ - \ \\n To set up a Smart Agent, declare all the sources you want to use (Knowledge\ - \ Boxes, Perplexity, etc.) in the Sources section of the left menu. Then,\ - \ create a new workflow in the Workflows section and add a Smart Agent in\ - \ the Retrieval step. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":1,\"start\":373,\"end\":825},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Chat mode \\n If you\ - \ want to use the Smart Agent in a chat interface, you need the following:\ - \ \\n \\n Enable the Session history option in the Smart Agent configuration.\ - \ This will allow the Smart Agent to take into account the previous conversation\ - \ when planning its next steps. \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":7,\"start\":2167,\"\ - end\":2442},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Then connect your sources\ - \ as Registered Agents to the Smart Agent. It is very important that you provide\ - \ an extensive description of each registered agent, so that the Smart Agent\ - \ can understand what each source is about and when to use it. \\n\",\"labels\"\ - :[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\"\ - :0,\"index\":2,\"start\":825,\"end\":1068},\"fuzzy_result\":false},{\"score\"\ - :0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"\ - field\":\"page\",\"text\":\" \\n reactive: The Smart Agent will decide what\ - \ to do first, and will plan the next steps based on the information it retrieves.\ - \ It is expected to be faster. \\n plan_execute: The Smart Agent will plan\ - \ all the steps in advance, and will execute them. It will be slower but more\ - \ accurate when processing complex questions. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":4,\"\ - start\":1321,\"end\":1640},\"fuzzy_result\":false},{\"score\":0.0,\"rid\"\ - :\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"t\",\"field\":\"page\"\ - ,\"text\":\" Also select the proper function for each registered agent, depending\ - \ on the type of source (MCP agents will not need one, the functions are provided\ - \ dynamically by the MCP server). \\n In the Smart Agent configuration, you\ - \ can select the planning mode: \\n\",\"labels\":[],\"start_seconds\":null,\"\ - end_seconds\":null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1068,\"\ - end\":1321},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" In the Summarize Agent,\ - \ enable the Conversational mode. This will allow the Summarize Agent to generate\ - \ a final answer that will not repeat the information already provided in\ - \ the previous conversation, and will be more natural for a chat interface.\ - \ \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\"\ - :{\"page_number\":0,\"index\":8,\"start\":2442,\"end\":2694},\"fuzzy_result\"\ - :false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\"\ - :\"t\",\"field\":\"page\",\"text\":\" Custom frontend \\n You can also create\ - \ a custom frontend for your Retrieval Agent. This allows you to have full\ - \ control over the user interface and user experience. \\n You can directly\ - \ implement the API calls to your Retrieval Agent in your frontend code (see\ - \ the Websocket API section for more details), or you can use the JavaScript\ - \ SDK provided by Agentic to simplify the integration. \\n\",\"labels\":[],\"\ - start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"\ - index\":2,\"start\":617,\"end\":1005},\"fuzzy_result\":false},{\"score\":0.0,\"\ - rid\":\"2a0d73029ad24c599d6dc1e117a41897\",\"field_type\":\"t\",\"field\"\ - :\"page\",\"text\":\" MCP \\n If you want to integrate your Retrieval Agent\ - \ with a broader AI-based system (for example Claude Cowork or Copilot), it\ - \ is reachable throught MCP. \\n The MCP endpoint is visible on the agent\ - \ home page on the dashboard.\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":3,\"start\":1005,\"end\":1230},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" \\n id: deploy \\n title:\ - \ Deploy \\n \\n Deploy \\n Your Agentic Retrieval Agent can be deployed in\ - \ different ways, depending on your needs and the environment you are working\ - \ in. \\n Ready-to-use widget \\n The easiest way to deploy your Retrieval\ - \ Agent is to use the ready-to-use widget. You can embed it in your website\ - \ or application, and it will provide a chat user interface for interacting\ - \ with the agent. \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\"\ - :null,\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":401},\"\ - fuzzy_result\":false},{\"score\":0.0,\"rid\":\"2a0d73029ad24c599d6dc1e117a41897\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"text\":\" Go to the Widgets section\ - \ in the left menu, and click on Create widget to create a new widget. You\ - \ can customize its appearance, and then copy the generated code snippet to\ - \ embed it in your website or application. \\n\",\"labels\":[],\"start_seconds\"\ - :null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"\ - start\":401,\"end\":617},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\"\ - :5294}},\"query\":\"\",\"total\":5294,\"page_number\":0,\"page_size\":20,\"\ - next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\"\ - :0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\",\"field_type\":\"t\",\"\ - field\":\"page\",\"labels\":[]},{\"score\":0.0,\"rid\":\"007b445866574d7ca69891d388663cc6\"\ - ,\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"\ - rid\":\"b28e388104644fc5bba68c0cd44e01d4\",\"field_type\":\"a\",\"field\"\ - :\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"b28e388104644fc5bba68c0cd44e01d4\"\ - ,\"field_type\":\"t\",\"field\":\"page\",\"labels\":[]},{\"score\":0.0,\"\ - 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'*/*' @@ -7566,7 +2517,7 @@ interactions: Connection: - keep-alive Content-Length: - - '379' + - '352' Host: - europe-1.nuclia.cloud User-Agent: @@ -7579,2107 +2530,1612 @@ interactions: uri: https://europe-1.nuclia.cloud/api/v1/kb/df8b4c24-2807-4888-ad6c-ae97357a638b/find response: body: - string: "{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\"\ - ,\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\"\ - ,\"title\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-12-09T11:47:37.548412\",\"modified\":\"2026-07-14T12:48:09.327828\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../../../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../../../globals.md) / [Ask](../README.md)\ - \ / ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\\ - n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\ - \\_key\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\\ - n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\\ - n\\n***\\n\\n### type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined\ - \ in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\"\ - ,\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"\ - 2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > namespaces >\ - \ Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"\ - }}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - :{\"score\":0.5382946133613586,\"score_type\":\"VECTOR\",\"order\":17,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem\ - \ \\n Interface: ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens\ - \ \\n \\n customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\\ - n \\n type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\"\ - 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,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: features\\ntitle:\ - \ Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic RAG widgets allows\ - \ you to embed the Agentic RAG search experience directly into your website\ - \ or web application through a simple HTML snippet:\\n\\n```html\\n\\\ - n\\n\\\ - n```\\n\\nThe easiest way to explore the different features of the widgets\ - \ is to use the [Agentic RAG Dashboard](https://rag.progress.cloud/) in the\ - \ Widgets section and to play with the different options.\\n\\nThe _Embed\ - \ widget_ button will generate the HTML snippet for you.\\n\\n## Widget types\\\ - n\\nThere are 3 different types of widgets:\\n\\n- **Embedded in page**: the\ - \ search input is embedded in a page and the results are displayed under the\ - \ input. Once the initial answer is displayed, the user can click on _Ask\ - \ more_ to access the full chat interface. Note: For the correct reading of\ - \ the results, the width of the widget container should not be less than 384px.\\\ - n\\n Web components:\\n\\n ```html\\n \\\ - n \\n ```\\n\\n- **Chat mode**:\ - \ displays directly the full chat interface.\\n\\n Web component:\\n\\n \ - \ ```html\\n \\n ```\\n\\n- **Popup modal**:\ - \ the search inout and the results are displayed in a popup modal.\\n\\n \ - \ Web component:\\n\\n ```html\\n \\n ```\\\ - n\\n## The `features` parameter\\n\\nThe `features` parameter allows you to\ - \ customize the behavior of the widget. It is a comma-separated list of features\ - \ among the following:\\n\\n- `filter`: display a filter dropdown in the search\ - \ bar.\\n- `navigateToFile`: open the file in the browser when clicking on\ - \ the result (by default, the file is displayed in the viewer).\\n- `navigateToLink`:\ - \ open the link in the browser when clicking on the result (by default, the\ - \ link is displayed in the viewer).\\n- `permalink`: add the search query\ - \ and criteria to the URL, allowing the widget to re-render the same results\ - \ upon loading.\\n- `relations`: display an info card on the right side of\ - \ the widget listing all the relations of the entity mentioned in the user\ - \ query.\\n- `suggestions`: display a list of suggested resource titles matching\ - \ the user input.\\n- `suggestLabels`: display a list of suggestions based\ - \ on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar.\\n- `displayMetadata`:\ - \ display the metadata associated with the resource in the result rows.\\\ - n- `answers`: trigger the answer generation process when the user makes a\ - \ search.\\n- `hideResults`: hide the search results, only the generative\ - \ answer will be displayed.\\n- `hideThumbnails`: hide the thumbnails associated\ - \ with the resource in the result rows.\\n- `displayFieldList`: display a\ - \ section listing all the fields of the resource in the right sidebar of the\ - \ viewer. This section is only visible for resources containing multiple fields.\\\ - n- `citations`: include citations in the generative answer.\\n- `rephrase`:\ - \ rephrase the user question in order to optimize the quality of the search\ - \ results.\\n- `debug`: display extra buttons to download the last request\ - \ full log of the debug metadata returned by the API. It must not be used\ - \ in production.\\n- `preferMarkdown`: require the generative answer to be\ - \ formatted in Markdown.\\n- `openNewTab`: open the link in a new tab when\ - \ clicking on the result.\\n- `orFilterLogic`: use the OR logic for filters\ - \ instead of the default AND logic.\\n- `noChatHistory`: the previous questions\ - \ and answers in the chat mode will not be passed as context when generating\ - \ a new answer.\\n- `showHidden`: display hidden resources in the search results.\\\ - n- `showAttachedImages`: display images attached to the matching paragraphs\ - \ in the search results.\\n\\n### Other parameters\\n\\n- `backend`: the URL\ - \ of the backend to use. Useful if you use your own proxy to access the Agentic\ - \ RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: the Knowledge\ - \ Box id.\\n- `placeholder`: the text displayed in the search bar when it\ - \ is empty.\\n- `lang`: the language of the widget. Currently supported: `ca`,\ - \ `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It is\ - \ not recommended to use it in production (the API key is meant to be injected\ - \ by your proxy).\\n- `account`: the account id.\\n- `state`: the publication\ - \ state of the Knowledge Box.\\n- `standalone`: set to `true` when using a\ - \ standalone NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy\ - \ to access the Agentic RAG API.\\n- `mode`: set to `dark` to display the\ - \ widget in dark mode.\\n- `filters`: define the filters offered to the user\ - \ in the search bar among `labels`, `entities`, `created` and `labelFamilies`.\\\ - n- `preselected_filters`: define filters that will be applied by default to\ - \ any query.\\n- `csspath`: the path to the CSS file to use to customize the\ - \ widget style.\\n- `prompt`: the prompt to use for the generative model.\ - \ It must use `{context}` and `{question}` variables.\\n- `system_prompt`:\ - \ the system prompt to use for the generative model.\\n- `rephrase_prompt`:\ - \ the prompt to use when optimizing the user query to get the best search\ - \ results.\\n- `generativemodel`: the generative model to use for the answer\ - \ generation.\\n- `rag_strategies`: the RAG strategies to apply to the retrieved\ - \ paragraphs.\\n- `rag_images_strategies`: the RAG strategies to apply to\ - \ the retrieved images.\\n- `not_enough_data_message`: the message to display\ - \ when there is not enough data to generate an answer.\\n- `ask_to_resource`:\ - \ the resource ID to use as context for the generative model.\\n- `max_tokens`:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question).\\n- `max_output_tokens`:\ - \ the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - \ 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\\ - n- `json_schema`: the JSON schema to use to get a JSON answer from the generative\ - \ model.\\n- `vectorset`: the embedding model to use for the semantic search.\\\ - n- `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`:\ - \ custom metatada to add in API calls for auditing purposes.\\n\",\"format\"\ - :\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ features \\n title: Features \\n \\n Widgets features \\n The Agentic RAG\ - \ widgets allows you to embed the Agentic RAG search experience directly into\ - \ your website or web application through a simple HTML snippet: \\n ```html\ - \ \\n \\n \\n \\n ``` \\n The easiest way to explore the different features\ - \ of the widgets is to use the Agentic RAG Dashboard in the Widgets section\ - \ and to play with the different options. \\n The Embed widget button will\ - \ generate the HTML snippet for you. \\n Widget types \\n There are 3 different\ - \ types of widgets: \\n \\n Embedded in page: the search input is embedded\ - \ in a page and the results are displayed under the input. Once the initial\ - \ answer is displayed, the user can click on Ask more to access the full chat\ - \ interface. Note: For the correct reading of the results, the width of the\ - \ widget container should not be less than 384px. \\n \\n Web components:\ - \ \\n html \\n \\n \\n \\n Chat mode: displays directly the full chat interface.\ - \ \\n \\n Web component: \\n html \\n \\n \\n Popup modal: the search inout\ - \ and the results are displayed in a popup modal. \\n \\n Web component: \\\ - n html \\n \\n The features parameter \\n The features parameter allows you\ - \ to customize the behavior of the widget. It is a comma-separated list of\ - \ features among the following: \\n \\n filter: display a filter dropdown\ - \ in the search bar. \\n navigateToFile: open the file in the browser when\ - \ clicking on the result (by default, the file is displayed in the viewer).\ - \ \\n navigateToLink: open the link in the browser when clicking on the result\ - \ (by default, the link is displayed in the viewer). \\n permalink: add the\ - \ search query and criteria to the URL, allowing the widget to re-render the\ - \ same results upon loading. \\n relations: display an info card on the right\ - \ side of the widget listing all the relations of the entity mentioned in\ - \ the user query. \\n suggestions: display a list of suggested resource titles\ - \ matching the user input. \\n suggestLabels: display a list of suggestions\ - \ based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs:\ - \ display a list of suggestions based on the NERs extracted from the user\ - \ query when the user starts typing in the search bar. \\n displayMetadata:\ - \ display the metadata associated with the resource in the result rows. \\\ - n answers: trigger the answer generation process when the user makes a search.\ - \ \\n hideResults: hide the search results, only the generative answer will\ - \ be displayed. \\n hideThumbnails: hide the thumbnails associated with the\ - \ resource in the result rows. \\n displayFieldList: display a section listing\ - \ all the fields of the resource in the right sidebar of the viewer. This\ - \ section is only visible for resources containing multiple fields. \\n citations:\ - \ include citations in the generative answer. \\n rephrase: rephrase the user\ - \ question in order to optimize the quality of the search results. \\n debug:\ - \ display extra buttons to download the last request full log of the debug\ - \ metadata returned by the API. It must not be used in production. \\n preferMarkdown:\ - \ require the generative answer to be formatted in Markdown. \\n openNewTab:\ - \ open the link in a new tab when clicking on the result. \\n orFilterLogic:\ - \ use the OR logic for filters instead of the default AND logic. \\n noChatHistory:\ - \ the previous questions and answers in the chat mode will not be passed as\ - \ context when generating a new answer. \\n showHidden: display hidden resources\ - \ in the search results. \\n showAttachedImages: display images attached to\ - \ the matching paragraphs in the search results. \\n \\n Other parameters\ - \ \\n \\n backend: the URL of the backend to use. Useful if you use your own\ - \ proxy to access the Agentic RAG API. \\n zone: the zone to use. \\n knowledgebox:\ - \ the Knowledge Box id. \\n placeholder: the text displayed in the search\ - \ bar when it is empty. \\n lang: the language of the widget. Currently supported:\ - \ ca, fr, en, es. Default: en. \\n apikey: the API key to use. It is not recommended\ - \ to use it in production (the API key is meant to be injected by your proxy).\ - \ \\n account: the account id. \\n state: the publication state of the Knowledge\ - \ Box. \\n standalone: set to true when using a standalone NucliaDB instance.\ - \ \\n proxy: set to true when using a proxy to access the Agentic RAG API.\ - \ \\n mode: set to dark to display the widget in dark mode. \\n filters: define\ - \ the filters offered to the user in the search bar among labels, entities,\ - \ created and labelFamilies. \\n preselected_filters: define filters that\ - \ will be applied by default to any query. \\n csspath: the path to the CSS\ - \ file to use to customize the widget style. \\n prompt: the prompt to use\ - \ for the generative model. It must use {context} and {question} variables.\ - \ \\n system_prompt: the system prompt to use for the generative model. \\\ - n rephrase_prompt: the prompt to use when optimizing the user query to get\ - \ the best search results. \\n generativemodel: the generative model to use\ - \ for the answer generation. \\n rag_strategies: the RAG strategies to apply\ - \ to the retrieved paragraphs. \\n rag_images_strategies: the RAG strategies\ - \ to apply to the retrieved images. \\n not_enough_data_message: the message\ - \ to display when there is not enough data to generate an answer. \\n ask_to_resource:\ - \ the resource ID to use as context for the generative model. \\n max_tokens:\ - \ the maximum number of input tokens to put in the final context (including\ - \ the prompt, the retrieved results and the user question). \\n max_output_tokens:\ - \ the maximum number of tokens to generate. \\n max_paragraphs: the maximum\ - \ number of paragraphs to pass in the context to the generative model (default:\ - 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text\":\"JSON\",\"label\":\"ORG\",\"positions\":[{\"start\":5610,\"end\":5614},{\"\ - start\":5638,\"end\":5642}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:04.788053Z\"\ - ,\"last_understanding\":\"2026-06-09T08:08:04.303970Z\",\"last_extract\":\"\ - 2026-06-09T08:08:01.001532Z\",\"last_processing_start\":\"2026-06-09T08:08:00.904137Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/the Knowledge Box\"\ - :{\"position\":[{\"start\":4031,\"end\":4048}],\"entity\":\"the Knowledge\ - \ Box\"},\"PRODUCT/Agentic RAG\":{\"position\":[{\"start\":61,\"end\":72},{\"\ - start\":105,\"end\":116}],\"entity\":\"Agentic RAG\"},\"PRODUCT/CSS\":{\"\ - position\":[{\"start\":4476,\"end\":4479}],\"entity\":\"CSS\"},\"ORG/Markdown\"\ - :{\"position\":[{\"start\":3046,\"end\":3054}],\"entity\":\"Markdown\"},\"\ - ORG/NucliaDB\":{\"position\":[{\"start\":4100,\"end\":4108}],\"entity\":\"\ - NucliaDB\"},\"ORG/JSON\":{\"position\":[{\"start\":5610,\"end\":5614},{\"\ - start\":5638,\"end\":5642}],\"entity\":\"JSON\"}},\"relations\":[],\"mime_type\"\ - :\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\"\ - :{\"title\":{\"value\":\"docs > rag > advanced > widget > features\",\"extracted\"\ - :{\"text\":{\"text\":\"docs > rag > advanced > widget > features\"}}}}},\"\ - security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\"\ - :{\"score\":0.6372341513633728,\"score_type\":\"VECTOR\",\"order\":1,\"text\"\ - :\" max_tokens: the maximum number of input tokens to put in the final context\ - \ (including the prompt, the retrieved results and the user question). \\\ - n max_output_tokens: the maximum number of tokens to generate. \\n\",\"id\"\ - :\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\"\ - ,\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\\ - n\\n# Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\\ - n\\n***\\n\\n### line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\\ - n\\n***\\n\\n### text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\\ - n\\n***\\n\\n### width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\\ - n\\n***\\n\\n### x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\\ - n\\n***\\n\\n### y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface:\ - \ PageToken \\n Properties \\n height \\n \\n height: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 \\n \\n\ - \ line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250\ - \ \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248\ - \ \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246\ - \ \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"\ - processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"\ - 2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ PageToken\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\"\ - :0.5384760499000549,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\\ - n Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249\ - \ \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251\ - \ \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"220eb37c167f4eb9bb8e9454e7ba8cf5\":{\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5\"\ - ,\"slug\":\"docs-develop-python-sdk-05-search-md\",\"title\":\"docs > develop\ - \ > python sdk > 05 search\",\"summary\":\"\",\"icon\":\"text/markdown\",\"\ - thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\"\ - :[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\"\ - :[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-04-15T14:48:33.723913\",\"modified\":\"2026-06-09T08:08:04.866092\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/search\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# Search and answer generation\\\ - n\\n## Search\\n\\nNuclia supports 2 different search endpoints:\\n\\n- `search`:\ - \ returns several result sets according the different search techniques (full-text,\ - \ fuzzy, semantic).\\n- `find`: returns a single result set where all different\ - \ results are merged into a hierarchical structure.\\n\\nBoth endpoints support\ - \ the same query parameters.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search\ - \ search --query=\\\"My search\\\"\\n nuclia kb search find --query=\\\"\ - My search\\\" --filters=\\\"['/icon/application/pdf','/classification.labels/region/Asia']\\\ - \"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n search\ - \ = sdk.NucliaSearch()\\n search.search(query=\\\"My search\\\", filters=['/icon/application/pdf',\ - \ '/classification.labels/region/Asia'])\\n search.find(query=\\\"My search\\\ - \")\\n ```\\n\\nGet JSON output:\\n\\n```bash\\nnuclia kb search find --query=\\\ - \"My search\\\" --json\\n```\\n\\nGet YAML output:\\n\\n```bash\\nnuclia kb\ - \ search search --query=\\\"My search\\\" --yaml\\n```\\n\\n## Generative\ - \ answer\\n\\nBased on a `find` request, Nuclia uses a generative AI to answer\ - \ the question based on the context without hallucinations and with the find\ - \ result and relations.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from\ - \ nuclia import sdk\\n search = sdk.NucliaSearch()\\n search.ask(query=\\\ - \"My question\\\")\\n ```\\n\\n You can also use the `AskRequest` item to\ - \ configure the request with all the parameters supported:\\n\\n ```python\\\ - n from nuclia import sdk\\n from nucliadb_models.search import AskRequest\\\ - n\\n search = sdk.NucliaSearch()\\n query = AskRequest(query=\\\"My question\\\ - \", prefer_markdown=True, citations=True)\\n search.ask(query=query)\\n \ - \ ```\\n\\n### Reasoning\\n\\nSome LLMs support reasoning. In some models,\ - \ reasoning is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter.\\n\\n- SDK:\\\ - n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import AskRequest, Reasoning\\n\\n search = sdk.NucliaSearch()\\n query\ - \ = AskRequest(\\n query=\\\"My question with extra reasoning effort\\\"\ - ,\\n max_tokens=5000,\\n reasoning=Reasoning(\\n display=True,\ - \ # Show reasoning in the response\\n effort=\\\"low\\\", \ - \ # Can be \\\"low\\\", \\\"medium\\\", or \\\"high\\\"\\n budget_tokens=1024\ - \ # How many tokens reasoning can use\\n ),\\n )\\n search.ask(query=query)\\\ - n ```\\n\\nModel Support for Reasoning Options:\\n\\n* **OpenAI models**\ - \ \u2192 support `effort` only.\\n* **Google & Anthropic models** \u2192 support\ - \ `budget_tokens` only.\\n\\n:::tip\\nIf you send just one of these values\ - \ (`effort` or `budget_tokens`), Nuclia will automatically fill in the other\ - \ for you.\\n:::\\n\\n:::warning\\n* Enabling reasoning can use additional\ - \ tokens, which may increase your usage costs.\\n* You may need to increase\ - \ `max_tokens` to give the LLM enough room to reason and generate an answer.\\\ - n:::\\n\\n## Filtering\\n\\nAny endpoint that involves search (`search`, `find`\ - \ and `ask`) also support more advanced filtering expressions. Expressions\ - \ can have one of the following operators:\\n\\n- `all`: this is the default.\ - \ Will make search return results containing all specified filter labels.\\\ - n- `any`: returns results containing at least one of the labels.\\n- `none`:\ - \ returns results that do not contain any of the labels.\\n- `not_all`: returns\ - \ results that do not contain all specified labels.\\n\\nNote that multiple\ - \ expressions can be chained in the `filters` parameter and the conjunction\ - \ of all of them will be computed.\\n\\nHere are some examples:\\n\\n- CLI:\\\ - n\\n ```bash\\n nuclia kb search find --query=\\\"My search\\\" --filters=\\\ - \"[{'any':['/icon/application/pdf','/icon/image/mp4']}]\\\"\\n ```\\n\\n-\ - \ SDK:\\n\\n ```python\\n from nuclia import sdk\\n from nucliadb_models.search\ - \ import Filter\\n\\n search = sdk.NucliaSearch()\\n search.ask(\\n query=\\\ - \"My question\\\",\\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\\\ - n )\\n ```\\n\\n## Using RAG strategies\\n\\nRAG strategies can be used\ - \ to improve the quality of the answers by extending the search results passed\ - \ to the LLM as context.\\n\\n- CLI:\\n\\n ```bash\\n nuclia kb search ask\ - \ --query=\\\"My question\\\" --rag_strategies='[{\\\"name\\\":\\\"hierarchy\\\ - \"}]'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia import sdk\\n \ - \ search = sdk.NucliaSearch()\\n search.ask(query=\\\"My question\\\", rag_strategies=[{\\\ - \"name\\\": \\\"hierarchy\\\"}])\\n ```\\n\\nSee the [RAG strategies documentation](https://docs.rag.progress.cloud/docs/rag/rag-strategy)\ - \ for more information.\\n\\n## Complex queries\\n\\nThe Python SDK allows\ - \ to use all the options supported by the `/find` and `/ask` endpoints,\\\ - nbut not all of the options can be passed as specific parameter.\\nIn these\ - \ cases, you can just pass your query as a dictionnary in the `query` parameter.\\\ - n\\n- CLI:\\n\\n ```bash\\n nuclia kb search find --query='{\\\"query\\\"\ - : \\\"My search\\\", \\\"filters\\\": [\\\"/icon/application/pdf\\\", \\\"\ - /classification.labels/region/Asia\\\"]}'\\n nuclia kb search ask --query='{\\\ - \"query\\\": \\\"My search\\\",\\\"top_k\\\": 5}'\\n ```\\n\\n- SDK:\\n\\\ - n ```python\\n from nuclia import sdk\\n search = sdk.NucliaSearch()\\\ - n search.find(query={\\\"query\\\": \\\"My search\\\", \\\"filters\\\": [\\\ - \"/icon/application/pdf\\\", \\\"/classification.labels/region/Asia\\\"]})\\\ - n search.ask(query={\\\"query\\\": \\\"My search\\\",\\\"top_k\\\": 5})\\\ - n ```\\n\\n## Graph queries\\n\\nThe Python SDK allows graph queries supported\ - \ by the `/graph` endpoint. Although\\na bit cumbersome, the knowledge graph\ - \ can be queried as in this example:\\n\\n- CLI:\\n\\n ```bash\\n nuclia\ - \ kb search graph --query='{\\\"query\\\": {\\\"prop\\\": \\\"path\\\", \\\ - \"source\\\": {\\\"value\\\": \\\"Rust\\\"}, \\\"destination\\\": {\\\"value\\\ - \": \\\"Python\\\"}}}'\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia\ - \ import sdk\\n search = sdk.NucliaSearch()\\n search.graph(\\n query={\\\ - n \\\"query\\\": {\\n \\\"prop\\\": \\\"path\\\",\\\ - n \\\"source\\\": {\\\"value\\\": \\\"Rust\\\"},\\n \ - \ \\\"destination\\\": {\\\"value\\\": \\\"Python\\\"}\\n }\\\ - n }\\n )\\n ```\\n\\nFor more information about graph querying, please\ - \ refer to [Nuclia's graph\\ndoc](https://docs.rag.progress.cloud/docs/rag/advanced/graph)\ - \ or the [API reference](https://docs.rag.progress.cloud/docs/api#tag/Search/operation/graph_search_knowledgebox_kb__kbid__graph_post)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57ed2bd023399aee08015a57a948ebf2\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"Search and answer generation \\n Search \\n Nuclia supports 2 different\ - \ search endpoints: \\n \\n search: returns several result sets according\ - \ the different search techniques (full-text, fuzzy, semantic). \\n find:\ - \ returns a single result set where all different results are merged into\ - \ a hierarchical structure. \\n \\n Both endpoints support the same query\ - \ parameters. \\n \\n CLI: \\n \\n bash \\n nuclia kb search search --query=\ - \ My search \\n nuclia kb search find --query= My search --filters= ['/icon/application/pdf','/classification.labels/region/Asia']\ - \ \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.search(query= My search , filters=['/icon/application/pdf', '/classification.labels/region/Asia'])\ - \ \\n search.find(query= My search ) \\n Get JSON output: \\n bash \\n nuclia\ - \ kb search find --query= My search --json \\n Get YAML output: \\n bash \\\ - n nuclia kb search search --query= My search --yaml \\n Generative answer\ - \ \\n Based on a find request, Nuclia uses a generative AI to answer the question\ - \ based on the context without hallucinations and with the find result and\ - \ relations. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask --query= My\ - \ question \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\n search\ - \ = sdk.NucliaSearch() \\n search.ask(query= My question ) \\n You can also\ - \ use the AskRequest item to configure the request with all the parameters\ - \ supported: \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest \\n search = sdk.NucliaSearch() \\n query = AskRequest(query=\ - \ My question , prefer_markdown=True, citations=True) \\n search.ask(query=query)\ - \ \\n ``` \\n Reasoning \\n Some LLMs support reasoning. In some models, reasoning\ - \ is enabled by default, while in others it must be explicitly requested.\ - \ You can control this behavior using the reasoning parameter. \\n \\n SDK:\ - \ \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n \\n OpenAI models \u2192\ - \ support effort only. \\n Google & Anthropic models \u2192 support budget_tokens\ - \ only. \\n \\n :::tip \\n If you send just one of these values (effort or\ - \ budget_tokens), Nuclia will automatically fill in the other for you. \\\ - n ::: \\n :::warning \\n Enabling reasoning can use additional tokens, which\ - \ may increase your usage costs. \\n You may need to increase max_tokens to\ - \ give the LLM enough room to reason and generate an answer. \\n ::: \\n Filtering\ - \ \\n Any endpoint that involves search (search, find and ask) also support\ - \ more advanced filtering expressions. Expressions can have one of the following\ - \ operators: \\n \\n all: this is the default. Will make search return results\ - \ containing all specified filter labels. \\n any: returns results containing\ - \ at least one of the labels. \\n none: returns results that do not contain\ - \ any of the labels. \\n not_all: returns results that do not contain all\ - \ specified labels. \\n \\n Note that multiple expressions can be chained\ - \ in the filters parameter and the conjunction of all of them will be computed.\ - \ \\n Here are some examples: \\n \\n CLI: \\n \\n bash \\n nuclia kb search\ - \ find --query= My search --filters= [{'any':['/icon/application/pdf','/icon/image/mp4']}]\ - \ \\n \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import Filter \\n search = sdk.NucliaSearch() \\n search.ask( \\n query=\ - \ My question , \\n filters=[Filter(any=['/classification.labels/region/Europe','/classification.labels/region/Asia'])],\ - \ \\n ) \\n ``` \\n Using RAG strategies \\n RAG strategies can be used to\ - \ improve the quality of the answers by extending the search results passed\ - \ to the LLM as context. \\n \\n CLI: \\n \\n bash \\n nuclia kb search ask\ - \ --query= My question --rag_strategies='[{ name : hierarchy }]' \\n \\n SDK:\ - \ \\n \\n python \\n from nuclia import sdk \\n search = sdk.NucliaSearch()\ - \ \\n search.ask(query= My question , rag_strategies=[{ name : hierarchy }])\ - \ \\n See the RAG strategies documentation for more information. \\n Complex\ - \ queries \\n The Python SDK allows to use all the options supported by the\ - \ /find and /ask endpoints, \\n but not all of the options can be passed as\ - \ specific parameter. \\n In these cases, you can just pass your query as\ - \ a dictionnary in the query parameter. \\n \\n CLI: \\n \\n bash \\n nuclia\ - \ kb search find --query='{ query : My search , filters : [ /icon/application/pdf\ - \ , /classification.labels/region/Asia ]}' \\n nuclia kb search ask --query='{\ - \ query : My search , top_k : 5}' \\n \\n SDK: \\n \\n python \\n from nuclia\ - \ import sdk \\n search = sdk.NucliaSearch() \\n search.find(query={ query\ - \ : My search , filters : [ /icon/application/pdf , /classification.labels/region/Asia\ - \ ]}) \\n search.ask(query={ query : My search , top_k : 5}) \\n Graph queries\ - \ \\n The Python SDK allows graph queries supported by the /graph endpoint.\ - \ Although \\n a bit cumbersome, the knowledge graph can be queried as in\ - \ this example: \\n \\n CLI: \\n \\n bash \\n nuclia kb search graph --query='{\ - \ query : { prop : path , source : { value : Rust }, destination : { value\ - \ : Python }}}' \\n \\n SDK: \\n \\n python \\n from nuclia import sdk \\\ - n search = sdk.NucliaSearch() \\n search.graph( \\n query={ \\n query : {\ - \ \\n prop : path , \\n source : { value : Rust }, \\n destination : { value\ - \ : Python } \\n } \\n } \\n ) \\n For more information about graph querying,\ - \ please refer to Nuclia's graph \\n doc or the API reference\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":305,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - 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:\"OpenAI\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":2279,\"end\":2285}]},{\"\ - text\":\"Europe\",\"label\":\"LOC\",\"positions\":[{\"start\":3677,\"end\"\ - :3683}]}]}},\"classifications\":[],\"last_index\":\"2026-06-09T08:08:25.563584Z\"\ - ,\"last_understanding\":\"2026-06-09T08:08:25.117490Z\",\"last_extract\":\"\ - 2026-06-09T08:08:22.113416Z\",\"last_processing_start\":\"2026-06-09T08:08:22.055332Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{\"ORG/Nuclia\":{\"position\"\ - :[{\"start\":955,\"end\":961},{\"start\":2451,\"end\":2457},{\"start\":5501,\"\ - end\":5507}],\"entity\":\"Nuclia\"},\"ORG/JSON\":{\"position\":[{\"start\"\ - :760,\"end\":764}],\"entity\":\"JSON\"},\"LOC/Asia\":{\"position\":[{\"start\"\ - :531,\"end\":535},{\"start\":713,\"end\":717},{\"start\":3716,\"end\":3720},{\"\ - start\":4602,\"end\":4606},{\"start\":4860,\"end\":4864}],\"entity\":\"Asia\"\ - },\"PRODUCT/OpenAI\":{\"position\":[{\"start\":2279,\"end\":2285}],\"entity\"\ - :\"OpenAI\"},\"LOC/Europe\":{\"position\":[{\"start\":3677,\"end\":3683}],\"\ - entity\":\"Europe\"}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"\ - split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"\ - value\":\"docs > develop > python sdk > 05 search\",\"extracted\":{\"text\"\ - :{\"text\":\"docs > develop > python sdk > 05 search\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - :{\"score\":0.5409069061279297,\"score_type\":\"VECTOR\",\"order\":15,\"text\"\ - :\" \\n SDK: \\n \\n ```python \\n from nuclia import sdk \\n from nucliadb_models.search\ - \ import AskRequest, Reasoning \\n search = sdk.NucliaSearch() \\n query =\ - \ AskRequest( \\n query= My question with extra reasoning effort , \\n max_tokens=5000,\ - \ \\n reasoning=Reasoning( \\n display=True, # Show reasoning in the response\ - \ \\n effort= low , # Can be low , medium , or high \\n budget_tokens=1024\ - \ # How many tokens reasoning can use \\n ), \\n ) \\n search.ask(query=query)\ - \ \\n ``` \\n Model Support for Reasoning Options: \\n\",\"id\":\"220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\"\ - :1790,\"end\":2276,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"c27a1e5f5ddb4b118921345d713401b8\":{\"id\"\ - :\"c27a1e5f5ddb4b118921345d713401b8\",\"slug\":\"docs-develop-js-sdk-interfaces-ConversationalStrategy-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > ConversationalStrategy\"\ - ,\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-04-15T14:50:50.062716\",\"modified\":\"2026-07-14T12:51:44.618051\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ConversationalStrategy\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ConversationalStrategy\\\ - n\\n# Interface: ConversationalStrategy\\n\\n## Properties\\n\\n### attachments\\\ - \\_images\\n\\n> **attachments\\\\_images**: `boolean`\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:385](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L385)\\\ - n\\n***\\n\\n### attachments\\\\_text\\n\\n> **attachments\\\\_text**: `boolean`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:384](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L384)\\\ - n\\n***\\n\\n### full\\n\\n> **full**: `boolean`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:386](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L386)\\\ - n\\n***\\n\\n### max\\\\_messages?\\n\\n> `optional` **max\\\\_messages**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:387](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L387)\\\ - n\\n***\\n\\n### name\\n\\n> **name**: [`CONVERSATION`](../enumerations/RagStrategyName.md#conversation)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:383](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L383)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"57a22a6451d1dd8d7ef574dfc6cae218\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ConversationalStrategy\ - \ \\n Interface: ConversationalStrategy \\n Properties \\n attachments_images\ - \ \\n \\n attachments_images: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:385\ - \ \\n \\n attachments_text \\n \\n attachments_text: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:384 \\n \\n full \\n \\\ - n full: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:386\ - \ \\n \\n max_messages? \\n \\n optional max_messages: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:387 \\n \\n name \\n \\\ - n name: CONVERSATION \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:383\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":351,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":351,\"key\":\"\"}]},{\"start\":351,\"end\":554,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":352,\"end\":450,\"key\":\"\"},{\"start\":450,\"end\":554,\"key\":\"\ - \"}]},{\"start\":554,\"end\":636,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":555,\"end\"\ - :636,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:46.119337Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:45.741783Z\",\"last_extract\":\"\ - 2026-07-14T12:51:45.425207Z\",\"last_processing_start\":\"2026-07-14T12:51:45.404045Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ ConversationalStrategy\",\"extracted\":{\"text\":{\"text\":\"docs > develop\ - \ > js sdk > interfaces > ConversationalStrategy\"}}}}},\"security\":null,\"\ - fields\":{\"/t/page\":{\"paragraphs\":{\"c27a1e5f5ddb4b118921345d713401b8/t/page/351-554\"\ - :{\"score\":0.5450109839439392,\"score_type\":\"VECTOR\",\"order\":12,\"text\"\ - :\" full: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:386\ - \ \\n \\n max_messages? \\n \\n optional max_messages: number \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:387 \\n \\n name \\n \\\ - n\",\"id\":\"c27a1e5f5ddb4b118921345d713401b8/t/page/351-554\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":351,\"\ - end\":554,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\ - \",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\"\ - ,\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\\ - n\\n# Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\\ - n\\n> `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\\ - n\\n***\\n\\n### effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\\ - n Interface: ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional\ - \ budget_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n \\n optional effort: NumericReasoningEffort \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:648\",\"split_text\"\ - :{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":118,\"\ - key\":\"\"},{\"start\":118,\"end\":224,\"key\":\"\"}]},{\"start\":224,\"end\"\ - :329,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":227,\"end\":329,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:49:31.596307Z\",\"last_understanding\":\"2026-07-14T12:49:31.194629Z\"\ - ,\"last_extract\":\"2026-07-14T12:49:31.030734Z\",\"last_processing_start\"\ - :\"2026-07-14T12:49:30.980505Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - :{\"score\":0.5525065660476685,\"score_type\":\"VECTOR\",\"order\":10,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface:\ - \ ReasoningConfig \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647\ - \ \\n \\n effort? \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\"\ - ,\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs\ - \ > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\"\ - ,\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\\ - n\\n# Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\ - \\_USED\\n\\n> **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\\ - n\\n***\\n\\n### BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"\ - bytes_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\\ - n\\n***\\n\\n### CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"\ - chars_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\\ - n\\n***\\n\\n### MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\\ - _PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\\ - n\\n***\\n\\n### MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\ - \\_PROCESSED**: `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\\ - n\\n***\\n\\n### NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\\ - n\\n***\\n\\n### PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"\ - pages_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\\ - n\\n***\\n\\n### PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**:\ - \ `\\\"paragraphs_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\\ - n\\n***\\n\\n### PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\ - \\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\\ - n\\n***\\n\\n### RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**:\ - \ `\\\"resources_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\\ - n\\n***\\n\\n### SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**:\ - \ `\\\"searches_performed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\\ - n\\n***\\n\\n### SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\ - \\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\\ - n\\n***\\n\\n### SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**:\ - \ `\\\"suggestions_performed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\\ - n\\n***\\n\\n### TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\ - \"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration:\ - \ UsageType \\n Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED:\ - \ ai_tokens_used \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206\ - \ \\n \\n BYTES_PROCESSED \\n \\n BYTES_PROCESSED: bytes_processed \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 \\n \\n CHARS_PROCESSED\ - \ \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n MEDIA_FILES_PROCESSED \\n \\n MEDIA_FILES_PROCESSED: media_files_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\\ - n MEDIA_SECONDS_PROCESSED \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\\ - n NUCLIA_TOKENS \\n \\n NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:207 \\n \\n PAGES_PROCESSED\ - \ \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n PARAGRAPHS_PROCESSED \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\\ - n PRE_PROCESSING_TIME \\n \\n PRE_PROCESSING_TIME: pre_processing_time \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 \\n \\n RESOURCES_PROCESSED\ - \ \\n \\n RESOURCES_PROCESSED: resources_processed \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED \\\ - n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\\ - n SUGGESTIONS_PERFORMED \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\\ - n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: train_seconds \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\"\ - :[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"\ - end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\"\ - ,\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\"\ - :\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > enumerations > UsageType\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\"\ - :{\"score\":0.5411338210105896,\"score_type\":\"VECTOR\",\"order\":14,\"text\"\ - :\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration:\ - \ UsageType \\n Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED:\ - \ ai_tokens_used \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206\ - \ \\n \\n BYTES_PROCESSED \\n \\n BYTES_PROCESSED: bytes_processed \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 \\n \\n CHARS_PROCESSED\ - \ \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:198\ - \ \\n \\n MEDIA_FILES_PROCESSED \\n \\n MEDIA_FILES_PROCESSED: media_files_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\\ - n MEDIA_SECONDS_PROCESSED \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\\ - n NUCLIA_TOKENS \\n \\n NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/db.models.ts:207 \\n \\n PAGES_PROCESSED\ - \ \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:201\ - \ \\n \\n PARAGRAPHS_PROCESSED \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\\ - n PRE_PROCESSING_TIME \\n \\n PRE_PROCESSING_TIME: pre_processing_time \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 \\n \\n RESOURCES_PROCESSED\ - \ \\n \\n RESOURCES_PROCESSED: resources_processed \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED \\\ - n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204\ - \ \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\\ - n SUGGESTIONS_PERFORMED \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\\ - n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: train_seconds \\n \\n Defined in \\\ - n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\"\ - :\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > Consumption\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\"\ - ,\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\\ - n\\n# Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\ - \\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\\ - n\\n***\\n\\n### normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface:\ - \ Consumption \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens:\ - \ TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\"\ - :[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\"\ - ,\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"\ - 2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ Consumption\",\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk\ - \ > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\"\ - :0.5509729981422424,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\"@nuclia/core\ - \ \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption\ - \ \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230\ - \ \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"\ - id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\"\ - :[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"\ - reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\"\ - ,\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\\ - n\\n# Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\\ - n\\n> `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\\ - n\\n***\\n\\n### driver?\\n\\n> `optional` **driver**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\\ - n\\n***\\n\\n### input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\\ - n\\n***\\n\\n### max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\\ - n\\n***\\n\\n### output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\\ - n\\n#### default\\\\_max?\\n\\n> `optional` **default\\\\_max**: `number`\\\ - n\\n#### max\\n\\n> **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\\ - n\\n***\\n\\n### prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface:\ - \ ModelConfig \\n Properties \\n assume_role? \\n \\n optional assume_role:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592\ - \ \\n \\n driver? \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n \\\ - n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588\ - \ \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\"\ - :[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"\ - key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\"\ - :480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"\ - end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\ - \"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\"\ - :806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"\ - entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\"\ - :\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\"\ - ,\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\"\ - :\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\"\ - :{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - :{\"score\":0.5994514226913452,\"score_type\":\"VECTOR\",\"order\":3,\"text\"\ - :\" \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591\ - \ \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max?\ - \ \\n \\n optional default_max: number \\n \\n max \\n \\n max: number \\\ - n \\n min? \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\"\ - :480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\"\ - :{\"score\":0.5779294371604919,\"score_type\":\"VECTOR\",\"order\":8,\"text\"\ - :\" \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590\ - \ \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max:\ - \ number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n\ - \ libs/sdk-core/src/lib/db/kb/kb.models.ts:587 \\n \\n max_images? \\n\",\"\ - id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"\ - ],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"\ - start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\"\ - :false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"\ - 4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\"\ - ,\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs > rag > advanced\ - \ > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\"\ - :\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"\ - en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"\ - relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\"\ - :[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: consumption\\\ - ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\"\ - ;\\nimport TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\\ - n\\nAgentic RAG is a license and consumption-based service. This means that\ - \ you pay for the computational resources you consume. The consumption is\ - \ measured in **Agentic RAG tokens**.\\nAll public 3rd-party LLMs base their\ - \ pricing on the number of tokens consumed. In the LLM world, a token is around\ - \ 4-5 characters on average, which might fit an entire word or be split into\ - \ parts. The number of tokens is proportional to the amount of text, measured\ - \ in chunks of 4-5 characters. It closely relates to words but not entirely.\ - \ The longer a sentence is, the more tokens it will consume to read or to\ - \ generate it.\\nSince all these 3rd-party LLMs have different pricing, Agentic\ - \ RAG tokens serve to normalize the cost across all of them.\\n\\n## How Tokens\ - \ Are Consumed in RAG\\n\\nWhen a user asks a question to your Knowledge Box,\ - \ the token consumption process follows these steps:\\n\\n1. **Question Processing**:\ - \ The system finds the most relevant paragraphs to answer the question\\n2.\ - \ **Context Assembly**: These paragraphs are used as context when calling\ - \ the LLM model\\n3. **Prompt Creation**: Agentic RAG assembles the **prompt**,\ - \ **context**, and **question** into a single string\\n4. **LLM Processing**:\ - \ This complete string is sent to the LLM, corresponding to a certain number\ - \ of **input tokens**\\n5. **Answer Generation**: The LLM generates the answer,\ - \ which corresponds to a certain number of **output tokens**\\n\\n**Total\ - \ consumption** = Input tokens + Output tokens + Image tokens\\n\\n### Factors\ - \ That Impact Token Consumption\\n\\nToken consumption is directly affected\ - \ by:\\n\\n- **Large context**: Results from using RAG strategies like \\\"\ - Full resource\\\" or \\\"Neighbouring paragraphs\\\", or from using the `extra_context`\ - \ parameter\\n- **Long questions**: More detailed or complex questions require\ - \ more input tokens\\n- **Long prompts**: Extensive system prompts increase\ - \ the input token count\\n- **Detailed answers**: Comprehensive responses\ - \ require more output tokens\\n- **Images in context**: When using multimodal\ - \ models, images included in the retrieved context significantly increase\ - \ token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\\ - n### Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing\ - \ token consumption is to fine-tune your request parameters:\\n\\n- **Optimize\ - \ prompts**: Ensure your prompts are concise and focused, avoiding unnecessary\ - \ verbosity\\n- **Control resource scope**: When using the \\\"Full resource\\\ - \" strategy, use the `count` attribute to limit the number of resources returned\\\ - n- **Tune neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy,\ - \ optimize the `before` and `after` attributes to balance context quality\ - \ with token efficiency\\n- **Manage summary length**: When using the \\\"\ - Hierarchical\\\" strategy, ensure that resource summaries are appropriately\ - \ sized\\n- **Choose efficient models**: Select LLMs that offer better token\ - \ efficiency (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT\ - \ 4o)\\n\\n### Strategy 2: Set Hard Limits\\n\\nYou can implement safeguards\ - \ against excessive token consumption:\\n\\nUse the `max_tokens` parameter\ - \ on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post)\ - \ to set hard limits on:\\n- **Context size**: Limits the amount of retrieved\ - \ information sent to the LLM\\n- **Answer length**: Limits the length of\ - \ the generated response\\n\\n#### Important Considerations\\n\\n**Context\ - \ Limitations**:\\n- Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with\\n- Balance between\ - \ cost control and answer quality\\n\\n**Answer Length Limitations**:\\n-\ - \ The LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way\\n- **Recommended approach**: Include length requirements\ - \ in your prompt (e.g., \\\"Please answer in less than 200 words\\\") rather\ - \ than relying solely on hard limits\\n- This allows the LLM to naturally\ - \ conclude its response within the desired length\\n\\n## How to Monitor Token\ - \ Consumption\\n\\n### Understanding Token Consumption Data\\n\\nYou can receive\ - \ detailed token consumption information from the following endpoints that\ - \ utilize LLM models: `ask`, `chat`, `remi`, `query`, `sentence`, `summarize`,\ - \ `tokens`, and `rerank`.\\n\\n:::note\\nThe `rephrase` endpoint currently\ - \ does not support token consumption monitoring.\\n:::\\n\\nTo receive token\ - \ consumption data, you must include the following header in your request:\\\ - n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption data is provided\ - \ in different formats depending on the response type:\\n- **Streaming responses**\ - \ (`application/x-ndjson`): Token consumption appears as a separate JSON chunk\ - \ with type \\\"consumption\\\"\\n- **Standard responses** (`application/json`):\ - \ Token consumption is included in a \\\"consumption\\\" field within the\ - \ main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": {\\\ - n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\ - \": {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \ - \ \\\"image\\\": 0.0\\n },\\n \\\"customer_key_tokens\\\ - \": {\\n \\\"input\\\": 0.0,\\n \\\"output\\\": 0.0,\\n\ - \ \\\"image\\\": 0.0\\n }\\n }\\n }\\n ```\\n \ - \ \\n\\n\\n### Understanding Token Types\\n\\n**Normalized\ - \ Tokens** (`normalized_tokens`):\\n- These represent the number of Agentic\ - \ RAG tokens consumed and that you will be billed for\\n- Values are normalized\ - \ across different LLM providers for consistent billing\\n- Include separate\ - \ counts for:\\n - `input`: Tokens used for the prompt, context, and question\\\ - n - `output`: Tokens used for the generated response\\n - `image`: Tokens\ - \ used for image processing (when applicable)\\n\\n**Customer Key Tokens**\ - \ (`customer_key_tokens`):\\n- These represent tokens consumed when using\ - \ your own LLM API keys\\n- These tokens are **not billed** by Agentic RAG\ - \ since you're using your own API keys\\n- Values are also normalized for\ - \ comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\"\ - ,\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"\ - split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id: consumption\ - \ \\n title: Token consumption \\n \\n import Tabs from @theme/Tabs ; \\n\ - \ import TabItem from @theme/TabItem ; \\n Token consumption \\n Agentic RAG\ - \ is a license and consumption-based service. This means that you pay for\ - \ the computational resources you consume. The consumption is measured in\ - \ Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on\ - \ the number of tokens consumed. In the LLM world, a token is around 4-5 characters\ - \ on average, which might fit an entire word or be split into parts. The number\ - \ of tokens is proportional to the amount of text, measured in chunks of 4-5\ - \ characters. It closely relates to words but not entirely. The longer a sentence\ - \ is, the more tokens it will consume to read or to generate it. \\n Since\ - \ all these 3rd-party LLMs have different pricing, Agentic RAG tokens serve\ - \ to normalize the cost across all of them. \\n How Tokens Are Consumed in\ - \ RAG \\n When a user asks a question to your Knowledge Box, the token consumption\ - \ process follows these steps: \\n \\n Question Processing: The system finds\ - \ the most relevant paragraphs to answer the question \\n Context Assembly:\ - \ These paragraphs are used as context when calling the LLM model \\n Prompt\ - \ Creation: Agentic RAG assembles the prompt, context, and question into a\ - \ single string \\n LLM Processing: This complete string is sent to the LLM,\ - \ corresponding to a certain number of input tokens \\n Answer Generation:\ - \ The LLM generates the answer, which corresponds to a certain number of output\ - \ tokens \\n \\n Total consumption = Input tokens + Output tokens + Image\ - \ tokens \\n Factors That Impact Token Consumption \\n Token consumption is\ - \ directly affected by: \\n \\n Large context: Results from using RAG strategies\ - \ like Full resource or Neighbouring paragraphs , or from using the extra_context\ - \ parameter \\n Long questions: More detailed or complex questions require\ - \ more input tokens \\n Long prompts: Extensive system prompts increase the\ - \ input token count \\n Detailed answers: Comprehensive responses require\ - \ more output tokens \\n Images in context: When using multimodal models,\ - \ images included in the retrieved context significantly increase token consumption\ - \ \\n \\n How to Limit and Control Token Consumption \\n Strategy 1: Optimize\ - \ Your Parameters \\n The first approach to reducing token consumption is\ - \ to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure your\ - \ prompts are concise and focused, avoiding unnecessary verbosity \\n Control\ - \ resource scope: When using the Full resource strategy, use the count attribute\ - \ to limit the number of resources returned \\n Tune neighboring context:\ - \ For the Neighbouring paragraphs strategy, optimize the before and after\ - \ attributes to balance context quality with token efficiency \\n Manage summary\ - \ length: When using the Hierarchical strategy, ensure that resource summaries\ - \ are appropriately sized \\n Choose efficient models: Select LLMs that offer\ - \ better token efficiency (typically, ChatGPT 4o-mini is more cost-effective\ - \ than ChatGPT 4o) \\n \\n Strategy 2: Set Hard Limits \\n You can implement\ - \ safeguards against excessive token consumption: \\n Use the max_tokens parameter\ - \ on the /ask endpoint to set hard limits on: \\n - Context size: Limits the\ - \ amount of retrieved information sent to the LLM \\n - Answer length: Limits\ - \ the length of the generated response \\n Important Considerations \\n Context\ - \ Limitations: \\n - Restricting context size may result in less relevant\ - \ answers since the LLM has less information to work with \\n - Balance between\ - \ cost control and answer quality \\n Answer Length Limitations: \\n - The\ - \ LLM might not complete its response if it hits the token limit, potentially\ - \ cutting sentences mid-way \\n - Recommended approach: Include length requirements\ - \ in your prompt (e.g., Please answer in less than 200 words ) rather than\ - \ relying solely on hard limits \\n - This allows the LLM to naturally conclude\ - \ its response within the desired length \\n How to Monitor Token Consumption\ - \ \\n Understanding Token Consumption Data \\n You can receive detailed token\ - \ consumption information from the following endpoints that utilize LLM models:\ - \ ask, chat, remi, query, sentence, summarize, tokens, and rerank. \\n :::note\ - \ \\n The rephrase endpoint currently does not support token consumption monitoring.\ - \ \\n ::: \\n To receive token consumption data, you must include the following\ - \ header in your request: \\n X-SHOW-CONSUMPTION: true \\n The token consumption\ - \ data is provided in different formats depending on the response type: \\\ - n - Streaming responses (application/x-ndjson): Token consumption appears\ - \ as a separate JSON chunk with type consumption \\n - Standard responses\ - \ (application/json): Token consumption is included in a consumption field\ - \ within the main response \\n Token Consumption Response Format \\n \\n \\\ - n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens :\ - \ { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\\ - n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens\ - \ : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n }\ - \ \\n \\n \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens):\ - \ \\n - These represent the number of Agentic RAG tokens consumed and that\ - \ you will be billed for \\n - Values are normalized across different LLM\ - \ providers for consistent billing \\n - Include separate counts for: \\n\ - \ - input: Tokens used for the prompt, context, and question \\n - output:\ - \ Tokens used for the generated response \\n - image: Tokens used for image\ - \ processing (when applicable) \\n Customer Key Tokens (customer_key_tokens):\ - \ \\n - These represent tokens consumed when using your own LLM API keys \\\ - n - These tokens are not billed by Agentic RAG since you're using your own\ - \ API keys \\n - Values are also normalized for comparison purposes across\ - \ different providers\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\"\ - :{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":319,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3,\"end\":198,\"key\":\"\"},{\"start\":198,\"end\":267,\"key\"\ - :\"\"},{\"start\":267,\"end\":319,\"key\":\"\"}]},{\"start\":319,\"end\":747,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - 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:\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\"\ - ,\"title\":\"docs > rag > advanced > openai api compatible models\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\"\ - ,\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: openai-api-compatible-models\\\ - ntitle: Connect to any OpenAI API compatible LLM\\n---\\n\\n# Connect to any\ - \ OpenAI API compatible LLM\\n\\nAgentic RAG allows you to connect to any\ - \ OpenAI API compatible LLM. This means that you can use any LLM that has\ - \ an API compatible with the OpenAI API which has become a standard in the\ - \ industry.\\n\\nMany of the options for self-hosted LLMs, open-source LLMs\ - \ hosted by cloud providers or commercial LLMs are compatible with the OpenAI\ - \ API. This means that you can use them with Agentic RAG without any modifications.\\\ - n\\n## Configuration\\n\\nTo modify your knowledge box configuration you can\ - \ do so in three manners, through the API, the Nuclia CLI / SDK or the Agentic\ - \ RAG dashboard.\\n\\nThe Agentic RAG dashboard offers the most user-friendly\ - \ way to modify the configuration of your knowledge box and we will use it\ - \ in this example.\\n\\nWe will be setting up a connection to the **Phi 4\ - \ Reasoning Plus** model, hosted by **OpenRouter** which offers a wide range\ - \ of open-source and commercial models compatible with the OpenAI API. We\ - \ can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free),\ - \ the API parameters are located under the **API** tab.\\n\\n1. **Open the\ - \ AI Models page** \\n In the left sidebar under **Advanced**, click **AI\ - \ Models**.\\n2. **Select \u201COpenAI API Compatible Model\u201D** \\n \ - \ From the models list, choose **OpenAI API Compatible Model**.\\n3. **Enable\ - \ custom Key** \\n Toggle the option for using you own `OpenAI API Compatible\ - \ Key` if it is not already enabled.\\n4. **Fill in the configuration parameters**\\\ - n\\n - **API Key**:\\n - Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key.\\n - Example: We will set this to our OpenRouter API key.\\\ - n - **API URL**:\\n - Description: The URL of the API endpoint for your\ - \ LLM. This may be shared between multiple models.\\n - Example: For OpenRouter,\ - \ it is the same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\\ - n - Description: The name of the model you want to use, it needs to exactly\ - \ match the name of the model in the API.\\n - Example: For Phi 4 Reasoning\ - \ Plus in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\\ - n - **Maximum supported input tokens**:\\n - Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only.\\n - Example: For Phi 4 Reasoning Plus, the total context\ - \ size is `32768` tokens, as we want to leave room for the output, we will\ - \ set the maximum supported input tokens as `32768 - 1024 = 31744`.\\n -\ - \ **Maximum supported output tokens**:\\n - Description: The maximum number\ - \ of tokens that the model can generate as output. Again, we should keep in\ - \ mind that this value summed to the **Maximum supported input tokens** should\ - \ not exceed the total context size supported by the model.\\n - Example:\ - \ For Phi 4 Reasoning Plus, the maximum output tokens is specified at `32768`,\ - \ but we already reserved `31744` for the input tokens, so we will set this\ - \ to `32768 - 31744 = 1024`.\\n - **Model Features**:\\n - Description:\ - \ Under this section you will find multiple toggles related to features supported\ - \ by the model, these vary from model to model, but most often the default\ - \ values are well suited to most use cases. The most relevant toggle is for\ - \ `Image Support` which allows you to use images as input for the model.\\\ - n - Example: Image input is not supported by Phi 4 Reasoning Plus, so\ - \ we will leave it disabled.\\n\\n5. **Save** \\n Click **Save changes**.\\\ - n\\n6. **Test your model** \\n Run a sample query in Agentic RAG or via\ - \ API/CLI. Adjust your prompt templates and token settings as needed.\\n\"\ - ,\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\"\ - :\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" \\n id:\ - \ openai-api-compatible-models \\n title: Connect to any OpenAI API compatible\ - \ LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic RAG allows\ - \ you to connect to any OpenAI API compatible LLM. This means that you can\ - \ use any LLM that has an API compatible with the OpenAI API which has become\ - \ a standard in the industry. \\n Many of the options for self-hosted LLMs,\ - \ open-source LLMs hosted by cloud providers or commercial LLMs are compatible\ - \ with the OpenAI API. This means that you can use them with Agentic RAG without\ - \ any modifications. \\n Configuration \\n To modify your knowledge box configuration\ - \ you can do so in three manners, through the API, the Nuclia CLI / SDK or\ - \ the Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most\ - \ user-friendly way to modify the configuration of your knowledge box and\ - \ we will use it in this example. \\n We will be setting up a connection to\ - \ the Phi 4 Reasoning Plus model, hosted by OpenRouter which offers a wide\ - \ range of open-source and commercial models compatible with the OpenAI API.\ - \ We can see more information about this specific model here, the API parameters\ - \ are located under the API tab. \\n \\n Open the AI Models page \\n In the\ - \ left sidebar under Advanced, click AI Models. \\n Select OpenAI API Compatible\ - \ Model \\n From the models list, choose OpenAI API Compatible Model. \\n\ - \ Enable custom Key \\n Toggle the option for using you own OpenAI API Compatible\ - \ Key if it is not already enabled. \\n \\n Fill in the configuration parameters\ - \ \\n \\n \\n API Key: \\n \\n Description: The API key for your LLM. This\ - \ is the key that you would use as an authorization header in the API. You\ - \ may leave this blank if the endpoint you are connecting to does not require\ - \ an API key. \\n Example: We will set this to our OpenRouter API key. \\\ - n \\n \\n API URL: \\n Description: The URL of the API endpoint for your LLM.\ - \ This may be shared between multiple models. \\n Example: For OpenRouter,\ - \ it is the same for all models: https://openrouter.ai/api/v1 \\n \\n \\n\ - \ Model: \\n Description: The name of the model you want to use, it needs\ - \ to exactly match the name of the model in the API. \\n Example: For Phi\ - \ 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free.\ - \ \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum\ - \ number of tokens that the model can accept as input. Be mindful that this\ - \ takes into account the tokens used in the prompt, query and context. Also\ - \ take note that some models may provide their context window as the total\ - \ between input and output tokens, while others may provide it as the input\ - \ tokens only. \\n Example: For Phi 4 Reasoning Plus, the total context size\ - \ is 32768 tokens, as we want to leave room for the output, we will set the\ - \ maximum supported input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum\ - \ supported output tokens: \\n Description: The maximum number of tokens that\ - \ the model can generate as output. Again, we should keep in mind that this\ - \ value summed to the Maximum supported input tokens should not exceed the\ - \ total context size supported by the model. \\n Example: For Phi 4 Reasoning\ - \ Plus, the maximum output tokens is specified at 32768, but we already reserved\ - \ 31744 for the input tokens, so we will set this to 32768 - 31744 = 1024.\ - \ \\n \\n \\n \\n Model Features: \\n \\n Description: Under this section\ - \ you will find multiple toggles related to features supported by the model,\ - \ these vary from model to model, but most often the default values are well\ - \ suited to most use cases. The most relevant toggle is for Image Support\ - \ which allows you to use images as input for the model. \\n Example: Image\ - \ input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled.\ - \ \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model\ - \ \\n Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt\ - \ templates and token settings as needed. \\n \\n \",\"split_text\":{},\"\ - deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[\"https://openrouter.ai/api/v1\"\ - ],\"paragraphs\":[{\"start\":0,\"end\":330,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3,\"\ - end\":201,\"key\":\"\"},{\"start\":201,\"end\":330,\"key\":\"\"}]},{\"start\"\ - :330,\"end\":549,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":331,\"end\":471,\"key\"\ - :\"\"},{\"start\":471,\"end\":549,\"key\":\"\"}]},{\"start\":549,\"end\":858,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":550,\"end\":711,\"key\":\"\"},{\"start\":711,\"\ - end\":858,\"key\":\"\"}]},{\"start\":858,\"end\":1156,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":859,\"end\":1045,\"key\":\"\"},{\"start\":1045,\"end\":1156,\"key\"\ - :\"\"}]},{\"start\":1156,\"end\":1450,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1159,\"\ - end\":1240,\"key\":\"\"},{\"start\":1240,\"end\":1337,\"key\":\"\"},{\"start\"\ - :1337,\"end\":1450,\"key\":\"\"}]},{\"start\":1450,\"end\":1715,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1453,\"end\":1548,\"key\":\"\"},{\"start\":1548,\"end\":1622,\"\ - key\":\"\"},{\"start\":1622,\"end\":1715,\"key\":\"\"}]},{\"start\":1715,\"\ - end\":1977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1716,\"end\":1775,\"key\":\"\"},{\"start\":1775,\"\ - end\":1841,\"key\":\"\"},{\"start\":1841,\"end\":1887,\"key\":\"\"},{\"start\"\ - :1887,\"end\":1977,\"key\":\"\"}]},{\"start\":1977,\"end\":2202,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1978,\"end\":2101,\"key\":\"\"},{\"start\":2101,\"end\":2202,\"\ - key\":\"\"}]},{\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2207,\"\ - end\":2319,\"key\":\"\"},{\"start\":2319,\"end\":2409,\"key\":\"\"},{\"start\"\ - :2409,\"end\":2575,\"key\":\"\"}]},{\"start\":2575,\"end\":2804,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":2576,\"end\":2770,\"key\":\"\"},{\"start\":2770,\"end\":2804,\"\ - key\":\"\"}]},{\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2805,\"\ - end\":2886,\"key\":\"\"},{\"start\":2886,\"end\":3043,\"key\":\"\"}]},{\"\ - start\":3043,\"end\":3250,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":3044,\"end\":3233,\"\ - key\":\"\"},{\"start\":3233,\"end\":3250,\"key\":\"\"}]},{\"start\":3250,\"\ - end\":3563,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":3253,\"end\":3461,\"key\":\"\"},{\"start\":3461,\"\ - end\":3563,\"key\":\"\"}]},{\"start\":3563,\"end\":3826,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":3564,\"end\":3665,\"key\":\"\"},{\"start\":3665,\"end\":3698,\"\ - key\":\"\"},{\"start\":3698,\"end\":3766,\"key\":\"\"},{\"start\":3766,\"\ - end\":3826,\"key\":\"\"}]}],\"ner\":{\"Key Toggle\":\"PRODUCT\",\"AI Models\"\ - :\"ORG\",\"Phi 4 Reasoning Plus\":\"LAW\",\"Agentic RAG\":\"PRODUCT\",\"OpenRouter\"\ - :\"ORG\"},\"entities\":{\"processor\":{\"entities\":[{\"text\":\"Agentic RAG\"\ - ,\"label\":\"PRODUCT\",\"positions\":[{\"start\":509,\"end\":520},{\"start\"\ - :715,\"end\":726},{\"start\":3738,\"end\":3749}]},{\"text\":\"Phi 4 Reasoning\ - \ Plus\",\"label\":\"LAW\",\"positions\":[{\"start\":901,\"end\":921},{\"\ - start\":2114,\"end\":2134},{\"start\":2589,\"end\":2609},{\"start\":3605,\"\ - end\":3625}]},{\"text\":\"OpenRouter\",\"label\":\"ORG\",\"positions\":[{\"\ - start\":939,\"end\":949},{\"start\":1749,\"end\":1759},{\"start\":1900,\"\ - end\":1910}]},{\"text\":\"AI Models\",\"label\":\"ORG\",\"positions\":[{\"\ - start\":1168,\"end\":1177},{\"start\":1227,\"end\":1236}]},{\"text\":\"Key\ - \ Toggle\",\"label\":\"PRODUCT\",\"positions\":[{\"start\":1351,\"end\":1363}]}]}},\"\ - classifications\":[],\"last_index\":\"2026-06-09T08:07:49.472289Z\",\"last_understanding\"\ - :\"2026-06-09T08:07:49.013593Z\",\"last_extract\":\"2026-06-09T08:07:46.559309Z\"\ - ,\"last_processing_start\":\"2026-06-09T08:07:46.510042Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{\"LAW/Phi 4 Reasoning Plus\":{\"position\"\ - :[{\"start\":901,\"end\":921},{\"start\":2114,\"end\":2134},{\"start\":2589,\"\ - end\":2609},{\"start\":3605,\"end\":3625}],\"entity\":\"Phi 4 Reasoning Plus\"\ - },\"ORG/AI Models\":{\"position\":[{\"start\":1168,\"end\":1177},{\"start\"\ - :1227,\"end\":1236}],\"entity\":\"AI Models\"},\"PRODUCT/Agentic RAG\":{\"\ - position\":[{\"start\":509,\"end\":520},{\"start\":715,\"end\":726},{\"start\"\ - :3738,\"end\":3749}],\"entity\":\"Agentic RAG\"},\"PRODUCT/Key Toggle\":{\"\ - position\":[{\"start\":1351,\"end\":1363}],\"entity\":\"Key Toggle\"},\"ORG/OpenRouter\"\ - :{\"position\":[{\"start\":939,\"end\":949},{\"start\":1749,\"end\":1759},{\"\ - start\":1900,\"end\":1910}],\"entity\":\"OpenRouter\"}},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > rag > advanced > openai api compatible\ - \ models\",\"extracted\":{\"text\":{\"text\":\"docs > rag > advanced > openai\ - \ api compatible models\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"\ - paragraphs\":{\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\":{\"score\"\ - :0.5885962843894958,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" Description:\ - \ The maximum number of tokens that the model can generate as output. Again,\ - \ we should keep in mind that this value summed to the Maximum supported input\ - \ tokens should not exceed the total context size supported by the model.\ - \ \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"\ - end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.5845638513565063,\"\ - score_type\":\"VECTOR\",\"order\":7,\"text\":\" \\n \\n Maximum supported\ - \ input tokens: \\n Description: The maximum number of tokens that the model\ - \ can accept as input. Be mindful that this takes into account the tokens\ - \ used in the prompt, query and context. Also take note that some models may\ - \ provide their context window as the total between input and output tokens,\ - \ while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\"\ - :2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - :{\"score\":0.5330213904380798,\"score_type\":\"VECTOR\",\"order\":19,\"text\"\ - :\" Example: For Phi 4 Reasoning Plus, the total context size is 32768 tokens,\ - \ as we want to leave room for the output, we will set the maximum supported\ - \ input tokens as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output\ - \ tokens: \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\"\ - :2575,\"end\":2804,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null}}}}},\"24ad6997e1fe4a109d67d7802a083678\":{\"id\"\ - :\"24ad6997e1fe4a109d67d7802a083678\",\"slug\":\"docs-develop-js-sdk-interfaces-TokenConsumption-md\"\ - ,\"title\":\"docs > develop > js sdk > interfaces > TokenConsumption\",\"\ - summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\"\ - :{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"\ - PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"\ - fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"\ - created\":\"2025-12-09T11:48:34.202249\",\"modified\":\"2026-07-14T12:49:12.506050\"\ - ,\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\"\ - :false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/TokenConsumption\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / TokenConsumption\\\ - n\\n# Interface: TokenConsumption\\n\\n## Properties\\n\\n### image\\n\\n>\ - \ **image**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:236](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L236)\\\ - n\\n***\\n\\n### input\\n\\n> **input**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/search/ask.models.ts:234](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L234)\\\ - n\\n***\\n\\n### output\\n\\n> **output**: `number`\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:235](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L235)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"7bb8b1c3910c7d06c2a257bc3f1cff47\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / TokenConsumption\ - \ \\n Interface: TokenConsumption \\n Properties \\n image \\n \\n image:\ - \ number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:236\ - \ \\n \\n input \\n \\n input: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:234\ - \ \\n \\n output \\n \\n output: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:235\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":205,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":205,\"key\":\"\"}]},{\"start\":205,\"end\":384,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":206,\"end\":384,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\"\ - :{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:13.998338Z\"\ - ,\"last_understanding\":\"2026-07-14T12:49:13.762284Z\",\"last_extract\":\"\ - 2026-07-14T12:49:13.588922Z\",\"last_processing_start\":\"2026-07-14T12:49:13.570246Z\"\ - ,\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"\ - mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"\ - generics\":{\"title\":{\"value\":\"docs > develop > js sdk > interfaces >\ - \ TokenConsumption\",\"extracted\":{\"text\":{\"text\":\"docs > develop >\ - \ js sdk > interfaces > TokenConsumption\"}}}}},\"security\":null,\"fields\"\ - :{\"/a/title\":{\"paragraphs\":{\"24ad6997e1fe4a109d67d7802a083678/a/title/0-55\"\ - :{\"score\":0.5375195741653442,\"score_type\":\"VECTOR\",\"order\":18,\"text\"\ - :\"docs > develop > js sdk > interfaces > TokenConsumption\",\"id\":\"24ad6997e1fe4a109d67d7802a083678/a/title/0-55\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\"\ - :0,\"end\":55,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\"\ - :\"docs > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\"\ - :\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\"\ - :{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"\ - usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\"\ - ,\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\\ - n\\n# Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\\ - _history?\\n\\n> `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\\ - n\\n***\\n\\n### context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\\ - n\\n***\\n\\n### format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\\ - n\\n***\\n\\n### json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\\ - n\\n***\\n\\n### query\\\\_context?\\n\\n> `optional` **query\\\\_context**:\ - \ `string`[] \\\\| `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\ - \\_context\\\\_images**: `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**:\ - \ `string`\\n\\n#### content\\\\_type\\n\\n> **content\\\\_type**: `string`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\\ - n\\n***\\n\\n### query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\\ - _context\\\\_order**: `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`:\ - \ `string`\\\\]: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\\ - n\\n***\\n\\n### rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\\ - n\\n***\\n\\n### retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\\ - n\\n***\\n\\n### system?\\n\\n> `optional` **system**: `string`\\n\\n####\ - \ Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\\ - n\\n***\\n\\n### truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\\ - n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\\ - n\\n***\\n\\n### user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\\ - n\\n#### prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions\ - \ \\n Interface: PredictAnswerOptions \\n Properties \\n chat_history? \\\ - n \\n optional chat_history: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242\ - \ \\n \\n citation_threshold? \\n \\n optional citation_threshold: number\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 \\n \\n context? \\\ - n \\n optional context: ContextEntry[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243\ - \ \\n \\n format_prompt? \\n \\n optional format_prompt: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 \\n \\n generative_model?\ - \ \\n \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n \\n optional query_context: string[] \\\\| object \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\\ - n \\n query_context_images? \\n \\n optional query_context_images: object\ - \ \\n \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\\ - n content_type: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252\ - \ \\n \\n query_context_order? \\n \\n optional query_context_order: object\ - \ \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context?\ - \ \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259\ - \ \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\\ - n \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241\ - \ \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt?\ - \ \\n \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string\ - \ \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\"\ - :257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\"\ - :\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"\ - start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"\ - end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\ - \"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"\ - kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\"\ - :1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\"\ - :1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\"\ - ,\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\"\ - :\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\"\ - :1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"\ - end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"\ - key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"\ - end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"\ - start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"\ - key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"\ - classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\"\ - :\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\"\ - ,\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"\ - en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"\ - },\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\"\ - :{\"value\":\"docs > develop > js sdk > interfaces > PredictAnswerOptions\"\ - ,\"extracted\":{\"text\":{\"text\":\"docs > develop > js sdk > interfaces\ - \ > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\"\ - :{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"\ - score\":0.6208906173706055,\"score_type\":\"VECTOR\",\"order\":2,\"text\"\ - :\" \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\\ - n query_context? \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\"\ - :977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\"\ - :{\"score\":0.5950271487236023,\"score_type\":\"VECTOR\",\"order\":4,\"text\"\ - :\" \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250\ - \ \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens?\ - \ \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"\ - end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\"\ - ,\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs\ - \ > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\"\ - :\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\"\ - :\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\"\ - :{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\"\ - :{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\"\ - ,\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\"\ - ,\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\"\ - ,\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\"\ - :[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\\ - n\\n# Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\\ - n\\n## Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\ - \\_json\\\\_schema**: `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\\ - n\\n***\\n\\n### audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**:\ - \ `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\\ - n\\n***\\n\\n### citation\\\\_threshold?\\n\\n> `optional` **citation\\\\\ - _threshold**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\\ - n\\n***\\n\\n### citations?\\n\\n> `optional` **citations**: `boolean` \\\\\ - | `\\\"none\\\"` \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\\ - nIt will return the text blocks that have been effectively used to build each\ - \ section of the answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\\ - n\\n***\\n\\n### debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\\ - n\\n***\\n\\n### extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**:\ - \ `string`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\\ - n\\n***\\n\\n### extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\ - \\_context\\\\_images**: `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\\ - n\\n***\\n\\n### ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\\ - n\\n#### Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\\ - n\\n***\\n\\n### features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\\ - n\\n***\\n\\n### field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\\ - _type\\\\_filter**: [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\\ - n\\n***\\n\\n### fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n####\ - \ Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\\ - n\\n***\\n\\n### filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**:\ - \ [`FilterExpression`](FilterExpression.md)\\n\\n#### Inherited from\\n\\\ - n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\\ - n\\n***\\n\\n### filters?\\n\\n> `optional` **filters**: `string`[] \\\\|\ - \ [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\\ - n\\n***\\n\\n### generative\\\\_model?\\n\\n> `optional` **generative\\\\\ - _model**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\\ - n\\n***\\n\\n### highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\\ - n\\n***\\n\\n### keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**:\ - \ `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited\ - \ from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\\ - n\\n***\\n\\n### max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\ - \ \\\\| `object`\\n\\nDefines the maximum number of tokens that the model\ - \ will take as context.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\\ - n\\n***\\n\\n### min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number`\ - \ \\\\| [`MinScore`](MinScore.md)\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\\ - n\\n***\\n\\n### prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\\ - n\\n***\\n\\n### prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\\ - n\\n***\\n\\n### query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\\ - n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\\ - _type\\n\\n> **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\\ - n\\n***\\n\\n### rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\\ - _images\\\\_strategies**: [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\\ - n\\n***\\n\\n### rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**:\ - \ [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### Defined in\\\ - n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\\ - _creation\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\\ - n\\n***\\n\\n### range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\ - \\_creation\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\ - \\_modification\\\\_end**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\\ - n\\n***\\n\\n### range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\ - \\_modification\\\\_start**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\\ - n\\n***\\n\\n### rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\\ - n\\n***\\n\\n### reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\\ - n\\n***\\n\\n### rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\\ - n\\n***\\n\\n### reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\\ - n\\n***\\n\\n### resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**:\ - \ `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\\ - n\\n***\\n\\n### search\\\\_configuration?\\n\\n> `optional` **search\\\\\ - _configuration**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\\ - n\\n***\\n\\n### security?\\n\\n> `optional` **security**: `object`\\n\\n####\ - \ groups\\n\\n> **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\\ - n\\n***\\n\\n### show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\\ - n\\n***\\n\\n### show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**:\ - \ `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\\ - n\\n***\\n\\n### show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\\ - n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\\ - n\\n***\\n\\n### synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\\ - n\\n***\\n\\n### top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\\ - n\\n***\\n\\n### vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\\ - n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\\ - n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface:\ - \ ChatOptions \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\\ - n answer_json_schema? \\n \\n optional answer_json_schema: object \\n \\n\ - \ Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 \\n\ - \ \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index\ - \ Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\\ - n \\n citation_threshold? \\n \\n optional citation_threshold: number \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116\ - \ \\n \\n citations? \\n \\n optional citations: boolean \\\\| none \\\\|\ - \ default \\\\| llm_footnotes \\n \\n It will return the text blocks that\ - \ have been effectively used to build each section of the answer. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 \\n \\n debug?\ - \ \\n \\n optional debug: boolean \\n \\n Inherited from \\n BaseSearchOptions.debug\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 \\\ - n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images?\ - \ \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118\ - \ \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[]\ - \ \\n \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\\ - n features? \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117\ - \ \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[]\ - \ \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields?\ - \ \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\\ - n \\n filter_expression? \\n \\n optional filter_expression: FilterExpression\ - \ \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters?\ - \ \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63\ - \ \\n \\n generative_model? \\n \\n optional generative_model: string \\n\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108\ - \ \\n \\n highlight? \\n \\n optional highlight: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76\ - \ \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\\ - | Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\\ - n max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines\ - \ the maximum number of tokens that the model will take as context. \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from\ - \ \\n BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66\ - \ \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n\ - \ \\n prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image?\ - \ \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded:\ - \ string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies?\ - \ \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies?\ - \ \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106\ - \ \\n \\n range_creation_end? \\n \\n optional range_creation_end: string\ - \ \\n \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start?\ - \ \\n \\n optional range_creation_start: string \\n \\n Inherited from \\\ - n BaseSearchOptions.range_creation_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67\ - \ \\n \\n range_modification_end? \\n \\n optional range_modification_end:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\\ - n \\n range_modification_start? \\n \\n optional range_modification_start:\ - \ string \\n \\n Inherited from \\n BaseSearchOptions.range_modification_start\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 \\\ - n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited\ - \ from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84\ - \ \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase?\ - \ \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\\ - n \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from\ - \ \\n BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83\ - \ \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\\ - n \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in\ - \ \\n libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration?\ - \ \\n \\n optional search_configuration: string \\n \\n Inherited from \\\ - n BaseSearchOptions.search_configuration \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86\ - \ \\n \\n security? \\n \\n optional security: object \\n \\n groups \\n \\\ - n groups: string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\\ - n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\\ - n show? \\n \\n optional show: ResourceProperties[] \\n \\n Inherited from\ - \ \\n BaseSearchOptions.show \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71\ - \ \\n \\n show_consumption? \\n \\n optional show_consumption: boolean \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127\ - \ \\n \\n show_hidden? \\n \\n optional show_hidden: boolean \\n \\n Inherited\ - \ from \\n BaseSearchOptions.show_hidden \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80\ - \ \\n \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined\ - \ in \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k?\ - \ \\n \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k\ - \ \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\\ - n \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from\ - \ \\n BaseSearchOptions.vectorset \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:78\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[\"BaseSearchOptions.security\",\"BaseSearchOptions.show\"],\"paragraphs\"\ - :[{\"start\":0,\"end\":276,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":149,\"\ - key\":\"\"},{\"start\":149,\"end\":276,\"key\":\"\"}]},{\"start\":276,\"end\"\ - :499,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":279,\"end\":499,\"key\":\"\"}]},{\"start\":499,\"\ - end\":796,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\"\ - :[],\"sentences\":[{\"start\":502,\"end\":627,\"key\":\"\"},{\"start\":627,\"\ - end\":796,\"key\":\"\"}]},{\"start\":796,\"end\":1034,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":797,\"end\":878,\"key\":\"\"},{\"start\":878,\"end\":1034,\"key\"\ - :\"\"}]},{\"start\":1034,\"end\":1488,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1037,\"\ - end\":1170,\"key\":\"\"},{\"start\":1170,\"end\":1296,\"key\":\"\"},{\"start\"\ - :1296,\"end\":1488,\"key\":\"\"}]},{\"start\":1488,\"end\":1796,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":1491,\"end\":1618,\"key\":\"\"},{\"start\":1618,\"end\":1796,\"\ - key\":\"\"}]},{\"start\":1796,\"end\":2148,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1799,\"\ - end\":1965,\"key\":\"\"},{\"start\":1965,\"end\":2148,\"key\":\"\"}]},{\"\ - start\":2148,\"end\":2450,\"start_seconds\":[],\"end_seconds\":[],\"kind\"\ - :\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2151,\"end\":2330,\"\ - 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}},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"\ - deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs > develop\ - \ > js sdk > interfaces > ChatOptions\",\"extracted\":{\"text\":{\"text\"\ - :\"docs > develop > js sdk > interfaces > ChatOptions\"}}}}},\"security\"\ - :null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\"\ - :{\"score\":0.6589934825897217,\"score_type\":\"VECTOR\",\"order\":0,\"text\"\ - :\" \\n optional max_tokens: number \\\\| object \\n \\n Defines the maximum\ - \ number of tokens that the model will take as context. \\n Defined in \\\ - n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score?\ - \ \\n\",\"id\":\"e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\",\"labels\"\ - :[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":2808,\"\ - end\":3011,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - 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:[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"\ - data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md)\ - \ \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / TokenAnnotation\\\ - n\\n# Interface: TokenAnnotation\\n\\n## Properties\\n\\n### end\\n\\n> **end**:\ - \ `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:403](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L403)\\\ - n\\n***\\n\\n### klass\\n\\n> **klass**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:401](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L401)\\\ - n\\n***\\n\\n### start\\n\\n> **start**: `number`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:402](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L402)\\\ - n\\n***\\n\\n### token\\n\\n> **token**: `string`\\n\\n#### Defined in\\n\\\ - n[libs/sdk-core/src/lib/db/resource/resource.models.ts:400](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L400)\\\ - n\",\"format\":\"MARKDOWN\",\"md5\":\"3aff3aba398b874c24844661b882ad66\",\"\ - extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"\ - text\":\"@nuclia/core \u2022 Docs \\n \\n @nuclia/core / TokenAnnotation \\\ - n Interface: TokenAnnotation \\n Properties \\n end \\n \\n end: number \\\ - n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:403\ - \ \\n \\n klass \\n \\n klass: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:401\ - \ \\n \\n start \\n \\n start: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:402\ - \ \\n \\n token \\n \\n token: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:400\"\ - ,\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\"\ - :[],\"paragraphs\":[{\"start\":0,\"end\":206,\"start_seconds\":[],\"end_seconds\"\ - :[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"\ - end\":206,\"key\":\"\"}]},{\"start\":206,\"end\":410,\"start_seconds\":[],\"\ - end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"\ - start\":207,\"end\":410,\"key\":\"\"}]},{\"start\":410,\"end\":499,\"start_seconds\"\ - :[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\"\ - :[{\"start\":411,\"end\":499,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"\ - 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"{\"resources\":{\"6e8250e6b5264156988657a221fd5e94\":{\"id\":\"6e8250e6b5264156988657a221fd5e94\",\"slug\":\"docs-develop-js-sdk-namespaces-Ask-interfaces-ConsumptionAskResponseItem-md\",\"title\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > 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ConsumptionAskResponseItem\\n\\n# Interface: ConsumptionAskResponseItem\\n\\n## + Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> **customer\\\\_key\\\\_tokens**: + [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L127)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](../../../interfaces/TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L126)\\n\\n***\\n\\n### + type\\n\\n> **type**: `\\\"consumption\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:125](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L125)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"3d72cf5a8634c4719acbe4304e79b48d\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n type: consumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:125\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":397,\"key\":\"\"}]},{\"start\":397,\"end\":483,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":398,\"end\":483,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:48:23.234967Z\",\"last_understanding\":\"2026-07-14T12:48:22.068785Z\",\"last_extract\":\"2026-07-14T12:48:21.777104Z\",\"last_processing_start\":\"2026-07-14T12:48:21.755089Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > namespaces > Ask > interfaces > ConsumptionAskResponseItem\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\":{\"score\":0.599697470664978,\"score_type\":\"VECTOR\",\"order\":11,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Ask / ConsumptionAskResponseItem \\n Interface: + ConsumptionAskResponseItem \\n Properties \\n customer_key_tokens \\n \\n + customer_key_tokens: TokenConsumption \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:127 + \\n \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \\n \\n + type \\n \\n\",\"id\":\"6e8250e6b5264156988657a221fd5e94/t/page/0-397\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":397,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"9f7036a7a6694700b72d52eb58a8326c\":{\"id\":\"9f7036a7a6694700b72d52eb58a8326c\",\"slug\":\"docs-rag-advanced-widget-features-md\",\"title\":\"docs + > rag > advanced > widget > features\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2024-07-18T14:32:26.204740\",\"modified\":\"2026-06-09T08:07:51.648767\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + features\\ntitle: Features\\n---\\n\\n# Widgets features\\n\\nThe Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet:\\n\\n```html\\n\\n\\n\\n```\\n\\nThe + easiest way to explore the different features of the widgets is to use the + [Agentic RAG Dashboard](https://rag.progress.cloud/) in the Widgets section + and to play with the different options.\\n\\nThe _Embed widget_ button will + generate the HTML snippet for you.\\n\\n## Widget types\\n\\nThere are 3 different + types of widgets:\\n\\n- **Embedded in page**: the search input is embedded + in a page and the results are displayed under the input. Once the initial + answer is displayed, the user can click on _Ask more_ to access the full chat + interface. Note: For the correct reading of the results, the width of the + widget container should not be less than 384px.\\n\\n Web components:\\n\\n + \ ```html\\n \\n \\n + \ ```\\n\\n- **Chat mode**: displays directly the full chat interface.\\n\\n + \ Web component:\\n\\n ```html\\n \\n ```\\n\\n- + **Popup modal**: the search inout and the results are displayed in a popup + modal.\\n\\n Web component:\\n\\n ```html\\n \\n + \ ```\\n\\n## The `features` parameter\\n\\nThe `features` parameter allows + you to customize the behavior of the widget. It is a comma-separated list + of features among the following:\\n\\n- `filter`: display a filter dropdown + in the search bar.\\n- `navigateToFile`: open the file in the browser when + clicking on the result (by default, the file is displayed in the viewer).\\n- + `navigateToLink`: open the link in the browser when clicking on the result + (by default, the link is displayed in the viewer).\\n- `permalink`: add the + search query and criteria to the URL, allowing the widget to re-render the + same results upon loading.\\n- `relations`: display an info card on the right + side of the widget listing all the relations of the entity mentioned in the + user query.\\n- `suggestions`: display a list of suggested resource titles + matching the user input.\\n- `suggestLabels`: display a list of suggestions + based on the labels when the user starts typing in the search bar.\\n- `autocompleteFromNERs`: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar.\\n- `displayMetadata`: display + the metadata associated with the resource in the result rows.\\n- `answers`: + trigger the answer generation process when the user makes a search.\\n- `hideResults`: + hide the search results, only the generative answer will be displayed.\\n- + `hideThumbnails`: hide the thumbnails associated with the resource in the + result rows.\\n- `displayFieldList`: display a section listing all the fields + of the resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields.\\n- `citations`: include citations + in the generative answer.\\n- `rephrase`: rephrase the user question in order + to optimize the quality of the search results.\\n- `debug`: display extra + buttons to download the last request full log of the debug metadata returned + by the API. It must not be used in production.\\n- `preferMarkdown`: require + the generative answer to be formatted in Markdown.\\n- `openNewTab`: open + the link in a new tab when clicking on the result.\\n- `orFilterLogic`: use + the OR logic for filters instead of the default AND logic.\\n- `noChatHistory`: + the previous questions and answers in the chat mode will not be passed as + context when generating a new answer.\\n- `showHidden`: display hidden resources + in the search results.\\n- `showAttachedImages`: display images attached to + the matching paragraphs in the search results.\\n\\n### Other parameters\\n\\n- + `backend`: the URL of the backend to use. Useful if you use your own proxy + to access the Agentic RAG API.\\n- `zone`: the zone to use.\\n- `knowledgebox`: + the Knowledge Box id.\\n- `placeholder`: the text displayed in the search + bar when it is empty.\\n- `lang`: the language of the widget. Currently supported: + `ca`, `fr`, `en`, `es`. Default: `en`.\\n- `apikey`: the API key to use. It + is not recommended to use it in production (the API key is meant to be injected + by your proxy).\\n- `account`: the account id.\\n- `state`: the publication + state of the Knowledge Box.\\n- `standalone`: set to `true` when using a standalone + NucliaDB instance.\\n- `proxy`: set to `true` when using a proxy to access + the Agentic RAG API.\\n- `mode`: set to `dark` to display the widget in dark + mode.\\n- `filters`: define the filters offered to the user in the search + bar among `labels`, `entities`, `created` and `labelFamilies`.\\n- `preselected_filters`: + define filters that will be applied by default to any query.\\n- `csspath`: + the path to the CSS file to use to customize the widget style.\\n- `prompt`: + the prompt to use for the generative model. It must use `{context}` and `{question}` + variables.\\n- `system_prompt`: the system prompt to use for the generative + model.\\n- `rephrase_prompt`: the prompt to use when optimizing the user query + to get the best search results.\\n- `generativemodel`: the generative model + to use for the answer generation.\\n- `rag_strategies`: the RAG strategies + to apply to the retrieved paragraphs.\\n- `rag_images_strategies`: the RAG + strategies to apply to the retrieved images.\\n- `not_enough_data_message`: + the message to display when there is not enough data to generate an answer.\\n- + `ask_to_resource`: the resource ID to use as context for the generative model.\\n- + `max_tokens`: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question).\\n- `max_output_tokens`: + the maximum number of tokens to generate.\\n- `max_paragraphs`: the maximum + number of paragraphs to pass in the context to the generative model (default: + 20).\\n- `query_prepend`: the hard-coded text to prepend to the user query.\\n- + `json_schema`: the JSON schema to use to get a JSON answer from the generative + model.\\n- `vectorset`: the embedding model to use for the semantic search.\\n- + `chat_placeholder`: the placeholder to display in the chat input.\\n- `audit_metadata`: + custom metatada to add in API calls for auditing purposes.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"8f3044c65d6a551153cea36353a2cdac\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: features \\n title: Features \\n \\n Widgets features \\n The Agentic + RAG widgets allows you to embed the Agentic RAG search experience directly + into your website or web application through a simple HTML snippet: \\n ```html + \\n \\n \\n \\n ``` \\n The easiest way to explore the different features + of the widgets is to use the Agentic RAG Dashboard in the Widgets section + and to play with the different options. \\n The Embed widget button will generate + the HTML snippet for you. \\n Widget types \\n There are 3 different types + of widgets: \\n \\n Embedded in page: the search input is embedded in a page + and the results are displayed under the input. Once the initial answer is + displayed, the user can click on Ask more to access the full chat interface. + Note: For the correct reading of the results, the width of the widget container + should not be less than 384px. \\n \\n Web components: \\n html \\n \\n \\n + \\n Chat mode: displays directly the full chat interface. \\n \\n Web component: + \\n html \\n \\n \\n Popup modal: the search inout and the results are displayed + in a popup modal. \\n \\n Web component: \\n html \\n \\n The features parameter + \\n The features parameter allows you to customize the behavior of the widget. + It is a comma-separated list of features among the following: \\n \\n filter: + display a filter dropdown in the search bar. \\n navigateToFile: open the + file in the browser when clicking on the result (by default, the file is displayed + in the viewer). \\n navigateToLink: open the link in the browser when clicking + on the result (by default, the link is displayed in the viewer). \\n permalink: + add the search query and criteria to the URL, allowing the widget to re-render + the same results upon loading. \\n relations: display an info card on the + right side of the widget listing all the relations of the entity mentioned + in the user query. \\n suggestions: display a list of suggested resource titles + matching the user input. \\n suggestLabels: display a list of suggestions + based on the labels when the user starts typing in the search bar. \\n autocompleteFromNERs: + display a list of suggestions based on the NERs extracted from the user query + when the user starts typing in the search bar. \\n displayMetadata: display + the metadata associated with the resource in the result rows. \\n answers: + trigger the answer generation process when the user makes a search. \\n hideResults: + hide the search results, only the generative answer will be displayed. \\n + hideThumbnails: hide the thumbnails associated with the resource in the result + rows. \\n displayFieldList: display a section listing all the fields of the + resource in the right sidebar of the viewer. This section is only visible + for resources containing multiple fields. \\n citations: include citations + in the generative answer. \\n rephrase: rephrase the user question in order + to optimize the quality of the search results. \\n debug: display extra buttons + to download the last request full log of the debug metadata returned by the + API. It must not be used in production. \\n preferMarkdown: require the generative + answer to be formatted in Markdown. \\n openNewTab: open the link in a new + tab when clicking on the result. \\n orFilterLogic: use the OR logic for filters + instead of the default AND logic. \\n noChatHistory: the previous questions + and answers in the chat mode will not be passed as context when generating + a new answer. \\n showHidden: display hidden resources in the search results. + \\n showAttachedImages: display images attached to the matching paragraphs + in the search results. \\n \\n Other parameters \\n \\n backend: the URL of + the backend to use. Useful if you use your own proxy to access the Agentic + RAG API. \\n zone: the zone to use. \\n knowledgebox: the Knowledge Box id. + \\n placeholder: the text displayed in the search bar when it is empty. \\n + lang: the language of the widget. Currently supported: ca, fr, en, es. Default: + en. \\n apikey: the API key to use. It is not recommended to use it in production + (the API key is meant to be injected by your proxy). \\n account: the account + id. \\n state: the publication state of the Knowledge Box. \\n standalone: + set to true when using a standalone NucliaDB instance. \\n proxy: set to true + when using a proxy to access the Agentic RAG API. \\n mode: set to dark to + display the widget in dark mode. \\n filters: define the filters offered to + the user in the search bar among labels, entities, created and labelFamilies. + \\n preselected_filters: define filters that will be applied by default to + any query. \\n csspath: the path to the CSS file to use to customize the widget + style. \\n prompt: the prompt to use for the generative model. It must use + {context} and {question} variables. \\n system_prompt: the system prompt to + use for the generative model. \\n rephrase_prompt: the prompt to use when + optimizing the user query to get the best search results. \\n generativemodel: + the generative model to use for the answer generation. \\n rag_strategies: + the RAG strategies to apply to the retrieved paragraphs. \\n rag_images_strategies: + the RAG strategies to apply to the retrieved images. \\n not_enough_data_message: + the message to display when there is not enough data to generate an answer. + \\n ask_to_resource: the resource ID to use as context for the generative + model. \\n max_tokens: the maximum number of input tokens to put in the final + context (including the prompt, the retrieved results and the user question). + \\n max_output_tokens: the maximum number of tokens to generate. \\n max_paragraphs: + the maximum number of paragraphs to pass in the context to the generative + model (default: 20). \\n query_prepend: the hard-coded text to prepend to + the user query. \\n json_schema: the JSON schema to use to get a JSON answer + from the generative model. \\n vectorset: the embedding model to use for the + semantic search. \\n chat_placeholder: the placeholder to display in the chat + input. \\n audit_metadata: custom metatada to add in API calls for auditing + purposes. \\n 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features\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\":{\"score\":0.7151519060134888,\"score_type\":\"VECTOR\",\"order\":0,\"text\":\" + max_tokens: the maximum number of input tokens to put in the final context + (including the prompt, the retrieved results and the user question). \\n max_output_tokens: + the maximum number of tokens to generate. \\n\",\"id\":\"9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":20,\"start\":5203,\"end\":5412,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"f02da6c4bdf34596a89a8106f4b0ea9f\":{\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f\",\"slug\":\"docs-develop-js-sdk-interfaces-PageToken-md\",\"title\":\"docs + > develop > js sdk > interfaces > PageToken\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:49:28.645864\",\"modified\":\"2026-07-14T12:51:05.803476\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PageToken\\n\\n# + Interface: PageToken\\n\\n## Properties\\n\\n### height\\n\\n> **height**: + `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L249)\\n\\n***\\n\\n### + line\\n\\n> **line**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L251)\\n\\n***\\n\\n### + text\\n\\n> **text**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L250)\\n\\n***\\n\\n### + width\\n\\n> **width**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L248)\\n\\n***\\n\\n### + x\\n\\n> **x**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L246)\\n\\n***\\n\\n### + y\\n\\n> **y**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"e72ffebe78398e6654aceec2addfd240\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n text: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:250 + \\n \\n width \\n \\n width: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:248 + \\n \\n x \\n \\n x: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:246 + \\n \\n y \\n \\n y: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":299,\"key\":\"\"}]},{\"start\":299,\"end\":592,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":300,\"end\":592,\"key\":\"\"}]},{\"start\":592,\"end\":677,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":593,\"end\":677,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:07.412857Z\",\"last_understanding\":\"2026-07-14T12:51:07.180359Z\",\"last_extract\":\"2026-07-14T12:51:06.857039Z\",\"last_processing_start\":\"2026-07-14T12:51:06.837097Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PageToken\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PageToken\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\":{\"score\":0.5957225561141968,\"score_type\":\"VECTOR\",\"order\":14,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PageToken \\n Interface: PageToken \\n + Properties \\n height \\n \\n height: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 + \\n \\n line \\n \\n line: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 + \\n \\n text \\n \\n\",\"id\":\"f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":299,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"1b2a9e67b9f14a0cb81efaa05b8793b8\":{\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8\",\"slug\":\"docs-develop-js-sdk-interfaces-AugmentedField-md\",\"title\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:00:05.937520\",\"modified\":\"2026-07-14T12:49:32.163110\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/AugmentedField\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / AugmentedField\\n\\n# + Interface: AugmentedField\\n\\n## Properties\\n\\n### applied\\\\_data\\\\_augmentation\\n\\n> + **applied\\\\_data\\\\_augmentation**: [`AppliedDataAugmentation`](AppliedDataAugmentation.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:571](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L571)\\n\\n***\\n\\n### + input\\\\_nuclia\\\\_tokens\\n\\n> **input\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:572](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L572)\\n\\n***\\n\\n### + metadata\\n\\n> **metadata**: [`FieldMetadata`](FieldMetadata.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:570](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L570)\\n\\n***\\n\\n### + output\\\\_nuclia\\\\_tokens\\n\\n> **output\\\\_nuclia\\\\_tokens**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:573](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L573)\\n\\n***\\n\\n### + time\\n\\n> **time**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/resource/resource.models.ts:574](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/resource/resource.models.ts#L574)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"1e8e95b886439059ae4ec717c9bcf283\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n time: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:574\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":641,\"key\":\"\"}]},{\"start\":641,\"end\":729,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":642,\"end\":729,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:49:33.585675Z\",\"last_understanding\":\"2026-07-14T12:49:33.340252Z\",\"last_extract\":\"2026-07-14T12:49:32.941746Z\",\"last_processing_start\":\"2026-07-14T12:49:32.917812Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > AugmentedField\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > AugmentedField\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\":{\"score\":0.5881041884422302,\"score_type\":\"VECTOR\",\"order\":19,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / AugmentedField \\n Interface: AugmentedField + \\n Properties \\n applied_data_augmentation \\n \\n applied_data_augmentation: + AppliedDataAugmentation \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:571 + \\n \\n input_nuclia_tokens \\n \\n input_nuclia_tokens: number \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:572 \\n \\n metadata + \\n \\n metadata: FieldMetadata \\n \\n Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:570 + \\n \\n output_nuclia_tokens \\n \\n output_nuclia_tokens: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/resource/resource.models.ts:573 \\n + \\n time \\n \\n\",\"id\":\"1b2a9e67b9f14a0cb81efaa05b8793b8/t/page/0-641\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":641,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"44d05174f1954331b62f5e4026f2b01a\":{\"id\":\"44d05174f1954331b62f5e4026f2b01a\",\"slug\":\"docs-develop-python-sdk-14-rao-md\",\"title\":\"docs + > develop > python sdk > 14 rao\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-22T13:38:09.919472\",\"modified\":\"2026-06-09T08:08:04.176136\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/python-sdk/rao\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"# + Retrieval Agents Orchestrator\\n\\nInteract with Nuclia's Retrieval Agents + Orchestrator to have intelligent conversations over several knowledge sources + with persistent session management and real-time streaming responses.\\n\\n## + Prerequisites\\n\\nInstall the Nuclia SDK:\\n\\n```sh\\npip install nuclia\\n```\\n\\nEnsure + you have:\\n- A valid Nuclia authentication token (see [Authentication](02-auth.md))\\n- + Access to a configured Retrieval Agent\\n\\n## Overview\\n\\nThe nuclia.py + library provides several ways to interact with your Retrieval Agents Orchestrators:\\n\\n- + **Interactive CLI**: A rich, user-friendly terminal interface (recommended)\\n- + **Standard CLI**: Direct access to raw websocket messages for debugging\\n- + **Session Management**: Create and manage persistent conversation sessions\\n- + **Programmatic API**: Python SDK for building custom applications\\n\\n\\n## + Listing Available Agents\\n\\nDiscover what Retrieval Agents Orchestrators + you have access to.\\n\\n- CLI:\\n\\n ```sh\\n nuclia agents list\\n ```\\n\\n- + SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n all_agents = agents.list()\\n\\n for agent + in all_agents:\\n print(f\\\"Agent: {agent.title} ({agent.id})\\\")\\n + \ print(f\\\" Slug: {agent.slug}\\\")\\n print(f\\\" Zone: {agent.zone}\\\")\\n + \ ```\\n\\n### Getting a Specific Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents get --account=\\\"my-account\\\" --id=\\\"agent-uuid\\\" --zone=\\\"europe-1\\\"\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agents import NucliaAgents\\n\\n + \ agents = NucliaAgents()\\n agent_details = agents.get(\\n account=\\\"my-account\\\",\\n + \ id=\\\"agent-uuid\\\",\\n zone=\\\"europe-1\\\"\\n )\\n print(agent_details)\\n + \ ```\\n\\n### Setting a Default Agent\\n\\n- CLI:\\n\\n ```sh\\n nuclia + agents default [AGENT_SLUG or AGENT_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agents import NucliaAgents\\n\\n agents = NucliaAgents()\\n + \ agents.default(\\\"my-agent\\\")\\n ```\\n\\nThis sets the default agent + for all subsequent operations.\\n\\n## Interactive CLI (Recommended)\\n\\nThe + interactive CLI provides a beautiful, real-time interface for conversing with + your Retrieval Agents Orchestrator.\\n\\n### Starting the Interactive CLI\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent cli interact\\n ```\\n\\n- SDK:\\n\\n + \ ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ agent.cli.interact()\\n ```\\n\\nThis launches an interactive terminal + session where you can:\\n- Ask questions and see streaming responses\\n- View + processing steps in real-time\\n- Manage conversation sessions\\n- See retrieved + context and citations\\n\\n### Interactive CLI Commands\\n\\nThe CLI supports + several commands (prefix with `/`):\\n\\n| Command | Description |\\n|---------|-------------|\\n| + `/help` | Show available commands |\\n| `/new_session` | Create a new persistent + session |\\n| `/list_sessions` | List all your sessions |\\n| `/change_session` + | Switch to a different session, use 'ephemeral' for a temporary session |\\n| + `/clear` | Clear the screen |\\n| `/exit` | Exit the CLI |\\n\\nPlease note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option **Agent with memory** enabled during creation.\\n\\n## Session + Management\\n\\nSessions allow you to maintain conversation context across + multiple interactions.\\n\\n> This feature will only be available if you checked + **Agent with memory** during the creation of your Retrieval Agents Orchestrator.\\n\\n### + Creating a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent session new --name=\\\"My + Research Session\\\"\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n session_uuid = agent.session.new(\\\"My + Research Session\\\")\\n print(f\\\"Created session: {session_uuid}\\\")\\n + \ ```\\n\\n### Listing Sessions\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session list\\n ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent + import NucliaAgent\\n\\n agent = NucliaAgent()\\n sessions = agent.session.list()\\n + \ for session in sessions.resources:\\n print(f\\\"{session.title}: {session.id}\\\")\\n + \ ```\\n\\n### Getting a Session\\n\\n- CLI:\\n\\n ```sh\\n nuclia agent + session get --session_uuid=[SESSION_UUID]\\n ```\\n\\n- SDK:\\n\\n ```python\\n + \ from nuclia.sdk.agent import NucliaAgent\\n\\n agent = NucliaAgent()\\n + \ session = agent.session.get(session_uuid)\\n print(f\\\"Session: {session.title}\\\")\\n + \ print(f\\\"Created: {session.created}\\\")\\n ```\\n\\n### Deleting a Session\\n\\n- + CLI:\\n\\n ```sh\\n nuclia agent session delete --session_uuid=[SESSION_UUID]\\n + \ ```\\n\\n- SDK:\\n\\n ```python\\n from nuclia.sdk.agent import NucliaAgent\\n\\n + \ agent = NucliaAgent()\\n agent.session.delete(session_uuid)\\n ```\\n\\n## + Interaction\\n\\nAside from the interactive CLI, you can interact with your + Retrieval Agents Orchestrator with the simple CLI or programmatically using + the SDK.\\n\\n### Basic Interaction\\n\\n\\n**CLI:**\\n```bash\\nnuclia agent + interact \\\"What is Eric known for?\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over streaming responses\\nfor response in agent.interact(\\n question=\\\"What + is Eric known for?\\\"\\n):\\n if response.operation == \\\"ANSWER\\\" + and response.answer:\\n print(response.answer)\\n elif response.step:\\n + \ print(f\\\"Processing: {response.step.module}\\\")\\n```\\n\\nNot + supplying a `session_uuid` when calling `interact` will use an ephemeral session + by default. To maintain context, provide a persistent session UUID.\\n\\n### + Using Persistent Sessions\\n\\n**CLI:**\\n```bash\\nnuclia agent sessions + new \\\"Customer Support Chat\\\"\\n# Note the session UUID returned\\nnuclia + agent interact \\\"What are your business hours?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\nnuclia + agent interact \\\"Are you open on weekends?\\\" --session_uuid=\\\"SESSION_UUID\\\"\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Create + a session\\nsession_uuid = agent.session.new(\\\"Customer Support Chat\\\")\\n\\n# + Have a conversation with context\\nfor response in agent.interact(\\n session_uuid=session_uuid,\\n + \ question=\\\"What are your business hours?\\\"\\n):\\n if response.answer:\\n + \ print(response.answer)\\n\\n# Follow-up question maintains context\\nfor + response in agent.interact(\\n session_uuid=session_uuid,\\n question=\\\"Are + you open on weekends?\\\"\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\n## + Understanding Response Types\\n\\nWhen interacting with an agent, you receive + a stream of `AragAnswer` objects with different operations:\\n\\n| Operation + | Description |\\n|-----------|-------------|\\n| `START` | Interaction has + begun |\\n| `ANSWER` | Processing step or partial answer |\\n| `DONE` | Interaction + complete |\\n| `ERROR` | An error occurred |\\n| `AGENT_REQUEST` | Agent needs + user feedback |\\n\\n### Response Attributes\\n\\nEach response may contain:\\n\\n- + **`step`**: Information about the current processing step\\n - `module`: + The module being executed (e.g., \\\"rephrase\\\", \\\"basic_ask\\\", \\\"remi\\\")\\n + \ - `title`: Display title for the step\\n - `value`: Result of the step\\n + \ - `reason`: Explanation for the step\\n - `timeit`: Time taken in seconds\\n + \ - `input_nuclia_tokens`/`output_nuclia_tokens`: Token usage\\n\\n- **`context`**: + Retrieved context from the knowledge base\\n - `chunks`: List of retrieved + text chunks with sources\\n - `summary`: Summary of the context or partial + answer\\n\\n- **`answer`**: The final answer text (Markdown formatted)\\n\\n- + **`generated_text`**: Intermediate generated text\\n\\n- **`possible_answer`**: + Alternative answer being considered\\n\\n- **`exception`**: Error details + if something went wrong\\n\\n### Processing Responses\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\nfrom nuclia_models.agent.interaction + import AnswerOperation\\n\\nagent = NucliaAgent()\\n\\nfor response in agent.interact(question=\\\"Tell + me about AI\\\"):\\n if response.operation == AnswerOperation.START:\\n + \ print(\\\"Starting...\\\")\\n \\n elif response.step:\\n print(f\\\"Step: + {response.step.module} ({response.step.timeit:.2f}s)\\\")\\n \\n elif + response.context:\\n print(f\\\"Retrieved {len(response.context.chunks)} + chunks\\\")\\n for chunk in response.context.chunks:\\n print(f\\\" + \ - {chunk.title}: {chunk.text[:100]}...\\\")\\n \\n elif response.answer:\\n + \ print(f\\\"\\\\nFinal Answer:\\\\n{response.answer}\\\")\\n \\n + \ elif response.operation == AnswerOperation.DONE:\\n print(\\\"Complete!\\\")\\n + \ \\n elif response.operation == AnswerOperation.ERROR:\\n print(f\\\"Error: + {response.exception.detail if response.exception else 'Unknown'}\\\")\\n```\\n\\n## + Standard CLI for Raw Messages\\n\\nFor debugging or advanced use cases, you + can access raw websocket messages programmatically:\\n\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\n\\n# Iterate + over all messages\\nfor message in agent.interact(\\n question=\\\"What + is RAO?\\\"\\n):\\n # message is an AragAnswer object with all raw data\\n + \ print(f\\\"Operation: {message.operation}\\\")\\n print(f\\\"Raw message: + {message.model_dump_json(indent=2)}\\\")\\n```\\n\\nThis gives you direct + access to all websocket message data for debugging or custom processing.\\n\\n## + Advanced Features\\n\\n### Agent Feedback Requests\\n\\nAgents can request + additional input from users during processing:\\n\\n```python\\nfrom nuclia.sdk.agent + import NucliaAgent\\nfrom nuclia_models.agent.interaction import AnswerOperation\\n\\nagent + = NucliaAgent()\\ngenerator = agent.interact(question=\\\"Help me with X\\\")\\n\\nfor + response in generator:\\n if response.operation == AnswerOperation.AGENT_REQUEST:\\n + \ # Agent is requesting user input\\n user_input = input(f\\\"Agent + asks: {response.feedback.question}\\\\n> \\\")\\n # Send response back\\n + \ generator.send(user_input)\\n elif response.answer:\\n print(response.answer)\\n```\\n\\n### + Error Handling\\n\\n```python\\nfrom nuclia.sdk.agent import NucliaAgent\\nfrom + nuclia.exceptions import RaoAPIException\\n\\nagent = NucliaAgent()\\n\\ntry:\\n + \ for response in agent.interact(question=\\\"Hello?\\\"):\\n if + response.exception:\\n print(f\\\"Agent error: {response.exception.detail}\\\")\\n + \ elif response.answer:\\n print(response.answer)\\nexcept + RaoAPIException as e:\\n print(f\\\"API error: {e.detail}\\\")\\nexcept + Exception as e:\\n print(f\\\"Unexpected error: {e}\\\")\\n```\\n\\n### + Passing Custom Headers to MCP\\n\\nIf your Retrieval Agents Orchestrator requires + custom headers for MCP Agents, you can pass them as follows:\\n\\n**CLI:**\\n```bash\\nnuclia + agent interact \\\"What is AI?\\\" --headers '{\\\"X-Custom-Header\\\": \\\"value\\\"}'\\n```\\n\\n**SDK:**\\n```python\\nfrom + nuclia.sdk.agent import NucliaAgent\\n\\nagent = NucliaAgent()\\nfor response + in agent.interact(\\n question=\\\"What is AI?\\\",\\n headers={\\\"X-Custom-Header\\\": + \\\"value\\\"}\\n):\\n if response.answer:\\n print(response.answer)\\n```\\n\\nPlease + ensure that the 'Allowed Headers' configuration in your MCP agent includes + any custom headers you wish to use.\\n\\n## Best Practices\\n\\n1. **Use Sessions + for Context**: Create sessions when you need multi-turn conversations with + context retention\\n2. **Use Ephemeral Sessions for One-offs**: Don't supply + a session UUID for using agents in a stateless manner.\\n3. **Stream for UX**: + Process responses as they arrive for better user experience\\n4. **Handle + All Operations**: Check for different operation types (START, ANSWER, DONE, + ERROR) when processing responses\\n5. **Clean Up Sessions**: Delete sessions + when done to avoid clutter\\n6. **Use Interactive CLI**: For manual testing + and exploration, the interactive CLI provides the best experience\\n\",\"format\":\"MARKDOWN\",\"md5\":\"bbaaf8cccd2b664ba4f7daf47d1f2bf4\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"Retrieval + Agents Orchestrator \\n Interact with Nuclia's Retrieval Agents Orchestrator + to have intelligent conversations over several knowledge sources with persistent + session management and real-time streaming responses. \\n Prerequisites \\n + Install the Nuclia SDK: \\n sh \\n pip install nuclia \\n Ensure you have: + \\n - A valid Nuclia authentication token (see Authentication) \\n - Access + to a configured Retrieval Agent \\n Overview \\n The nuclia.py library provides + several ways to interact with your Retrieval Agents Orchestrators: \\n \\n + Interactive CLI: A rich, user-friendly terminal interface (recommended) \\n + Standard CLI: Direct access to raw websocket messages for debugging \\n Session + Management: Create and manage persistent conversation sessions \\n Programmatic + API: Python SDK for building custom applications \\n \\n Listing Available + Agents \\n Discover what Retrieval Agents Orchestrators you have access to. + \\n \\n CLI: \\n \\n sh \\n nuclia agents list \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() + \\n all_agents = agents.list() \\n for agent in all_agents: \\n print(f Agent: + {agent.title} ({agent.id}) ) \\n print(f Slug: {agent.slug} ) \\n print(f + Zone: {agent.zone} ) \\n ``` \\n Getting a Specific Agent \\n \\n CLI: \\n + \\n sh \\n nuclia agents get --account= my-account --id= agent-uuid --zone= + europe-1 \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agents import + NucliaAgents \\n agents = NucliaAgents() \\n agent_details = agents.get( \\n + account= my-account , \\n id= agent-uuid , \\n zone= europe-1 \\n ) \\n print(agent_details) + \\n ``` \\n Setting a Default Agent \\n \\n CLI: \\n \\n sh \\n nuclia agents + default [AGENT_SLUG or AGENT_UUID] \\n \\n SDK: \\n \\n ```python \\n from + nuclia.sdk.agents import NucliaAgents \\n agents = NucliaAgents() \\n agents.default( + my-agent ) \\n ``` \\n This sets the default agent for all subsequent operations. + \\n Interactive CLI (Recommended) \\n The interactive CLI provides a beautiful, + real-time interface for conversing with your Retrieval Agents Orchestrator. + \\n Starting the Interactive CLI \\n \\n CLI: \\n \\n sh \\n nuclia agent + cli interact \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n agent.cli.interact() \\n ``` \\n + This launches an interactive terminal session where you can: \\n - Ask questions + and see streaming responses \\n - View processing steps in real-time \\n - + Manage conversation sessions \\n - See retrieved context and citations \\n + Interactive CLI Commands \\n The CLI supports several commands (prefix with + /): \\n | Command | Description | \\n |---------|-------------| \\n | /help + | Show available commands | \\n | /new_session | Create a new persistent session + | \\n | /list_sessions | List all your sessions | \\n | /change_session | + Switch to a different session, use 'ephemeral' for a temporary session | \\n + | /clear | Clear the screen | \\n | /exit | Exit the CLI | \\n Please note + that all commands related to sessions require a Retrieval Agent Orchestrator + with the option Agent with memory enabled during creation. \\n Session Management + \\n Sessions allow you to maintain conversation context across multiple interactions. + \\n \\n This feature will only be available if you checked Agent with memory + during the creation of your Retrieval Agents Orchestrator. \\n \\n Creating + a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session new --name= My + Research Session \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session_uuid = agent.session.new( + My Research Session ) \\n print(f Created session: {session_uuid} ) \\n ``` + \\n Listing Sessions \\n \\n CLI: \\n \\n sh \\n nuclia agent session list + \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent import NucliaAgent + \\n agent = NucliaAgent() \\n sessions = agent.session.list() \\n for session + in sessions.resources: \\n print(f {session.title}: {session.id} ) \\n ``` + \\n Getting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent session get + --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python \\n from nuclia.sdk.agent + import NucliaAgent \\n agent = NucliaAgent() \\n session = agent.session.get(session_uuid) + \\n print(f Session: {session.title} ) \\n print(f Created: {session.created} + ) \\n ``` \\n Deleting a Session \\n \\n CLI: \\n \\n sh \\n nuclia agent + session delete --session_uuid=[SESSION_UUID] \\n \\n SDK: \\n \\n ```python + \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n + agent.session.delete(session_uuid) \\n ``` \\n Interaction \\n Aside from + the interactive CLI, you can interact with your Retrieval Agents Orchestrator + with the simple CLI or programmatically using the SDK. \\n Basic Interaction + \\n CLI: \\n bash \\n nuclia agent interact What is Eric known for? \\n SDK: + \\n ```python \\n from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() + \\n Iterate over streaming responses \\n for response in agent.interact( \\n + question= What is Eric known for? \\n ): \\n if response.operation == ANSWER + and response.answer: \\n print(response.answer) \\n elif response.step: \\n + print(f Processing: {response.step.module} ) \\n ``` \\n Not supplying a session_uuid + when calling interact will use an ephemeral session by default. To maintain + context, provide a persistent session UUID. \\n Using Persistent Sessions + \\n CLI: \\n ```bash \\n nuclia agent sessions new Customer Support Chat \\n + Note the session UUID returned \\n nuclia agent interact What are your business + hours? --session_uuid= SESSION_UUID \\n nuclia agent interact Are you open + on weekends? --session_uuid= SESSION_UUID \\n ``` \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n Create + a session \\n session_uuid = agent.session.new( Customer Support Chat ) \\n + Have a conversation with context \\n for response in agent.interact( \\n session_uuid=session_uuid, + \\n question= What are your business hours? \\n ): \\n if response.answer: + \\n print(response.answer) \\n Follow-up question maintains context \\n for + response in agent.interact( \\n session_uuid=session_uuid, \\n question= Are + you open on weekends? \\n ): \\n if response.answer: \\n print(response.answer) + \\n ``` \\n Understanding Response Types \\n When interacting with an agent, + you receive a stream of AragAnswer objects with different operations: \\n + | Operation | Description | \\n |-----------|-------------| \\n | START | + Interaction has begun | \\n | ANSWER | Processing step or partial answer | + \\n | DONE | Interaction complete | \\n | ERROR | An error occurred | \\n + | AGENT_REQUEST | Agent needs user feedback | \\n Response Attributes \\n + Each response may contain: \\n \\n step: Information about the current processing + step \\n module: The module being executed (e.g., rephrase , basic_ask , remi + ) \\n title: Display title for the step \\n value: Result of the step \\n + reason: Explanation for the step \\n timeit: Time taken in seconds \\n \\n + input_nuclia_tokens/output_nuclia_tokens: Token usage \\n \\n \\n context: + Retrieved context from the knowledge base \\n \\n chunks: List of retrieved + text chunks with sources \\n \\n summary: Summary of the context or partial + answer \\n \\n \\n answer: The final answer text (Markdown formatted) \\n + \\n \\n generated_text: Intermediate generated text \\n \\n \\n possible_answer: + Alternative answer being considered \\n \\n \\n exception: Error details if + something went wrong \\n \\n \\n Processing Responses \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n for response in agent.interact(question= + Tell me about AI ): \\n if response.operation == AnswerOperation.START: \\n + print( Starting... ) \\n elif response.step: \\n print(f Step: {response.step.module} + ({response.step.timeit:.2f}s) ) \\n \\n elif response.context: \\n print(f + Retrieved {len(response.context.chunks)} chunks ) \\n for chunk in response.context.chunks: + \\n print(f - {chunk.title}: {chunk.text[:100]}... ) \\n \\n elif response.answer: + \\n print(f \\\\nFinal Answer:\\\\n{response.answer} ) \\n \\n elif response.operation + == AnswerOperation.DONE: \\n print( Complete! ) \\n \\n elif response.operation + == AnswerOperation.ERROR: \\n print(f Error: {response.exception.detail if + response.exception else 'Unknown'} ) \\n \\n ``` \\n Standard CLI for Raw + Messages \\n For debugging or advanced use cases, you can access raw websocket + messages programmatically: \\n ```python \\n from nuclia.sdk.agent import + NucliaAgent \\n agent = NucliaAgent() \\n Iterate over all messages \\n for + message in agent.interact( \\n question= What is RAO? \\n ): \\n # message + is an AragAnswer object with all raw data \\n print(f Operation: {message.operation} + ) \\n print(f Raw message: {message.model_dump_json(indent=2)} ) \\n ``` \\n + This gives you direct access to all websocket message data for debugging or + custom processing. \\n Advanced Features \\n Agent Feedback Requests \\n Agents + can request additional input from users during processing: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n from nuclia_models.agent.interaction + import AnswerOperation \\n agent = NucliaAgent() \\n generator = agent.interact(question= + Help me with X ) \\n for response in generator: \\n if response.operation + == AnswerOperation.AGENT_REQUEST: \\n # Agent is requesting user input \\n + user_input = input(f Agent asks: {response.feedback.question}\\\\n> ) \\n + # Send response back \\n generator.send(user_input) \\n elif response.answer: + \\n print(response.answer) \\n ``` \\n Error Handling \\n ```python \\n from + nuclia.sdk.agent import NucliaAgent \\n from nuclia.exceptions import RaoAPIException + \\n agent = NucliaAgent() \\n try: \\n for response in agent.interact(question= + Hello? ): \\n if response.exception: \\n print(f Agent error: {response.exception.detail} + ) \\n elif response.answer: \\n print(response.answer) \\n except RaoAPIException + as e: \\n print(f API error: {e.detail} ) \\n except Exception as e: \\n print(f + Unexpected error: {e} ) \\n ``` \\n Passing Custom Headers to MCP \\n If your + Retrieval Agents Orchestrator requires custom headers for MCP Agents, you + can pass them as follows: \\n CLI: \\n bash \\n nuclia agent interact What + is AI? --headers '{ X-Custom-Header : value }' \\n SDK: \\n ```python \\n + from nuclia.sdk.agent import NucliaAgent \\n agent = NucliaAgent() \\n for + response in agent.interact( \\n question= What is AI? , \\n headers={ X-Custom-Header + : value } \\n ): \\n if response.answer: \\n print(response.answer) \\n ``` + \\n Please ensure that the 'Allowed Headers' configuration in your MCP agent + includes any custom headers you wish to use. \\n Best Practices \\n \\n Use + Sessions for Context: Create sessions when you need multi-turn conversations + with context retention \\n Use Ephemeral Sessions for One-offs: Don't supply + a session UUID for using agents in a stateless manner. \\n Stream for UX: + Process responses as they arrive for better user experience \\n Handle All + Operations: Check for different operation types (START, ANSWER, DONE, ERROR) + when processing responses \\n Clean Up Sessions: Delete sessions when done + to avoid clutter \\n Use Interactive CLI: For manual testing and exploration, + the interactive CLI provides the best experience \\n 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[`UsageMetric`](UsageMetric.md)\\n\\n## + Properties\\n\\n### details\\n\\n> **details**: [`NucliaTokensDetails`](NucliaTokensDetails.md)[]\\n\\n#### + Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`details`](UsageMetric.md#details)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n***\\n\\n### + name\\n\\n> **name**: `\\\"nuclia_tokens\\\"`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`name`](UsageMetric.md#name)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n***\\n\\n### + value\\n\\n> **value**: `number`\\n\\n#### Overrides\\n\\n[`UsageMetric`](UsageMetric.md).[`value`](UsageMetric.md#value)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"af7b6aa34935b09fa5ddc2badd6da04f\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / NucliaTokensMetric \\n Interface: NucliaTokensMetric + \\n Extends \\n \\n UsageMetric \\n \\n Properties \\n details \\n \\n details: + NucliaTokensDetails[] \\n \\n Overrides \\n UsageMetric.details \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n name \\n \\n name: + nuclia_tokens \\n \\n Overrides \\n UsageMetric.name \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:183 + \\n \\n value \\n \\n value: number \\n \\n Overrides \\n UsageMetric.value + \\n Defined in \\n 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\\n\",\"id\":\"8b3e0ef630a346d1b591143309db87ec/t/page/0-281\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":281,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0d01e250360a4d6c91f3baf2de5e7d38\":{\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38\",\"slug\":\"docs-develop-js-sdk-interfaces-ReasoningConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-02-24T11:15:01.495997\",\"modified\":\"2026-07-14T12:49:30.245034\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ReasoningConfig\\n\\n# + Interface: ReasoningConfig\\n\\n## Properties\\n\\n### budget\\\\_tokens?\\n\\n> + `optional` **budget\\\\_tokens**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:647](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L647)\\n\\n***\\n\\n### + effort?\\n\\n> `optional` **effort**: [`NumericReasoningEffort`](../enumerations/NumericReasoningEffort.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:648](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L648)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"940c8be17c938dcc14e4abb000b0f8b5\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n \\n optional effort: NumericReasoningEffort \\n \\n Defined in \\n 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> develop > js sdk > interfaces > ReasoningConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ReasoningConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\":{\"score\":0.6052000522613525,\"score_type\":\"VECTOR\",\"order\":9,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ReasoningConfig \\n Interface: ReasoningConfig + \\n Properties \\n budget_tokens? \\n \\n optional budget_tokens: number \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 \\n \\n effort? + \\n\",\"id\":\"0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":224,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"66b6f0dbd883413e98fbbf5b59f049b9\":{\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9\",\"slug\":\"docs-develop-js-sdk-enumerations-UsageType-md\",\"title\":\"docs + > develop > js sdk > enumerations > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tl\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T09:59:02.349911\",\"modified\":\"2026-07-14T12:54:11.386825\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / UsageType\\n\\n# + Enumeration: UsageType\\n\\n## Enumeration Members\\n\\n### AI\\\\_TOKENS\\\\_USED\\n\\n> + **AI\\\\_TOKENS\\\\_USED**: `\\\"ai_tokens_used\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:206](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L206)\\n\\n***\\n\\n### + BYTES\\\\_PROCESSED\\n\\n> **BYTES\\\\_PROCESSED**: `\\\"bytes_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:197](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L197)\\n\\n***\\n\\n### + CHARS\\\\_PROCESSED\\n\\n> **CHARS\\\\_PROCESSED**: `\\\"chars_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:198](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L198)\\n\\n***\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n> **MEDIA\\\\_FILES\\\\_PROCESSED**: `\\\"media_files_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:200](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L200)\\n\\n***\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n> **MEDIA\\\\_SECONDS\\\\_PROCESSED**: + `\\\"media_seconds_processed\\\"`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:199](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L199)\\n\\n***\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n> **NUCLIA\\\\_TOKENS**: `\\\"nuclia_tokens_billed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:207](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L207)\\n\\n***\\n\\n### + PAGES\\\\_PROCESSED\\n\\n> **PAGES\\\\_PROCESSED**: `\\\"pages_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:201](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L201)\\n\\n***\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n> **PARAGRAPHS\\\\_PROCESSED**: `\\\"paragraphs_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:202](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L202)\\n\\n***\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n> **PRE\\\\_PROCESSING\\\\_TIME**: `\\\"pre_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:194](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L194)\\n\\n***\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n> **RESOURCES\\\\_PROCESSED**: `\\\"resources_processed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:196](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L196)\\n\\n***\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n> **SEARCHES\\\\_PERFORMED**: `\\\"searches_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:204](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L204)\\n\\n***\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n> **SLOW\\\\_PROCESSING\\\\_TIME**: `\\\"slow_processing_time\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:195](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L195)\\n\\n***\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n> **SUGGESTIONS\\\\_PERFORMED**: `\\\"suggestions_performed\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:205](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L205)\\n\\n***\\n\\n### + TRAIN\\\\_SECONDS\\n\\n> **TRAIN\\\\_SECONDS**: `\\\"train_seconds\\\"`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:203](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/db.models.ts#L203)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"a6825b7bf9d5960444b0981f205811a3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":1833,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:54:13.123173Z\",\"last_understanding\":\"2026-07-14T12:54:12.864112Z\",\"last_extract\":\"2026-07-14T12:54:12.463871Z\",\"last_processing_start\":\"2026-07-14T12:54:12.433665Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > enumerations > UsageType\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > enumerations > UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\":{\"score\":0.6058452725410461,\"score_type\":\"VECTOR\",\"order\":8,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / UsageType \\n Enumeration: UsageType \\n + Enumeration Members \\n AI_TOKENS_USED \\n \\n AI_TOKENS_USED: ai_tokens_used + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:206 \\n \\n BYTES_PROCESSED + \\n \\n BYTES_PROCESSED: bytes_processed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:197 + \\n \\n CHARS_PROCESSED \\n \\n CHARS_PROCESSED: chars_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:198 \\n \\n MEDIA_FILES_PROCESSED + \\n \\n MEDIA_FILES_PROCESSED: media_files_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:200 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \\n MEDIA_SECONDS_PROCESSED: media_seconds_processed \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:199 \\n \\n NUCLIA_TOKENS \\n \\n + NUCLIA_TOKENS: nuclia_tokens_billed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:207 + \\n \\n PAGES_PROCESSED \\n \\n PAGES_PROCESSED: pages_processed \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:201 \\n \\n PARAGRAPHS_PROCESSED + \\n \\n PARAGRAPHS_PROCESSED: paragraphs_processed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:202 \\n \\n PRE_PROCESSING_TIME \\n + \\n PRE_PROCESSING_TIME: pre_processing_time \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:194 + \\n \\n RESOURCES_PROCESSED \\n \\n RESOURCES_PROCESSED: resources_processed + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:196 \\n \\n SEARCHES_PERFORMED + \\n \\n SEARCHES_PERFORMED: searches_performed \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:204 + \\n \\n SLOW_PROCESSING_TIME \\n \\n SLOW_PROCESSING_TIME: slow_processing_time + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:195 \\n \\n SUGGESTIONS_PERFORMED + \\n \\n SUGGESTIONS_PERFORMED: suggestions_performed \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:205 \\n \\n TRAIN_SECONDS \\n \\n TRAIN_SECONDS: + train_seconds \\n \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:203\",\"id\":\"66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":1833,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"0a003c3f724e45e392a9c8d1ce8800c1\":{\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1\",\"slug\":\"docs-develop-js-sdk-interfaces-Consumption-md\",\"title\":\"docs + > develop > js sdk > interfaces > Consumption\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-12-09T11:51:46.788864\",\"modified\":\"2026-07-14T12:51:15.300757\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / Consumption\\n\\n# + Interface: Consumption\\n\\n## Properties\\n\\n### customer\\\\_key\\\\_tokens\\n\\n> + **customer\\\\_key\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:230](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L230)\\n\\n***\\n\\n### + normalized\\\\_tokens\\n\\n> **normalized\\\\_tokens**: [`TokenConsumption`](TokenConsumption.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:229](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L229)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"654b0dfe17ab08959c78891dd24c3424\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":349,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:16.563837Z\",\"last_understanding\":\"2026-07-14T12:51:16.208384Z\",\"last_extract\":\"2026-07-14T12:51:15.984803Z\",\"last_processing_start\":\"2026-07-14T12:51:15.966495Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > Consumption\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > Consumption\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\":{\"score\":0.6083368062973022,\"score_type\":\"VECTOR\",\"order\":7,\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / Consumption \\n Interface: Consumption + \\n Properties \\n customer_key_tokens \\n \\n customer_key_tokens: TokenConsumption + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:230 \\n + \\n normalized_tokens \\n \\n normalized_tokens: TokenConsumption \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:229\",\"id\":\"0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":349,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"4039d76b0fff4962900836ab3fdec9f7\":{\"id\":\"4039d76b0fff4962900836ab3fdec9f7\",\"slug\":\"docs-rag-advanced-consumption-mdx\",\"title\":\"docs + > rag > advanced > consumption.\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-10-03T08:32:26.341394\",\"modified\":\"2026-06-09T08:18:16.730959\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/consumption\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + consumption\\ntitle: Token consumption\\n---\\n\\nimport Tabs from \\\"@theme/Tabs\\\";\\nimport + TabItem from \\\"@theme/TabItem\\\";\\n\\n# Token consumption\\n\\nAgentic + RAG is a license and consumption-based service. This means that you pay for + the computational resources you consume. The consumption is measured in **Agentic + RAG tokens**.\\nAll public 3rd-party LLMs base their pricing on the number + of tokens consumed. In the LLM world, a token is around 4-5 characters on + average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it.\\nSince all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them.\\n\\n## How Tokens Are Consumed in RAG\\n\\nWhen + a user asks a question to your Knowledge Box, the token consumption process + follows these steps:\\n\\n1. **Question Processing**: The system finds the + most relevant paragraphs to answer the question\\n2. **Context Assembly**: + These paragraphs are used as context when calling the LLM model\\n3. **Prompt + Creation**: Agentic RAG assembles the **prompt**, **context**, and **question** + into a single string\\n4. **LLM Processing**: This complete string is sent + to the LLM, corresponding to a certain number of **input tokens**\\n5. **Answer + Generation**: The LLM generates the answer, which corresponds to a certain + number of **output tokens**\\n\\n**Total consumption** = Input tokens + Output + tokens + Image tokens\\n\\n### Factors That Impact Token Consumption\\n\\nToken + consumption is directly affected by:\\n\\n- **Large context**: Results from + using RAG strategies like \\\"Full resource\\\" or \\\"Neighbouring paragraphs\\\", + or from using the `extra_context` parameter\\n- **Long questions**: More detailed + or complex questions require more input tokens\\n- **Long prompts**: Extensive + system prompts increase the input token count\\n- **Detailed answers**: Comprehensive + responses require more output tokens\\n- **Images in context**: When using + multimodal models, images included in the retrieved context significantly + increase token consumption\\n\\n## How to Limit and Control Token Consumption\\n\\n### + Strategy 1: Optimize Your Parameters\\n\\nThe first approach to reducing token + consumption is to fine-tune your request parameters:\\n\\n- **Optimize prompts**: + Ensure your prompts are concise and focused, avoiding unnecessary verbosity\\n- + **Control resource scope**: When using the \\\"Full resource\\\" strategy, + use the `count` attribute to limit the number of resources returned\\n- **Tune + neighboring context**: For the \\\"Neighbouring paragraphs\\\" strategy, optimize + the `before` and `after` attributes to balance context quality with token + efficiency\\n- **Manage summary length**: When using the \\\"Hierarchical\\\" + strategy, ensure that resource summaries are appropriately sized\\n- **Choose + efficient models**: Select LLMs that offer better token efficiency (typically, + ChatGPT 4o-mini is more cost-effective than ChatGPT 4o)\\n\\n### Strategy + 2: Set Hard Limits\\n\\nYou can implement safeguards against excessive token + consumption:\\n\\nUse the `max_tokens` parameter on the [`/ask` endpoint](/docs/api#tag/Search/operation/ask_knowledgebox_endpoint_kb__kbid__ask_post) + to set hard limits on:\\n- **Context size**: Limits the amount of retrieved + information sent to the LLM\\n- **Answer length**: Limits the length of the + generated response\\n\\n#### Important Considerations\\n\\n**Context Limitations**:\\n- + Restricting context size may result in less relevant answers since the LLM + has less information to work with\\n- Balance between cost control and answer + quality\\n\\n**Answer Length Limitations**:\\n- The LLM might not complete + its response if it hits the token limit, potentially cutting sentences mid-way\\n- + **Recommended approach**: Include length requirements in your prompt (e.g., + \\\"Please answer in less than 200 words\\\") rather than relying solely on + hard limits\\n- This allows the LLM to naturally conclude its response within + the desired length\\n\\n## How to Monitor Token Consumption\\n\\n### Understanding + Token Consumption Data\\n\\nYou can receive detailed token consumption information + from the following endpoints that utilize LLM models: `ask`, `chat`, `remi`, + `query`, `sentence`, `summarize`, `tokens`, and `rerank`.\\n\\n:::note\\nThe + `rephrase` endpoint currently does not support token consumption monitoring.\\n:::\\n\\nTo + receive token consumption data, you must include the following header in your + request:\\n```\\nX-SHOW-CONSUMPTION: true\\n```\\n\\nThe token consumption + data is provided in different formats depending on the response type:\\n- + **Streaming responses** (`application/x-ndjson`): Token consumption appears + as a separate JSON chunk with type \\\"consumption\\\"\\n- **Standard responses** + (`application/json`): Token consumption is included in a \\\"consumption\\\" + field within the main response\\n\\n### Token Consumption Response Format\\n\\n\\n \\n ```json\\n {\\n \\\"item\\\": + {\\n \\\"type\\\": \\\"consumption\\\",\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n \\n ```json\\n {\\n \\\"consumption\\\": {\\n \\\"normalized_tokens\\\": + {\\n \\\"input\\\": 13,\\n \\\"output\\\": 34,\\n \\\"image\\\": + 0.0\\n },\\n \\\"customer_key_tokens\\\": {\\n \\\"input\\\": + 0.0,\\n \\\"output\\\": 0.0,\\n \\\"image\\\": 0.0\\n }\\n + \ }\\n }\\n ```\\n \\n\\n\\n### Understanding + Token Types\\n\\n**Normalized Tokens** (`normalized_tokens`):\\n- These represent + the number of Agentic RAG tokens consumed and that you will be billed for\\n- + Values are normalized across different LLM providers for consistent billing\\n- + Include separate counts for:\\n - `input`: Tokens used for the prompt, context, + and question\\n - `output`: Tokens used for the generated response\\n - + `image`: Tokens used for image processing (when applicable)\\n\\n**Customer + Key Tokens** (`customer_key_tokens`):\\n- These represent tokens consumed + when using your own LLM API keys\\n- These tokens are **not billed** by Agentic + RAG since you're using your own API keys\\n- Values are also normalized for + comparison purposes across different providers\\n\",\"format\":\"MARKDOWN\",\"md5\":\"204fe47dbd7eb38d465d05fa7538e51e\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: consumption \\n title: Token consumption \\n \\n import Tabs from + @theme/Tabs ; \\n import TabItem from @theme/TabItem ; \\n Token consumption + \\n Agentic RAG is a license and consumption-based service. This means that + you pay for the computational resources you consume. The consumption is measured + in Agentic RAG tokens. \\n All public 3rd-party LLMs base their pricing on + the number of tokens consumed. In the LLM world, a token is around 4-5 characters + on average, which might fit an entire word or be split into parts. The number + of tokens is proportional to the amount of text, measured in chunks of 4-5 + characters. It closely relates to words but not entirely. The longer a sentence + is, the more tokens it will consume to read or to generate it. \\n Since all + these 3rd-party LLMs have different pricing, Agentic RAG tokens serve to normalize + the cost across all of them. \\n How Tokens Are Consumed in RAG \\n When a + user asks a question to your Knowledge Box, the token consumption process + follows these steps: \\n \\n Question Processing: The system finds the most + relevant paragraphs to answer the question \\n Context Assembly: These paragraphs + are used as context when calling the LLM model \\n Prompt Creation: Agentic + RAG assembles the prompt, context, and question into a single string \\n LLM + Processing: This complete string is sent to the LLM, corresponding to a certain + number of input tokens \\n Answer Generation: The LLM generates the answer, + which corresponds to a certain number of output tokens \\n \\n Total consumption + = Input tokens + Output tokens + Image tokens \\n Factors That Impact Token + Consumption \\n Token consumption is directly affected by: \\n \\n Large context: + Results from using RAG strategies like Full resource or Neighbouring paragraphs + , or from using the extra_context parameter \\n Long questions: More detailed + or complex questions require more input tokens \\n Long prompts: Extensive + system prompts increase the input token count \\n Detailed answers: Comprehensive + responses require more output tokens \\n Images in context: When using multimodal + models, images included in the retrieved context significantly increase token + consumption \\n \\n How to Limit and Control Token Consumption \\n Strategy + 1: Optimize Your Parameters \\n The first approach to reducing token consumption + is to fine-tune your request parameters: \\n \\n Optimize prompts: Ensure + your prompts are concise and focused, avoiding unnecessary verbosity \\n Control + resource scope: When using the Full resource strategy, use the count attribute + to limit the number of resources returned \\n Tune neighboring context: For + the Neighbouring paragraphs strategy, optimize the before and after attributes + to balance context quality with token efficiency \\n Manage summary length: + When using the Hierarchical strategy, ensure that resource summaries are appropriately + sized \\n Choose efficient models: Select LLMs that offer better token efficiency + (typically, ChatGPT 4o-mini is more cost-effective than ChatGPT 4o) \\n \\n + Strategy 2: Set Hard Limits \\n You can implement safeguards against excessive + token consumption: \\n Use the max_tokens parameter on the /ask endpoint to + set hard limits on: \\n - Context size: Limits the amount of retrieved information + sent to the LLM \\n - Answer length: Limits the length of the generated response + \\n Important Considerations \\n Context Limitations: \\n - Restricting context + size may result in less relevant answers since the LLM has less information + to work with \\n - Balance between cost control and answer quality \\n Answer + Length Limitations: \\n - The LLM might not complete its response if it hits + the token limit, potentially cutting sentences mid-way \\n - Recommended approach: + Include length requirements in your prompt (e.g., Please answer in less than + 200 words ) rather than relying solely on hard limits \\n - This allows the + LLM to naturally conclude its response within the desired length \\n How to + Monitor Token Consumption \\n Understanding Token Consumption Data \\n You + can receive detailed token consumption information from the following endpoints + that utilize LLM models: ask, chat, remi, query, sentence, summarize, tokens, + and rerank. \\n :::note \\n The rephrase endpoint currently does not support + token consumption monitoring. \\n ::: \\n To receive token consumption data, + you must include the following header in your request: \\n X-SHOW-CONSUMPTION: + true \\n The token consumption data is provided in different formats depending + on the response type: \\n - Streaming responses (application/x-ndjson): Token + consumption appears as a separate JSON chunk with type consumption \\n - Standard + responses (application/json): Token consumption is included in a consumption + field within the main response \\n Token Consumption Response Format \\n \\n + \\n json \\n { \\n item : { \\n type : consumption , \\n normalized_tokens + : { \\n input : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens + : { \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n + \\n \\n json \\n { \\n consumption : { \\n normalized_tokens : { \\n input + : 13, \\n output : 34, \\n image : 0.0 \\n }, \\n customer_key_tokens : { + \\n input : 0.0, \\n output : 0.0, \\n image : 0.0 \\n } \\n } \\n } \\n \\n + \\n Understanding Token Types \\n Normalized Tokens (normalized_tokens): \\n + - These represent the number of Agentic RAG tokens consumed and that you will + be billed for \\n - Values are normalized across different LLM providers for + consistent billing \\n - Include separate counts for: \\n - input: Tokens + used for the prompt, context, and question \\n - output: Tokens used for the + generated response \\n - image: Tokens used for image processing (when applicable) + \\n Customer Key Tokens (customer_key_tokens): \\n - These represent tokens + consumed when using your own LLM API keys \\n - These tokens are not billed + by Agentic RAG since you're using your own API keys \\n - Values are also + normalized for comparison purposes across different 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Context size: Limits the amount of retrieved information sent to the LLM + \\n - Answer length: Limits the length of the generated response \\n Important + Considerations \\n Context Limitations: \\n\",\"id\":\"4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":6,\"start\":3075,\"end\":3340,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"89cc367c149e4f6eab0e06a743d1edba\":{\"id\":\"89cc367c149e4f6eab0e06a743d1edba\",\"slug\":\"docs-rag-advanced-openai-api-compatible-models-md\",\"title\":\"docs + > rag > advanced > openai api compatible models\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-05-23T07:21:32.894218\",\"modified\":\"2026-06-09T08:07:43.359058\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + openai-api-compatible-models\\ntitle: Connect to any OpenAI API compatible + LLM\\n---\\n\\n# Connect to any OpenAI API compatible LLM\\n\\nAgentic RAG + allows you to connect to any OpenAI API compatible LLM. This means that you + can use any LLM that has an API compatible with the OpenAI API which has become + a standard in the industry.\\n\\nMany of the options for self-hosted LLMs, + open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications.\\n\\n## Configuration\\n\\nTo modify your knowledge box + configuration you can do so in three manners, through the API, the Nuclia + CLI / SDK or the Agentic RAG dashboard.\\n\\nThe Agentic RAG dashboard offers + the most user-friendly way to modify the configuration of your knowledge box + and we will use it in this example.\\n\\nWe will be setting up a connection + to the **Phi 4 Reasoning Plus** model, hosted by **OpenRouter** which offers + a wide range of open-source and commercial models compatible with the OpenAI + API. We can see more information about this specific model [here](https://openrouter.ai/microsoft/phi-4-reasoning-plus:free), + the API parameters are located under the **API** tab.\\n\\n1. **Open the AI + Models page** \\n In the left sidebar under **Advanced**, click **AI Models**.\\n2. + **Select \u201COpenAI API Compatible Model\u201D** \\n From the models + list, choose **OpenAI API Compatible Model**.\\n3. **Enable custom Key** \\n + \ Toggle the option for using you own `OpenAI API Compatible Key` if it is + not already enabled.\\n4. **Fill in the configuration parameters**\\n\\n - + **API Key**:\\n - Description: The API key for your LLM. This is the key + that you would use as an authorization header in the API. You may leave this + blank if the endpoint you are connecting to does not require an API key.\\n + \ - Example: We will set this to our OpenRouter API key.\\n - **API URL**:\\n + \ - Description: The URL of the API endpoint for your LLM. This may be + shared between multiple models.\\n - Example: For OpenRouter, it is the + same for all models: `https://openrouter.ai/api/v1`\\n - **Model**:\\n - + Description: The name of the model you want to use, it needs to exactly match + the name of the model in the API.\\n - Example: For Phi 4 Reasoning Plus + in the OpenRouter API, it is `microsoft/phi-4-reasoning-plus:free`.\\n - + **Maximum supported input tokens**:\\n - Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only.\\n - + Example: For Phi 4 Reasoning Plus, the total context size is `32768` tokens, + as we want to leave room for the output, we will set the maximum supported + input tokens as `32768 - 1024 = 31744`.\\n - **Maximum supported output + tokens**:\\n - Description: The maximum number of tokens that the model + can generate as output. Again, we should keep in mind that this value summed + to the **Maximum supported input tokens** should not exceed the total context + size supported by the model.\\n - Example: For Phi 4 Reasoning Plus, the + maximum output tokens is specified at `32768`, but we already reserved `31744` + for the input tokens, so we will set this to `32768 - 31744 = 1024`.\\n - + **Model Features**:\\n - Description: Under this section you will find + multiple toggles related to features supported by the model, these vary from + model to model, but most often the default values are well suited to most + use cases. The most relevant toggle is for `Image Support` which allows you + to use images as input for the model.\\n - Example: Image input is not + supported by Phi 4 Reasoning Plus, so we will leave it disabled.\\n\\n5. **Save** + \ \\n Click **Save changes**.\\n\\n6. **Test your model** \\n Run a sample + query in Agentic RAG or via API/CLI. Adjust your prompt templates and token + settings as needed.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"37cd8aff748addd04a363fa50828d1fe\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: openai-api-compatible-models \\n title: Connect to any OpenAI API + compatible LLM \\n \\n Connect to any OpenAI API compatible LLM \\n Agentic + RAG allows you to connect to any OpenAI API compatible LLM. This means that + you can use any LLM that has an API compatible with the OpenAI API which has + become a standard in the industry. \\n Many of the options for self-hosted + LLMs, open-source LLMs hosted by cloud providers or commercial LLMs are compatible + with the OpenAI API. This means that you can use them with Agentic RAG without + any modifications. \\n Configuration \\n To modify your knowledge box configuration + you can do so in three manners, through the API, the Nuclia CLI / SDK or the + Agentic RAG dashboard. \\n The Agentic RAG dashboard offers the most user-friendly + way to modify the configuration of your knowledge box and we will use it in + this example. \\n We will be setting up a connection to the Phi 4 Reasoning + Plus model, hosted by OpenRouter which offers a wide range of open-source + and commercial models compatible with the OpenAI API. We can see more information + about this specific model here, the API parameters are located under the API + tab. \\n \\n Open the AI Models page \\n In the left sidebar under Advanced, + click AI Models. \\n Select OpenAI API Compatible Model \\n From the models + list, choose OpenAI API Compatible Model. \\n Enable custom Key \\n Toggle + the option for using you own OpenAI API Compatible Key if it is not already + enabled. \\n \\n Fill in the configuration parameters \\n \\n \\n API Key: + \\n \\n Description: The API key for your LLM. This is the key that you would + use as an authorization header in the API. You may leave this blank if the + endpoint you are connecting to does not require an API key. \\n Example: We + will set this to our OpenRouter API key. \\n \\n \\n API URL: \\n Description: + The URL of the API endpoint for your LLM. This may be shared between multiple + models. \\n Example: For OpenRouter, it is the same for all models: https://openrouter.ai/api/v1 + \\n \\n \\n Model: \\n Description: The name of the model you want to use, + it needs to exactly match the name of the model in the API. \\n Example: For + Phi 4 Reasoning Plus in the OpenRouter API, it is microsoft/phi-4-reasoning-plus:free. + \\n \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n Example: + For Phi 4 Reasoning Plus, the total context size is 32768 tokens, as we want + to leave room for the output, we will set the maximum supported input tokens + as 32768 - 1024 = 31744. \\n \\n \\n Maximum supported output tokens: \\n + Description: The maximum number of tokens that the model can generate as output. + Again, we should keep in mind that this value summed to the Maximum supported + input tokens should not exceed the total context size supported by the model. + \\n Example: For Phi 4 Reasoning Plus, the maximum output tokens is specified + at 32768, but we already reserved 31744 for the input tokens, so we will set + this to 32768 - 31744 = 1024. \\n \\n \\n \\n Model Features: \\n \\n Description: + Under this section you will find multiple toggles related to features supported + by the model, these vary from model to model, but most often the default values + are well suited to most use cases. The most relevant toggle is for Image Support + which allows you to use images as input for the model. \\n Example: Image + input is not supported by Phi 4 Reasoning Plus, so we will leave it disabled. + \\n \\n \\n \\n Save \\n Click Save changes. \\n \\n \\n Test your model \\n + Run a sample query in Agentic RAG or via API/CLI. Adjust your prompt templates + and token settings as needed. \\n \\n 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\\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":10,\"start\":2804,\"end\":3043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\":{\"score\":0.63338303565979,\"score_type\":\"VECTOR\",\"order\":5,\"text\":\" + \\n \\n Maximum supported input tokens: \\n Description: The maximum number + of tokens that the model can accept as input. Be mindful that this takes into + account the tokens used in the prompt, query and context. Also take note that + some models may provide their context window as the total between input and + output tokens, while others may provide it as the input tokens only. \\n\",\"id\":\"89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":8,\"start\":2202,\"end\":2575,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"43004f553e534ffe9c9e735856bd9b23\":{\"id\":\"43004f553e534ffe9c9e735856bd9b23\",\"slug\":\"docs-develop-js-sdk-interfaces-ModelConfig-md\",\"title\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-01-08T16:32:44.621593\",\"modified\":\"2026-07-14T12:49:58.922592\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ModelConfig\\n\\n# + Interface: ModelConfig\\n\\n## Properties\\n\\n### assume\\\\_role?\\n\\n> + `optional` **assume\\\\_role**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:592](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L592)\\n\\n***\\n\\n### + driver?\\n\\n> `optional` **driver**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:590](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L590)\\n\\n***\\n\\n### + input\\\\_tokens\\n\\n> **input\\\\_tokens**: `object`\\n\\n#### max\\n\\n> + **max**: `number`\\n\\n#### min?\\n\\n> `optional` **min**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:587](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L587)\\n\\n***\\n\\n### + max\\\\_images?\\n\\n> `optional` **max\\\\_images**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:591](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L591)\\n\\n***\\n\\n### + output\\\\_tokens\\n\\n> **output\\\\_tokens**: `object`\\n\\n#### default\\\\_max?\\n\\n> + `optional` **default\\\\_max**: `number`\\n\\n#### max\\n\\n> **max**: `number`\\n\\n#### + min?\\n\\n> `optional` **min**: `number`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:588](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L588)\\n\\n***\\n\\n### + prompt\\\\_id?\\n\\n> `optional` **prompt\\\\_id**: `string`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:589](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L589)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"0ce41cb0494bcb99176527165e321fec\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ModelConfig \\n Interface: ModelConfig + \\n Properties \\n assume_role? \\n \\n optional assume_role: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:592 \\n \\n driver? + \\n \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n \\n optional max_images: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \\n \\n output_tokens \\n + \\n output_tokens: object \\n \\n default_max? \\n \\n optional default_max: + number \\n \\n max \\n \\n max: number \\n \\n min? \\n \\n optional min: + number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:588 + \\n \\n prompt_id? \\n \\n optional prompt_id: string \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:589\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":212,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":108,\"key\":\"\"},{\"start\":108,\"end\":212,\"key\":\"\"}]},{\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":215,\"end\":380,\"key\":\"\"},{\"start\":380,\"end\":480,\"key\":\"\"}]},{\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":483,\"end\":638,\"key\":\"\"},{\"start\":638,\"end\":703,\"key\":\"\"}]},{\"start\":703,\"end\":895,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":704,\"end\":806,\"key\":\"\"},{\"start\":806,\"end\":895,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:01.191702Z\",\"last_understanding\":\"2026-07-14T12:50:00.962823Z\",\"last_extract\":\"2026-07-14T12:50:00.418522Z\",\"last_processing_start\":\"2026-07-14T12:50:00.364723Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > ModelConfig\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > ModelConfig\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\":{\"score\":0.6202770471572876,\"score_type\":\"VECTOR\",\"order\":6,\"text\":\" + \\n optional max_images: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 + \\n \\n output_tokens \\n \\n output_tokens: object \\n \\n default_max? \\n + \\n optional default_max: number \\n \\n max \\n \\n max: number \\n \\n min? + \\n \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/480-703\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":480,\"end\":703,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\":{\"score\":0.594592273235321,\"score_type\":\"VECTOR\",\"order\":16,\"text\":\" + \\n optional driver: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 + \\n \\n input_tokens \\n \\n input_tokens: object \\n \\n max \\n \\n max: + number \\n \\n min? \\n \\n optional min: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 + \\n \\n max_images? \\n\",\"id\":\"43004f553e534ffe9c9e735856bd9b23/t/page/212-480\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":1,\"start\":212,\"end\":480,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"42a4cd5a30314e94aa079ed0cfe81381\":{\"id\":\"42a4cd5a30314e94aa079ed0cfe81381\",\"slug\":\"docs-develop-js-sdk-interfaces-SplitStrategy-md\",\"title\":\"docs + > develop > js sdk > interfaces > SplitStrategy\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:02:38.468609\",\"modified\":\"2026-07-14T12:51:02.450318\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/SplitStrategy\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / SplitStrategy\\n\\n# + Interface: SplitStrategy\\n\\n## Properties\\n\\n### custom\\\\_split?\\n\\n> + `optional` **custom\\\\_split**: [`CustomSplitStrategy`](../enumerations/CustomSplitStrategy.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:537](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L537)\\n\\n***\\n\\n### + llm\\\\_split?\\n\\n> `optional` **llm\\\\_split**: [`SplitLLMConfig`](SplitLLMConfig.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:538](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L538)\\n\\n***\\n\\n### + manual\\\\_split?\\n\\n> `optional` **manual\\\\_split**: `object`\\n\\n#### + splitter\\n\\n> **splitter**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:539](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L539)\\n\\n***\\n\\n### + max\\\\_paragraph?\\n\\n> `optional` **max\\\\_paragraph**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:536](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L536)\\n\\n***\\n\\n### + name?\\n\\n> `optional` **name**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/kb/kb.models.ts:535](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/kb/kb.models.ts#L535)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d24d884112df2f67b327ac6d6302ddf1\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / SplitStrategy \\n Interface: SplitStrategy + \\n Properties \\n custom_split? \\n \\n optional custom_split: CustomSplitStrategy + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:537 \\n \\n + llm_split? \\n \\n optional llm_split: SplitLLMConfig \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/kb/kb.models.ts:538 \\n \\n manual_split? \\n \\n + optional manual_split: object \\n \\n splitter \\n \\n splitter: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:539 \\n \\n max_paragraph? + \\n \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 + \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":234,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":113,\"key\":\"\"},{\"start\":113,\"end\":234,\"key\":\"\"}]},{\"start\":234,\"end\":502,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":237,\"end\":356,\"key\":\"\"},{\"start\":356,\"end\":502,\"key\":\"\"}]},{\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":505,\"end\":612,\"key\":\"\"},{\"start\":612,\"end\":696,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:51:04.701538Z\",\"last_understanding\":\"2026-07-14T12:51:04.225754Z\",\"last_extract\":\"2026-07-14T12:51:03.819024Z\",\"last_processing_start\":\"2026-07-14T12:51:03.798124Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > SplitStrategy\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > SplitStrategy\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\":{\"score\":0.5957005620002747,\"score_type\":\"VECTOR\",\"order\":15,\"text\":\" + \\n optional max_paragraph: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:536 + \\n \\n name? \\n \\n optional name: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/kb/kb.models.ts:535\",\"id\":\"42a4cd5a30314e94aa079ed0cfe81381/t/page/502-696\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":2,\"start\":502,\"end\":696,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"b14cf452a3434839a04c111f2ea4dc51\":{\"id\":\"b14cf452a3434839a04c111f2ea4dc51\",\"slug\":\"docs-develop-js-sdk-enums-UsageType-md\",\"title\":\"docs + > develop > js sdk > enums > UsageType\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\",\"tn\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:51:41.582110\",\"modified\":\"2026-06-09T08:13:34.112208\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/enums/UsageType\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[@nuclia/core](../README.md) + / [Exports](../modules.md) / UsageType\\n\\n# Enumeration: UsageType\\n\\n## + Table of contents\\n\\n### Enumeration Members\\n\\n- [AI\\\\_TOKENS\\\\_USED](UsageType.md#ai_tokens_used)\\n- + [BYTES\\\\_PROCESSED](UsageType.md#bytes_processed)\\n- [CHARS\\\\_PROCESSED](UsageType.md#chars_processed)\\n- + [MEDIA\\\\_FILES\\\\_PROCESSED](UsageType.md#media_files_processed)\\n- [MEDIA\\\\_SECONDS\\\\_PROCESSED](UsageType.md#media_seconds_processed)\\n- + [NUCLIA\\\\_TOKENS](UsageType.md#nuclia_tokens)\\n- [PAGES\\\\_PROCESSED](UsageType.md#pages_processed)\\n- + [PARAGRAPHS\\\\_PROCESSED](UsageType.md#paragraphs_processed)\\n- [PRE\\\\_PROCESSING\\\\_TIME](UsageType.md#pre_processing_time)\\n- + [RESOURCES\\\\_PROCESSED](UsageType.md#resources_processed)\\n- [SEARCHES\\\\_PERFORMED](UsageType.md#searches_performed)\\n- + [SLOW\\\\_PROCESSING\\\\_TIME](UsageType.md#slow_processing_time)\\n- [SUGGESTIONS\\\\_PERFORMED](UsageType.md#suggestions_performed)\\n- + [TRAIN\\\\_SECONDS](UsageType.md#train_seconds)\\n\\n## Enumeration Members\\n\\n### + AI\\\\_TOKENS\\\\_USED\\n\\n\u2022 **AI\\\\_TOKENS\\\\_USED** = ``\\\"ai_tokens_used\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:190](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L190)\\n\\n___\\n\\n### + BYTES\\\\_PROCESSED\\n\\n\u2022 **BYTES\\\\_PROCESSED** = ``\\\"bytes_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:181](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L181)\\n\\n___\\n\\n### + CHARS\\\\_PROCESSED\\n\\n\u2022 **CHARS\\\\_PROCESSED** = ``\\\"chars_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:182](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L182)\\n\\n___\\n\\n### + MEDIA\\\\_FILES\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_FILES\\\\_PROCESSED** + = ``\\\"media_files_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:184](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L184)\\n\\n___\\n\\n### + MEDIA\\\\_SECONDS\\\\_PROCESSED\\n\\n\u2022 **MEDIA\\\\_SECONDS\\\\_PROCESSED** + = ``\\\"media_seconds_processed\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:183](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L183)\\n\\n___\\n\\n### + NUCLIA\\\\_TOKENS\\n\\n\u2022 **NUCLIA\\\\_TOKENS** = ``\\\"nuclia_tokens_billed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:191](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L191)\\n\\n___\\n\\n### + PAGES\\\\_PROCESSED\\n\\n\u2022 **PAGES\\\\_PROCESSED** = ``\\\"pages_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:185](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L185)\\n\\n___\\n\\n### + PARAGRAPHS\\\\_PROCESSED\\n\\n\u2022 **PARAGRAPHS\\\\_PROCESSED** = ``\\\"paragraphs_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:186](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L186)\\n\\n___\\n\\n### + PRE\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **PRE\\\\_PROCESSING\\\\_TIME** = + ``\\\"pre_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:178](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L178)\\n\\n___\\n\\n### + RESOURCES\\\\_PROCESSED\\n\\n\u2022 **RESOURCES\\\\_PROCESSED** = ``\\\"resources_processed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:180](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L180)\\n\\n___\\n\\n### + SEARCHES\\\\_PERFORMED\\n\\n\u2022 **SEARCHES\\\\_PERFORMED** = ``\\\"searches_performed\\\"``\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:188](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L188)\\n\\n___\\n\\n### + SLOW\\\\_PROCESSING\\\\_TIME\\n\\n\u2022 **SLOW\\\\_PROCESSING\\\\_TIME** + = ``\\\"slow_processing_time\\\"``\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/db.models.ts:179](https://github.com/nuclia/frontend/blob/55a20097/libs/sdk-core/src/lib/db/db.models.ts#L179)\\n\\n___\\n\\n### + SUGGESTIONS\\\\_PERFORMED\\n\\n\u2022 **SUGGESTIONS\\\\_PERFORMED** = ``\\\"suggestions_performed\\\"``\\n\\n#### + Defined 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TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined 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UsageType\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\":{\"score\":0.5916038155555725,\"score_type\":\"VECTOR\",\"order\":17,\"text\":\"@nuclia/core + / Exports / UsageType \\n Enumeration: UsageType \\n Table of contents \\n + Enumeration Members \\n \\n AI_TOKENS_USED \\n BYTES_PROCESSED \\n CHARS_PROCESSED + \\n MEDIA_FILES_PROCESSED \\n MEDIA_SECONDS_PROCESSED \\n NUCLIA_TOKENS \\n + PAGES_PROCESSED \\n PARAGRAPHS_PROCESSED \\n PRE_PROCESSING_TIME \\n RESOURCES_PROCESSED + \\n SEARCHES_PERFORMED \\n SLOW_PROCESSING_TIME \\n SUGGESTIONS_PERFORMED + \\n TRAIN_SECONDS \\n \\n Enumeration Members \\n AI_TOKENS_USED \\n \u2022 + AI_TOKENS_USED = ai_tokens_used \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:190 + \\n \\n BYTES_PROCESSED \\n \u2022 BYTES_PROCESSED = bytes_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:181 \\n \\n CHARS_PROCESSED \\n + \u2022 CHARS_PROCESSED = chars_processed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:182 + \\n \\n MEDIA_FILES_PROCESSED \\n \u2022 MEDIA_FILES_PROCESSED = media_files_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:184 \\n \\n MEDIA_SECONDS_PROCESSED + \\n \u2022 MEDIA_SECONDS_PROCESSED = media_seconds_processed \\n Defined in + \\n libs/sdk-core/src/lib/db/db.models.ts:183 \\n \\n NUCLIA_TOKENS \\n \u2022 + NUCLIA_TOKENS = nuclia_tokens_billed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:191 + \\n \\n PAGES_PROCESSED \\n \u2022 PAGES_PROCESSED = pages_processed \\n Defined + in \\n libs/sdk-core/src/lib/db/db.models.ts:185 \\n \\n PARAGRAPHS_PROCESSED + \\n \u2022 PARAGRAPHS_PROCESSED = paragraphs_processed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:186 \\n \\n PRE_PROCESSING_TIME \\n + \u2022 PRE_PROCESSING_TIME = pre_processing_time \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:178 + \\n \\n RESOURCES_PROCESSED \\n \u2022 RESOURCES_PROCESSED = resources_processed + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:180 \\n \\n SEARCHES_PERFORMED + \\n \u2022 SEARCHES_PERFORMED = searches_performed \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:188 + \\n \\n SLOW_PROCESSING_TIME \\n \u2022 SLOW_PROCESSING_TIME = slow_processing_time + \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:179 \\n \\n SUGGESTIONS_PERFORMED + \\n \u2022 SUGGESTIONS_PERFORMED = suggestions_performed \\n Defined in \\n + libs/sdk-core/src/lib/db/db.models.ts:189 \\n \\n TRAIN_SECONDS \\n \u2022 + TRAIN_SECONDS = train_seconds \\n Defined in \\n libs/sdk-core/src/lib/db/db.models.ts:187\",\"id\":\"b14cf452a3434839a04c111f2ea4dc51/t/page/0-2139\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":0,\"start\":0,\"end\":2139,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"dd41482018924facb5dbb87a7d53f122\":{\"id\":\"dd41482018924facb5dbb87a7d53f122\",\"slug\":\"docs-ingestion-how-to-rate-limiting-md\",\"title\":\"docs + > ingestion > how to > rate limiting\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:52:05.339624\",\"modified\":\"2026-06-09T08:18:12.259849\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/ingestion/how-to/rate-limiting\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"---\\nid: + rate-limiting\\ntitle: Manage rate limiting\\n---\\n\\n# Manage rate limiting\\n\\nRate + limits are an essential aspect of the Agentic RAG platform, ensuring fair + usage and optimal performance for all users interacting with Agentic RAG APIs. + This document outlines the rate limits enforced by Agentic RAG and provides + guidelines for handling rate-limited responses effectively.\\n\\n## Introduction\\n\\nAgentic + RAG can apply two types of limits to its APIs:\\n\\n- **Regular API rate limits**: + By default, the sum of all authenticated requests in a Agentic RAG account + cannot exceed 2400 requests per minute. Note that this limit can be customized + on a per-account basis. Please contact [Agentic RAG's support team](mailto:support@nuclia.com) + if you need an increase.\\n\\n- **Ingestion back pressure limits**: Agentic + RAG implements a back-pressure mechanism to manage ingestion pipeline overload. + This mainly affects endpoints for uploading data and creating or updating + resources.\\n\\n## Handling Rate-Limited Responses\\n\\nAgentic RAG adheres + to [the HTTP standard](https://datatracker.ietf.org/doc/html/rfc6585#section-4) + and will return a response with a `429` status codes when the limits are exceeded.\\n\\nThe + official Agentic RAG API clients already have built-in mechanisms for retrying + requests when rate limits are encountered:\\n\\n- [Nuclia Python client](/docs/develop/python-sdk/README)\\n- + [Nuclia JavaScript client](/docs/develop/js-sdk/)\\n\\nHowever, if you are + interacting directly with the API, we recommend using an [exponential backoff + retry strategy](https://en.wikipedia.org/wiki/Exponential_backoff) when limits + are reached.\\n\\nWhen ingestion back pressure rate limits are hit, the response + will include a `try_after` key with an estimated UTC time for retrying the + request. You can use this value for retry logic as an alternative to the exponential + backoff strategy.\\n\\n## Example 1: Regular API rate limits\\n\\nHere's an + example of how to implement an exponential backoff retry strategy in Python:\\n\\n```python\\nimport + time\\nimport requests\\n\\ndef make_request_with_exponential_backoff(url, + headers, max_retries=5):\\n retries = 0\\n while retries < max_retries:\\n + \ response = requests.get(url, headers=headers)\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429:\\n wait_time = 2 ** retries # Exponential backoff: 2^retries\\n + \ print(f\\\"Rate limit exceeded. Retrying in {wait_time} seconds...\\\")\\n + \ time.sleep(wait_time)\\n retries += 1\\n else:\\n + \ response.raise_for_status()\\n raise Exception(\\\"Max retries + exceeded\\\")\\n\\n# Example usage\\nurl = \\\"https://your-endpoint\\\"\\nheaders + = {\\\"Authorization\\\": \\\"Bearer YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_exponential_backoff(url, + headers)\\nprint(data)\\n```\\n\\n## Example 2: Ingestion back pressure limits\\n\\nHere's + an example of how to use the try_after key from the response to manage rate + limits:\\n\\n```python\\nimport time\\nfrom datetime import datetime\\n\\nimport + requests\\n\\n\\ndef make_request_with_try_after_info(url, headers, max_retries=5):\\n + \ retries = 0\\n while retries < max_retries:\\n response = requests.get(url, + headers=headers)\\n response_body = response.json()\\n if response.status_code + == 200:\\n return response.json()\\n elif response.status_code + == 429 and \\\"try_after\\\" in response_body:\\n try_after = response_body[\\\"try_after\\\"]\\n + \ retry_time = datetime.strptime(try_after, \\\"%Y-%m-%dT%H:%M:%S.%fZ\\\")\\n + \ wait_time = (retry_time - datetime.utcnow()).total_seconds()\\n + \ print(\\n f\\\"Rate limit exceeded. Retrying at + {retry_time} (in {wait_time} seconds)...\\\"\\n )\\n time.sleep(wait_time)\\n + \ retries += 1\\n else:\\n response.raise_for_status()\\n + \ raise Exception(\\\"Max retries exceeded\\\")\\n\\n\\n# Example usage\\nurl + = \\\"https://your-endpoint\\\"\\nheaders = {\\\"Authorization\\\": \\\"Bearer + YOUR_ACCESS_TOKEN\\\"}\\ndata = make_request_with_try_after_info(url, headers)\\nprint(data)\\n```\\n\\nThese + examples demonstrate how to handle rate limits effectively, ensuring that + your application respects the limits and retries appropriately.\\n\",\"format\":\"MARKDOWN\",\"md5\":\"ed11945857f4618eec7ed0d1b16ffa44\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\" + \\n id: rate-limiting \\n title: Manage rate limiting \\n \\n Manage rate + limiting \\n Rate limits are an essential aspect of the Agentic RAG platform, + ensuring fair usage and optimal performance for all users interacting with + Agentic RAG APIs. This document outlines the rate limits enforced by Agentic + RAG and provides guidelines for handling rate-limited responses effectively. + \\n Introduction \\n Agentic RAG can apply two types of limits to its APIs: + \\n \\n \\n Regular API rate limits: By default, the sum of all authenticated + requests in a Agentic RAG account cannot exceed 2400 requests per minute. + Note that this limit can be customized on a per-account basis. Please contact + Agentic RAG's support team if you need an increase. \\n \\n \\n Ingestion + back pressure limits: Agentic RAG implements a back-pressure mechanism to + manage ingestion pipeline overload. This mainly affects endpoints for uploading + data and creating or updating resources. \\n \\n \\n Handling Rate-Limited + Responses \\n Agentic RAG adheres to the HTTP standard and will return a response + with a 429 status codes when the limits are exceeded. \\n The official Agentic + RAG API clients already have built-in mechanisms for retrying requests when + rate limits are encountered: \\n \\n Nuclia Python client \\n Nuclia JavaScript + client \\n \\n However, if you are interacting directly with the API, we recommend + using an exponential backoff retry strategy when limits are reached. \\n When + ingestion back pressure rate limits are hit, the response will include a try_after + key with an estimated UTC time for retrying the request. You can use this + value for retry logic as an alternative to the exponential backoff strategy. + \\n Example 1: Regular API rate limits \\n Here's an example of how to implement + an exponential backoff retry strategy in Python: \\n ```python \\n import + time \\n import requests \\n def make_request_with_exponential_backoff(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n if response.status_code + == 200: \\n return response.json() \\n elif response.status_code == 429: \\n + wait_time = 2 retries # Exponential backoff: 2^retries \\n print(f Rate limit + exceeded. Retrying in {wait_time} seconds... ) \\n time.sleep(wait_time) \\n + retries += 1 \\n else: \\n response.raise_for_status() \\n raise Exception( + Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint \\n + headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_exponential_backoff(url, + headers) \\n print(data) \\n ``` \\n Example 2: Ingestion back pressure limits + \\n Here's an example of how to use the try_after key from the response to + manage rate limits: \\n ```python \\n import time \\n from datetime import + datetime \\n import requests \\n def make_request_with_try_after_info(url, + headers, max_retries=5): \\n retries = 0 \\n while retries < max_retries: + \\n response = requests.get(url, headers=headers) \\n response_body = response.json() + \\n if response.status_code == 200: \\n return response.json() \\n elif response.status_code + == 429 and try_after in response_body: \\n try_after = response_body[ try_after + ] \\n retry_time = datetime.strptime(try_after, %Y-%m-%dT%H:%M:%S.%fZ ) \\n + wait_time = (retry_time - datetime.utcnow()).total_seconds() \\n print( \\n + f Rate limit exceeded. Retrying at {retry_time} (in {wait_time} seconds)... + \\n ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries 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API\"},\"TIME/UTC\":{\"position\":[{\"start\":1526,\"end\":1529}],\"entity\":\"UTC\"},\"PRODUCT/Python\":{\"position\":[{\"start\":1232,\"end\":1238},{\"start\":1773,\"end\":1779}],\"entity\":\"Python\"},\"TIME/seconds\":{\"position\":[{\"start\":2199,\"end\":2206}],\"entity\":\"seconds\"}},\"relations\":[{\"relation\":\"OTHER\",\"label\":\"operating + system\",\"metadata\":{\"paragraph_id\":\"dd41482018924facb5dbb87a7d53f122/t/page/1222-1656\",\"source_start\":1248,\"source_end\":1254,\"to_start\":1232,\"to_end\":1238},\"from\":{\"value\":\"Nuclia\",\"type\":\"entity\",\"group\":\"ORG\"},\"to\":{\"value\":\"Python\",\"type\":\"entity\",\"group\":\"PRODUCT\"}}],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > ingestion > how to > rate limiting\",\"extracted\":{\"text\":{\"text\":\"docs + > ingestion > how to > rate limiting\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\":{\"score\":0.590923011302948,\"score_type\":\"VECTOR\",\"order\":18,\"text\":\" + ) \\n time.sleep(wait_time) \\n retries += 1 \\n else: \\n response.raise_for_status() + \\n raise Exception( Max retries exceeded ) \\n Example usage \\n url = https://your-endpoint + \\n headers = { Authorization : Bearer YOUR_ACCESS_TOKEN } \\n data = make_request_with_try_after_info(url, + headers) \\n print(data) \\n ``` \\n These examples demonstrate how to handle + rate limits effectively, ensuring that your application respects the limits + and retries appropriately.\",\"id\":\"dd41482018924facb5dbb87a7d53f122/t/page/3310-3757\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":9,\"start\":3310,\"end\":3757,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"50aac6f34b6d47de8e3b01f8b2de6e9c\":{\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c\",\"slug\":\"docs-develop-js-sdk-interfaces-PredictAnswerOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-08-26T10:03:11.443619\",\"modified\":\"2026-07-14T12:50:46.385594\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / PredictAnswerOptions\\n\\n# + Interface: PredictAnswerOptions\\n\\n## Properties\\n\\n### chat\\\\_history?\\n\\n> + `optional` **chat\\\\_history**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:242](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L242)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:249](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L249)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:248](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L248)\\n\\n***\\n\\n### + context?\\n\\n> `optional` **context**: [`ContextEntry`](../namespaces/Ask/interfaces/ContextEntry.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:243](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L243)\\n\\n***\\n\\n### + format\\\\_prompt?\\n\\n> `optional` **format\\\\_prompt**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:258](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L258)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:250](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L250)\\n\\n***\\n\\n### + json\\\\_schema?\\n\\n> `optional` **json\\\\_schema**: `object`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:257](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L257)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:251](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L251)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:256](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L256)\\n\\n***\\n\\n### + query\\\\_context?\\n\\n> `optional` **query\\\\_context**: `string`[] \\\\| + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:244](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L244)\\n\\n***\\n\\n### + query\\\\_context\\\\_images?\\n\\n> `optional` **query\\\\_context\\\\_images**: + `object`\\n\\n#### b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:252](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L252)\\n\\n***\\n\\n### + query\\\\_context\\\\_order?\\n\\n> `optional` **query\\\\_context\\\\_order**: + `object`\\n\\n#### Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:245](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L245)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:260](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L260)\\n\\n***\\n\\n### + rerank\\\\_context?\\n\\n> `optional` **rerank\\\\_context**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:259](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L259)\\n\\n***\\n\\n### + retrieval?\\n\\n> `optional` **retrieval**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:240](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L240)\\n\\n***\\n\\n### + system?\\n\\n> `optional` **system**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:241](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L241)\\n\\n***\\n\\n### + truncate?\\n\\n> `optional` **truncate**: `boolean`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:246](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L246)\\n\\n***\\n\\n### + user\\\\_prompt?\\n\\n> `optional` **user\\\\_prompt**: `object`\\n\\n#### + prompt\\n\\n> **prompt**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/ask.models.ts:247](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/ask.models.ts#L247)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"470be50f3c6aeeff5ae3310ed007b9b3\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / PredictAnswerOptions \\n Interface: PredictAnswerOptions + \\n Properties \\n chat_history? \\n \\n optional chat_history: ContextEntry[] + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:242 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:249 \\n \\n citations? + \\n \\n optional citations: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:248 + \\n \\n context? \\n \\n optional context: ContextEntry[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:243 \\n \\n format_prompt? + \\n \\n optional format_prompt: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:258 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 \\n \\n json_schema? + \\n \\n optional json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 + \\n \\n max_tokens? \\n \\n optional max_tokens: number \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 \\n \\n prefer_markdown? + \\n \\n optional prefer_markdown: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 + \\n \\n query_context? \\n \\n optional query_context: string[] \\\\| object + \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:244 \\n + \\n query_context_images? \\n \\n optional query_context_images: object \\n + \\n b64encoded \\n \\n b64encoded: string \\n \\n content_type \\n \\n content_type: + string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:252 + \\n \\n query_context_order? \\n \\n optional query_context_order: object + \\n \\n Index Signature \\n [key: string]: number \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:245 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:260 \\n \\n rerank_context? + \\n \\n optional rerank_context: boolean \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:259 + \\n \\n retrieval? \\n \\n optional retrieval: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/ask.models.ts:240 \\n \\n system? \\n + \\n optional system: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:241 + \\n \\n truncate? \\n \\n optional truncate: boolean \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/ask.models.ts:246 \\n \\n user_prompt? \\n + \\n optional user_prompt: object \\n \\n prompt \\n \\n prompt: string \\n + \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:247\",\"split_text\":{},\"deleted_splits\":[]},\"metadata\":{\"metadata\":{\"links\":[],\"paragraphs\":[{\"start\":0,\"end\":257,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":0,\"end\":127,\"key\":\"\"},{\"start\":127,\"end\":257,\"key\":\"\"}]},{\"start\":257,\"end\":491,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":260,\"end\":382,\"key\":\"\"},{\"start\":382,\"end\":491,\"key\":\"\"}]},{\"start\":491,\"end\":739,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":494,\"end\":617,\"key\":\"\"},{\"start\":617,\"end\":739,\"key\":\"\"}]},{\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":742,\"end\":864,\"key\":\"\"},{\"start\":864,\"end\":977,\"key\":\"\"}]},{\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":980,\"end\":1100,\"key\":\"\"},{\"start\":1100,\"end\":1221,\"key\":\"\"}]},{\"start\":1221,\"end\":1575,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1224,\"end\":1364,\"key\":\"\"},{\"start\":1364,\"end\":1575,\"key\":\"\"}]},{\"start\":1575,\"end\":1866,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1578,\"end\":1743,\"key\":\"\"},{\"start\":1743,\"end\":1866,\"key\":\"\"}]},{\"start\":1866,\"end\":2096,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":1869,\"end\":1988,\"key\":\"\"},{\"start\":1988,\"end\":2096,\"key\":\"\"}]},{\"start\":2096,\"end\":2320,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2099,\"end\":2208,\"key\":\"\"},{\"start\":2208,\"end\":2320,\"key\":\"\"}]},{\"start\":2320,\"end\":2449,\"start_seconds\":[],\"end_seconds\":[],\"kind\":\"TEXT\",\"classifications\":[],\"sentences\":[{\"start\":2323,\"end\":2449,\"key\":\"\"}]}],\"ner\":{},\"entities\":{\"processor\":{\"entities\":[]}},\"classifications\":[],\"last_index\":\"2026-07-14T12:50:49.024021Z\",\"last_understanding\":\"2026-07-14T12:50:48.461687Z\",\"last_extract\":\"2026-07-14T12:50:47.086918Z\",\"last_processing_start\":\"2026-07-14T12:50:47.048628Z\",\"language\":\"en\",\"summary\":\"\",\"positions\":{},\"relations\":[],\"mime_type\":\"text/markdown\"},\"split_metadata\":{},\"deleted_splits\":[]}}}},\"generics\":{\"title\":{\"value\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\",\"extracted\":{\"text\":{\"text\":\"docs + > develop > js sdk > interfaces > PredictAnswerOptions\"}}}}},\"security\":null,\"fields\":{\"/t/page\":{\"paragraphs\":{\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\":{\"score\":0.6482165455818176,\"score_type\":\"VECTOR\",\"order\":3,\"text\":\" + \\n optional max_tokens: number \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:251 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \\n \\n query_context? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":4,\"start\":977,\"end\":1221,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\":{\"score\":0.5993967652320862,\"score_type\":\"VECTOR\",\"order\":12,\"text\":\" + \\n optional generative_model: string \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:250 + \\n \\n json_schema? \\n \\n optional json_schema: object \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/ask.models.ts:257 \\n \\n max_tokens? + \\n\",\"id\":\"50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":0,\"index\":3,\"start\":739,\"end\":977,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null}}}}},\"e8525e64c5b44982b958d32cf6090613\":{\"id\":\"e8525e64c5b44982b958d32cf6090613\",\"slug\":\"docs-develop-js-sdk-interfaces-ChatOptions-md\",\"title\":\"docs + > develop > js sdk > interfaces > ChatOptions\",\"summary\":\"\",\"icon\":\"text/markdown\",\"thumbnail\":\"\",\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[],\"relations\":[]},\"fieldmetadata\":[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2025-04-15T14:50:37.546010\",\"modified\":\"2026-07-14T12:51:04.368823\",\"last_seqid\":0,\"last_account_seq\":null,\"queue\":\"private\",\"hidden\":false,\"origin\":{\"source_id\":\"\",\"url\":\"https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\",\"metadata\":{},\"tags\":[],\"collaborators\":[],\"filename\":\"\",\"related\":[],\"path\":\"\",\"source\":\"API\"},\"extra\":null,\"relations\":null,\"data\":{\"texts\":{\"page\":{\"value\":{\"body\":\"[**@nuclia/core**](../README.md) + \u2022 **Docs**\\n\\n***\\n\\n[@nuclia/core](../globals.md) / ChatOptions\\n\\n# + Interface: ChatOptions\\n\\n## Extends\\n\\n- [`BaseSearchOptions`](BaseSearchOptions.md)\\n\\n## + Properties\\n\\n### answer\\\\_json\\\\_schema?\\n\\n> `optional` **answer\\\\_json\\\\_schema**: + `object`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:114](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L114)\\n\\n***\\n\\n### + audit\\\\_metadata?\\n\\n> `optional` **audit\\\\_metadata**: `object`\\n\\n#### + Index Signature\\n\\n \\\\[`key`: `string`\\\\]: `string`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`audit_metadata`](BaseSearchOptions.md#audit_metadata)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:81](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L81)\\n\\n***\\n\\n### + citation\\\\_threshold?\\n\\n> `optional` **citation\\\\_threshold**: `number`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:116](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L116)\\n\\n***\\n\\n### + citations?\\n\\n> `optional` **citations**: `boolean` \\\\| `\\\"none\\\"` + \\\\| `\\\"default\\\"` \\\\| `\\\"llm_footnotes\\\"`\\n\\nIt will return + the text blocks that have been effectively used to build each section of the + answer.\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:105](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L105)\\n\\n***\\n\\n### + debug?\\n\\n> `optional` **debug**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`debug`](BaseSearchOptions.md#debug)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:79](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L79)\\n\\n***\\n\\n### + extra\\\\_context?\\n\\n> `optional` **extra\\\\_context**: `string`[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:115](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L115)\\n\\n***\\n\\n### + extra\\\\_context\\\\_images?\\n\\n> `optional` **extra\\\\_context\\\\_images**: + `object`[]\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:118](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L118)\\n\\n***\\n\\n### + ~~extracted?~~\\n\\n> `optional` **extracted**: [`ExtractedDataTypes`](../enumerations/ExtractedDataTypes.md)[]\\n\\n#### + Deprecated\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`extracted`](BaseSearchOptions.md#extracted)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:73](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L73)\\n\\n***\\n\\n### + features?\\n\\n> `optional` **features**: [`Features`](../namespaces/Ask/enumerations/Features.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:117](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L117)\\n\\n***\\n\\n### + field\\\\_type\\\\_filter?\\n\\n> `optional` **field\\\\_type\\\\_filter**: + [`FIELD_TYPE`](../enumerations/FIELD_TYPE.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`field_type_filter`](BaseSearchOptions.md#field_type_filter)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:74](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L74)\\n\\n***\\n\\n### + fields?\\n\\n> `optional` **fields**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`fields`](BaseSearchOptions.md#fields)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:62](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L62)\\n\\n***\\n\\n### + filter\\\\_expression?\\n\\n> `optional` **filter\\\\_expression**: [`FilterExpression`](FilterExpression.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filter_expression`](BaseSearchOptions.md#filter_expression)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:64](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L64)\\n\\n***\\n\\n### + filters?\\n\\n> `optional` **filters**: `string`[] \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`filters`](BaseSearchOptions.md#filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:63](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L63)\\n\\n***\\n\\n### + generative\\\\_model?\\n\\n> `optional` **generative\\\\_model**: `string`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:108](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L108)\\n\\n***\\n\\n### + highlight?\\n\\n> `optional` **highlight**: `boolean`\\n\\n#### Inherited + from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`highlight`](BaseSearchOptions.md#highlight)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:76](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L76)\\n\\n***\\n\\n### + keyword\\\\_filters?\\n\\n> `optional` **keyword\\\\_filters**: `string`[] + \\\\| [`Filter`](../type-aliases/Filter.md)[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`keyword_filters`](BaseSearchOptions.md#keyword_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:65](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L65)\\n\\n***\\n\\n### + max\\\\_tokens?\\n\\n> `optional` **max\\\\_tokens**: `number` \\\\| `object`\\n\\nDefines + the maximum number of tokens that the model will take as context.\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:112](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L112)\\n\\n***\\n\\n### + min\\\\_score?\\n\\n> `optional` **min\\\\_score**: `number` \\\\| [`MinScore`](MinScore.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`min_score`](BaseSearchOptions.md#min_score)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:66](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L66)\\n\\n***\\n\\n### + prefer\\\\_markdown?\\n\\n> `optional` **prefer\\\\_markdown**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:113](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L113)\\n\\n***\\n\\n### + prompt?\\n\\n> `optional` **prompt**: `string` \\\\| [`Prompts`](Prompts.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:101](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L101)\\n\\n***\\n\\n### + query\\\\_image?\\n\\n> `optional` **query\\\\_image**: `object`\\n\\n#### + b64encoded\\n\\n> **b64encoded**: `string`\\n\\n#### content\\\\_type\\n\\n> + **content\\\\_type**: `string`\\n\\n#### Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:122](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L122)\\n\\n***\\n\\n### + rag\\\\_images\\\\_strategies?\\n\\n> `optional` **rag\\\\_images\\\\_strategies**: + [`RAGImageStrategy`](../type-aliases/RAGImageStrategy.md)[]\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:107](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L107)\\n\\n***\\n\\n### + rag\\\\_strategies?\\n\\n> `optional` **rag\\\\_strategies**: [`RAGStrategy`](../type-aliases/RAGStrategy.md)[]\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:106](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L106)\\n\\n***\\n\\n### + range\\\\_creation\\\\_end?\\n\\n> `optional` **range\\\\_creation\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_end`](BaseSearchOptions.md#range_creation_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:68](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L68)\\n\\n***\\n\\n### + range\\\\_creation\\\\_start?\\n\\n> `optional` **range\\\\_creation\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_creation_start`](BaseSearchOptions.md#range_creation_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:67](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L67)\\n\\n***\\n\\n### + range\\\\_modification\\\\_end?\\n\\n> `optional` **range\\\\_modification\\\\_end**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_end`](BaseSearchOptions.md#range_modification_end)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:70](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L70)\\n\\n***\\n\\n### + range\\\\_modification\\\\_start?\\n\\n> `optional` **range\\\\_modification\\\\_start**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`range_modification_start`](BaseSearchOptions.md#range_modification_start)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:69](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L69)\\n\\n***\\n\\n### + rank\\\\_fusion?\\n\\n> `optional` **rank\\\\_fusion**: [`RankFusion`](RankFusion.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rank_fusion`](BaseSearchOptions.md#rank_fusion)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:84](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L84)\\n\\n***\\n\\n### + reasoning?\\n\\n> `optional` **reasoning**: [`ReasoningParam`](../type-aliases/ReasoningParam.md)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:126](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L126)\\n\\n***\\n\\n### + rephrase?\\n\\n> `optional` **rephrase**: `boolean`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`rephrase`](BaseSearchOptions.md#rephrase)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:77](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L77)\\n\\n***\\n\\n### + reranker?\\n\\n> `optional` **reranker**: [`Reranker`](../enumerations/Reranker.md)\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`reranker`](BaseSearchOptions.md#reranker)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:83](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L83)\\n\\n***\\n\\n### + resource\\\\_filters?\\n\\n> `optional` **resource\\\\_filters**: `string`[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`resource_filters`](BaseSearchOptions.md#resource_filters)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:75](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L75)\\n\\n***\\n\\n### + search\\\\_configuration?\\n\\n> `optional` **search\\\\_configuration**: + `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`search_configuration`](BaseSearchOptions.md#search_configuration)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:86](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L86)\\n\\n***\\n\\n### + security?\\n\\n> `optional` **security**: `object`\\n\\n#### groups\\n\\n> + **groups**: `string`[]\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`security`](BaseSearchOptions.md#security)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:85](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L85)\\n\\n***\\n\\n### + show?\\n\\n> `optional` **show**: [`ResourceProperties`](../enumerations/ResourceProperties.md)[]\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show`](BaseSearchOptions.md#show)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:71](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L71)\\n\\n***\\n\\n### + show\\\\_consumption?\\n\\n> `optional` **show\\\\_consumption**: `boolean`\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:127](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L127)\\n\\n***\\n\\n### + show\\\\_hidden?\\n\\n> `optional` **show\\\\_hidden**: `boolean`\\n\\n#### + Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`show_hidden`](BaseSearchOptions.md#show_hidden)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:80](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L80)\\n\\n***\\n\\n### + synchronous?\\n\\n> `optional` **synchronous**: `boolean`\\n\\n#### Defined + in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:100](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L100)\\n\\n***\\n\\n### + top\\\\_k?\\n\\n> `optional` **top\\\\_k**: `number`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`top_k`](BaseSearchOptions.md#top_k)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:82](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L82)\\n\\n***\\n\\n### + vectorset?\\n\\n> `optional` **vectorset**: `string`\\n\\n#### Inherited from\\n\\n[`BaseSearchOptions`](BaseSearchOptions.md).[`vectorset`](BaseSearchOptions.md#vectorset)\\n\\n#### + Defined in\\n\\n[libs/sdk-core/src/lib/db/search/search.models.ts:78](https://github.com/nuclia/frontend/blob/f1c1a5898c3d1fd8929aad7c184789dbbea1b896/libs/sdk-core/src/lib/db/search/search.models.ts#L78)\\n\",\"format\":\"MARKDOWN\",\"md5\":\"d6b2f3f31ba0af4fa58490cdf6abeb52\",\"extract_strategy\":\"\",\"split_strategy\":\"\"},\"extracted\":{\"text\":{\"text\":\"@nuclia/core + \u2022 Docs \\n \\n @nuclia/core / ChatOptions \\n Interface: ChatOptions + \\n Extends \\n \\n BaseSearchOptions \\n \\n Properties \\n answer_json_schema? + \\n \\n optional answer_json_schema: object \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:114 + \\n \\n audit_metadata? \\n \\n optional audit_metadata: object \\n \\n Index + Signature \\n [key: string]: string \\n Inherited from \\n BaseSearchOptions.audit_metadata + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:81 \\n + \\n citation_threshold? \\n \\n optional citation_threshold: number \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:116 \\n \\n + citations? \\n \\n optional citations: boolean \\\\| none \\\\| default \\\\| + llm_footnotes \\n \\n It will return the text blocks that have been effectively + used to build each section of the answer. \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:105 + \\n \\n debug? \\n \\n optional debug: boolean \\n \\n Inherited from \\n + BaseSearchOptions.debug \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:79 + \\n \\n extra_context? \\n \\n optional extra_context: string[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:115 \\n \\n extra_context_images? + \\n \\n optional extra_context_images: object[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:118 + \\n \\n ~~extracted?~~ \\n \\n optional extracted: ExtractedDataTypes[] \\n + \\n Deprecated \\n Inherited from \\n BaseSearchOptions.extracted \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:73 \\n \\n features? + \\n \\n optional features: Features[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:117 + \\n \\n field_type_filter? \\n \\n optional field_type_filter: FIELD_TYPE[] + \\n \\n Inherited from \\n BaseSearchOptions.field_type_filter \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:74 \\n \\n fields? + \\n \\n optional fields: string[] \\n \\n Inherited from \\n BaseSearchOptions.fields + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:62 \\n + \\n filter_expression? \\n \\n optional filter_expression: FilterExpression + \\n \\n Inherited from \\n BaseSearchOptions.filter_expression \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:64 \\n \\n filters? + \\n \\n optional filters: string[] \\\\| Filter[] \\n \\n Inherited from \\n + BaseSearchOptions.filters \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:63 + \\n \\n generative_model? \\n \\n optional generative_model: string \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:108 \\n \\n + highlight? \\n \\n optional highlight: boolean \\n \\n Inherited from \\n + BaseSearchOptions.highlight \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:76 + \\n \\n keyword_filters? \\n \\n optional keyword_filters: string[] \\\\| + Filter[] \\n \\n Inherited from \\n BaseSearchOptions.keyword_filters \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:65 \\n \\n + max_tokens? \\n \\n optional max_tokens: number \\\\| object \\n \\n Defines + the maximum number of tokens that the model will take as context. \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:112 \\n \\n min_score? + \\n \\n optional min_score: number \\\\| MinScore \\n \\n Inherited from \\n + BaseSearchOptions.min_score \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:66 + \\n \\n prefer_markdown? \\n \\n optional prefer_markdown: boolean \\n \\n + Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:113 \\n \\n + prompt? \\n \\n optional prompt: string \\\\| Prompts \\n \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:101 \\n \\n query_image? + \\n \\n optional query_image: object \\n \\n b64encoded \\n \\n b64encoded: + string \\n \\n content_type \\n \\n content_type: string \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:122 \\n \\n rag_images_strategies? + \\n \\n optional rag_images_strategies: RAGImageStrategy[] \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:107 \\n \\n rag_strategies? + \\n \\n optional rag_strategies: RAGStrategy[] \\n \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:106 + \\n \\n range_creation_end? \\n \\n optional range_creation_end: string \\n + \\n Inherited from \\n BaseSearchOptions.range_creation_end \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:68 \\n \\n range_creation_start? + \\n \\n optional range_creation_start: string \\n \\n Inherited from \\n BaseSearchOptions.range_creation_start + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:67 \\n + \\n range_modification_end? \\n \\n optional range_modification_end: string + \\n \\n Inherited from \\n BaseSearchOptions.range_modification_end \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:70 \\n \\n range_modification_start? + \\n \\n optional range_modification_start: string \\n \\n Inherited from \\n + BaseSearchOptions.range_modification_start \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:69 + \\n \\n rank_fusion? \\n \\n optional rank_fusion: RankFusion \\n \\n Inherited + from \\n BaseSearchOptions.rank_fusion \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:84 + \\n \\n reasoning? \\n \\n optional reasoning: ReasoningParam \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:126 \\n \\n rephrase? + \\n \\n optional rephrase: boolean \\n \\n Inherited from \\n BaseSearchOptions.rephrase + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:77 \\n + \\n reranker? \\n \\n optional reranker: Reranker \\n \\n Inherited from \\n + BaseSearchOptions.reranker \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:83 + \\n \\n resource_filters? \\n \\n optional resource_filters: string[] \\n + \\n Inherited from \\n BaseSearchOptions.resource_filters \\n Defined in \\n + libs/sdk-core/src/lib/db/search/search.models.ts:75 \\n \\n search_configuration? + \\n \\n optional search_configuration: string \\n \\n Inherited from \\n BaseSearchOptions.search_configuration + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:86 \\n + \\n security? \\n \\n optional security: object \\n \\n groups \\n \\n groups: + string[] \\n \\n Inherited from \\n BaseSearchOptions.security \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:85 \\n \\n show? \\n + \\n optional show: ResourceProperties[] \\n \\n Inherited from \\n BaseSearchOptions.show + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:71 \\n + \\n show_consumption? \\n \\n optional show_consumption: boolean \\n \\n Defined + in \\n libs/sdk-core/src/lib/db/search/search.models.ts:127 \\n \\n show_hidden? + \\n \\n optional show_hidden: boolean \\n \\n Inherited from \\n BaseSearchOptions.show_hidden + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:80 \\n + \\n synchronous? \\n \\n optional synchronous: boolean \\n \\n Defined in + \\n libs/sdk-core/src/lib/db/search/search.models.ts:100 \\n \\n top_k? \\n + \\n optional top_k: number \\n \\n Inherited from \\n BaseSearchOptions.top_k + \\n Defined in \\n libs/sdk-core/src/lib/db/search/search.models.ts:82 \\n + \\n vectorset? \\n \\n optional vectorset: string \\n \\n Inherited from \\n + BaseSearchOptions.vectorset \\n Defined in \\n 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TokenConsumption \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:126 \n \n type \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/namespaces/Ask/interfaces/ConsumptionAskResponseItem\n", - "9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412": " max_tokens: the maximum - number of input tokens to put in the final context (including the prompt, the - retrieved results and the user question). \n max_output_tokens: the maximum - number of tokens to generate. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n", - "f02da6c4bdf34596a89a8106f4b0ea9f/t/page/0-299": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / PageToken \n Interface: PageToken \n Properties \n height - \n \n height: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:249 - \n \n line \n \n line: number \n \n Defined in \n libs/sdk-core/src/lib/db/resource/resource.models.ts:251 - \n \n text \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PageToken\n", - "220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276": " \n SDK: \n \n ```python - \n from nuclia import sdk \n from nucliadb_models.search import AskRequest, - Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( \n query= My - question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n", - "c27a1e5f5ddb4b118921345d713401b8/t/page/351-554": " full: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:386 \n \n max_messages? \n \n - optional max_messages: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:387 - \n \n name \n \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ConversationalStrategy\n", - "0d01e250360a4d6c91f3baf2de5e7d38/t/page/0-224": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / ReasoningConfig \n Interface: ReasoningConfig \n Properties - \n budget_tokens? \n \n optional budget_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:647 - \n \n effort? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ReasoningConfig\n", - "66b6f0dbd883413e98fbbf5b59f049b9/t/page/0-1833": "@nuclia/core \u2022 Docs - \n \n @nuclia/core / UsageType \n Enumeration: UsageType \n Enumeration Members - \n AI_TOKENS_USED \n \n AI_TOKENS_USED: ai_tokens_used \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:206 - \n \n BYTES_PROCESSED \n \n BYTES_PROCESSED: bytes_processed \n \n Defined in - \n libs/sdk-core/src/lib/db/db.models.ts:197 \n \n CHARS_PROCESSED \n \n CHARS_PROCESSED: - chars_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:198 - \n \n MEDIA_FILES_PROCESSED \n \n MEDIA_FILES_PROCESSED: media_files_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:200 \n \n MEDIA_SECONDS_PROCESSED - \n \n MEDIA_SECONDS_PROCESSED: media_seconds_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:199 - \n \n NUCLIA_TOKENS \n \n NUCLIA_TOKENS: nuclia_tokens_billed \n \n Defined - in \n libs/sdk-core/src/lib/db/db.models.ts:207 \n \n PAGES_PROCESSED \n \n - PAGES_PROCESSED: pages_processed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:201 - \n \n PARAGRAPHS_PROCESSED \n \n PARAGRAPHS_PROCESSED: paragraphs_processed - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:202 \n \n PRE_PROCESSING_TIME - \n \n PRE_PROCESSING_TIME: pre_processing_time \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:194 - \n \n RESOURCES_PROCESSED \n \n RESOURCES_PROCESSED: resources_processed \n - \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:196 \n \n SEARCHES_PERFORMED - \n \n SEARCHES_PERFORMED: searches_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:204 - \n \n SLOW_PROCESSING_TIME \n \n SLOW_PROCESSING_TIME: slow_processing_time - \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:195 \n \n SUGGESTIONS_PERFORMED - \n \n SUGGESTIONS_PERFORMED: suggestions_performed \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:205 - \n \n TRAIN_SECONDS \n \n TRAIN_SECONDS: train_seconds \n \n Defined in \n libs/sdk-core/src/lib/db/db.models.ts:203\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/enumerations/UsageType\n", - "0a003c3f724e45e392a9c8d1ce8800c1/t/page/0-349": "@nuclia/core \u2022 Docs \n - \n @nuclia/core / Consumption \n Interface: Consumption \n Properties \n customer_key_tokens - \n \n customer_key_tokens: TokenConsumption \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:230 - \n \n normalized_tokens \n \n normalized_tokens: TokenConsumption \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:229\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/Consumption\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/480-703": " \n optional max_images: - string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 \n \n - output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "43004f553e534ffe9c9e735856bd9b23/t/page/212-480": " \n optional driver: string - \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 \n \n input_tokens - \n \n input_tokens: object \n \n max \n \n max: number \n \n min? \n \n optional - min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n", - "4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340": " Use the max_tokens parameter - on the /ask endpoint to set hard limits on: \n - Context size: Limits the amount - of retrieved information sent to the LLM \n - Answer length: Limits the length - of the generated response \n Important Considerations \n Context Limitations: - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043": " Description: The maximum - number of tokens that the model can generate as output. Again, we should keep - in mind that this value summed to the Maximum supported input tokens should - not exceed the total context size supported by the model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575": " \n \n Maximum supported - input tokens: \n Description: The maximum number of tokens that the model can - accept as input. Be mindful that this takes into account the tokens used in - the prompt, query and context. Also take note that some models may provide their - context window as the total between input and output tokens, while others may - provide it as the input tokens only. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n", - "89cc367c149e4f6eab0e06a743d1edba/t/page/2575-2804": " Example: For Phi 4 Reasoning - Plus, the total context size is 32768 tokens, as we want to leave room for the - output, we will set the maximum supported input tokens as 32768 - 1024 = 31744. - \n \n \n Maximum supported output tokens: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n", - "24ad6997e1fe4a109d67d7802a083678/a/title/0-55": "docs > develop > js sdk > - interfaces > TokenConsumption\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/TokenConsumption\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221": " \n optional max_tokens: - number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n", - "50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977": " \n optional generative_model: - string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n", - "e8525e64c5b44982b958d32cf6090613/t/page/2808-3011": " \n optional max_tokens: - number \\| object \n \n Defines the maximum number of tokens that the model - will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? 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headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 - Content-Length: - - '1433' - access-control-expose-headers: - - X-NUCLIA-TRACE-ID - content-type: - - application/json - date: - - Wed, 15 Jul 2026 07:06:05 GMT - nuclia-learning-model: - - bge-reranker-v2-m3 - via: - - 1.1 google - x-envoy-upstream-service-time: - - '152' - x-nuclia-trace-id: - - 54b731e2a4fe555568d925e1c3513d6d - status: - code: 200 - message: OK -- request: - body: '{"question": "", "retrieval": true, "user_id": "rao_answer_summary-ask", - "system": null, "chat_history": [], "context": [], "query_context": {}, "query_context_order": - {}, "truncate": true, "user_prompt": {"prompt": "\nBased on the provided context - and user question, perform the following tasks:\n\n1. Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. The `citations` field - consists ONLY in a list of block IDs that are relevant to the answer, following - these rules:\n - Use the format: block-AB\n - Just mention the block IDs, - do NOT include any other text.\n - Just mention the blocks actually relevant - and that contain information used in the answer, do NOT include blocks that - are not relevant.\n - No duplicates.\n9. Do NOT hallucinate block IDs. Only - use those provided in the context.\n10. Your output must be a JSON object with - the following fields:\n - \"reason\": Explain your reasoning for the answer - or validation.\n - \"answer\": Provide a partial or complete answer to the - user query strictly from the information in the context. If there isn''t enough - information to even provide a partial answer, leave ''answer'' empty.\n - - \"missing_info_query\": If the context is insufficient, specify what information - is missing in a query shape; otherwise, leave it empty. Just return the query - needed to retrieve the missing information.\n - \"useful\": Indicate if the - context is useful to answer the question (\"yes\" or \"no\").\n - \"citations\": - List the IDs of the blocks relevant to the answer, if any (e.g., [\"block-AB\", - \"block-CD\"]).\n11. **IMPORTANT** If any extra instructions are provided, you - MUST follow them carefully when generating the answer field. These instructions - may specify the format, style, tools to use, or other requirements for the answer.\n\n\nExplica - c\u00f3mo usar el par\u00e1metro `max_tokens` en espa\u00f1ol, y proporciona - un enlace a la documentaci\u00f3n oficial.\n\n\n\nContext:\n\n**block-AA**\n\n## - Chunk: 4039d76b0fff4962900836ab3fdec9f7/t/page/3075-3340\nTags: /k/text\n``` Use - the max_tokens parameter on the /ask endpoint to set hard limits on: \n - Context - size: Limits the amount of retrieved information sent to the LLM \n - Answer - length: Limits the length of the generated response \n Important Considerations - \n Context Limitations: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/consumption\n - ```\n\n\n---\"\n\n\n**block-AB**\n\n## Chunk: 9f7036a7a6694700b72d52eb58a8326c/t/page/5203-5412\nTags: - /k/text\n``` max_tokens: the maximum number of input tokens to put in the final - context (including the prompt, the retrieved results and the user question). - \n max_output_tokens: the maximum number of tokens to generate. \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/rag/advanced/widget/features\n ```\n\n\n---\"\n\n\n**block-AC**\n\n## - Chunk: 220eb37c167f4eb9bb8e9454e7ba8cf5/t/page/1790-2276\nTags: /k/text\n``` \n - SDK: \n \n ```python \n from nuclia import sdk \n from nucliadb_models.search - import AskRequest, Reasoning \n search = sdk.NucliaSearch() \n query = AskRequest( - \n query= My question with extra reasoning effort , \n max_tokens=5000, \n reasoning=Reasoning( - \n display=True, # Show reasoning in the response \n effort= low , # Can be - low , medium , or high \n budget_tokens=1024 # How many tokens reasoning can - use \n ), \n ) \n search.ask(query=query) \n ``` \n Model Support for Reasoning - Options: \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/python-sdk/search\n - ```\n\n\n---\"\n\n\n**block-AD**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2808-3011\nTags: - /k/text\n``` \n optional max_tokens: number \\| object \n \n Defines the maximum - number of tokens that the model will take as context. \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:112 - \n \n min_score? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n - ```\n\n\n---\"\n\n\n**block-AE**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/977-1221\nTags: - /k/text\n``` \n optional max_tokens: number \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:251 - \n \n prefer_markdown? \n \n optional prefer_markdown: boolean \n \n Defined - in \n libs/sdk-core/src/lib/db/search/ask.models.ts:256 \n \n query_context? - \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AF**\n\n## Chunk: e8525e64c5b44982b958d32cf6090613/t/page/2450-2808\nTags: - /k/text\n``` \n optional highlight: boolean \n \n Inherited from \n BaseSearchOptions.highlight - \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:76 \n \n keyword_filters? - \n \n optional keyword_filters: string[] \\| Filter[] \n \n Inherited from \n - BaseSearchOptions.keyword_filters \n Defined in \n libs/sdk-core/src/lib/db/search/search.models.ts:65 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ChatOptions\n - ```\n\n\n---\"\n\n\n**block-AG**\n\n## Chunk: 50aac6f34b6d47de8e3b01f8b2de6e9c/t/page/739-977\nTags: - /k/text\n``` \n optional generative_model: string \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:250 - \n \n json_schema? \n \n optional json_schema: object \n \n Defined in \n libs/sdk-core/src/lib/db/search/ask.models.ts:257 - \n \n max_tokens? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/PredictAnswerOptions\n - ```\n\n\n---\"\n\n\n**block-AH**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/212-480\nTags: - /k/text\n``` \n optional driver: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:590 - \n \n input_tokens \n \n input_tokens: object \n \n max \n \n max: number \n - \n min? \n \n optional min: number \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:587 - \n \n max_images? \n\n\n\nLink: https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n - ```\n\n\n---\"\n\n\n**block-AI**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2804-3043\nTags: - /k/text\n``` Description: The maximum number of tokens that the model can generate - as output. Again, we should keep in mind that this value summed to the Maximum - supported input tokens should not exceed the total context size supported by - the model. \n\n\n\nLink: https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n - ```\n\n\n---\"\n\n\n**block-AJ**\n\n## Chunk: 89cc367c149e4f6eab0e06a743d1edba/t/page/2202-2575\nTags: - /k/text\n``` \n \n Maximum supported input tokens: \n Description: The maximum - number of tokens that the model can accept as input. Be mindful that this takes - into account the tokens used in the prompt, query and context. Also take note - that some models may provide their context window as the total between input - and output tokens, while others may provide it as the input tokens only. \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/rag/advanced/openai-api-compatible-models\n - ```\n\n\n---\"\n\n\n**block-AK**\n\n## Chunk: 43004f553e534ffe9c9e735856bd9b23/t/page/480-703\nTags: - /k/text\n``` \n optional max_images: string \n \n Defined in \n libs/sdk-core/src/lib/db/kb/kb.models.ts:591 - \n \n output_tokens \n \n output_tokens: object \n \n default_max? \n \n optional - default_max: number \n \n max \n \n max: number \n \n min? \n \n\n\n\nLink: - https://docs.rag.progress.cloud/docs/develop/js-sdk/interfaces/ModelConfig\n - ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": false, "citation_threshold": - null, "generative_model": "chatgpt-azure-4o-mini", "max_tokens": 8192, "query_context_images": - {}, "prefer_markdown": null, "json_schema": {"title": "validate_or_answer", - "description": "Validate or answer", "parameters": {"type": "object", "properties": - {"reason": {"type": "string", "description": "Reasoning for the answer or validation"}, - "answer": {"type": "string", "description": "Partial or complete answer to the - user query from the information in the context."}, "missing_info_query": {"type": - "string", "description": "Query needed to retrieve the missing information in - case the context is not enough to answer the question. 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El tama\u00f1o del contexto: - limita la cantidad de informaci\u00f3n recuperada enviada al modelo de lenguaje. - 2. La longitud de la respuesta generada: limita la longitud de la respuesta - generada. 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All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7 \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":83,\"start\":16815,\"\ - end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"score\":0.0004974744515493512,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":21,\"\ - start\":4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"score\":0.000124362253700383,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":96,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.0001214817602885887,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":84,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":74,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":11,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":9,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":233,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.0000494715895911213,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":41,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004400118632474914,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":246,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":209,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - :{\"score\":0.0000370529705833178,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 54\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Semaphore PI 2026.1 Priorities\ - \ \\n \\n Technical Debt \\n \\n \u2022 Angular to \\n React \\n completion\ - \ \\n \\n Ease of \\n Modelling \\n \\n \u2022 Performance \\n improvements\ - \ \\n \\n AI enhancement \\n \\n \u2022 LLM use in: \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":53,\"index\":262,\"\ - start\":51783,\"end\":52072,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":281,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003095910506090149,\"score_type\":\"RERANKER\",\"order\":15,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":140,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - :{\"score\":0.00003024195939360652,\"score_type\":\"RERANKER\",\"order\":16,\"\ - text\":\" \\n Deliver a robust and \\n \\n secure platform that \\n instills\ - \ trust and \\n \\n reliability. \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":57,\"index\":270,\"\ - start\":53971,\"end\":54093,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - :{\"score\":0.000029083439585519955,\"score_type\":\"RERANKER\",\"order\"\ - :17,\"text\":\"3) Progress Semaphore \\n \\n Semaphore \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":51,\"index\":249,\"\ - start\":49860,\"end\":49896,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - :{\"score\":0.000027108802896691486,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" \\n Empowering developers \\n \\n with streamlined tools \\\ - n and processes to \\n \\n accelerate development \\n \\n cycles and boost\ - \ overall \\n productivity \\n \\n Enhancing our platform \\n \\n with advanced\ - \ \\n observability and \\n \\n cost-efficiency \\n \\n measures to ensure\ - \ \\n robust performance \\n \\n and optimal resource \\n utilization. \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":49,\"index\":243,\"\ - start\":48609,\"end\":48921,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026688529032981023,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. 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All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":0,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.0001214817602885887,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":0,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":0,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":0,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":0,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.0000494715895911213,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":0,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004400118632474914,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":0,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":0,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - :{\"score\":0.0000370529705833178,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 54\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Semaphore PI 2026.1 Priorities\ - \ \\n \\n Technical Debt \\n \\n \u2022 Angular to \\n React \\n completion\ - \ \\n \\n Ease of \\n Modelling \\n \\n \u2022 Performance \\n improvements\ - \ \\n \\n AI enhancement \\n \\n \u2022 LLM use in: \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":53,\"index\":0,\"\ - start\":51783,\"end\":52072,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":0,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003095910506090149,\"score_type\":\"RERANKER\",\"order\":15,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":0,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - :{\"score\":0.00003024195939360652,\"score_type\":\"RERANKER\",\"order\":16,\"\ - text\":\" \\n Deliver a robust and \\n \\n secure platform that \\n instills\ - \ trust and \\n \\n reliability. \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":57,\"index\":0,\"\ - start\":53971,\"end\":54093,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - :{\"score\":0.000029083439585519955,\"score_type\":\"RERANKER\",\"order\"\ - :17,\"text\":\"3) Progress Semaphore \\n \\n Semaphore \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":51,\"index\":0,\"\ - start\":49860,\"end\":49896,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":false,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - :{\"score\":0.000027108802896691486,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" \\n Empowering developers \\n \\n with streamlined tools \\\ - n and processes to \\n \\n accelerate development \\n \\n cycles and boost\ - \ overall \\n productivity \\n \\n Enhancing our platform \\n \\n with advanced\ - \ \\n observability and \\n \\n cost-efficiency \\n \\n measures to ensure\ - \ \\n robust performance \\n \\n and optimal resource \\n utilization. \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":49,\"index\":0,\"\ - start\":48609,\"end\":48921,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026688529032981023,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. 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Visible objects include the person's face, hair,\ - \ and smile. The background consists of a stone wall wi\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"text\":\"me decorative elements. \\n\\n\\n \\n\\n\\n a portrait photo\ - \ of a man with a beard and wavy hair. He is wearing a dark shirt with a floral\ - \ pattern\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"text\":\"tps://www.progress.com/ \\n \\n \\n\\n\\n \\n 8\xA9 2025 Progress\ - \ Software Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n AI is no longer optional. \\n It is the engine of \\n transformation.\ - \ \\n \\n 8\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or a\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"text\":\"ffiliates. All rights reserved. \\n \\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"text\":\"ps://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a blurred\ - \ motion photograph, capturing the rapid movement of city streets at night.\ - \ Visible objects include tall buildings, streetlights, and vehicles. The\ - \ scene depicts a bustling urban environment with dynamic light \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"text\":\"s indicating high-speed traffic. \\n\\n\\n \\n Practices are\ - \ changing \\n \\n \u2022 Market analysis \\n \u2022 Feature definition \\\ - n \u2022 Threat modelling \\n \u2022 Code suggestions \\n \u2022 Code analysis\ - \ \\n \u2022 UI scaffolding \\n \u2022 Test coverage \\n \u2022 Penetration\ - \ testing \\n \u2022 Documentation \\n \u2022 Defect analysi\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"text\":\" \\n \\n \\n\\n\\n \\n 3\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"text\":\"ress\u2019 \\n\\n\\n a slide from a presentation titled \\\"Celebrating\ - \ Innovation from PI 3...\\\". The slide features a festive background with\ - \ colorful confetti. Visible objects include the title text, bullet points,\ - \ and a logo at the bottom left corner. The scene shows a celebration atmosphere\ - \ with bright colors and decorative\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"text\":\"ents. \\n\\n\\n \\n 4\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Important\ - \ Dates \\n \\n 11 December (Thursday) \\n \\n Draft Plan Presentations \\\ - n \\n 15 December (Monday) \\n \\n Final Plan Presentations \\n \\n 17 December\ - \ (Wednesday) \\n \\n Start of PI 2026.1 \\n \\n 18 December (Thursday) \\\ - n \\n Innovation Demos \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"text\":\"\\n \\n \\n\\n\\n \\n\\n\\n a portrait photograph of a man smiling\ - \ at the camera. He is wearing a yellow shirt with a patterned background.\ - \ Visible objects include his face, shirt, and background. The scene is likely\ - \ an indoor setting, possibly a conference o\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"text\":\"nt space. \\n\\n\\n \\n\\n\\n a portrait of a smiling man with\ - \ short hair and a beard. He is wearing a black shirt. The backgro\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"text\":\"us tools and equipment, and a classic car parked in the background.\ - \ The context suggests a professional environment focused on automotive repairs.\ - \ \\n\\n\\n \\n 15\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n https://www.progress.com/ \\\ - n \\n \\n\\n\\n \\n\\n\\n a vintage photograph showing a woman working at\ - \ a computer desk in an automotive workshop. Visible objects include a desktop\ - \ computer with a monitor, keyboard, and printer on the desk, a telephone\ - \ being used by the woman, and shelves stocked with various automotive parts\ - \ in the background. The scene captures a moment of work in a mode\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"text\":\"rn automotive repair shop. \\n\\n\\n \\n \\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"text\":\"2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n https://www.progress.com/ \\n \\\ - n \\n\\n\\n \\n\\n\\n a digital screenshot of a mobile phone interface,\ - \ specifically the Wineria Santine app. The screen displays various menu options\ - \ such as \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\"\ - \ \\\"Sensitiveness,\\\" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\ - \" and \\\"New mascara-international brands 06.\\\" Visible objects include\ - \ a woman sitting on a bench holding a smartphone, with storefronts in the\ - \ background featuring signage in Hindi. \\n\\n\\n \\n 17\xA9 2025 Progress\ - \ Software Corporation and/or its subsidiaries or affiliates. All r\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"text\":\"s reserved. \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\\ - n AAPFOINFAERT COMFROMDE\\n\u201CTUsEBALI NOW_16 2050 G-\u20ACARAEE\\n[DEWAUS\ - \ EG TO NELIMAL LK\\nENIOY @0F9 UN JOURTEY. \\n\\n\\n a digital interface\ - \ displaying an image of a car in a modern urban setti\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"text\":\"ng. Visible objects include the car's front view, a city skyline\ - \ in the background, and a person standing in front of the vehicle. The context\ - \ suggests a professional environment, possibly related to automotive services\ - \ or tech\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"text\":\"ous urban elements such as buildings, trees, and street signs.\ - \ The scene captures a moment of everyday life in an urban environment. \\\ - n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition.\ - \ Visible objects include a bowl, a sp\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"text\":\"a knife, and various fruits such as apples and oranges. The scene\ - \ depicts a kitchen setting with a focus on the fruit bowl, suggesting a theme\ - \ of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a green\ - \ arrow pointing to the right, indicatin\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"text\":\"s plain white. \\n\\n\\n \\n\\n\\n a portrait photograph of a\ - \ young man wearing a light blue shirt. He has dark hair and a friendly expression.\ - \ The background is plain white, providing no distractions from the subject.\ - \ Visible objects include his face and shirt. Context suggests he might be\ - \ a student or professional in a field related to \\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"text\":\"ffiliates. All rights reserved. \\n \\n What is the best damp-proofing\ - \ product for \\n concrete piles that are in constant contact with \\n standing\ - \ water, is it available, how is it applied, \\n \\n Hi, how can I help? \\\ - n \\n and what qualifications staff require to apply it? \\n \\n Submit \\\ - n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"text\":\"tograph of two construction workers standing near a body of water,\ - \ likely a lake or river, with several concrete pillars marked with numbers\ - \ in the background. They are wearing safety helmets and high-visibility jackets.\ - \ The scene depicts them observing or discussing something on a clipboard.\ - \ Visible objects include the concrete pillars, th\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"text\":\"er, and the sky. Context suggests they are involved in a construction\ - \ project. \\n\\n\\n \\n 20\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n ?Documents\ - \ \\n \\n Searching for a Suitable Product \\n \\n + damp-proofing \\n \\\ - n + concrete piles \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"text\":\"e interpretation of the \\n \\n query, the data to initially fetch\ - \ and the additional \\n \\n data required \\n \\n Guardrails \\n \\n The\ - \ Full Agentic Workflow \\n \\n Agentic Framework \\n \\n RAG MCP | HDP \\\ - n \\n Data under management \\n \\n (fully enriched and indexed) \\n \\n federated\ - \ model of data retrieval \\n \\n (no enrichment, not indexed) \\n \\n Comprehensive\ - \ \\n \\n and actionable \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"text\":\"r \\n \\n Guardrails \\n \\n Data fabric / \\n \\n Knowledge Graph\ - \ \\n Other data sources \\n \\n LLM for response \\n \\n generation \\n \\\ - n https://www.progress.com/ \\n \\n \\n\\n\\n \\n Pro \\n duct 360 \\n \\\ - n P \\n u \\n \\n b \\n lic \\n \\n S \\n a \\n fe \\n \\n ty \\n \\n Self\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"text\":\"ice Policy \\n Search \\n \\n K \\n n \\n \\n o \\n w \\n \\n\ - \ le \\n d \\n \\n g \\n e \\n \\n M \\n a \\n n \\n \\n a \\n g \\n \\n e\ - \ \\n m \\n \\n e \\n n \\n \\n t \\n \\n Fraud \\n Identity \\n \\n Search\ - \ & \\n Dis \\n \\n cover \\n y \\n \\n Software (ISV/OEM) \\n \\n Manufacturing\ - \ \\n \\n Government \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"text\":\"ement or direction. Visible objects include the arrow itself and\ - \ a few indistinct shapes that might be part of the background. The scene\ - \ shows a blurred, dark background with a green arrow pointing to the right.\ - \ \\n\\n\\n \\n 19\xA9 2025 Progress Software Corporation and/or its subsidia\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"text\":\"s. Visible objects include buildings, trees, and pedestrians.\ - \ The scene captures the essence of urban life, highlighting the blend of\ - \ architecture and nature. \\n\\n\\n \\n\\n\\n a digital artwork depicting\ - \ a street scene with various elements. Visible objects include buildings,\ - \ trees, and pedestria\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"text\":\"he scene captures the essence of urban life, highlighting the\ - \ blend of architecture and nature. \\n\\n\\n \\n 12\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n AI is no longer optional. \\n It is the engine of \\n transf\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"text\":\"ion. \\n \\n 12\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n MODERNIZATION \\n /m\u0251d\u0259rn\u026A\u02C8\ - ze\u026A\u0283\u026An/ \\n \\n Modernization is the process of \\n updating\ - \ something or making it work \\n in a contemporary s\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"text\":\"g. \\n \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork that appears\ - \ to be a still life composition. Visible objects include a bowl, a spoon,\ - \ a knife, and various fruits such as apples and oranges. The scene depicts\ - \ a kitchen setting with a focus on the fruit bowl, suggesting a theme of\ - \ healthy eating or culinary preparation. \\n\\n\\n \\n 14\xA9 2025 Progress\ - \ Software Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n https://www\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"text\":\"ress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a black-and-white photograph\ - \ depicting two people in a garage setting, likely a car repair shop. The\ - \ woman is seated at a desk writing on a clipboard, while the man stands behind\ - \ her, leaning slightly towards her. Visible objects include a vintage telephone,\ - \ a metal workbench with v\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"text\":\" \\n\\n\\n a geometric abstract artwork featuring a three-dimensional\ - \ shape composed of overlapping triangles in varying shades of green. The\ - \ scene shows a modern, minimalist interior with a black background. Visible\ - \ objects include a sleek, black chair and a small table with a simple design.\ - \ Context suggests a contemporary living space or office environ\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"text\":\" \\n\\n\\n \\n\\n\\n a geometric abstract graphic with a three-dimensional\ - \ cube-like shape composed of green and yellow gradients. It shows a modern,\ - \ minimalist design with clean lines and a vibrant color palette. Visible\ - \ objects include the cube and its gradient colors. The scene is set against\ - \ a black background, emphasizing the geometr\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"text\":\"a signature, likely from a person's handwritten document or digital\ - \ representation. Visible objects include the signature itself and possibly\ - \ some ink residue. The scene shows a close-up view of a person's handwriting,\ - \ with the signature prominently displayed against a dark background.\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"text\":\"althy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital\ - \ artwork that appears to be a still life composition. Visible objects include\ - \ a bowl, a spoon, a knife, and various fruits such as apples and oranges.\ - \ The scene depicts a kitchen setting with a focus on the fruit bowl, suggesting\ - \ a theme of healthy eating or culinary preparation. \\n\\n\\n \\n Strategic\ - \ Product Themes \\n \\n Accelerate \\n \\n Time to Value\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"text\":\"Reduce Total \\n \\n Cost of \\n \\n Ownership \\n \\n Progress\ - \ \\n \\n on Your AI \\n \\n Journey \\n \\n Support Data \\n \\n Security\ - \ \\n \\n \\n \\n\\n\\n \\n\\n\\n a halftone dot pattern, which creates\ - \ a gradient effect from white to black. It shows a blurred background with\ - \ a series of dots that gradually fade into darkness. Visible objects include\ - \ dots and gradients. The scene is likely an abstract representation of a\ - \ digital interface or a graphic\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"text\":\"gn element. \\n\\n\\n \\n 37\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Leverage\ - \ our \\n \\n GenAI \\n \\n Developments \\n \\n Harness the power of \\n\ - \ artificial intelligence \\n \\n and drive your AI \\n \\n st\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"text\":\"y. \\n \\n Adopt Our Latest Releases \\n \\n Security & \\n \\\ - n Compliance \\n \\n Protect your systems \\n \\n and your data with up- \\\ - n to-date security \\n \\n patches, dependency \\n updates, and \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"text\":\"atory \\n compliance alignment. \\n \\n Platform \\n \\n Support\ - \ \\n \\n Sta\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"text\":\"rent with the \\n \\n latest platforms, \\n frameworks, and \\\ - n \\n standards to ensure \\n compatibility and long- \\n \\n term support\ - \ \\n \\n \xC7\xC7 \\n Modernization and \\n \\n Future-Readiness \\n \\n\ - \ Embrace our latest \\n \\n features and \\n advancements and \\n \\n accelerate\ - \ your data \\n agility journey. \\n \\n Performance & \\n \\n Reliability\ - \ \\n \\n Benefit from ongoing \\n \\n fixes and optimizations \\n th\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"text\":\" https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital\ - \ photograph showing two people working at a desk. The man is seated, looking\ - \ at the computer screen, while the woman stands behind him, smiling. Visible\ - \ objects include a desktop computer with a keyboard and mouse, a pen holder,\ - \ and some\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"text\":\"ments on the desk. The background features a green wall with charts\ - \ and graphs. \\n\\n\\n \\n\\n\\n a geometric logo consisting of three overlapping\ - \ hexagons arranged in a triangular formation. The background is black, and\ - \ the logo is rendered in shades of green. Visible objects include the hexagons\ - \ and their intersecting lines. Context suggests a modern, tech-oriented brand\ - \ identity. \\n\\n\\n \\n 10\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"text\":\" rights reserved.\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n From Triad\ - \ to Quartet \\n \\n Product Management \\n \\n Viability & Scope \\n \\n\ - \ Engineering \\n \\n Feasibility & Delivery \\n \\n Architecture \\n \\n\ - \ Sustainability & Scale \\n \\n Customer Experience \\n \\n Desirability\ - \ \\n \\n https://www.pro\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"text\":\".com/ \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital thumbs-up icon. It shows a thumbs-up gesture with the thumb\ - \ extended upwards and the index finger curled into a triangle. Visible objects\ - \ include the thumbnail icon itself, the thumb, and the index finger. The\ - \ context is likely a digital interface or a social media platform where users\ - \ can express approval\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"text\":\"emporary reporting \\n \\n structure while we select a \\n \\\ - n product leadership team, \\n \\n and align P\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"text\":\"d \\n \\n OpenEdge product \\n management and product \\n \\n\ - \ operations. \\n \\n Creation of a new enterprise \\n \\n architecture function\ - \ led by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\\ - n enterprise architect: Nathan \\n van \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"text\":\". \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting line changes\ - \ for \\n \\n HDP \\n \\n \u2022 Temporary structure for \\n \\n the documentation\ - \ \\n function \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital artwork depicting a street scene with various el\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"text\":\" a kitchen setting with a focus on the fruit bowl, suggesting\ - \ a theme of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n\ - \ a digital artwork that appears to be a still life composition. Visible objects\ - \ include a bowl, a spoon, a knife, and various fruits such as apple\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"text\":\"s and oranges. The scene depicts a kitchen setting with a focus\ - \ on the fruit bowl, suggesting a theme of healthy eating or culinary preparation.\ - \ \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition.\ - \ Visible o\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"text\":\"e a bowl, a spoon, a knife, and va\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"text\":\"s fruits such as apples and oranges. The scene depicts a kitchen\ - \ setting with a focus on the fruit bowl, suggesting a theme of healthy eating\ - \ or culinary preparation. \\n\\n\\n \\n\\n\\n a digital artwork that appears\ - \ to be a still\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"text\":\"Government\\nim (SL/Fed)\\n\\nMedia &\\nPublishing\\n\\nMayes\ - \ oyand \\n\\n\\n a circular diagram with six segments, each representing\ - \ a different aspect of security management. The segments are \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"text\":\"ch, Managed Healthcare, Government (SL/Fed), Public Safety, Fraud\ - \ Identity, and Financial Services & Insurance. Visible objects include icons\ - \ for Software (ISV/OEM) Manufacturing, Life Sciences Pharma Biotec\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"text\":\"h, Media & Publishing, and Fraud I\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"text\":\" \\n and classification workflows. \\n \\n Knowledge Graph Design\ - \ & \\n \\n Deployment \u2013 Build enterprise \\n knowledge graphs for semantic\ - \ \\n search and AI. \\n \\n Ontology Development & \\n \\n Governance \u2013\ - \ Create and govern \\n domain-specific ontologies for \\n accuracy. \\n \\\ - n AI & NLP Enablement \u2013 Implement \\n \\n NLP and semantic AI for smarter\ - \ \\n content retrieval. \\n \\n h\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"text\":\"//www.progress.com/ \\n \\n \\n\\n\\n \\n 32\xA9 2025 Progres\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"text\":\"ftware Corporation and/or its subsidiaries or affiliates. All\ - \ rights reserved. \\n \\n Progress Data Platform \\n \\n Acquisition \\n\ - \ Customer \\n \\n Renewal \\n Expansion \\n \\n Customer \\n \\n Renewal\ - \ \\n Expansion \\n \\n Acquisition \\n Customer \\n \\n Renewal \\n Expansion\ - \ \\n \\n Acqu\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"text\":\"isition \\n \\n Acquisition \\n Customer \\n \\n Renewal \\n Expansion\ - \ \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"text\":\"quisition \\n Customer \\n \\n Renewal \\n Expansion \\n \\n Acquisition\ - \ \\n Customer \\n \\n Renewal \\n Expansion \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital graphic artifact, likely representing\ - \ a logo or branding element. It shows a stylized green geometric shape with\ - \ three arrows pointing upwards, set against a white background. Visible objects\ - \ include the green shapes and arrows, as well as s\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"text\":\"ects include the green shapes and arrows, as well as some black\ - \ elements that could represent additional details or text. The scene is minimalistic,\ - \ focusing on the geometric shapes and their directional emphasis. \\n\\n\\\ - n \\n 33\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Progress Data Platform \\n \\n\ - \ \xC7\xC7 \\n \\n \u2713 Simplified Licensing Terms \u2013 Harmonized E\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"text\":\"ULA and clear discounting guidelin\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"text\":\"\\n \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal\ - \ desk and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent\ - \ Pricing Models \u2013 Unified price list and predictable cost structures.\ - \ \\n \\n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for\ - \ easier entry and adoption. \\n \\n \u2713 Integrated Support Experience\ - \ \u2013 Unified help resources and onboarding for smoother engagement. \\\ - n \\n \u2713 Automation in Fulfillment \u2013 Salesforce migration\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"text\":\" faster provisioning and order accuracy. \\n \\n \u2713 Unified\ - \ UI \u2013 Improved experience across product set to increase efficiency\ - \ and user engagement. \\n \\n Customer Experience \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a graphic illustration featuring three stylized\ - \ icons: a wave symbol, a green checkmark icon, and a smiley face icon. The\ - \ scene shows a modern digital interface with these icons, suggesting a focus\ - \ on user feedback or approval. Visible objects include the wave symbol, the\ - \ green checkmark icon, and the smiley face icon. Context includes a digital\ - \ environment with a clean, minimalist design. \\n\\n\\n \\n\\n\\n a digital\ - \ artwork that appears to be a still life composition. Visible objects include\ - \ a bowl, a spoon, a knife, and various fruits such as apples and oranges.\ - \ The scene depicts a kitchen setting with a focus on the fruit bowl, suggesting\ - \ a theme of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n\ - \ a digital image of a \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"text\":\"cape scene. Visible objects include trees, grass, rocks, and water.\ - \ The scene depicts a serene natural environment with lush greenery and calm\ - \ waters. \\n\\n\\n \\n 34\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n Intersection\ - \ of \\n Moment-in-Time \\n Pain and Long-term \\n Business Impact \\n \\\ - n Long-Term \\n \\n Transformation \\n Help to solve today's pains \\n \\\ - n repeatedly \\n \\n until long-term impact \\n \\n becomes instilled. \\\ - n \\n AI-\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50832-51252\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50832-51252\"\ - ,\"text\":\" Hosting \\n \\n \u2022We are experiencing ever more \\n \\n demand\ - \ for hosting products in PDC \\n \\n \u2022 If you want to host your product\ - \ in \\n \\n PDC\u2026 \\n \\n \u27A2How do you get started? \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n 41\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n First Steps\ - \ \\n \\n \u2022 Investigate whether PDC is really the \\n \\n correct place\ - \ for your product \\n \\n \u2022 There may be te\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50832-51252\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51262-51486\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51262-51486\"\ - ,\"text\":\"al or business \\n \\n limitations that may be a deal-breaker\ - \ \\n \\n \u27A2Read up on what PDC is \\n \\n \u27A2 Investigate PDC hosting\ - \ \\n \\n requirements \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\\ - n \\n 42\xA9 2025 Progress Software Corporation and/or its\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51262-51486\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51496-51778\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51496-51778\"\ - ,\"text\":\"idiaries or affiliates. All rights reserved. 42 \\n \\n Getting\ - \ Started \\n \\n \u2022 Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss\ - \ \\n \\n \u27A2 Provide as many details as required \\n \\n including timeframes\ - \ \\n \\n \u27A2 Meeting will be arranged between key \\n \\n stakeholders\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51496-51778\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"text\":\" \\n \\n \u2022 PDC engineering reviews before deployment \\n\ - \ \\n \u2022 PDC engineering deploys and tests \\n \\n \u2022 Product is deployed!\ - \ \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n 44\xA9 2025 Progress\ - \ Soft\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"text\":\" Corporation and/or its subsidiaries or affiliates. All rights\ - \ reserved. 44 \\n \\n Product Hosting Notes \\n \\n \u2022 Current lead times\ - \ for product \\n \\n deployment a\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"text\":\"urrently 3-6 months \\n \\n \u2022 A Product Hosting Guidelines\ - \ \\n \\n document describes the process and the \\n \\n technical details\ - \ for deploying a new \\n \\n product/service in PDC (contact Stephen \\n\ - \ \\n Rice for details) \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\\ - n Alltools Edit\\n1c)\\n\\n= menu) \xA5y Product Hosting Guideli. x @ \xA9\ - \ # Signin - a x\\nConvert E-Sign Findtextortoo Q 6% a 8\\n| @\\nProduct Hosting\ - \ Guidelines\\nLast updated by | Stephen Rice | 3 Dec 2025 at 14:36 GMT i(k)\\\ - n\\nZ.\\n\\n2\\n\\nContents\\n* Introduction\\n+ Terminology and Overview\\\ - n+ Product Hosting Features\\n+ Is PDC the correct place to host your service?\\\ - n+ PDC Product Deployment \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"text\":\"ess\\n+ Exploration - Need is identif\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"text\":\"for product to be hosted\\n+ Preparation - Product and PDC teams\ - \ create implementati...\\n\\n+ Product Updates - Product team implements\ - \ required cha...\\n\\n+ PDC Review - PDC team reviews design documentation\ - \ an...\\n+ PDC Integration - PDC deploys product and performs inte..\\n\\\ - n+ Operations On\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"text\":\"or. \\n\\n\\n \\n 47\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n Platform \\\ - n \\n Functionality \\n \\n Support \\n \\n Support for additional \\n \\\ - n PDP products and \\n services as well as \\n \\n regional deployment \\\ - n \\n options \\n \\n PDC On-Going Themes \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"text\":\"Secure and \\n \\n Trustworthy \\n \\n Platform \\n \\n Operational\ - \ \\n \\n Support \\n \\n Performance \\n \\n and Scalability \\n \\n Enhance\ - \ product \\n \\n performance to elevate \\n customer satisfaction and \\\ - n \\n retention \\n \\n Equipping the Cloud \\n \\n Operations team with \\\ - n advanced tools to better \\n assist clients and drive \\n \\n revenue generation\ - \ \\n \\n Continuo\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"text\":\"nitoring \\n \\n and enhancement of \\n security measures to \\\ - n \\n safeguard customer data \\n \\n integrity \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene\ - \ with a focus on the intersection of two roads. Visible objects include cars,\ - \ pedestrians, and buildings. \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"text\":\"cene captures a moment of everyday life, highlighting urban dynamics\ - \ and human activity. \\n\\n\\n \\n\\n\\n a digital artwork depicting a street\ - \ scene with a focus on the intersection of two roads. Visible objects include\ - \ cars, pedestrians, and buildings. The scene captures a moment of everyday\ - \ life, highlighting urban dyn\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"text\":\" and human activity. \\n\\n\\n \\n\\n\\n a digital artwork depicting\ - \ a street scene with a focus on the intersection o\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"text\":\" roads. Visible objec\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"text\":\"SL/Fed) \\n \\n Life \\n \\n Sciences \\n \\n Pharma \\n \\n Biotech\ - \ \\n \\n Financial \\n \\n Services & \\n \\n Insurance \\n \\n Managed \\\ - n \\n Healthcare \\n \\n Media & \\n \\n Publishing \\n \\n \\n \\n\\n\\\ - n \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\n \\\ - n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n value\ - \ through \\n \\n added AD\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"text\":\" products or \\n \\n leveling up tiers \\n \\n \xC7\xC7 \\n \\\ - n New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\n \\n adopting\ - \ Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer \\n \\\ - n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinf\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"text\":\"g ongoing \\n \\n value of current \\n \\n product(s) in use \\\ - n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork\ - \ depicting a street scene with a focus on the intersection of two roads.\ - \ Visible objects include cars, pedestrians, and buildings. The scene captures\ - \ a moment of everyday life, highlighting \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"text\":\"res a moment of everyday life, highlighting urban dynamics and\ - \ human activity. \\n\\n\\n \\n 25\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Long-standing\ - \ Innovation \\n \\n Customer \\n \\n Renewal \\n E\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"text\":\"xpansion \\n \\n Customer \\n \\n Renewal \\n Expansion \\n \\\ - n Customer \\n \\n Renewal \\n Expansion \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital graphic artifact, likely representing\ - \ a logo or branding element. It shows a stylized green arrow pointing upwards\ - \ against a white ba\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"text\":\"ckground. Visible objects include the green arrow and the white\ - \ background. Context suggests a modern, tech-oriented environment, possibly\ - \ related to technology or innovation. \\n\\n\\n \\n\\n\\n a digital graphic\ - \ artifact, likely representing a logo or branding element. It shows a stylized\ - \ green geometric shape with three arrows pointing upwards, set against a\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"text\":\"white background. Visible objects \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"text\":\"-boarding - Operations team is provided wi...\\n+ Retrospective\ - \ - Review process and discuss how any issues...\\n+ Technical Requirements\\\ - n\\n+ Reverse Proxy Support\\n\\n+ specif .\\n\\n+ Network Connections Requirements\\\ - n\\n+ Helm Charts\\n\\n+ Load Bala\\n\\n2 p \\n\\n\\n a screenshot of a webpage\ - \ titled \\\"Product Hosting Guideli\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"text\":\"nes\\\". The page contains a list of contents, including sections\ - \ on introduction, product deployment process, and hosting requirements. Visible\ - \ objects include the menu bar at the top with options like \\\"Menu\\\",\ - \ \\\"Product Hosting Guideli...\\\", \\\"Create\\\", \\\"Sign in\\\", \\\"\ - All tools\\\", \\\"Edit\\\", \\\"Convert\\\", \\\"E-Sign\\\", \\\"Find text\ - \ or t\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"text\":\", and \\\"Share\\\". The background is white, and there are no\ - \ other graphics or images present. \\n\\n\\n \\n Product Innovation \\n\ - \ \\n \\n \\n\\n\\n \\n 46\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n Progress \\\ - n Data Cloud \\n \\n https://www.progress.com/ \\n \\n\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54401-54690\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54401-54690\"\ - ,\"text\":\" 3) Progress DataCloud \\n\\n\\n a logo for Progress DataCloud.\ - \ It shows a green square with a white arrow pointing upwards, indicating\ - \ progress or growth. Visible objects include the green square and the white\ - \ arrow. The scene shows a modern office environment with a desk, chair, and\ - \ computer \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54401-54690\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50145-50233\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50145-50233\"\ - ,\"text\":\"ity. The scene shows a globe centered within the circle, surrounded\ - \ by these six segment\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50145-50233\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50238-50546\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50238-50546\"\ - ,\"text\":\"\\n @uHHEERy\\n\\nquxx== \xA9\\n\\nERB EBB BEEBE Baees wm \xAE\ - \\n\\nSd\\n\\n@nunuun 8 \\n\\n\\n a flowchart or diagram that illustrates\ - \ the process of data integration. Visible objects include arrows representing\ - \ different stages, icons indicating various processes, and a circular loop\ - \ symbolizing a continuous cycle. The scene shows a n\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50238-50546\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47858-48111\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47858-48111\"\ - ,\"text\":\"liver a more \\n \\n resilient platform. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still\ - \ life composition. Visible objects include a bowl, a spoon, a knife, and\ - \ various fruits such as apples and oranges. The scene de\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47858-48111\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"text\":\"7\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Congrats getting to the next\ - \ level! \\n \\n Employee Location Scrum Team New Position \\n Abhishek Tiwari\ - \ Hyderabad Mangalayan QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers\ - \ Software Engineer, Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software\ - \ Engineer, Principal 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software\ - \ Engineer, Senior 1 \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal\ - \ \\n \\n Brahmananda Gouni Hyderabad - Senior Manager, Software Engineering\ - \ \\n \\n Brian Tang USA Quicksilver Software Engineer, Principal \\n \\n\ - \ Hari Krishna Tirunagari Hyderabad Mangalayan QA Engineer, Principal 3 \\\ - n \\n Hemanandh S Hyderabad Avengers Software Engineer, Principal 3 \\n \\\ - n Jonathan Miller USA Kalimba Software Engineer, Senior \\n \\n Kunal Basarkar\ - \ Hyderabad Avengers Software Engineer, Senior Principal \\n \\n Nitesh Mehta\ - \ Hyderabad Harmonica Software Engineer, Senior Principal \\n \\n Saivenkat\ - \ Chepuri Hyderabad Conquerors Software Engineer, Principal 1 \\n \\n Sathyanarayana\ - \ Gundoji Hyderabad Mangalayan Software Engineer, Senior 1 \\n \\n Srikar\ - \ Veeramallu Hyderabad Atlantis Software Engineer, Principal 1 \\n \\n Stephen\ - \ Brown USA Apollo Software Engineer, Principal \\n \\n Sumit Pritmani Hyderabad\ - \ Transformers Software Engineer, Senior 2 \\n \\n Vikram Reddy Deva Hyderabad\ - \ Avengers QA Engineer, Senior 2 \\n \\n https://www.progress.com/\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"text\":\"5\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"text\":\"24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"text\":\"6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"text\":\"26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"text\":\"New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers\ - \ \\n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\\ - n Customer \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\\ - n \\n reinforcing ongoing \\n \\n value of current \\n \\n product(s) in use\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"text\":\"and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"text\":\"2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat!\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"text\":\"48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"text\":\"Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"text\":\"\u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"text\":\"42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - ,\"text\":\"54\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Semaphore PI 2026.1 Priorities\ - \ \\n \\n Technical Debt \\n \\n \u2022 Angular to \\n React \\n completion\ - \ \\n \\n Ease of \\n Modelling \\n \\n \u2022 Performance \\n improvements\ - \ \\n \\n AI enhancement \\n \\n \u2022 LLM use in:\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/51783-52072\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"text\":\"59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - 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All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":4,\"index\":14,\"\ - start\":3054,\"end\":3424,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"score\":0.0012893283274024725,\"score_type\":\"RERANKER\",\"order\":2,\"\ - text\":\" \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7 \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":83,\"start\":16815,\"\ - end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"score\":0.00049553596181795,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":21,\"\ - start\":4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":96,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":84,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":74,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":11,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":9,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":233,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":41,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":246,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":209,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":281,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":140,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026480842279852368,\"score_type\":\"RERANKER\",\"order\"\ - :15,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"labels\":[\"/k/inception\"],\"position\":{\"page_number\":8,\"index\":34,\"\ - start\":7044,\"end\":7374,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_8_0.jpg\",\"is_a_table\"\ - :false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"score\":0.000018925147742265835,\"score_type\":\"RERANKER\",\"order\"\ - :16,\"text\":\" \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal\ - \ desk and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent\ - \ Pricing Models \u2013 Unified price list and predictable cost structures.\ - \ \\n \\n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for\ - \ easier entry and adoption. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":32,\"index\":161,\"\ - start\":31881,\"end\":32155,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"score\":0.00001805851934477687,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\" \\n 44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"labels\":[],\"position\":{\"page_number\":43,\"index\":212,\"start\":41776,\"\ - end\":42205,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"score\":0.000017231572201126255,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" Modernize the Corticon \\n \\n user experience with a \\n\ - \ new Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and\ - \ \\n accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule\ - \ \\n \\n automation and \\n delivery with AI that \\n supports end-to-end\ - \ \\n \\n project creation, \\n testing, and \\n \\n maintenance. \\n\",\"\ - id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"labels\":[],\"position\":{\"page_number\":57,\"index\":268,\"start\":53354,\"\ - end\":53655,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"score\":0.000016701422282494605,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - 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All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":0,\"start\"\ - :4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":0,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":0,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":0,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":0,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":0,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":0,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":0,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":0,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":0,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":0,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026480842279852368,\"score_type\":\"RERANKER\",\"order\"\ - :15,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"labels\":[\"/k/inception\"],\"position\":{\"page_number\":8,\"index\":0,\"\ - start\":7044,\"end\":7374,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_8_0.jpg\",\"is_a_table\"\ - :false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"score\":0.000018925147742265835,\"score_type\":\"RERANKER\",\"order\"\ - :16,\"text\":\" \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal\ - \ desk and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent\ - \ Pricing Models \u2013 Unified price list and predictable cost structures.\ - \ \\n \\n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for\ - \ easier entry and adoption. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":32,\"index\":0,\"\ - start\":31881,\"end\":32155,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"score\":0.00001805851934477687,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\" \\n 44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"labels\":[],\"position\":{\"page_number\":43,\"index\":0,\"start\":41776,\"\ - end\":42205,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"score\":0.000017231572201126255,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" Modernize the Corticon \\n \\n user experience with a \\n\ - \ new Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and\ - \ \\n accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule\ - \ \\n \\n automation and \\n delivery with AI that \\n supports end-to-end\ - \ \\n \\n project creation, \\n testing, and \\n \\n maintenance. \\n\",\"\ - id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"labels\":[],\"position\":{\"page_number\":57,\"index\":0,\"start\":53354,\"\ - end\":53655,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"score\":0.000016701422282494605,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - \" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"\ - New mascara-international brands 06.\\\" Visible objects include a woman sitting\ - \ on a bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - ,\"labels\":[],\"position\":{\"page_number\":15,\"index\":0,\"start\":11635,\"\ - end\":12070,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":\"image_15_0.jpg\",\"is_a_table\":false}}}}}},\"\ - query\":\"New employees at ADP\",\"total\":20,\"page_number\":0,\"page_size\"\ - :20,\"next_page\":false,\"autofilters\":[],\"min_score\":{\"bm25\":0.0},\"\ - best_matches\":[\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - 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Visible objects include the person's face, hair,\ - \ and smile. The background consists of a stone wall wi\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"text\":\"me decorative elements. \\n\\n\\n \\n\\n\\n a portrait photo\ - \ of a man with a beard and wavy hair. He is wearing a dark shirt with a floral\ - \ pattern\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"text\":\"tps://www.progress.com/ \\n \\n \\n\\n\\n \\n 8\xA9 2025 Progress\ - \ Software Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n AI is no longer optional. \\n It is the engine of \\n transformation.\ - \ \\n \\n 8\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or a\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"text\":\"ffiliates. All rights reserved. \\n \\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"text\":\"ps://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a blurred\ - \ motion photograph, capturing the rapid movement of city streets at night.\ - \ Visible objects include tall buildings, streetlights, and vehicles. The\ - \ scene depicts a bustling urban environment with dynamic light \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"text\":\"s indicating high-speed traffic. \\n\\n\\n \\n Practices are\ - \ changing \\n \\n \u2022 Market analysis \\n \u2022 Feature definition \\\ - n \u2022 Threat modelling \\n \u2022 Code suggestions \\n \u2022 Code analysis\ - \ \\n \u2022 UI scaffolding \\n \u2022 Test coverage \\n \u2022 Penetration\ - \ testing \\n \u2022 Documentation \\n \u2022 Defect analysi\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"text\":\" \\n \\n \\n\\n\\n \\n 3\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"text\":\"ress\u2019 \\n\\n\\n a slide from a presentation titled \\\"Celebrating\ - \ Innovation from PI 3...\\\". The slide features a festive background with\ - \ colorful confetti. Visible objects include the title text, bullet points,\ - \ and a logo at the bottom left corner. The scene shows a celebration atmosphere\ - \ with bright colors and decorative\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"text\":\"ents. \\n\\n\\n \\n 4\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Important\ - \ Dates \\n \\n 11 December (Thursday) \\n \\n Draft Plan Presentations \\\ - n \\n 15 December (Monday) \\n \\n Final Plan Presentations \\n \\n 17 December\ - \ (Wednesday) \\n \\n Start of PI 2026.1 \\n \\n 18 December (Thursday) \\\ - n \\n Innovation Demos \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"text\":\"\\n \\n \\n\\n\\n \\n\\n\\n a portrait photograph of a man smiling\ - \ at the camera. He is wearing a yellow shirt with a patterned background.\ - \ Visible objects include his face, shirt, and background. The scene is likely\ - \ an indoor setting, possibly a conference o\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"text\":\"nt space. \\n\\n\\n \\n\\n\\n a portrait of a smiling man with\ - \ short hair and a beard. He is wearing a black shirt. The backgro\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"text\":\"rights reserved. \\n \\n What is the best damp-proofing product\ - \ for \\n concrete piles that are in constant contact with \\n standing water,\ - \ is it available, how is it applied, \\n \\n Hi, how can I help? \\n \\n\ - \ and what qualifications staff require to apply it? \\n \\n Submit \\n \\\ - n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a photograph of\ - \ two construction workers standing near a body of water, likely a lake or\ - \ river, with several concrete pillars marked with numbers in the background.\ - \ They are wearing safety helmets and high-visibility jackets. The scene depicts\ - \ them observing or discussing something on a clipboard. Visible objects include\ - \ the con\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"text\":\"crete pillars, the water, and the \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"text\":\" Context suggests they are involved in a construction project.\ - \ \\n\\n\\n \\n 20\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n ?Documents \\n \\n Searching\ - \ for a Suitable Product \\n \\n + damp-proofing \\n \\n + concrete piles\ - \ \\n \\n + standing water \\n \\n damp-proofing \\n \\n concrete piles \\\ - n \\n standing water \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n\ - \ \\n 21\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Comprehensive \\n \\n and actionable\ - \ \\n \\n answer \\n \\n Searching for a Suitable Product \\n \\n Scan tech\ - \ specs to \\n \\n find suitable products \\n \\n Retrieve additional \\n\ - \ \\n information about \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"text\":\"ication, safety and \\n \\n qualifications \\n \\n Check stock\ - \ management \\n \\n system to see if a suitable \\n \\n product is in stock\ - \ \\n \\n What is the best damp-proofing \\n \\n product for concrete piles\ - \ that are \\n \\n in constant contact with sta\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"text\":\"nding \\n \\n water, is it available, how is it \\n \\n applied,\ - \ and what qualifications \\n \\n staff require to apply it? \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n 22\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affilia\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"text\":\"ion \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\ - \ Pro \\n duct 360 \\n \\n P \\n u \\n \\n b \\n lic \\n \\n S \\n a \\n fe\ - \ \\n \\n ty \\n \\n Self-service Policy \\n Search \\n \\n K \\n n \\n \\\ - n o \\n w \\n \\n le \\n d \\n \\n g \\n e \\n \\n M \\n a \\n n \\n \\n a\ - \ \\n g \\n \\n e \\n m \\n \\n e \\n n \\n \\n t \\n \\n Fraud \\n \\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"text\":\"ity \\n \\n Search & \\n Dis \\n \\n cover \\n y \\n \\n Software\ - \ (ISV/OEM) \\n \\n Manufacturing \\n \\n Government \\n \\n (SL/Fed) \\n\ - \ \\n Life \\n \\n Sciences \\n \\n Pharma \\n \\n Biotech \\n \\n Financial\ - \ \\n \\n Services & \\n \\n Insurance \\n \\n Managed \\n \\n Healthcare\ - \ \\n \\n Media & \\n \\n Publ\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"text\":\"s plain white. \\n\\n\\n \\n\\n\\n a portrait photograph of a\ - \ young man wearing a light blue shirt. He has dark hair and a friendly expression.\ - \ The background is plain white, providing no distractions from the subject.\ - \ Visible objects include his face and shirt. Context suggests he might be\ - \ a student or professional in a field related to \\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"text\":\"w \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"text\":\"a focus on the intersection of two roads. Visible objects include\ - \ cars, pedestrians, and buildings. The scene captures a moment of everyday\ - \ life, highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\\ - n a digital artwork depicting a street scene with a focus on the intersection\ - \ of two roads. Visible objects include cars, pedestrians, and bu\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"text\":\"gs. The scene captures a moment of everyday life, highlighting\ - \ urban dynamics and human activity. \\n\\n\\n \\n 25\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n Long-standing Innovation \\n \\n Customer \\n \\n Renewal \\n Expansion\ - \ \\n \\n Cust\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"text\":\"ometric shapes representing buildings, windows, and roofs. The\ - \ scene shows a modern architectural design with green accents. \\n\\n\\n\ - \ \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\xC7\ - \ \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge 13\ - \ \u2013 Latest version \\n supporting \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"text\":\"compliance \\n \\n 12 and GenAI Workshops \u2013 Free \\n hands-on\ - \ training \\n \\n QSM \u2013 Quality and security \\n management tools \\\ - n \\n MCP Connector for ABL - AI Code \\n Assistant to improve code quality\ - \ \\n \\n Agentic RAG \u2013 AI-driven contex\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"text\":\"\\n knowledge retrieval \\n \\n Forrester Total Economic Impact\ - \ \u2013 \\n Validated ROI and business value \\n \\n PUG Challenge \u2013\ - \ Community- \\n \\n driven learning and networking \\n \\n Customer Advisory\ - \ Board \u2013 Customer \\n \\n advisory for stra\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"text\":\"g \\n \\n \\n \\n\\n\\n \\n 24\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Progress\ - \ ADP: GTM Motions \\n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase\ - \ account \\n \\n value through \\n \\n added ADP \\n \\n products or \\n\ - \ \\n leveling up tiers \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"text\":\" a modern urban setting. Visible objects include the car's front\ - \ view, a city skyline in the background, and a person standing in front of\ - \ the vehicle. The context suggests a professional environment, possibly related\ - \ to automotive services or technology. \\n\\n\\n \\n 18\xA9 2025 Progress\ - \ Software\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"text\":\"oration and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a geometric\ - \ shape, likely a polygon or a three-dimensional object, rendered in yellow\ - \ with black outlines. It shows a simple scene with a few objects:\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"text\":\"ctangular box-like structure and a small square inside it. The\ - \ background is black. \\n\\n\\n \\n\\n\\n a digital artwork depicting a\ - \ street scene with a focus on a person walking down the sidewalk. Visible\ - \ objects include a person, a car, and various urban elements such as buildings,\ - \ tree\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"text\":\"d street signs. The scene captures a moment of everyday life in\ - \ an urban environment. \\n\\n\\n \\n\\n\\n a digital artwork that appears\ - \ to be a still life composition. Visible objects include a bowl, a spoon,\ - \ a knife, and various fruits such as apples and oranges. The scene depicts\ - \ a kitchen setting with a focus on the fruit bowl, suggesting a theme of\ - \ healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a green arrow\ - \ pointing to the right,\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"text\":\"cating movement or direction. Visible objects include the arrow\ - \ itself and a few indistinct shapes that might be part of the background.\ - \ The scene shows a blurred, dark background with a green arrow pointing to\ - \ the right. \\n\\n\\n \\n 19\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"text\":\" \\n\\n\\n a geometric abstract artwork featuring a three-dimensional\ - \ shape composed of overlapping triangles in varying shades of green. The\ - \ scene shows a modern, minimalist interior with a black background. Visible\ - \ objects include a sleek, black chair and a small table with a simple design.\ - \ Context suggests a contemporary living space or office environ\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"text\":\" \\n\\n\\n \\n\\n\\n a geometric abstract graphic with a three-dimensional\ - \ cube-like shape composed of green and yellow gradients. It shows a modern,\ - \ minimalist design with clean lines and a vibrant color palette. Visible\ - \ objects include the cube and its gradient colors. The scene is set against\ - \ a black background, emphasizing the geometr\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"text\":\"a signature, likely from a person's handwritten document or digital\ - \ representation. Visible objects include the signature itself and possibly\ - \ some ink residue. The scene shows a close-up view of a person's handwriting,\ - \ with the signature prominently displayed against a dark background.\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"text\":\"ftware Corporation and/or its subsidiaries or affiliates. All\ - \ rights reserved. \\n \\n Leverage our \\n \\n GenAI \\n \\n Developments\ - \ \\n \\n Harness the power of \\n artificial intelligence \\n \\n and drive\ - \ your AI \\n \\n strategy. \\n \\n Adopt Our Latest Releases \\n \\n Security\ - \ & \\n \\n Compliance \\n \\n Protect your systems \\n \\n and your data\ - \ with up- \\n to-date security \\n \\n patches, dependency \\n updates, and\ - \ regulatory \\n complian\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"text\":\"ignment. \\n \\n Platform \\n \\n Support \\n \\n Stay current\ - \ with the \\n \\n latest platforms, \\n frameworks, and \\n \\n standards\ - \ to ensure \\n compatibility and long- \\n \\n term support \\n \\n \xC7\xC7\ - \ \\n Modernization and \\n \\n Future-Readiness \\n \\n Embrace our latest\ - \ \\n \\n features and \\n advancements and \\n \\n accelerate your data \\\ - n agility journey. \\n \\n Performance & \\n \\n Reliability \\n \\n Benefit\ - \ from ongoing \\n \\n fixes and optimizations \\n that del\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"text\":\"a more \\n \\n resilient platform. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still\ - \ life composition. Visible objects include a bowl, a spoon, a knife, and\ - \ various fruits such as apples and oranges. The sc\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"text\":\"epicts a kitchen setting with a focus on the fruit bowl, suggesting\ - \ a theme of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n\ - \ a digital artwork that appears to be a still \\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"text\":\"composition. Visible objects include a bowl, a spoon, a knife,\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"text\":\"various fruits such as apples and oranges. The scene depicts a\ - \ kitchen setting with a focus on the fruit bowl, suggesting a theme of healthy\ - \ eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital artwork that\ - \ appears to be a still life composition. Visible objects include a bowl,\ - \ a spoon, a knife, and various fruits such as apples and oranges. The scene\ - \ depicts a k\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"text\":\" https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital\ - \ photograph showing two people working at a desk. The man is seated, looking\ - \ at the computer screen, while the woman stands behind him, smiling. Visible\ - \ objects include a desktop computer with a keyboard and mouse, a pen holder,\ - \ and some\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"text\":\"ments on the desk. The background features a green wall with charts\ - \ and graphs. \\n\\n\\n \\n\\n\\n a geometric logo consisting of three overlapping\ - \ hexagons arranged in a triangular formation. The background is black, and\ - \ the logo is rendered in shades of green. Visible objects include the hexagons\ - \ and their intersecting lines. Context suggests a modern, tech-oriented brand\ - \ identity. \\n\\n\\n \\n 10\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"text\":\" rights reserved.\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n From Triad\ - \ to Quartet \\n \\n Product Management \\n \\n Viability & Scope \\n \\n\ - \ Engineering \\n \\n Feasibility & Delivery \\n \\n Architecture \\n \\n\ - \ Sustainability & Scale \\n \\n Customer Experience \\n \\n Desirability\ - \ \\n \\n https://www.pro\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"text\":\".com/ \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital thumbs-up icon. It shows a thumbs-up gesture with the thumb\ - \ extended upwards and the index finger curled into a triangle. Visible objects\ - \ include the thumbnail icon itself, the thumb, and the index finger. The\ - \ context is likely a digital interface or a social media platform where users\ - \ can express approval\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"text\":\"emporary reporting \\n \\n structure while we select a \\n \\\ - n product leadership team, \\n \\n and align P\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"text\":\"d \\n \\n OpenEdge product \\n management and product \\n \\n\ - \ operations. \\n \\n Creation of a new enterprise \\n \\n architecture function\ - \ led by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\\ - n enterprise architect: Nathan \\n van \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"text\":\". \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting line changes\ - \ for \\n \\n HDP \\n \\n \u2022 Temporary structure for \\n \\n the documentation\ - \ \\n function \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital artwork depicting a street scene with various el\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"text\":\" oranges. The scene depicts a kitchen setting with a focus on\ - \ the fruit bowl, suggesting a theme of healthy eating or culinary preparation.\ - \ \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition.\ - \ Visible objects include a bowl, a spoon, a knife, and v\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"text\":\"arious fruits such as apples and oranges. The scene depicts a\ - \ kitchen setting with a focus on the fruit bowl, suggesting a theme of healthy\ - \ eating or culinary preparation. \\n\\n\\n \\n Integrate \\n Our Teams \\\ - n \\n Our Products \\n \\n Our \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"text\":\"Use Cases \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\\ - n Software (ISV/OEM)\\nManufacturing\\n\\nLife\\nSciences\\nPharma\\nBiotech\\\ - n\\n&\\nS\\n@\\n<\\noO\\n=\\no\\ni=)\\n3\\ncc\\n3\\nS\\nt\\n\\nFinancial\\\ - nServices &\\nInsurance\\n\\nManaged\\nHealthcare\\n\\nGovernment\\nim (SL/Fed)\\\ - n\\nMedia &\\nPublishing\\n\\nMayes oyand \\n\\n\\n a circular diagram with\ - \ six segments, each representing \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"text\":\"a different aspect of security man\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"text\":\"ent. The segments are labeled Product 360, Self-service Policy\ - \ Search, Managed Healthcare, Government (SL/Fed), Public Safety, Fraud Identity,\ - \ and Financial Services & Insurance. Visible objects include icons for Softwar\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"text\":\"V/OEM) Manufacturing, Life Sciences Pharma Biotech, Media & Publishing,\ - \ and Fraud Identity. The scene shows a globe centered within the circle,\ - \ surrounded by these six segments\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"text\":\" @uHHEERy\\n\\nquxx== \xA9\\n\\nERB EBB BEEBE B\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"text\":\" wm \xAE\\n\\nSd\\n\\n@nunuun 8 \\n\\n\\n a flowchart or diagram\ - \ that illustrates the process of data integration. Visible objects include\ - \ arrows representing different stages, icons indicating various processes,\ - \ and a circu\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"text\":\"lar loop symbolizing a continuous \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"text\":\"a digital graphic artifact, likely representing a logo or branding\ - \ element. It shows a stylized green geometric shape with three arrows pointing\ - \ upwards, set against a white background. Visible objects include the green\ - \ shapes and arrows, as well as some black elements that could represent additional\ - \ details or text. The scene context suggests a mo\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"text\":\" tech-oriented environment, possibly related to \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"text\":\"nology or innovation. \\n\\n\\n \\n\\n\\n a digital graphic artifact,\ - \ likely a logo or an icon. It shows a green geometric shape with three arrows\ - \ pointing upwards, set against a white background. Visible objects include\ - \ the green shapes and arrows, as well as som\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"text\":\"e black elements that could represent additional details or t\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"text\":\" The scene is minimalistic, focusing on the geometric shapes and\ - \ their directional emphasis. \\n\\n\\n \\n 33\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n Progress Data Platform \\n \\n \xC7\xC7 \\n \\n \u2713 Simplified\ - \ Licensing Terms \u2013 Harmonized EULA and clear discounting guidelines.\ - \ \\n \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal desk and\ - \ legal alignme\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"text\":\"ion for faster provisioning and order accuracy. \\n \\n \u2713\ - \ Unified UI \u2013 Improved experience across product set to increase efficiency\ - \ and user engagement. \\n \\n Customer Experience \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a graphic illustration featuring three stylized\ - \ icons: a wave symbol, a green checkmark icon, and a smiley face icon. The\ - \ scene shows a modern digital interface with these icons, sugg\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"text\":\"esting a focus on user feedback or\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"text\":\"althy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital\ - \ image of a landscape scene. Visible objects include trees, grass, rocks,\ - \ and water. The scene depicts a serene natural environment with lush greenery\ - \ and calm waters. \\n\\n\\n \\n 34\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Intersection\ - \ of \\n Moment-in-Time \\n Pain and Long-term \\n Business Impact \\n \\\ - n Long-Term \\n \\n Transformation \\n Help to solve today's pains \\n \\\ - n repeatedly \\n \\n until long-term impact \\n \\n becomes instilled. \\\ - n \\n AI-Driven \\n \\n Foundation \\n Build a robust AI \\n \\n foundation\ - \ that \\n \\n accelerates insights and \\n \\n decision-making \\n for long-term\ - \ success. \\n \\n Customer-Centric \\n \\n Approach \\n Focus on meeting\ - \ \\n \\n customers at their \\n \\n current state to address \\n \\n immediate\ - \ \\n pain points effectively. \\n \\n https://www.progress.com/ \\n \\n \ - \ \\n\\n\\n \\n\\n\\n a long-exposure photograph capturing the dynamic movement\ - \ of vehic\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"text\":\"n an intricate highway network at night. Visible objects include\ - \ multiple intersecting highways with light trails indicating the speed and\ - \ flow of traffic. The scene is set in a bustling urban area, likely a city\ - \ center, with illuminated buildings and streets in the background. \\n\\\ - n\\n \\n Progress Data Platform: Vertical Concentration \\n \\n \xC7\xC7\ - \ \\n \\n Manufacturing \\n \\n \xC7\xC7 \\n \\n Healthcare \\n \\n \xC7\xC7\ - \ \\n \\n Government \\n \\n \xC7\xC7 \\n \\n Media & \\n \\n Publishing \\\ - n \\n \xC7\xC7 \\n \\n FinServ/ \\n \\n Insuranc\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"text\":\"ions team with \\n advanced tools to better \\n assist clients\ - \ and drive \\n \\n revenue generation \\n \\n Continuous monitoring \\n \\\ - n and enhancement of \\n security measures to \\n \\n safeguard customer data\ - \ \\n \\n integrity \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \ - \ \\n\\n\\n a digital artwork depicting a street sc\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"text\":\"ith a focus on the intersection of two roads. Visible objects\ - \ include cars, pedestrians, and buildings. The scene captures a moment of\ - \ everyday life, highlighting urban dynamics and human activity. \\n\\n\\\ - n \\n\\n\\n a digital artwork depicting a street scene with a focus on the\ - \ intersection of two roads. Visible objects include cars, pede\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"text\":\"ns, and buildings. The scene captures a moment of everyday life,\ - \ highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a digital\ - \ artwork depicting a street scene with a focus on the intersection of two\ - \ roads. Visible objects include cars, pedestrians, and buildings. The scene\ - \ captures \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"text\":\"ent of everyday life, highlighting urban dynamics and human activity.\ - \ \\n\\n\\n \\n\\n\\n a digital illustration featuring a gear-like blue background\ - \ with interconnected lines and nodes, symbolizing technology and data management.\ - \ In the foreground, there are two open books: one with a brain diagram on\ - \ the left and another\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"text\":\" a yellow brain icon on the right. Below the books, there is a\ - \ graph showing data trends. The scene suggest\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"text\":\"ocus on information t\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"text\":\" alignment \\n \\n World Tour \u2013 Global events \\n showcasing\ - \ OpenEdge innovation \\n \\n Community - Collaborative network for \\n learning,\ - \ sharing, and innovation \\n \\n Customer Validation Program - Early \\n\ - \ access to roadmap features and \\n \\n collaborative feedback. \\n \\n AppMod\ - \ \u2013 Separating US from \\n \\n database and business logic and API \\\ - n enablement \\n \\n Managed Database Administration \\n (MDB\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"text\":\"Proactive DB monitoring \\n \\n and expert management \\n \\n\ - \ Security Assessment \u2013 Identify \\n \\n vulnerabilities and strengthen\ - \ \\n protection \\n \\n Cloud Migration \u2013 Move OpenEdge \\n workloads\ - \ to cloud \\n \\n https://www.progress.com/ \\n\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"text\":\"\\n\\n \\n\\n\\n a digital artwork depicting a street scene with\ - \ various elements. Visible objects include buildings, trees, and pedestrians.\ - \ The scene captures the essence of urban life, highlighting the blend of\ - \ architecture and nature. \\n\\n\\n \\n\\n\\n a digital artwork depicting\ - \ a street scene with various elements.\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"text\":\"or its subsidiaries or affiliates. All rights reserved. \\n \\\ - n The Solution: Trusted, Intelligent Insights \\n \\n \xC7\xC7 \\n \\n MCP\ - \ Server \\n \\n AI You Can \\n \\n TRUST \\n \u2022 Unified Intelligence\ - \ \\n \\n \u2022 Faster Modernization \\n \\n \u2022 Trusted, Governed AI\ - \ \\n \\n https://\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"text\":\"www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a graphic representation\ - \ of a light bulb with bright yellow glow and sparkles around it. The scene\ - \ shows a sunny day with a clear blue sky, and the light bulb is placed on\ - \ a surface with a few decorative elements like stars and flowers. \\n\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"text\":\"\\n\\n \\n\\n\\n a digital artwork depicting a street scene with\ - \ a focus on the intersection of two roads. Visible objects include cars,\ - \ pedestrians, and buildings. The scene captures a moment of everyday life,\ - \ highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a logo,\ - \ featuring a gear with arrows pointing downwards. The gear is yellow with\ - \ black teeth, \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"text\":\"and the arrows are blue. The backg\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - ,\"text\":\" is green. Visible objects include the gear and arrows. Context\ - \ shows a factory setting with machinery and equipment. \\n\\n\\n \\n\\n\\\ - n a digital artwork depicting a street scene with a focus on the intersection\ - \ of two roads. Visible objects include cars, pedestrians, and buildings.\ - \ The scene captures a moment of everyday life, highlighting urban dynamics\ - \ and human activity. \\n\\n\\n \\n\\n\\n a stylized graphic representation\ - \ of a motorcyclis\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - ,\"text\":\"ing fast, likely on a racetrack. Visible objects include the rider's\ - \ silhouette, the motorcycle, and dynamic motion lines indicating speed. The\ - \ scene captures the thrill and intensity of racing, emphasizing rapid movement\ - \ and speed. \\n\\n\\n \\n\\n\\n a digital artwork that depicts a street\ - \ scene with various elements. Visible objects include buildings, trees, and\ - \ vehicles. The scene ca\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - ,\"text\":\"s the essence of urban life, highlighting the contrast between\ - \ nature and architecture. \\n\\n\\n \\n 28\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Progress\ - \ Corticon \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - ,\"text\":\"ity with modern platforms \\n \\n Unified Data + Rules \u2013\ - \ Decision logic \\n \\n with real-time, governed data for more \\n accurate\ - \ outcomes \\n \\n Accelerated Compliance \u2013 Regulatory \\n requirements\ - \ faster and with greater \\n \\n confidence \\n \\n Future-Ready Architecture\ - \ for \\n \\n Scalability \u2013 Modern, scalable \\n founda\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - ,\"text\":\"tion for advanced analytics and AI \\n \\n Targeted U.S. State\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - ,\"text\":\" https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital\ - \ artwork depicting a street scene with various elements such as buildings,\ - \ trees, and vehicles. Visible objects include a building with a large window,\ - \ a tree with green leaves, and a car parked on the side of the road. The\ - \ scene captures a typical urban environment with a mix of architectural styles\ - \ and natural elements. \\n\\n\\n \\n\\n\\n a graphic\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - ,\"text\":\" \xC7\xC7 \\n \\n Pharma \\n \\n \u2022 R&D \u2013 pr\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - ,\"text\":\"t \\n design, test, \\n \\n pricing, etc. \\n \\n \u2022 Knowledge\ - \ \\n \\n management \\n \\n \u2022 Supply chain \\n \\n management and \\\ - n \\n logistics \\n \\n \u2022 Safety \\n \\n \u2022 Compliance \\n \\n Provider:\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - ,\"text\":\"Clinical coding \\n \\n \u2022\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"text\":\"nes \\n \\n document describes the process and the \\n \\n technical\ - \ details for deploying a new \\n \\n product/service in PDC (contact Stephen\ - \ \\n \\n Rice for details) \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"text\":\" \\n \\n \\n\\n\\n Alltools Edit\\n1c)\\n\\n= menu) \xA5y Product\ - \ Hosting Guideli. x @ \xA9 # Signin - a x\\nConvert E-Sign Findtextortoo\ - \ Q 6% a 8\\n| @\\nProduct Hosting Guidelines\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"text\":\"t updated by | Stephen Rice | 3 Dec 2025 at 14:36 GMT i(k)\\n\\\ - nZ.\\n\\n2\\n\\nContents\\n* Introduction\\n+ Terminology and Overview\\n+\ - \ Product Hosting Features\\n+ Is PDC the correct place to host your service?\\\ - n+ PDC Product Deployment Process\\n+ Exploration - Need is identified for\ - \ product to be hosted\\n+ Preparation - Product and PDC teams create implementati...\\\ - n\\n+ Product Updates - Product team implements required cha...\\n\\n+ PDC\ - \ Review - PDC team reviews design documentation an...\\n+ PDC Integration\ - \ - PDC deploys product and performs inte..\\n\\n+ Operations On-boarding\ - \ - Operations team is provided wi...\\n+ Retrospective - Revi\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"text\":\"rocess and discuss how any issues..\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"text\":\"Technical Requirements\\n\\n+ Reverse Proxy Support\\n\\n+ specif\ - \ .\\n\\n+ Network Connections Requirements\\n\\n+ Helm Charts\\n\\n+ Load\ - \ Bala\\n\\n2 p \\n\\n\\n a screenshot of a webpage titled \\\"Product Hosting\ - \ Guidelines\\\". The page contains a list of contents, including sections\ - \ on introduction, product deplo\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"text\":\"r images present. \\n\\n\\n \\n Product Innovation \\n \\n \\\ - n \\n\\n\\n \\n 46\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress \\n Data Cloud \\n\ - \ \\n https://www.progress.com/ \\n \\n \\n\\n\\n 3) Progress DataCloud \\\ - n\\n\\n a logo for Progress DataCloud. It shows a green square with a white\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"text\":\"arrow pointing upwards, indicating progress or growth. Visible\ - \ objects include the green square and the white arrow. The s\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"text\":\"shows a modern office environment with a desk, chair, and computer\ - \ monitor. \\n\\n\\n \\n 47\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n Platform \\\ - n \\n Functionality \\n \\n Support \\n \\n Support for additional \\n \\\ - n PDP products and \\n services as we\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - ,\"text\":\"eople in a garage setting, likely a car repair shop. The woman\ - \ is seated at a desk writing on a clipboard, while the man stands behind\ - \ he\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - ,\"text\":\"aning slightly towards her. Visible objects include a vintage\ - \ telephone, a metal workbench with various tools and equipment, and a classic\ - \ car parked in the background. The context suggests a professional environment\ - \ focused on automotive repairs. \\n\\n\\n \\n 15\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n http\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - ,\"text\":\"ww.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a vintage photograph\ - \ showing a woman working at a computer desk in an automotive workshop. Visible\ - \ objects\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - ,\"text\":\" interface, specifically the Wineria Santine app. The screen displays\ - \ various menu options such as \\\"Rolls water transport,\\\" \\\"Faree 310,54\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - ,\"text\":\"04,00,00,\\\" \\\"Sensitiveness,\\\" \\\"Revenue to market 280\ - \ mnx,\\\" \\\"Delivery schedule,\\\" and \\\"New mascara-intern\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - ,\"text\":\"al brands 06.\\\" Visible objects include a woman sitting on a\ - \ bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi. \\n\\n\\n \\n 17\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n AAPFOINFAE\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - ,\"text\":\"MFROMDE\\n\u201CTUsEBALI NOW_16 2050 G-\u20ACARAEE\\n[DEWAUS EG\ - \ TO NELIMAL LK\\nENIOY @0F9 UN JOURTEY. \\n\\n\\n a digital interface displaying\ - \ an image of a c\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"text\":\"7\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Congrats getting to the next\ - \ level! \\n \\n Employee Location Scrum Team New Position \\n Abhishek Tiwari\ - \ Hyderabad Mangalayan QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers\ - \ Software Engineer, Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software\ - \ Engineer, Principal 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software\ - \ Engineer, Senior 1 \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal\ - \ \\n \\n Brahmananda Gouni Hyderabad - Senior Manager, Software Engineering\ - \ \\n \\n Brian Tang USA Quicksilver Software Engineer, Principal \\n \\n\ - \ Hari Krishna Tirunagari Hyderabad Mangalayan QA Engineer, Principal 3 \\\ - n \\n Hemanandh S Hyderabad Avengers Software Engineer, Principal 3 \\n \\\ - n Jonathan Miller USA Kalimba Software Engineer, Senior \\n \\n Kunal Basarkar\ - \ Hyderabad Avengers Software Engineer, Senior Principal \\n \\n Nitesh Mehta\ - \ Hyderabad Harmonica Software Engineer, Senior Principal \\n \\n Saivenkat\ - \ Chepuri Hyderabad Conquerors Software Engineer, Principal 1 \\n \\n Sathyanarayana\ - \ Gundoji Hyderabad Mangalayan Software Engineer, Senior 1 \\n \\n Srikar\ - \ Veeramallu Hyderabad Atlantis Software Engineer, Principal 1 \\n \\n Stephen\ - \ Brown USA Apollo Software Engineer, Principal \\n \\n Sumit Pritmani Hyderabad\ - \ Transformers Software Engineer, Senior 2 \\n \\n Vikram Reddy Deva Hyderabad\ - \ Avengers QA Engineer, Senior 2 \\n \\n https://www.progress.com/\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"text\":\"5\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"text\":\"24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"text\":\"6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"text\":\"26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"text\":\"New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers\ - \ \\n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\\ - n Customer \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\\ - n \\n reinforcing ongoing \\n \\n value of current \\n \\n product(s) in use\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"text\":\"and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"text\":\"2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat!\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"text\":\"48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"text\":\"Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"text\":\"\u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"text\":\"42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"text\":\"59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"text\":\"Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"text\":\"a digital photograph showing two people working at a desk. The\ - \ man is seated, looking at the computer screen, while the woman stands behind\ - \ him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"text\":\"\u2713 Faster Contract Approvals \u2013 Streamlined deal desk\ - \ and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent Pricing\ - \ Models \u2013 Unified price list and predictable cost structures. \\n \\\ - n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for easier\ - \ entry and adoption.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"text\":\"44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"text\":\"Modernize the Corticon \\n \\n user experience with a \\n new\ - \ Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and \\n\ - \ accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule \\\ - n \\n automation and \\n delivery with AI that \\n supports end-to-end \\\ - n \\n project creation, \\n testing, and \\n \\n maintenance.\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - ,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - \" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"\ - New mascara-international brands 06.\\\" Visible objects include a woman sitting\ - \ on a bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - 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The scene is set in a bustling urban + area, likely a city center, with illuminated buildings and streets in the + background. \\n\\n\\n \\n Progress Data Platform: Vertical Concentration + \\n \\n \xC7\xC7 \\n \\n Manufacturing \\n \\n \xC7\xC7 \\n\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":48,\"index\":240,\"start\":47858,\"end\":48111},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":61,\"index\":296,\"start\":58608,\"end\":58608},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"ce?\\n+ + PDC Product Deployment Process\\n+ Exploration - Need is identified for product + to be hosted\\n+ Preparation - Product and PDC teams create implementati...\\n\\n+ + Product Updates - Product team implements required cha...\\n\\n+ PDC Review + - PDC team reviews design documentation an...\\n+ PDC Integration - PDC deploys + pro\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":58,\"index\":283,\"start\":56177,\"end\":56491},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":0,\"index\":1,\"start\":86,\"end\":86},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"cene + with a focus on the intersection of two roads. 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Visible objects include the car's front view, a city skyline in the + background, and a person standing in front of the vehicle. The context sugge\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":16,\"index\":63,\"start\":12322,\"end\":12628},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":34,\"index\":179,\"start\":35484,\"end\":35484},\"fuzzy_result\":false},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"text\":\"com/ + \\n \\n \\n\\n\\n A \\n\\n\\n a digital illustration depicting a cloud computing + environment with a central server connected to multiple storage devices. 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The scene captures the + essence of urban life, highlighting the blend of architecture and nature. + \\n\\n\\n \\n\\n\\n a pie chart, which visually represents data divi\",\"labels\":[],\"start_seconds\":null,\"end_seconds\":null,\"position\":{\"page_number\":37,\"index\":204,\"start\":39868,\"end\":40218},\"fuzzy_result\":false}],\"facets\":{\"/k\":{\"/k/text\":109,\"/k/ocr\":100,\"/k/inception\":69}},\"query\":\"\",\"total\":278,\"page_number\":0,\"page_size\":20,\"next_page\":true,\"min_score\":0.0},\"fulltext\":{\"results\":[{\"score\":0.0,\"rid\":\"c9a5194ebf7df8267828c2836c851618\",\"field_type\":\"f\",\"field\":\"c9a5194ebf7df8267828c2836c851618\",\"labels\":[]},{\"score\":0.0,\"rid\":\"c9a5194ebf7df8267828c2836c851618\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"a\",\"field\":\"title\",\"labels\":[]},{\"score\":0.0,\"rid\":\"866f34bfe00a53e9265df7699d9b264b\",\"field_type\":\"f\",\"field\":\"866f34bfe00a53e9265df7699d9b264b\",\"labels\":[]}],\"facets\":{},\"query\":\"\",\"total\":4,\"page_number\":0,\"page_size\":20,\"next_page\":false,\"min_score\":0.0},\"shards\":[\"ea8e2a0d-1d34-4f0a-aac9-f9e26a3e48dd\"]}" headers: Alt-Svc: - - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000 + - h3=":443"; ma=2592000 Content-Length: - '11513' access-control-expose-headers: @@ -7518,13 +418,13 @@ interactions: content-type: - application/json date: - - Wed, 15 Jul 2026 07:06:28 GMT + - Wed, 15 Jul 2026 08:11:46 GMT via: - 1.1 google x-envoy-upstream-service-time: - - '84' + - '72' x-nuclia-trace-id: - - ae179f163faf7fb99ac691be6a0155dc + - 6ef6d9d55c3663b19fa9a9f78c3dbc56 status: code: 200 message: OK @@ -7551,284 +451,144 @@ interactions: uri: https://europe-1.stashify.cloud/api/v1/kb/e103caf3-f8cb-4161-a57c-aad1192d0666/find response: body: - string: "{\"resources\":{\"866f34bfe00a53e9265df7699d9b264b\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b\"\ - ,\"slug\":\"1fec86515e41e88ff6b36d14e6b5353c\",\"title\":\"Business Context\ - \ and Product Vision 2026 1_self_signed.pdf\",\"summary\":\"\",\"icon\":\"\ - application/pdf\",\"thumbnail\":\"/kb/e103caf3-f8cb-4161-a57c-aad1192d0666/resource/866f34bfe00a53e9265df7699d9b264b/file/866f34bfe00a53e9265df7699d9b264b/download/extracted/file_thumbnail\"\ - ,\"metadata\":{\"metadata\":{},\"language\":\"en\",\"languages\":[\"en\"],\"\ - status\":\"PROCESSED\"},\"usermetadata\":{\"classifications\":[]},\"fieldmetadata\"\ - :[],\"computedmetadata\":{\"field_classifications\":[]},\"created\":\"2026-03-06T20:13:26.353143\"\ - ,\"modified\":\"2026-03-06T20:13:26.337843\",\"last_seqid\":0,\"last_account_seq\"\ - :null,\"queue\":\"private\",\"hidden\":false,\"origin\":null,\"extra\":null,\"\ - relations\":null,\"data\":null,\"security\":null,\"fields\":{\"/f/866f34bfe00a53e9265df7699d9b264b\"\ - :{\"paragraphs\":{\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - :{\"score\":0.012722708284854889,\"score_type\":\"RERANKER\",\"order\":0,\"\ - text\":\" \\n 7\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Congrats getting to the next\ - \ level! \\n \\n Employee Location Scrum Team New Position \\n Abhishek Tiwari\ - \ Hyderabad Mangalayan QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers\ - \ Software Engineer, Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software\ - \ Engineer, Principal 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software\ - \ Engineer, Senior 1 \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal\ - \ \\n \\n Brahmananda Gouni Hyderabad - Senior Manager, Software Engineering\ - \ \\n \\n Brian Tang USA Quicksilver Software Engineer, Principal \\n \\n\ - \ Hari Krishna Tirunagari Hyderabad Mangalayan QA Engineer, Principal 3 \\\ - n \\n Hemanandh S Hyderabad Avengers Software Engineer, Principal 3 \\n \\\ - n Jonathan Miller USA Kalimba Software Engineer, Senior \\n \\n Kunal Basarkar\ - \ Hyderabad Avengers Software Engineer, Senior Principal \\n \\n Nitesh Mehta\ - \ Hyderabad Harmonica Software Engineer, Senior Principal \\n \\n Saivenkat\ - \ Chepuri Hyderabad Conquerors Software Engineer, Principal 1 \\n \\n Sathyanarayana\ - \ Gundoji Hyderabad Mangalayan Software Engineer, Senior 1 \\n \\n Srikar\ - \ Veeramallu Hyderabad Atlantis Software Engineer, Principal 1 \\n \\n Stephen\ - \ Brown USA Apollo Software Engineer, Principal \\n \\n Sumit Pritmani Hyderabad\ - \ Transformers Software Engineer, Senior 2 \\n \\n Vikram Reddy Deva Hyderabad\ - \ Avengers QA Engineer, Senior 2 \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"labels\":[],\"position\":{\"page_number\":6,\"index\":27,\"start\":4766,\"\ - end\":6189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - :{\"score\":0.0029121784027665854,\"score_type\":\"RERANKER\",\"order\":1,\"\ - text\":\" \\n 5\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":4,\"index\":14,\"\ - start\":3054,\"end\":3424,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"score\":0.0012893283274024725,\"score_type\":\"RERANKER\",\"order\":2,\"\ - text\":\" \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7 \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":83,\"start\":16815,\"\ - end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"score\":0.00049553596181795,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":21,\"\ - start\":4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":96,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":84,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":74,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":11,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":9,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":233,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":41,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":246,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":209,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":281,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":140,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026480842279852368,\"score_type\":\"RERANKER\",\"order\"\ - :15,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"labels\":[\"/k/inception\"],\"position\":{\"page_number\":8,\"index\":34,\"\ - start\":7044,\"end\":7374,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_8_0.jpg\",\"is_a_table\"\ - :false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"score\":0.000018925147742265835,\"score_type\":\"RERANKER\",\"order\"\ - :16,\"text\":\" \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal\ - \ desk and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent\ - \ Pricing Models \u2013 Unified price list and predictable cost structures.\ - \ \\n \\n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for\ - \ easier entry and adoption. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":32,\"index\":161,\"\ - start\":31881,\"end\":32155,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"\ - relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"score\":0.00001805851934477687,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\" \\n 44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"labels\":[],\"position\":{\"page_number\":43,\"index\":212,\"start\":41776,\"\ - end\":42205,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"score\":0.000017231572201126255,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" Modernize the Corticon \\n \\n user experience with a \\n\ - \ new Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and\ - \ \\n accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule\ - \ \\n \\n automation and \\n delivery with AI that \\n supports end-to-end\ - \ \\n \\n project creation, \\n testing, and \\n \\n maintenance. \\n\",\"\ - id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"labels\":[],\"position\":{\"page_number\":57,\"index\":268,\"start\":53354,\"\ - end\":53655,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\"\ - :null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"score\":0.000016701422282494605,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - \" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"\ - New mascara-international brands 06.\\\" Visible objects include a woman sitting\ - \ on a bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - ,\"labels\":[],\"position\":{\"page_number\":15,\"index\":60,\"start\":11635,\"\ - end\":12070,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":\"image_15_0.jpg\",\"is_a_table\":false,\"\ - relevant_relations\":null}}}}}},\"relations\":{\"entities\":{}},\"query\"\ - :\"New employees at ADP\",\"rephrased_query\":null,\"total\":11,\"page_number\"\ - :0,\"page_size\":20,\"next_page\":true,\"shards\":[\"ea8e2a0d-1d34-4f0a-aac9-f9e26a3e48dd\"\ - ],\"min_score\":{\"semantic\":0.4,\"bm25\":0.0},\"best_matches\":[\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - 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Senior Manager, Software Engineering \\n \\n Brian Tang + USA Quicksilver Software Engineer, Principal \\n \\n Hari Krishna Tirunagari + Hyderabad Mangalayan QA Engineer, Principal 3 \\n \\n Hemanandh S Hyderabad + Avengers Software Engineer, Principal 3 \\n \\n Jonathan Miller USA Kalimba + Software Engineer, Senior \\n \\n Kunal Basarkar Hyderabad Avengers Software + Engineer, Senior Principal \\n \\n Nitesh Mehta Hyderabad Harmonica Software + Engineer, Senior Principal \\n \\n Saivenkat Chepuri Hyderabad Conquerors + Software Engineer, Principal 1 \\n \\n Sathyanarayana Gundoji Hyderabad Mangalayan + Software Engineer, Senior 1 \\n \\n Srikar Veeramallu Hyderabad Atlantis Software + Engineer, Principal 1 \\n \\n Stephen Brown USA Apollo Software Engineer, + Principal \\n \\n Sumit Pritmani Hyderabad Transformers Software Engineer, + Senior 2 \\n \\n Vikram Reddy Deva Hyderabad Avengers QA Engineer, Senior + 2 \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\",\"labels\":[],\"position\":{\"page_number\":6,\"index\":27,\"start\":4766,\"end\":6189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\":{\"score\":0.0029121784027665854,\"score_type\":\"RERANKER\",\"order\":1,\"text\":\" + \\n 5\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Vamshi Shanigala + \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad \\n \\n Nathan + Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh \\n \\n Shivabalu + Thouta \\n Software Engineer, Senior \\n \\n MarkLogic \\n Hyderabad \\n \\n + https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":4,\"index\":14,\"start\":3054,\"end\":3424,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\":{\"score\":0.0012893283274024725,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" + \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress ADP: GTM Motions \\n \\n Expansion: + \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n value through \\n + \\n added ADP \\n \\n products or \\n \\n leveling up tiers \\n \\n \xC7\xC7 + \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\",\"labels\":[],\"position\":{\"page_number\":23,\"index\":83,\"start\":16815,\"end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\":{\"score\":0.00049553596181795,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" + \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Aleix Ruiz de Villa + \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\n Casimiro Pio Carrino + \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":21,\"start\":4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\":{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" + \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\xC7 \\n \\n Modernization + Services & Plus Ones Engagement \\n \\n OpenEdge 13 \u2013 Latest version + \\n supporting FIPS compliance \\n \\n 12 and GenAI Workshops \u2013 Free + \\n hands-on training \\n \\n QSM \u2013 Quality and security \\n management + tools \\n \\n MCP Connector for ABL - AI Code \\n Assistant to improve code + quality \\n \\n Agentic RAG \u2013 AI-driven contextual \\n knowledge retrieval + \\n \\n Forrester Total Economic Impact \u2013 \\n Validated ROI and business + value \\n \\n PUG Challenge \u2013 Community- \\n \\n driven learning and + networking \\n \\n Customer Advisory Board \u2013 Customer \\n \\n advisory + for strategic alignment \\n \\n World Tour \u2013 Global events \\n showcasing + OpenEdge innovation \\n \\n Community - Collaborative network for \\n learning, + sharing, and innovation \\n \\n Customer Validation Program - Early \\n access + to roadmap features and \\n \\n collaborative feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":96,\"start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\":{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\n \\n adopting + Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer \\n \\n + Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing ongoing + \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/ + \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\",\"labels\":[],\"position\":{\"page_number\":23,\"index\":84,\"start\":17085,\"end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\":{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + \\n and what qualifications staff require to apply it? \\n \\n Submit \\n + \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":74,\"start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\":{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\"Celebrating + Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack w/ MCP\\n\\nVanessa + Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\ndependency update + automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\",\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":11,\"start\":2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\":{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" + \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases \\n \\n ARC, + PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon 7.3.0 \\n + \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic IronBank + \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC 1.2.2/1.3.0 + \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog \\n \\n SAMM Level + 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution \\n \\n HDP 4.6.2 + \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage \\n \\n Innovation + \\n \\n Corticon AI \\n Generated User \\n Interface \\n \\n AI Video Production + \\n \\n Share your additional celebrations in the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":9,\"start\":1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\":{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\" + \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n PDC Priorities & Innovation \\n \\n \u2022 Functional + Expansion \u2013 Increase value proposition by on- \\n boarding additional + PDP (and other) services \\n \\n Business Priorities \\n \\n \u2022 MCP OpenEdge + ABL Service \\n \\n \u2022 Hybrid Data Pipeline Service \\n \\n \u2022 EULA + Acceptance Feedback Mechanism \\n \\n \u2022 Data Symphony Service \\n \\n + \u2022 Operational Support Shared Logging (CRIBL) \\n \\n Innovation Areas + \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":233,\"start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\":{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" + \\n Creation of a new enterprise \\n \\n architecture function led by \\n + \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise + architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting + line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\",\"labels\":[],\"position\":{\"page_number\":10,\"index\":41,\"start\":8847,\"end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\":{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\" + \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering TCO, + Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities \\n \\n + \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n \u2022 Nuclia-MarkLogic + Integration \\n \\n \u2022 MarkLogic Knowledge Hub \\n \\n \u2022 Flux as + PDC Service \\n \\n Innovation \\n \\n \u2022 Property Graphs \\n \\n \u2022 + Read from Replicas \\n \\n \u2022 Index Exclusion \\n \\n \u2022 Vector improvements + \\n \\n \u2022 Object storage improvement \\n \u2022 Support Kendo Theme Builder + in FastTrack \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\",\"labels\":[],\"position\":{\"page_number\":50,\"index\":246,\"start\":49179,\"end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\":{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"text\":\" + \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022 Contact the + PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide as many + details as required \\n \\n including timeframes \\n \\n \u27A2 Meeting will + be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/ + \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":209,\"start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\":{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\" + \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Corticon Priorities & Innovation \\n \\n \u2022 + Retention \u2013 Support, CVEs, encourage update to latest LTS \\n \\n \u2022 + Public Sector Expansion - Replicate success at State of Mississippi \\n \\n + \u2022 Platform Modernization - New UI and SaaS to enable more customer wins + \\n \\n Business Priorities \\n \\n \u2022 Modern UI \\n \\n \u2022 AI powered + user experience \\n \\n \u2022 SaaS offering in PDC \\n \\n \u2022 Symphony + rule deployment \\n \\n Innovation \\n \\n https://www.progress.com/ \\n \\n + \ \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\",\"labels\":[],\"position\":{\"page_number\":58,\"index\":281,\"start\":55634,\"end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\":{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"text\":\" + \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries and improved + resilience \\n \\n for mission-critical workloads. \\n \\n AI-Assistant \u2013 + Simplify the developer \\n \\n experience and lower the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":140,\"start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":null,\"is_a_table\":false,\"relevant_relations\":null},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\":{\"score\":0.000026480842279852368,\"score_type\":\"RERANKER\",\"order\":15,\"text\":\"a + digital photograph showing two people working at a desk. 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All rights reserved. \\n \\n Congrats getting to the next\ - \ level! \\n \\n Employee Location Scrum Team New Position \\n Abhishek Tiwari\ - \ Hyderabad Mangalayan QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers\ - \ Software Engineer, Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software\ - \ Engineer, Principal 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software\ - \ Engineer, Senior 1 \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal\ - \ \\n \\n Brahmananda Gouni Hyderabad - Senior Manager, Software Engineering\ - \ \\n \\n Brian Tang USA Quicksilver Software Engineer, Principal \\n \\n\ - \ Hari Krishna Tirunagari Hyderabad Mangalayan QA Engineer, Principal 3 \\\ - n \\n Hemanandh S Hyderabad Avengers Software Engineer, Principal 3 \\n \\\ - n Jonathan Miller USA Kalimba Software Engineer, Senior \\n \\n Kunal Basarkar\ - \ Hyderabad Avengers Software Engineer, Senior Principal \\n \\n Nitesh Mehta\ - \ Hyderabad Harmonica Software Engineer, Senior Principal \\n \\n Saivenkat\ - \ Chepuri Hyderabad Conquerors Software Engineer, Principal 1 \\n \\n Sathyanarayana\ - \ Gundoji Hyderabad Mangalayan Software Engineer, Senior 1 \\n \\n Srikar\ - \ Veeramallu Hyderabad Atlantis Software Engineer, Principal 1 \\n \\n Stephen\ - \ Brown USA Apollo Software Engineer, Principal \\n \\n Sumit Pritmani Hyderabad\ - \ Transformers Software Engineer, Senior 2 \\n \\n Vikram Reddy Deva Hyderabad\ - \ Avengers QA Engineer, Senior 2 \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"labels\":[],\"position\":{\"page_number\":6,\"index\":0,\"start\":4766,\"\ - end\":6189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - :{\"score\":0.0029121784027665854,\"score_type\":\"RERANKER\",\"order\":1,\"\ - text\":\" \\n 5\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":4,\"index\":0,\"start\"\ - :3054,\"end\":3424,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"score\":0.0012893283274024725,\"score_type\":\"RERANKER\",\"order\":2,\"\ - text\":\" \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7 \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":16815,\"\ - end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"score\":0.00049553596181795,\"score_type\":\"RERANKER\",\"order\":3,\"\ - text\":\" \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":0,\"start\"\ - :4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"\ - text\":\" \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":0,\"\ - start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"\ - text\":\" New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":17085,\"\ - end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"\ - text\":\" \\n and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":0,\"\ - start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"\ - text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":0,\"start\"\ - :2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\"\ - :false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"\ - text\":\" \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":0,\"start\"\ - :1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\"\ - :9,\"text\":\" \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/ \\n \\\ - n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":0,\"\ - start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"\ - text\":\" \\n Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"labels\":[],\"position\":{\"page_number\":10,\"index\":0,\"start\":8847,\"\ - end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"\ - text\":\" \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"labels\":[],\"position\":{\"page_number\":50,\"index\":0,\"start\":49179,\"\ - end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"\ - text\":\" \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\ - \ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":0,\"\ - start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"\ - text\":\" \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"labels\":[],\"position\":{\"page_number\":58,\"index\":0,\"start\":55634,\"\ - end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"\ - text\":\" \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":29,\"index\":0,\"\ - start\":27462,\"end\":27669,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"score\":0.000026480842279852368,\"score_type\":\"RERANKER\",\"order\"\ - :15,\"text\":\"a digital photograph showing two people working at a desk.\ - \ The man is seated, looking at the computer screen, while the woman stands\ - \ behind him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"labels\":[\"/k/inception\"],\"position\":{\"page_number\":8,\"index\":0,\"\ - start\":7044,\"end\":7374,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"reference\":\"image_8_0.jpg\",\"is_a_table\"\ - :false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"score\":0.000018925147742265835,\"score_type\":\"RERANKER\",\"order\"\ - :16,\"text\":\" \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal\ - \ desk and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent\ - \ Pricing Models \u2013 Unified price list and predictable cost structures.\ - \ \\n \\n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for\ - \ easier entry and adoption. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"labels\":[\"/k/text\"],\"position\":{\"page_number\":32,\"index\":0,\"\ - start\":31881,\"end\":32155,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\"\ - :false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"score\":0.00001805851934477687,\"score_type\":\"RERANKER\",\"order\":17,\"\ - text\":\" \\n 44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"labels\":[],\"position\":{\"page_number\":43,\"index\":0,\"start\":41776,\"\ - end\":42205,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"score\":0.000017231572201126255,\"score_type\":\"RERANKER\",\"order\"\ - :18,\"text\":\" Modernize the Corticon \\n \\n user experience with a \\n\ - \ new Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and\ - \ \\n accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule\ - \ \\n \\n automation and \\n delivery with AI that \\n supports end-to-end\ - \ \\n \\n project creation, \\n testing, and \\n \\n maintenance. \\n\",\"\ - id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"labels\":[],\"position\":{\"page_number\":57,\"index\":0,\"start\":53354,\"\ - end\":53655,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"score\":0.000016701422282494605,\"score_type\":\"RERANKER\",\"order\"\ - :19,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - \" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"\ - New mascara-international brands 06.\\\" Visible objects include a woman sitting\ - \ on a bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - ,\"labels\":[],\"position\":{\"page_number\":15,\"index\":0,\"start\":11635,\"\ - end\":12070,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"\ - page_with_visual\":true,\"reference\":\"image_15_0.jpg\",\"is_a_table\":false}}}}}},\"\ - query\":\"New employees at ADP\",\"total\":20,\"page_number\":0,\"page_size\"\ - :20,\"next_page\":false,\"autofilters\":[],\"min_score\":{\"bm25\":0.0},\"\ - best_matches\":[\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - 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Visible objects include the person's face, hair,\ - \ and smile. The background consists of a stone wall wi\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"text\":\"me decorative elements. \\n\\n\\n \\n\\n\\n a portrait photo\ - \ of a man with a beard and wavy hair. He is wearing a dark shirt with a floral\ - \ pattern\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"text\":\"tps://www.progress.com/ \\n \\n \\n\\n\\n \\n 8\xA9 2025 Progress\ - \ Software Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n AI is no longer optional. \\n It is the engine of \\n transformation.\ - \ \\n \\n 8\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or a\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"text\":\"ffiliates. All rights reserved. \\n \\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"text\":\"ps://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a blurred\ - \ motion photograph, capturing the rapid movement of city streets at night.\ - \ Visible objects include tall buildings, streetlights, and vehicles. The\ - \ scene depicts a bustling urban environment with dynamic light \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"text\":\"s indicating high-speed traffic. \\n\\n\\n \\n Practices are\ - \ changing \\n \\n \u2022 Market analysis \\n \u2022 Feature definition \\\ - n \u2022 Threat modelling \\n \u2022 Code suggestions \\n \u2022 Code analysis\ - \ \\n \u2022 UI scaffolding \\n \u2022 Test coverage \\n \u2022 Penetration\ - \ testing \\n \u2022 Documentation \\n \u2022 Defect analysi\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"text\":\" \\n \\n \\n\\n\\n \\n 3\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"text\":\"ress\u2019 \\n\\n\\n a slide from a presentation titled \\\"Celebrating\ - \ Innovation from PI 3...\\\". The slide features a festive background with\ - \ colorful confetti. Visible objects include the title text, bullet points,\ - \ and a logo at the bottom left corner. The scene shows a celebration atmosphere\ - \ with bright colors and decorative\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"text\":\"ents. \\n\\n\\n \\n 4\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Important\ - \ Dates \\n \\n 11 December (Thursday) \\n \\n Draft Plan Presentations \\\ - n \\n 15 December (Monday) \\n \\n Final Plan Presentations \\n \\n 17 December\ - \ (Wednesday) \\n \\n Start of PI 2026.1 \\n \\n 18 December (Thursday) \\\ - n \\n Innovation Demos \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"text\":\"\\n \\n \\n\\n\\n \\n\\n\\n a portrait photograph of a man smiling\ - \ at the camera. He is wearing a yellow shirt with a patterned background.\ - \ Visible objects include his face, shirt, and background. The scene is likely\ - \ an indoor setting, possibly a conference o\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"text\":\"nt space. \\n\\n\\n \\n\\n\\n a portrait of a smiling man with\ - \ short hair and a beard. He is wearing a black shirt. The backgro\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"text\":\"rights reserved. \\n \\n What is the best damp-proofing product\ - \ for \\n concrete piles that are in constant contact with \\n standing water,\ - \ is it available, how is it applied, \\n \\n Hi, how can I help? \\n \\n\ - \ and what qualifications staff require to apply it? \\n \\n Submit \\n \\\ - n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a photograph of\ - \ two construction workers standing near a body of water, likely a lake or\ - \ river, with several concrete pillars marked with numbers in the background.\ - \ They are wearing safety helmets and high-visibility jackets. The scene depicts\ - \ them observing or discussing something on a clipboard. Visible objects include\ - \ the con\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"text\":\"crete pillars, the water, and the \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"text\":\" Context suggests they are involved in a construction project.\ - \ \\n\\n\\n \\n 20\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n ?Documents \\n \\n Searching\ - \ for a Suitable Product \\n \\n + damp-proofing \\n \\n + concrete piles\ - \ \\n \\n + standing water \\n \\n damp-proofing \\n \\n concrete piles \\\ - n \\n standing water \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n\ - \ \\n 21\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Comprehensive \\n \\n and actionable\ - \ \\n \\n answer \\n \\n Searching for a Suitable Product \\n \\n Scan tech\ - \ specs to \\n \\n find suitable products \\n \\n Retrieve additional \\n\ - \ \\n information about \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"text\":\"ication, safety and \\n \\n qualifications \\n \\n Check stock\ - \ management \\n \\n system to see if a suitable \\n \\n product is in stock\ - \ \\n \\n What is the best damp-proofing \\n \\n product for concrete piles\ - \ that are \\n \\n in constant contact with sta\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"text\":\"nding \\n \\n water, is it available, how is it \\n \\n applied,\ - \ and what qualifications \\n \\n staff require to apply it? \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n 22\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affilia\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"text\":\"ion \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\ - \ Pro \\n duct 360 \\n \\n P \\n u \\n \\n b \\n lic \\n \\n S \\n a \\n fe\ - \ \\n \\n ty \\n \\n Self-service Policy \\n Search \\n \\n K \\n n \\n \\\ - n o \\n w \\n \\n le \\n d \\n \\n g \\n e \\n \\n M \\n a \\n n \\n \\n a\ - \ \\n g \\n \\n e \\n m \\n \\n e \\n n \\n \\n t \\n \\n Fraud \\n \\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"text\":\"ity \\n \\n Search & \\n Dis \\n \\n cover \\n y \\n \\n Software\ - \ (ISV/OEM) \\n \\n Manufacturing \\n \\n Government \\n \\n (SL/Fed) \\n\ - \ \\n Life \\n \\n Sciences \\n \\n Pharma \\n \\n Biotech \\n \\n Financial\ - \ \\n \\n Services & \\n \\n Insurance \\n \\n Managed \\n \\n Healthcare\ - \ \\n \\n Media & \\n \\n Publ\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"text\":\"s plain white. \\n\\n\\n \\n\\n\\n a portrait photograph of a\ - \ young man wearing a light blue shirt. He has dark hair and a friendly expression.\ - \ The background is plain white, providing no distractions from the subject.\ - \ Visible objects include his face and shirt. Context suggests he might be\ - \ a student or professional in a field related to \\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"text\":\"w \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\\ - n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer\ - \ \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing\ - \ ongoing \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"text\":\"a focus on the intersection of two roads. Visible objects include\ - \ cars, pedestrians, and buildings. The scene captures a moment of everyday\ - \ life, highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\\ - n a digital artwork depicting a street scene with a focus on the intersection\ - \ of two roads. Visible objects include cars, pedestrians, and bu\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"text\":\"gs. The scene captures a moment of everyday life, highlighting\ - \ urban dynamics and human activity. \\n\\n\\n \\n 25\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n Long-standing Innovation \\n \\n Customer \\n \\n Renewal \\n Expansion\ - \ \\n \\n Cust\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"text\":\"ometric shapes representing buildings, windows, and roofs. The\ - \ scene shows a modern architectural design with green accents. \\n\\n\\n\ - \ \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries or\ - \ affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\xC7\ - \ \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge 13\ - \ \u2013 Latest version \\n supporting \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"text\":\"compliance \\n \\n 12 and GenAI Workshops \u2013 Free \\n hands-on\ - \ training \\n \\n QSM \u2013 Quality and security \\n management tools \\\ - n \\n MCP Connector for ABL - AI Code \\n Assistant to improve code quality\ - \ \\n \\n Agentic RAG \u2013 AI-driven contex\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"text\":\"\\n knowledge retrieval \\n \\n Forrester Total Economic Impact\ - \ \u2013 \\n Validated ROI and business value \\n \\n PUG Challenge \u2013\ - \ Community- \\n \\n driven learning and networking \\n \\n Customer Advisory\ - \ Board \u2013 Customer \\n \\n advisory for stra\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"text\":\"g \\n \\n \\n \\n\\n\\n \\n 24\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Progress\ - \ ADP: GTM Motions \\n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase\ - \ account \\n \\n value through \\n \\n added ADP \\n \\n products or \\n\ - \ \\n leveling up tiers \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"text\":\" a modern urban setting. Visible objects include the car's front\ - \ view, a city skyline in the background, and a person standing in front of\ - \ the vehicle. The context suggests a professional environment, possibly related\ - \ to automotive services or technology. \\n\\n\\n \\n 18\xA9 2025 Progress\ - \ Software\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"text\":\"oration and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a geometric\ - \ shape, likely a polygon or a three-dimensional object, rendered in yellow\ - \ with black outlines. It shows a simple scene with a few objects:\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"text\":\"ctangular box-like structure and a small square inside it. The\ - \ background is black. \\n\\n\\n \\n\\n\\n a digital artwork depicting a\ - \ street scene with a focus on a person walking down the sidewalk. Visible\ - \ objects include a person, a car, and various urban elements such as buildings,\ - \ tree\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"text\":\"d street signs. The scene captures a moment of everyday life in\ - \ an urban environment. \\n\\n\\n \\n\\n\\n a digital artwork that appears\ - \ to be a still life composition. Visible objects include a bowl, a spoon,\ - \ a knife, and various fruits such as apples and oranges. The scene depicts\ - \ a kitchen setting with a focus on the fruit bowl, suggesting a theme of\ - \ healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a green arrow\ - \ pointing to the right,\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"text\":\"cating movement or direction. Visible objects include the arrow\ - \ itself and a few indistinct shapes that might be part of the background.\ - \ The scene shows a blurred, dark background with a green arrow pointing to\ - \ the right. \\n\\n\\n \\n 19\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"text\":\" \\n\\n\\n a geometric abstract artwork featuring a three-dimensional\ - \ shape composed of overlapping triangles in varying shades of green. The\ - \ scene shows a modern, minimalist interior with a black background. Visible\ - \ objects include a sleek, black chair and a small table with a simple design.\ - \ Context suggests a contemporary living space or office environ\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"text\":\" \\n\\n\\n \\n\\n\\n a geometric abstract graphic with a three-dimensional\ - \ cube-like shape composed of green and yellow gradients. It shows a modern,\ - \ minimalist design with clean lines and a vibrant color palette. Visible\ - \ objects include the cube and its gradient colors. The scene is set against\ - \ a black background, emphasizing the geometr\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"text\":\"a signature, likely from a person's handwritten document or digital\ - \ representation. Visible objects include the signature itself and possibly\ - \ some ink residue. The scene shows a close-up view of a person's handwriting,\ - \ with the signature prominently displayed against a dark background.\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"text\":\"ftware Corporation and/or its subsidiaries or affiliates. All\ - \ rights reserved. \\n \\n Leverage our \\n \\n GenAI \\n \\n Developments\ - \ \\n \\n Harness the power of \\n artificial intelligence \\n \\n and drive\ - \ your AI \\n \\n strategy. \\n \\n Adopt Our Latest Releases \\n \\n Security\ - \ & \\n \\n Compliance \\n \\n Protect your systems \\n \\n and your data\ - \ with up- \\n to-date security \\n \\n patches, dependency \\n updates, and\ - \ regulatory \\n complian\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"text\":\"ignment. \\n \\n Platform \\n \\n Support \\n \\n Stay current\ - \ with the \\n \\n latest platforms, \\n frameworks, and \\n \\n standards\ - \ to ensure \\n compatibility and long- \\n \\n term support \\n \\n \xC7\xC7\ - \ \\n Modernization and \\n \\n Future-Readiness \\n \\n Embrace our latest\ - \ \\n \\n features and \\n advancements and \\n \\n accelerate your data \\\ - n agility journey. \\n \\n Performance & \\n \\n Reliability \\n \\n Benefit\ - \ from ongoing \\n \\n fixes and optimizations \\n that del\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"text\":\"a more \\n \\n resilient platform. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still\ - \ life composition. Visible objects include a bowl, a spoon, a knife, and\ - \ various fruits such as apples and oranges. The sc\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"text\":\"epicts a kitchen setting with a focus on the fruit bowl, suggesting\ - \ a theme of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n\ - \ a digital artwork that appears to be a still \\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"text\":\"composition. Visible objects include a bowl, a spoon, a knife,\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"text\":\"various fruits such as apples and oranges. The scene depicts a\ - \ kitchen setting with a focus on the fruit bowl, suggesting a theme of healthy\ - \ eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital artwork that\ - \ appears to be a still life composition. Visible objects include a bowl,\ - \ a spoon, a knife, and various fruits such as apples and oranges. The scene\ - \ depicts a k\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"text\":\" https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital\ - \ photograph showing two people working at a desk. The man is seated, looking\ - \ at the computer screen, while the woman stands behind him, smiling. Visible\ - \ objects include a desktop computer with a keyboard and mouse, a pen holder,\ - \ and some\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"text\":\"ments on the desk. The background features a green wall with charts\ - \ and graphs. \\n\\n\\n \\n\\n\\n a geometric logo consisting of three overlapping\ - \ hexagons arranged in a triangular formation. The background is black, and\ - \ the logo is rendered in shades of green. Visible objects include the hexagons\ - \ and their intersecting lines. Context suggests a modern, tech-oriented brand\ - \ identity. \\n\\n\\n \\n 10\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"text\":\" rights reserved.\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n From Triad\ - \ to Quartet \\n \\n Product Management \\n \\n Viability & Scope \\n \\n\ - \ Engineering \\n \\n Feasibility & Delivery \\n \\n Architecture \\n \\n\ - \ Sustainability & Scale \\n \\n Customer Experience \\n \\n Desirability\ - \ \\n \\n https://www.pro\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"text\":\".com/ \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital thumbs-up icon. It shows a thumbs-up gesture with the thumb\ - \ extended upwards and the index finger curled into a triangle. Visible objects\ - \ include the thumbnail icon itself, the thumb, and the index finger. The\ - \ context is likely a digital interface or a social media platform where users\ - \ can express approval\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"text\":\"emporary reporting \\n \\n structure while we select a \\n \\\ - n product leadership team, \\n \\n and align P\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"text\":\"d \\n \\n OpenEdge product \\n management and product \\n \\n\ - \ operations. \\n \\n Creation of a new enterprise \\n \\n architecture function\ - \ led by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\\ - n enterprise architect: Nathan \\n van \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"text\":\". \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting line changes\ - \ for \\n \\n HDP \\n \\n \u2022 Temporary structure for \\n \\n the documentation\ - \ \\n function \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\\ - n\\n a digital artwork depicting a street scene with various el\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"text\":\" oranges. The scene depicts a kitchen setting with a focus on\ - \ the fruit bowl, suggesting a theme of healthy eating or culinary preparation.\ - \ \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition.\ - \ Visible objects include a bowl, a spoon, a knife, and v\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"text\":\"arious fruits such as apples and oranges. The scene depicts a\ - \ kitchen setting with a focus on the fruit bowl, suggesting a theme of healthy\ - \ eating or culinary preparation. \\n\\n\\n \\n Integrate \\n Our Teams \\\ - n \\n Our Products \\n \\n Our \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"text\":\"Use Cases \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\\ - n Software (ISV/OEM)\\nManufacturing\\n\\nLife\\nSciences\\nPharma\\nBiotech\\\ - n\\n&\\nS\\n@\\n<\\noO\\n=\\no\\ni=)\\n3\\ncc\\n3\\nS\\nt\\n\\nFinancial\\\ - nServices &\\nInsurance\\n\\nManaged\\nHealthcare\\n\\nGovernment\\nim (SL/Fed)\\\ - n\\nMedia &\\nPublishing\\n\\nMayes oyand \\n\\n\\n a circular diagram with\ - \ six segments, each representing \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"text\":\"a different aspect of security man\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"text\":\"ent. The segments are labeled Product 360, Self-service Policy\ - \ Search, Managed Healthcare, Government (SL/Fed), Public Safety, Fraud Identity,\ - \ and Financial Services & Insurance. Visible objects include icons for Softwar\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"text\":\"V/OEM) Manufacturing, Life Sciences Pharma Biotech, Media & Publishing,\ - \ and Fraud Identity. The scene shows a globe centered within the circle,\ - \ surrounded by these six segments\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"text\":\" @uHHEERy\\n\\nquxx== \xA9\\n\\nERB EBB BEEBE B\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"text\":\" wm \xAE\\n\\nSd\\n\\n@nunuun 8 \\n\\n\\n a flowchart or diagram\ - \ that illustrates the process of data integration. Visible objects include\ - \ arrows representing different stages, icons indicating various processes,\ - \ and a circu\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"text\":\"lar loop symbolizing a continuous \\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"text\":\"a digital graphic artifact, likely representing a logo or branding\ - \ element. It shows a stylized green geometric shape with three arrows pointing\ - \ upwards, set against a white background. Visible objects include the green\ - \ shapes and arrows, as well as some black elements that could represent additional\ - \ details or text. The scene context suggests a mo\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"text\":\" tech-oriented environment, possibly related to \\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"text\":\"nology or innovation. \\n\\n\\n \\n\\n\\n a digital graphic artifact,\ - \ likely a logo or an icon. It shows a green geometric shape with three arrows\ - \ pointing upwards, set against a white background. Visible objects include\ - \ the green shapes and arrows, as well as som\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"text\":\"e black elements that could represent additional details or t\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"text\":\" The scene is minimalistic, focusing on the geometric shapes and\ - \ their directional emphasis. \\n\\n\\n \\n 33\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n Progress Data Platform \\n \\n \xC7\xC7 \\n \\n \u2713 Simplified\ - \ Licensing Terms \u2013 Harmonized EULA and clear discounting guidelines.\ - \ \\n \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal desk and\ - \ legal alignme\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"text\":\"ion for faster provisioning and order accuracy. \\n \\n \u2713\ - \ Unified UI \u2013 Improved experience across product set to increase efficiency\ - \ and user engagement. \\n \\n Customer Experience \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n \\n\\n\\n a graphic illustration featuring three stylized\ - \ icons: a wave symbol, a green checkmark icon, and a smiley face icon. The\ - \ scene shows a modern digital interface with these icons, sugg\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"text\":\"esting a focus on user feedback or\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"text\":\"althy eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital\ - \ image of a landscape scene. Visible objects include trees, grass, rocks,\ - \ and water. The scene depicts a serene natural environment with lush greenery\ - \ and calm waters. \\n\\n\\n \\n 34\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Intersection\ - \ of \\n Moment-in-Time \\n Pain and Long-term \\n Business Impact \\n \\\ - n Long-Term \\n \\n Transformation \\n Help to solve today's pains \\n \\\ - n repeatedly \\n \\n until long-term impact \\n \\n becomes instilled. \\\ - n \\n AI-Driven \\n \\n Foundation \\n Build a robust AI \\n \\n foundation\ - \ that \\n \\n accelerates insights and \\n \\n decision-making \\n for long-term\ - \ success. \\n \\n Customer-Centric \\n \\n Approach \\n Focus on meeting\ - \ \\n \\n customers at their \\n \\n current state to address \\n \\n immediate\ - \ \\n pain points effectively. \\n \\n https://www.progress.com/ \\n \\n \ - \ \\n\\n\\n \\n\\n\\n a long-exposure photograph capturing the dynamic movement\ - \ of vehic\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"text\":\"n an intricate highway network at night. Visible objects include\ - \ multiple intersecting highways with light trails indicating the speed and\ - \ flow of traffic. The scene is set in a bustling urban area, likely a city\ - \ center, with illuminated buildings and streets in the background. \\n\\\ - n\\n \\n Progress Data Platform: Vertical Concentration \\n \\n \xC7\xC7\ - \ \\n \\n Manufacturing \\n \\n \xC7\xC7 \\n \\n Healthcare \\n \\n \xC7\xC7\ - \ \\n \\n Government \\n \\n \xC7\xC7 \\n \\n Media & \\n \\n Publishing \\\ - n \\n \xC7\xC7 \\n \\n FinServ/ \\n \\n Insuranc\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"text\":\"ions team with \\n advanced tools to better \\n assist clients\ - \ and drive \\n \\n revenue generation \\n \\n Continuous monitoring \\n \\\ - n and enhancement of \\n security measures to \\n \\n safeguard customer data\ - \ \\n \\n integrity \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \ - \ \\n\\n\\n a digital artwork depicting a street sc\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"text\":\"ith a focus on the intersection of two roads. Visible objects\ - \ include cars, pedestrians, and buildings. The scene captures a moment of\ - \ everyday life, highlighting urban dynamics and human activity. \\n\\n\\\ - n \\n\\n\\n a digital artwork depicting a street scene with a focus on the\ - \ intersection of two roads. Visible objects include cars, pede\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"text\":\"ns, and buildings. The scene captures a moment of everyday life,\ - \ highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a digital\ - \ artwork depicting a street scene with a focus on the intersection of two\ - \ roads. Visible objects include cars, pedestrians, and buildings. The scene\ - \ captures \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"text\":\"ent of everyday life, highlighting urban dynamics and human activity.\ - \ \\n\\n\\n \\n\\n\\n a digital illustration featuring a gear-like blue background\ - \ with interconnected lines and nodes, symbolizing technology and data management.\ - \ In the foreground, there are two open books: one with a brain diagram on\ - \ the left and another\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"text\":\" a yellow brain icon on the right. Below the books, there is a\ - \ graph showing data trends. The scene suggest\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"text\":\"ocus on information t\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"text\":\" alignment \\n \\n World Tour \u2013 Global events \\n showcasing\ - \ OpenEdge innovation \\n \\n Community - Collaborative network for \\n learning,\ - \ sharing, and innovation \\n \\n Customer Validation Program - Early \\n\ - \ access to roadmap features and \\n \\n collaborative feedback. \\n \\n AppMod\ - \ \u2013 Separating US from \\n \\n database and business logic and API \\\ - n enablement \\n \\n Managed Database Administration \\n (MDB\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"text\":\"Proactive DB monitoring \\n \\n and expert management \\n \\n\ - \ Security Assessment \u2013 Identify \\n \\n vulnerabilities and strengthen\ - \ \\n protection \\n \\n Cloud Migration \u2013 Move OpenEdge \\n workloads\ - \ to cloud \\n \\n https://www.progress.com/ \\n\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"text\":\"\\n\\n \\n\\n\\n a digital artwork depicting a street scene with\ - \ various elements. Visible objects include buildings, trees, and pedestrians.\ - \ The scene captures the essence of urban life, highlighting the blend of\ - \ architecture and nature. \\n\\n\\n \\n\\n\\n a digital artwork depicting\ - \ a street scene with various elements.\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"text\":\"or its subsidiaries or affiliates. All rights reserved. \\n \\\ - n The Solution: Trusted, Intelligent Insights \\n \\n \xC7\xC7 \\n \\n MCP\ - \ Server \\n \\n AI You Can \\n \\n TRUST \\n \u2022 Unified Intelligence\ - \ \\n \\n \u2022 Faster Modernization \\n \\n \u2022 Trusted, Governed AI\ - \ \\n \\n https://\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"text\":\"www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a graphic representation\ - \ of a light bulb with bright yellow glow and sparkles around it. The scene\ - \ shows a sunny day with a clear blue sky, and the light bulb is placed on\ - \ a surface with a few decorative elements like stars and flowers. \\n\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"text\":\"\\n\\n \\n\\n\\n a digital artwork depicting a street scene with\ - \ a focus on the intersection of two roads. Visible objects include cars,\ - \ pedestrians, and buildings. The scene captures a moment of everyday life,\ - \ highlighting urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a logo,\ - \ featuring a gear with arrows pointing downwards. The gear is yellow with\ - \ black teeth, \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"text\":\"and the arrows are blue. The backg\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - ,\"text\":\" is green. Visible objects include the gear and arrows. Context\ - \ shows a factory setting with machinery and equipment. \\n\\n\\n \\n\\n\\\ - n a digital artwork depicting a street scene with a focus on the intersection\ - \ of two roads. Visible objects include cars, pedestrians, and buildings.\ - \ The scene captures a moment of everyday life, highlighting urban dynamics\ - \ and human activity. \\n\\n\\n \\n\\n\\n a stylized graphic representation\ - \ of a motorcyclis\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - ,\"text\":\"ing fast, likely on a racetrack. Visible objects include the rider's\ - \ silhouette, the motorcycle, and dynamic motion lines indicating speed. The\ - \ scene captures the thrill and intensity of racing, emphasizing rapid movement\ - \ and speed. \\n\\n\\n \\n\\n\\n a digital artwork that depicts a street\ - \ scene with various elements. Visible objects include buildings, trees, and\ - \ vehicles. The scene ca\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - ,\"text\":\"s the essence of urban life, highlighting the contrast between\ - \ nature and architecture. \\n\\n\\n \\n 28\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Progress\ - \ Corticon \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - ,\"text\":\"ity with modern platforms \\n \\n Unified Data + Rules \u2013\ - \ Decision logic \\n \\n with real-time, governed data for more \\n accurate\ - \ outcomes \\n \\n Accelerated Compliance \u2013 Regulatory \\n requirements\ - \ faster and with greater \\n \\n confidence \\n \\n Future-Ready Architecture\ - \ for \\n \\n Scalability \u2013 Modern, scalable \\n founda\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - ,\"text\":\"tion for advanced analytics and AI \\n \\n Targeted U.S. State\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - ,\"text\":\" https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital\ - \ artwork depicting a street scene with various elements such as buildings,\ - \ trees, and vehicles. Visible objects include a building with a large window,\ - \ a tree with green leaves, and a car parked on the side of the road. The\ - \ scene captures a typical urban environment with a mix of architectural styles\ - \ and natural elements. \\n\\n\\n \\n\\n\\n a graphic\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - ,\"text\":\" \xC7\xC7 \\n \\n Pharma \\n \\n \u2022 R&D \u2013 pr\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - ,\"text\":\"t \\n design, test, \\n \\n pricing, etc. \\n \\n \u2022 Knowledge\ - \ \\n \\n management \\n \\n \u2022 Supply chain \\n \\n management and \\\ - n \\n logistics \\n \\n \u2022 Safety \\n \\n \u2022 Compliance \\n \\n Provider:\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - ,\"text\":\"Clinical coding \\n \\n \u2022\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"text\":\"nes \\n \\n document describes the process and the \\n \\n technical\ - \ details for deploying a new \\n \\n product/service in PDC (contact Stephen\ - \ \\n \\n Rice for details) \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA\ - \ AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"text\":\" \\n \\n \\n\\n\\n Alltools Edit\\n1c)\\n\\n= menu) \xA5y Product\ - \ Hosting Guideli. x @ \xA9 # Signin - a x\\nConvert E-Sign Findtextortoo\ - \ Q 6% a 8\\n| @\\nProduct Hosting Guidelines\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"text\":\"t updated by | Stephen Rice | 3 Dec 2025 at 14:36 GMT i(k)\\n\\\ - nZ.\\n\\n2\\n\\nContents\\n* Introduction\\n+ Terminology and Overview\\n+\ - \ Product Hosting Features\\n+ Is PDC the correct place to host your service?\\\ - n+ PDC Product Deployment Process\\n+ Exploration - Need is identified for\ - \ product to be hosted\\n+ Preparation - Product and PDC teams create implementati...\\\ - n\\n+ Product Updates - Product team implements required cha...\\n\\n+ PDC\ - \ Review - PDC team reviews design documentation an...\\n+ PDC Integration\ - \ - PDC deploys product and performs inte..\\n\\n+ Operations On-boarding\ - \ - Operations team is provided wi...\\n+ Retrospective - Revi\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"text\":\"rocess and discuss how any issues..\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"text\":\"Technical Requirements\\n\\n+ Reverse Proxy Support\\n\\n+ specif\ - \ .\\n\\n+ Network Connections Requirements\\n\\n+ Helm Charts\\n\\n+ Load\ - \ Bala\\n\\n2 p \\n\\n\\n a screenshot of a webpage titled \\\"Product Hosting\ - \ Guidelines\\\". The page contains a list of contents, including sections\ - \ on introduction, product deplo\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"text\":\"r images present. \\n\\n\\n \\n Product Innovation \\n \\n \\\ - n \\n\\n\\n \\n 46\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress \\n Data Cloud \\n\ - \ \\n https://www.progress.com/ \\n \\n \\n\\n\\n 3) Progress DataCloud \\\ - n\\n\\n a logo for Progress DataCloud. It shows a green square with a white\ - \ \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"text\":\"arrow pointing upwards, indicating progress or growth. Visible\ - \ objects include the green square and the white arrow. The s\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"text\":\"shows a modern office environment with a desk, chair, and computer\ - \ monitor. \\n\\n\\n \\n 47\xA9 2025 Progress Software Corporation and/or\ - \ its subsidiaries or affiliates. All rights reserved. \\n \\n Platform \\\ - n \\n Functionality \\n \\n Support \\n \\n Support for additional \\n \\\ - n PDP products and \\n services as we\\n\\nDOCUMENT METADATA AT ORIGIN:\\\ - ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - ,\"text\":\"eople in a garage setting, likely a car repair shop. The woman\ - \ is seated at a desk writing on a clipboard, while the man stands behind\ - \ he\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - ,\"text\":\"aning slightly towards her. Visible objects include a vintage\ - \ telephone, a metal workbench with various tools and equipment, and a classic\ - \ car parked in the background. The context suggests a professional environment\ - \ focused on automotive repairs. \\n\\n\\n \\n 15\xA9 2025 Progress Software\ - \ Corporation and/or its subsidiaries or affiliates. All rights reserved.\ - \ \\n \\n http\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - ,\"text\":\"ww.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a vintage photograph\ - \ showing a woman working at a computer desk in an automotive workshop. Visible\ - \ objects\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - ,\"text\":\" interface, specifically the Wineria Santine app. The screen displays\ - \ various menu options such as \\\"Rolls water transport,\\\" \\\"Faree 310,54\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - ,\"text\":\"04,00,00,\\\" \\\"Sensitiveness,\\\" \\\"Revenue to market 280\ - \ mnx,\\\" \\\"Delivery schedule,\\\" and \\\"New mascara-intern\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - ,\"text\":\"al brands 06.\\\" Visible objects include a woman sitting on a\ - \ bench holding a smartphone, with storefronts in the background featuring\ - \ signage in Hindi. \\n\\n\\n \\n 17\xA9 2025 Progress Software Corporation\ - \ and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.com/\ - \ \\n \\n \\n\\n\\n AAPFOINFAE\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - ,\"text\":\"MFROMDE\\n\u201CTUsEBALI NOW_16 2050 G-\u20ACARAEE\\n[DEWAUS EG\ - \ TO NELIMAL LK\\nENIOY @0F9 UN JOURTEY. \\n\\n\\n a digital interface displaying\ - \ an image of a c\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"text\":\"7\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Congrats getting to the next\ - \ level! \\n \\n Employee Location Scrum Team New Position \\n Abhishek Tiwari\ - \ Hyderabad Mangalayan QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers\ - \ Software Engineer, Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software\ - \ Engineer, Principal 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software\ - \ Engineer, Senior 1 \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal\ - \ \\n \\n Brahmananda Gouni Hyderabad - Senior Manager, Software Engineering\ - \ \\n \\n Brian Tang USA Quicksilver Software Engineer, Principal \\n \\n\ - \ Hari Krishna Tirunagari Hyderabad Mangalayan QA Engineer, Principal 3 \\\ - n \\n Hemanandh S Hyderabad Avengers Software Engineer, Principal 3 \\n \\\ - n Jonathan Miller USA Kalimba Software Engineer, Senior \\n \\n Kunal Basarkar\ - \ Hyderabad Avengers Software Engineer, Senior Principal \\n \\n Nitesh Mehta\ - \ Hyderabad Harmonica Software Engineer, Senior Principal \\n \\n Saivenkat\ - \ Chepuri Hyderabad Conquerors Software Engineer, Principal 1 \\n \\n Sathyanarayana\ - \ Gundoji Hyderabad Mangalayan Software Engineer, Senior 1 \\n \\n Srikar\ - \ Veeramallu Hyderabad Atlantis Software Engineer, Principal 1 \\n \\n Stephen\ - \ Brown USA Apollo Software Engineer, Principal \\n \\n Sumit Pritmani Hyderabad\ - \ Transformers Software Engineer, Senior 2 \\n \\n Vikram Reddy Deva Hyderabad\ - \ Avengers QA Engineer, Senior 2 \\n \\n https://www.progress.com/\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"text\":\"5\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Vamshi Shanigala \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad\ - \ \\n \\n Nathan Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh\ - \ \\n \\n Shivabalu Thouta \\n Software Engineer, Senior \\n \\n MarkLogic\ - \ \\n Hyderabad \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"text\":\"24\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress ADP: GTM Motions \\\ - n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n\ - \ value through \\n \\n added ADP \\n \\n products or \\n \\n leveling up\ - \ tiers \\n \\n \xC7\xC7\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"text\":\"6\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Welcome to the Team \\n \\n\ - \ Aleix Ruiz de Villa \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\\ - n Casimiro Pio Carrino \\n Scientific Advisor \\n \\n PDP \\n Spain\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"text\":\"26\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\ - \xC7 \\n \\n Modernization Services & Plus Ones Engagement \\n \\n OpenEdge\ - \ 13 \u2013 Latest version \\n supporting FIPS compliance \\n \\n 12 and GenAI\ - \ Workshops \u2013 Free \\n hands-on training \\n \\n QSM \u2013 Quality and\ - \ security \\n management tools \\n \\n MCP Connector for ABL - AI Code \\\ - n Assistant to improve code quality \\n \\n Agentic RAG \u2013 AI-driven contextual\ - \ \\n knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\\ - n Validated ROI and business value \\n \\n PUG Challenge \u2013 Community-\ - \ \\n \\n driven learning and networking \\n \\n Customer Advisory Board \u2013\ - \ Customer \\n \\n advisory for strategic alignment \\n \\n World Tour \u2013\ - \ Global events \\n showcasing OpenEdge innovation \\n \\n Community - Collaborative\ - \ network for \\n learning, sharing, and innovation \\n \\n Customer Validation\ - \ Program - Early \\n access to roadmap features and \\n \\n collaborative\ - \ feedback.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"text\":\"New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers\ - \ \\n \\n adopting Progress \\n \\n Data Platform \\n \\n portfolio \\n \\\ - n Customer \\n \\n Renewal \\n \\n Retain existing \\n \\n customers by \\\ - n \\n reinforcing ongoing \\n \\n value of current \\n \\n product(s) in use\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"text\":\"and what qualifications staff require to apply it? \\n \\n Submit\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"text\":\"Celebrating Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack\ - \ w/ MCP\\n\\nVanessa Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\\ - ndependency update automation\\n\\nProgress\u2019\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"text\":\"2\xA9 2022 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases\ - \ \\n \\n ARC, PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon\ - \ 7.3.0 \\n \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic\ - \ IronBank \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC\ - \ 1.2.2/1.3.0 \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog\ - \ \\n \\n SAMM Level 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution\ - \ \\n \\n HDP 4.6.2 \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage\ - \ \\n \\n Innovation \\n \\n Corticon AI \\n Generated User \\n Interface\ - \ \\n \\n AI Video Production \\n \\n Share your additional celebrations in\ - \ the meeting chat!\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"text\":\"48\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n PDC Priorities & Innovation\ - \ \\n \\n \u2022 Functional Expansion \u2013 Increase value proposition by\ - \ on- \\n boarding additional PDP (and other) services \\n \\n Business Priorities\ - \ \\n \\n \u2022 MCP OpenEdge ABL Service \\n \\n \u2022 Hybrid Data Pipeline\ - \ Service \\n \\n \u2022 EULA Acceptance Feedback Mechanism \\n \\n \u2022\ - \ Data Symphony Service \\n \\n \u2022 Operational Support Shared Logging\ - \ (CRIBL) \\n \\n Innovation Areas \\n \\n https://www.progress.com/\\n\\\ - nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"text\":\"Creation of a new enterprise \\n \\n architecture function led\ - \ by \\n \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise\ - \ architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting\ - \ line changes for \\n \\n HDP\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"text\":\"\u2022 Expansion/New Logos - Supporting the AI Journey, Lowering\ - \ TCO, Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities\ - \ \\n \\n \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n\ - \ \u2022 Nuclia-MarkLogic Integration \\n \\n \u2022 MarkLogic Knowledge Hub\ - \ \\n \\n \u2022 Flux as PDC Service \\n \\n Innovation \\n \\n \u2022 Property\ - \ Graphs \\n \\n \u2022 Read from Replicas \\n \\n \u2022 Index Exclusion\ - \ \\n \\n \u2022 Vector improvements \\n \\n \u2022 Object storage improvement\ - \ \\n \u2022 Support Kendo Theme Builder in FastTrack \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"text\":\"42\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022\ - \ Contact the PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide\ - \ as many details as required \\n \\n including timeframes \\n \\n \u27A2\ - \ Meeting will be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\\\ - n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\\ - nfilename: Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified:\ - \ 2025-12-11 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"text\":\"59\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. \\n \\n Corticon Priorities & Innovation\ - \ \\n \\n \u2022 Retention \u2013 Support, CVEs, encourage update to latest\ - \ LTS \\n \\n \u2022 Public Sector Expansion - Replicate success at State\ - \ of Mississippi \\n \\n \u2022 Platform Modernization - New UI and SaaS to\ - \ enable more customer wins \\n \\n Business Priorities \\n \\n \u2022 Modern\ - \ UI \\n \\n \u2022 AI powered user experience \\n \\n \u2022 SaaS offering\ - \ in PDC \\n \\n \u2022 Symphony rule deployment \\n \\n Innovation \\n \\\ - n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"text\":\"Performance & Reliability Upgrades \u2013 \\n \\n Faster queries\ - \ and improved resilience \\n \\n for mission-critical workloads. \\n \\n\ - \ AI-Assistant \u2013 Simplify the developer \\n \\n experience and lower\ - \ the learning curve.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"text\":\"a digital photograph showing two people working at a desk. The\ - \ man is seated, looking at the computer screen, while the woman stands behind\ - \ him, smiling. Visible objects include a desktop computer with a keyboard\ - \ and mouse, a pen holder, and some documents on the desk. The background\ - \ features a green wall with charts and graphs.\\n\\nDOCUMENT METADATA AT\ - \ ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business\ - \ Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"text\":\"\u2713 Faster Contract Approvals \u2013 Streamlined deal desk\ - \ and legal alignment for quicker turnaround. \\n \\n \u2713 Transparent Pricing\ - \ Models \u2013 Unified price list and predictable cost structures. \\n \\\ - n \u2713 Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for easier\ - \ entry and adoption.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11\ - \ 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026\ - \ 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test\ - \ folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"text\":\"44\xA9 2025 Progress Software Corporation and/or its subsidiaries\ - \ or affiliates. All rights reserved. 44 \\n \\n Product Hosting Notes \\\ - n \\n \u2022 Current lead times for product \\n \\n deployment are currently\ - \ 3-6 months \\n \\n \u2022 A Product Hosting Guidelines \\n \\n document\ - \ describes the process and the \\n \\n technical details for deploying a\ - \ new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice for details)\ - \ \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated:\ - \ 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product\ - \ Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\\ - npath: Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\\ - nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\\ - n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"text\":\"Modernize the Corticon \\n \\n user experience with a \\n new\ - \ Web UI for, \\n \\n designed to simplify \\n \\n rule authoring and \\n\ - \ accelerate adoption for \\n \\n new projects. \\n \\n Accelerate rule \\\ - n \\n automation and \\n delivery with AI that \\n supports end-to-end \\\ - n \\n project creation, \\n testing, and \\n \\n maintenance.\\n\\nDOCUMENT\ - \ METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename:\ - \ Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11\ - \ 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and\ - \ Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\\ - nsync_metadata:\\n auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\\ - n file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\"\ - :\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\"\ - ,\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - :{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\"\ - ,\"text\":\"a digital screenshot of a mobile phone interface, specifically\ - \ the Wineria Santine app. The screen displays various menu options such as\ - \ \\\"Rolls water transport,\\\" \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\ - \" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"\ - New mascara-international brands 06.\\\" Visible objects include a woman sitting\ - \ on a 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Principal \\n \\n Brahmananda + Gouni Hyderabad - Senior Manager, Software Engineering \\n \\n Brian Tang + USA Quicksilver Software Engineer, Principal \\n \\n Hari Krishna Tirunagari + Hyderabad Mangalayan QA Engineer, Principal 3 \\n \\n Hemanandh S Hyderabad + Avengers Software Engineer, Principal 3 \\n \\n Jonathan Miller USA Kalimba + Software Engineer, Senior \\n \\n Kunal Basarkar Hyderabad Avengers Software + Engineer, Senior Principal \\n \\n Nitesh Mehta Hyderabad Harmonica Software + Engineer, Senior Principal \\n \\n Saivenkat Chepuri Hyderabad Conquerors + Software Engineer, Principal 1 \\n \\n Sathyanarayana Gundoji Hyderabad Mangalayan + Software Engineer, Senior 1 \\n \\n Srikar Veeramallu Hyderabad Atlantis Software + Engineer, Principal 1 \\n \\n Stephen Brown USA Apollo Software Engineer, + Principal \\n \\n Sumit Pritmani Hyderabad Transformers Software Engineer, + Senior 2 \\n \\n Vikram Reddy Deva Hyderabad Avengers QA Engineer, Senior + 2 \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\",\"labels\":[],\"position\":{\"page_number\":6,\"index\":0,\"start\":4766,\"end\":6189,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\":{\"score\":0.0029121784027665854,\"score_type\":\"RERANKER\",\"order\":1,\"text\":\" + \\n 5\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Vamshi Shanigala + \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad \\n \\n Nathan + Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh \\n \\n Shivabalu + Thouta \\n Software Engineer, Senior \\n \\n MarkLogic \\n Hyderabad \\n \\n + https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":4,\"index\":0,\"start\":3054,\"end\":3424,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\":{\"score\":0.0012893283274024725,\"score_type\":\"RERANKER\",\"order\":2,\"text\":\" + \\n 24\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress ADP: GTM Motions \\n \\n Expansion: + \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n value through \\n + \\n added ADP \\n \\n products or \\n \\n leveling up tiers \\n \\n \xC7\xC7 + \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\",\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":16815,\"end\":17085,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\":{\"score\":0.00049553596181795,\"score_type\":\"RERANKER\",\"order\":3,\"text\":\" + \\n 6\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Aleix Ruiz de Villa + \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\n Casimiro Pio Carrino + \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":5,\"index\":0,\"start\":4148,\"end\":4399,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\":{\"score\":0.00012533752305898815,\"score_type\":\"RERANKER\",\"order\":4,\"text\":\" + \\n 26\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\xC7 \\n \\n Modernization + Services & Plus Ones Engagement \\n \\n OpenEdge 13 \u2013 Latest version + \\n supporting FIPS compliance \\n \\n 12 and GenAI Workshops \u2013 Free + \\n hands-on training \\n \\n QSM \u2013 Quality and security \\n management + tools \\n \\n MCP Connector for ABL - AI Code \\n Assistant to improve code + quality \\n \\n Agentic RAG \u2013 AI-driven contextual \\n knowledge retrieval + \\n \\n Forrester Total Economic Impact \u2013 \\n Validated ROI and business + value \\n \\n PUG Challenge \u2013 Community- \\n \\n driven learning and + networking \\n \\n Customer Advisory Board \u2013 Customer \\n \\n advisory + for strategic alignment \\n \\n World Tour \u2013 Global events \\n showcasing + OpenEdge innovation \\n \\n Community - Collaborative network for \\n learning, + sharing, and innovation \\n \\n Customer Validation Program - Early \\n access + to roadmap features and \\n \\n collaborative feedback. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":25,\"index\":0,\"start\":19109,\"end\":20048,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\":{\"score\":0.00012339458044152707,\"score_type\":\"RERANKER\",\"order\":5,\"text\":\" + New \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\n \\n adopting + Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer \\n \\n + Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing ongoing + \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/ + \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\",\"labels\":[],\"position\":{\"page_number\":23,\"index\":0,\"start\":17085,\"end\":17354,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\":{\"score\":0.00008818923379294574,\"score_type\":\"RERANKER\",\"order\":6,\"text\":\" + \\n and what qualifications staff require to apply it? \\n \\n Submit \\n + \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":18,\"index\":0,\"start\":14158,\"end\":14258,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\":{\"score\":0.00006014151585986838,\"score_type\":\"RERANKER\",\"order\":7,\"text\":\"Celebrating + Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack w/ MCP\\n\\nVanessa + Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\ndependency update + automation\\n\\nProgress\u2019\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\",\"labels\":[\"/k/ocr\"],\"position\":{\"page_number\":2,\"index\":0,\"start\":2193,\"end\":2360,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":\"image_2_0.jpg\",\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\":{\"score\":0.00005738759500673041,\"score_type\":\"RERANKER\",\"order\":8,\"text\":\" + \\n 2\xA9 2022 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases \\n \\n ARC, + PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon 7.3.0 \\n + \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic IronBank + \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC 1.2.2/1.3.0 + \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog \\n \\n SAMM Level + 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution \\n \\n HDP 4.6.2 + \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage \\n \\n Innovation + \\n \\n Corticon AI \\n Generated User \\n Interface \\n \\n AI Video Production + \\n \\n Share your additional celebrations in the meeting chat! \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":1,\"index\":0,\"start\":1388,\"end\":2043,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\":{\"score\":0.000054759766499046236,\"score_type\":\"RERANKER\",\"order\":9,\"text\":\" + \\n 48\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n PDC Priorities & Innovation \\n \\n \u2022 Functional + Expansion \u2013 Increase value proposition by on- \\n boarding additional + PDP (and other) services \\n \\n Business Priorities \\n \\n \u2022 MCP OpenEdge + ABL Service \\n \\n \u2022 Hybrid Data Pipeline Service \\n \\n \u2022 EULA + Acceptance Feedback Mechanism \\n \\n \u2022 Data Symphony Service \\n \\n + \u2022 Operational Support Shared Logging (CRIBL) \\n \\n Innovation Areas + \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":47,\"index\":0,\"start\":46719,\"end\":47227,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\":{\"score\":0.00004985958003089763,\"score_type\":\"RERANKER\",\"order\":10,\"text\":\" + \\n Creation of a new enterprise \\n \\n architecture function led by \\n + \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise + architect: Nathan \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting + line changes for \\n \\n HDP \\n \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\",\"labels\":[],\"position\":{\"page_number\":10,\"index\":0,\"start\":8847,\"end\":9067,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\":{\"score\":0.00004434627408045344,\"score_type\":\"RERANKER\",\"order\":11,\"text\":\" + \\n \u2022 Expansion/New Logos - Supporting the AI Journey, Lowering TCO, + Supporting \\n Hybrid Deployment Options \\n \\n Business Priorities \\n \\n + \u2022 Graviton Support \\n \\n \u2022 Monitoring Agent \\n \\n \u2022 Nuclia-MarkLogic + Integration \\n \\n \u2022 MarkLogic Knowledge Hub \\n \\n \u2022 Flux as + PDC Service \\n \\n Innovation \\n \\n \u2022 Property Graphs \\n \\n \u2022 + Read from Replicas \\n \\n \u2022 Index Exclusion \\n \\n \u2022 Vector improvements + \\n \\n \u2022 Object storage improvement \\n \u2022 Support Kendo Theme Builder + in FastTrack \\n \\n https://www.progress.com/ \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\",\"labels\":[],\"position\":{\"page_number\":50,\"index\":0,\"start\":49179,\"end\":49670,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\":{\"score\":0.00003822911094175652,\"score_type\":\"RERANKER\",\"order\":12,\"text\":\" + \\n 42\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022 Contact the + PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide as many + details as required \\n \\n including timeframes \\n \\n \u27A2 Meeting will + be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/ + \\n \\n \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":41,\"index\":0,\"start\":40988,\"end\":41333,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\":{\"score\":0.00003647853736765683,\"score_type\":\"RERANKER\",\"order\":13,\"text\":\" + \\n 59\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Corticon Priorities & Innovation \\n \\n \u2022 + Retention \u2013 Support, CVEs, encourage update to latest LTS \\n \\n \u2022 + Public Sector Expansion - Replicate success at State of Mississippi \\n \\n + \u2022 Platform Modernization - New UI and SaaS to enable more customer wins + \\n \\n Business Priorities \\n \\n \u2022 Modern UI \\n \\n \u2022 AI powered + user experience \\n \\n \u2022 SaaS offering in PDC \\n \\n \u2022 Symphony + rule deployment \\n \\n Innovation \\n \\n https://www.progress.com/ \\n \\n + \ \",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\",\"labels\":[],\"position\":{\"page_number\":58,\"index\":0,\"start\":55634,\"end\":56168,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27462-27669\":{\"score\":0.00003071818719035946,\"score_type\":\"RERANKER\",\"order\":14,\"text\":\" + \\n Performance & Reliability Upgrades \u2013 \\n \\n Faster queries and improved + resilience \\n \\n for mission-critical workloads. \\n \\n AI-Assistant \u2013 + Simplify the developer \\n \\n experience and lower the learning curve. 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The man is seated, + looking at the computer screen, while the woman stands behind him, smiling. + Visible objects include a desktop computer with a keyboard and mouse, a pen + holder, and some documents on the desk. The background features a green wall + with charts and graphs.\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\",\"labels\":[\"/k/inception\"],\"position\":{\"page_number\":8,\"index\":0,\"start\":7044,\"end\":7374,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"reference\":\"image_8_0.jpg\",\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\":{\"score\":0.000018925147742265835,\"score_type\":\"RERANKER\",\"order\":16,\"text\":\" + \\n \u2713 Faster Contract Approvals \u2013 Streamlined deal desk and legal + alignment for quicker turnaround. \\n \\n \u2713 Transparent Pricing Models + \u2013 Unified price list and predictable cost structures. \\n \\n \u2713 + Starter Bundles for Simplicity \u2013 Upcoming PDP SKU for easier entry and + adoption. \\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\",\"labels\":[\"/k/text\"],\"position\":{\"page_number\":32,\"index\":0,\"start\":31881,\"end\":32155,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41776-42205\":{\"score\":0.00001805851934477687,\"score_type\":\"RERANKER\",\"order\":17,\"text\":\" + \\n 44\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. 44 \\n \\n Product Hosting Notes \\n \\n \u2022 Current + lead times for product \\n \\n deployment are currently 3-6 months \\n \\n + \u2022 A Product Hosting Guidelines \\n \\n document describes the process + and the \\n \\n technical details for deploying a new \\n \\n product/service + in PDC (contact Stephen \\n \\n Rice for details) \\n \\n https://www.progress.com/ + \\n \\n 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\\n\",\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53354-53655\",\"labels\":[],\"position\":{\"page_number\":57,\"index\":0,\"start\":53354,\"end\":53655,\"start_seconds\":[],\"end_seconds\":[]},\"fuzzy_result\":false,\"page_with_visual\":true,\"is_a_table\":false},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11635-12070\":{\"score\":0.000016701422282494605,\"score_type\":\"RERANKER\",\"order\":19,\"text\":\"a + digital screenshot of a mobile phone interface, specifically the Wineria Santine + app. The screen displays various menu options such as \\\"Rolls water transport,\\\" + \\\"Faree 310,54,64,04,00,00,\\\" \\\"Sensitiveness,\\\" \\\"Revenue to market + 280 mnx,\\\" \\\"Delivery schedule,\\\" and \\\"New mascara-international + brands 06.\\\" Visible objects include a woman sitting on a bench holding + a smartphone, with storefronts in the background featuring signage in 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+ \\n \\n PDP \\n Spain \\n \\n https:/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4399-4431\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\",\"text\":\"progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a portrait of a person smiling at the camera. + Visible objects include the person's face, hair, and smile. The background + consists of a stone wall wi\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4440-4616\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\",\"text\":\"me + decorative elements. \\n\\n\\n \\n\\n\\n a portrait photo of a man with a + beard and wavy hair. He is wearing a dark shirt with a floral pattern\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4626-4761\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\",\"text\":\"tps://www.progress.com/ + \\n \\n \\n\\n\\n \\n 8\xA9 2025 Progress Software Corporation and/or its + subsidiaries or affiliates. All rights reserved. \\n \\n AI is no longer optional. + \\n It is the engine of \\n transformation. \\n \\n 8\xA9 2025 Progress Software + Corporation and/or its subsidiaries or a\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6193-6467\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\",\"text\":\"ffiliates. + All rights reserved. \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6467-6501\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\",\"text\":\"ps://www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a blurred motion photograph, capturing the rapid + movement of city streets at night. Visible objects include tall buildings, + streetlights, and vehicles. The scene depicts a bustling urban environment + with dynamic light \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6510-6765\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\",\"text\":\"s + indicating high-speed traffic. \\n\\n\\n \\n Practices are changing \\n \\n + \u2022 Market analysis \\n \u2022 Feature definition \\n \u2022 Threat modelling + \\n \u2022 Code suggestions \\n \u2022 Code analysis \\n \u2022 UI scaffolding + \\n \u2022 Test coverage \\n \u2022 Penetration testing \\n \u2022 Documentation + \\n \u2022 Defect analysi\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/6770-7035\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\",\"text\":\" + \\n \\n \\n\\n\\n \\n 3\xA9 2025 Progress Software Corporation and/or its + subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2053-2189\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\",\"text\":\"ress\u2019 + \\n\\n\\n a slide from a presentation titled \\\"Celebrating Innovation from + PI 3...\\\". The slide features a festive background with colorful confetti. + Visible objects include the title text, bullet points, and a logo at the bottom + left corner. The scene shows a celebration atmosphere with bright colors and + decorative\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2365-2680\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\",\"text\":\"ents. + \\n\\n\\n \\n 4\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates. All rights reserved. \\n \\n Important Dates \\n \\n 11 December + (Thursday) \\n \\n Draft Plan Presentations \\n \\n 15 December (Monday) \\n + \\n Final Plan Presentations \\n \\n 17 December (Wednesday) \\n \\n Start + of PI 2026.1 \\n \\n 18 December (Thursday) \\n \\n Innovation Demos \\n \\n + https://www.progress.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2685-3050\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\",\"text\":\"\\n + \\n \\n\\n\\n \\n\\n\\n a portrait photograph of a man smiling at the camera. + He is wearing a yellow shirt with a patterned background. Visible objects + include his face, shirt, and background. The scene is likely an indoor setting, + possibly a conference o\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3433-3678\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\",\"text\":\"nt + space. \\n\\n\\n \\n\\n\\n a portrait of a smiling man with short hair and + a beard. He is wearing a black shirt. The backgro\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3688-3804\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\",\"text\":\"rights + reserved. \\n \\n What is the best damp-proofing product for \\n concrete + piles that are in constant contact with \\n standing water, is it available, + how is it applied, \\n \\n Hi, how can I help? \\n \\n and what qualifications + staff require to apply it? \\n \\n Submit \\n \\n https://www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a photograph of two construction workers standing + near a body of water, likely a lake or river, with several concrete pillars + marked with numbers in the background. They are wearing safety helmets and + high-visibility jackets. The scene depicts them observing or discussing something + on a clipboard. Visible objects include the con\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15013-15646\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\",\"text\":\"crete + pillars, the water, and the \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15646-15680\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\",\"text\":\" + Context suggests they are involved in a construction project. \\n\\n\\n \\n + 20\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n ?Documents \\n \\n Searching for a Suitable Product + \\n \\n + damp-proofing \\n \\n + concrete piles \\n \\n + standing water + \\n \\n damp-proofing \\n \\n concrete piles \\n \\n standing water \\n \\n + https://www.progress.com/ \\n \\n \\n\\n\\n \\n 21\xA9 2025 Progress Software + Corporation and/or its subsidiaries or affiliates. All rights reserved. \\n + \\n Comprehensive \\n \\n and actionable \\n \\n answer \\n \\n Searching + for a Suitable Product \\n \\n Scan tech specs to \\n \\n find suitable products + \\n \\n Retrieve additional \\n \\n information about \\n \\n \\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/15684-16349\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\",\"text\":\"ication, + safety and \\n \\n qualifications \\n \\n Check stock management \\n \\n system + to see if a suitable \\n \\n product is in stock \\n \\n What is the best + damp-proofing \\n \\n product for concrete piles that are \\n \\n in constant + contact with sta\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16353-16582\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\",\"text\":\"nding + \\n \\n water, is it available, how is it \\n \\n applied, and what qualifications + \\n \\n staff require to apply it? \\n \\n https://www.progress.com/ \\n \\n + \ \\n\\n\\n \\n 22\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affilia\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16582-16809\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\",\"text\":\"ion + \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n Pro \\n duct 360 + \\n \\n P \\n u \\n \\n b \\n lic \\n \\n S \\n a \\n fe \\n \\n ty \\n \\n + Self-service Policy \\n Search \\n \\n K \\n n \\n \\n o \\n w \\n \\n le + \\n d \\n \\n g \\n e \\n \\n M \\n a \\n n \\n \\n a \\n g \\n \\n e \\n + m \\n \\n e \\n n \\n \\n t \\n \\n Fraud \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17363-17604\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\",\"text\":\"ity + \\n \\n Search & \\n Dis \\n \\n cover \\n y \\n \\n Software (ISV/OEM) \\n + \\n Manufacturing \\n \\n Government \\n \\n (SL/Fed) \\n \\n Life \\n \\n + Sciences \\n \\n Pharma \\n \\n Biotech \\n \\n Financial \\n \\n Services + & \\n \\n Insurance \\n \\n Managed \\n \\n Healthcare \\n \\n Media & \\n + \\n Publ\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17614-17855\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\",\"text\":\"s + plain white. \\n\\n\\n \\n\\n\\n a portrait photograph of a young man wearing + a light blue shirt. He has dark hair and a friendly expression. The background + is plain white, providing no distractions from the subject. Visible objects + include his face and shirt. Context suggests he might be a student or professional + in a field related to \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3814-4143\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\",\"text\":\"w + \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\n \\n adopting + Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer \\n \\n + Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing ongoing + \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene \\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18146-18464\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\",\"text\":\"a + focus on the intersection of two roads. Visible objects include cars, pedestrians, + and buildings. The scene captures a moment of everyday life, highlighting + urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a digital artwork + depicting a street scene with a focus on the intersection of two roads. Visible + objects include cars, pedestrians, and bu\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18474-18816\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\",\"text\":\"gs. + The scene captures a moment of everyday life, highlighting urban dynamics + and human activity. \\n\\n\\n \\n 25\xA9 2025 Progress Software Corporation + and/or its subsidiaries or affiliates. All rights reserved. \\n \\n Long-standing + Innovation \\n \\n Customer \\n \\n Renewal \\n Expansion \\n \\n Cust\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/18826-19104\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\",\"text\":\"ometric + shapes representing buildings, windows, and roofs. The scene shows a modern + architectural design with green accents. \\n\\n\\n \\n 26\xA9 2025 Progress + Software Corporation and/or its subsidiaries or affiliates. All rights reserved. + \\n \\n Progress OpenEdge \\n \\n \xC7\xC7 \\n \\n Modernization Services + & Plus Ones Engagement \\n \\n OpenEdge 13 \u2013 Latest version \\n supporting + \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20048-20404\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\",\"text\":\"compliance + \\n \\n 12 and GenAI Workshops \u2013 Free \\n hands-on training \\n \\n QSM + \u2013 Quality and security \\n management tools \\n \\n MCP Connector for + ABL - AI Code \\n Assistant to improve code quality \\n \\n Agentic RAG \u2013 + AI-driven contex\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20413-20634\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\",\"text\":\"\\n + knowledge retrieval \\n \\n Forrester Total Economic Impact \u2013 \\n Validated + ROI and business value \\n \\n PUG Challenge \u2013 Community- \\n \\n driven + learning and networking \\n \\n Customer Advisory Board \u2013 Customer \\n + \\n advisory for stra\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/20644-20865\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\",\"text\":\"g + \\n \\n \\n \\n\\n\\n \\n 24\xA9 2025 Progress Software Corporation and/or + its subsidiaries or affiliates. All rights reserved. \\n \\n Progress ADP: + GTM Motions \\n \\n Expansion: \\n Cross-sell & Upsell \\n \\n Increase account + \\n \\n value through \\n \\n added ADP \\n \\n products or \\n \\n leveling + up tiers \\n \\n \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17860-18137\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\",\"text\":\" + a modern urban setting. Visible objects include the car's front view, a city + skyline in the background, and a person standing in front of the vehicle. + The context suggests a professional environment, possibly related to automotive + services or technology. \\n\\n\\n \\n 18\xA9 2025 Progress Software\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13313-13602\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\",\"text\":\"oration + and/or its subsidiaries or affiliates. All rights reserved. \\n \\n https://www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a geometric shape, likely a polygon or a three-dimensional + object, rendered in yellow with black outlines. It shows a simple scene with + a few objects:\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13612-13874\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\",\"text\":\"ctangular + box-like structure and a small square inside it. The background is black. + \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene with a focus + on a person walking down the sidewalk. Visible objects include a person, a + car, and various urban elements such as buildings, tree\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/13879-14158\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\",\"text\":\"d + street signs. The scene captures a moment of everyday life in an urban environment. + \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition. + Visible objects include a bowl, a spoon, a knife, and various fruits such + as apples and oranges. The scene depicts a kitchen setting with a focus on + the fruit bowl, suggesting a theme of healthy eating or culinary preparation. + \\n\\n\\n \\n\\n\\n a green arrow pointing to the right,\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14267-14697\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\",\"text\":\"cating + movement or direction. Visible objects include the arrow itself and a few + indistinct shapes that might be part of the background. The scene shows a + blurred, dark background with a green arrow pointing to the right. \\n\\n\\n + \ \\n 19\xA9 2025 Progress Software Corporation and/or its subsidiaries or + affiliates. \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14702-15009\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\",\"text\":\" + \\n\\n\\n a geometric abstract artwork featuring a three-dimensional shape + composed of overlapping triangles in varying shades of green. The scene shows + a modern, minimalist interior with a black background. Visible objects include + a sleek, black chair and a small table with a simple design. Context suggests + a contemporary living space or office environ\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/694-1044\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\",\"text\":\" + \\n\\n\\n \\n\\n\\n a geometric abstract graphic with a three-dimensional + cube-like shape composed of green and yellow gradients. It shows a modern, + minimalist design with clean lines and a vibrant color palette. Visible objects + include the cube and its gradient colors. The scene is set against a black + background, emphasizing the geometr\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1054-1383\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\",\"text\":\"a + signature, likely from a person's handwritten document or digital representation. + Visible objects include the signature itself and possibly some ink residue. + The scene shows a close-up view of a person's handwriting, with the signature + prominently displayed against a dark background.\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/398-684\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\",\"text\":\"ftware + Corporation and/or its subsidiaries or affiliates. All rights reserved. \\n + \\n Leverage our \\n \\n GenAI \\n \\n Developments \\n \\n Harness the power + of \\n artificial intelligence \\n \\n and drive your AI \\n \\n strategy. + \\n \\n Adopt Our Latest Releases \\n \\n Security & \\n \\n Compliance \\n + \\n Protect your systems \\n \\n and your data with up- \\n to-date security + \\n \\n patches, dependency \\n updates, and regulatory \\n complian\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/45637-46038\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\",\"text\":\"ignment. + \\n \\n Platform \\n \\n Support \\n \\n Stay current with the \\n \\n latest + platforms, \\n frameworks, and \\n \\n standards to ensure \\n compatibility + and long- \\n \\n term support \\n \\n \xC7\xC7 \\n Modernization and \\n + \\n Future-Readiness \\n \\n Embrace our latest \\n \\n features and \\n advancements + and \\n \\n accelerate your data \\n agility journey. \\n \\n Performance + & \\n \\n Reliability \\n \\n Benefit from ongoing \\n \\n fixes and optimizations + \\n that del\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46048-46463\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\",\"text\":\"a + more \\n \\n resilient platform. \\n \\n https://www.progress.com/ \\n \\n + \ \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still life composition. + Visible objects include a bowl, a spoon, a knife, and various fruits such + as apples and oranges. The sc\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46473-46714\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\",\"text\":\"epicts + a kitchen setting with a focus on the fruit bowl, suggesting a theme of healthy + eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital artwork that + appears to be a still \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47236-47409\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\",\"text\":\"composition. + Visible objects include a bowl, a spoon, a knife,\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47414-47476\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\",\"text\":\"various + fruits such as apples and oranges. The scene depicts a kitchen setting with + a focus on the fruit bowl, suggesting a theme of healthy eating or culinary + preparation. \\n\\n\\n \\n\\n\\n a digital artwork that appears to be a still + life composition. Visible objects include a bowl, a spoon, a knife, and various + fruits such as apples and oranges. The scene depicts a k\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/47485-47848\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\",\"text\":\" + https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital photograph + showing two people working at a desk. The man is seated, looking at the computer + screen, while the woman stands behind him, smiling. Visible objects include + a desktop computer with a keyboard and mouse, a pen holder, and some\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7384-7671\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\",\"text\":\"ments + on the desk. The background features a green wall with charts and graphs. + \\n\\n\\n \\n\\n\\n a geometric logo consisting of three overlapping hexagons + arranged in a triangular formation. The background is black, and the logo + is rendered in shades of green. Visible objects include the hexagons and their + intersecting lines. Context suggests a modern, tech-oriented brand identity. + \\n\\n\\n \\n 10\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7676-8136\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\",\"text\":\" + rights reserved.\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates. All rights reserved. \\n \\n From Triad to Quartet \\n \\n + Product Management \\n \\n Viability & Scope \\n \\n Engineering \\n \\n Feasibility + & Delivery \\n \\n Architecture \\n \\n Sustainability & Scale \\n \\n Customer + Experience \\n \\n Desirability \\n \\n https://www.pro\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8145-8477\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\",\"text\":\".com/ + \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital thumbs-up + icon. It shows a thumbs-up gesture with the thumb extended upwards and the + index finger curled into a triangle. Visible objects include the thumbnail + icon itself, the thumb, and the index finger. The context is likely a digital + interface or a social media platform where users can express approval\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8482-8847\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\",\"text\":\"emporary + reporting \\n \\n structure while we select a \\n \\n product leadership team, + \\n \\n and align P\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9067-9162\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\",\"text\":\"d + \\n \\n OpenEdge product \\n management and product \\n \\n operations. \\n + \\n Creation of a new enterprise \\n \\n architecture function led by \\n + \\n James Kerr with a first \\n \\n dedicated PDP level \\n \\n enterprise + architect: Nathan \\n van \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9171-9392\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\",\"text\":\". + \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting line changes for \\n \\n HDP + \\n \\n \u2022 Temporary structure for \\n \\n the documentation \\n function + \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital + artwork depicting a street scene with various el\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/9402-9623\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\",\"text\":\" + oranges. The scene depicts a kitchen setting with a focus on the fruit bowl, + suggesting a theme of healthy eating or culinary preparation. \\n\\n\\n \\n\\n\\n + a digital artwork that appears to be a still life composition. Visible objects + include a bowl, a spoon, a knife, and v\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48116-48383\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\",\"text\":\"arious + fruits such as apples and oranges. The scene depicts a kitchen setting with + a focus on the fruit bowl, suggesting a theme of healthy eating or culinary + preparation. \\n\\n\\n \\n Integrate \\n Our Teams \\n \\n Our Products \\n + \\n Our \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48383-48609\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\",\"text\":\"Use + Cases \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n Software (ISV/OEM)\\nManufacturing\\n\\nLife\\nSciences\\nPharma\\nBiotech\\n\\n&\\nS\\n@\\n<\\noO\\n=\\no\\ni=)\\n3\\ncc\\n3\\nS\\nt\\n\\nFinancial\\nServices + &\\nInsurance\\n\\nManaged\\nHealthcare\\n\\nGovernment\\nim (SL/Fed)\\n\\nMedia + &\\nPublishing\\n\\nMayes oyand \\n\\n\\n a circular diagram with six segments, + each representing \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48609-48921\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\",\"text\":\"a + different aspect of security man\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48921-48955\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\",\"text\":\"ent. + The segments are labeled Product 360, Self-service Policy Search, Managed + Healthcare, Government (SL/Fed), Public Safety, Fraud Identity, and Financial + Services & Insurance. Visible objects include icons for Softwar\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/48959-49179\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\",\"text\":\"V/OEM) + Manufacturing, Life Sciences Pharma Biotech, Media & Publishing, and Fraud + Identity. The scene shows a globe centered within the circle, surrounded by + these six segments\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49679-49855\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\",\"text\":\" + @uHHEERy\\n\\nquxx== \xA9\\n\\nERB EBB BEEBE B\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49860-49896\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\",\"text\":\" + wm \xAE\\n\\nSd\\n\\n@nunuun 8 \\n\\n\\n a flowchart or diagram that illustrates + the process of data integration. Visible objects include arrows representing + different stages, icons indicating various processes, and a circu\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49900-50107\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\",\"text\":\"lar + loop symbolizing a continuous \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/50107-50141\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\",\"text\":\"a + digital graphic artifact, likely representing a logo or branding element. + It shows a stylized green geometric shape with three arrows pointing upwards, + set against a white background. Visible objects include the green shapes and + arrows, as well as some black elements that could represent additional details + or text. The scene context suggests a mo\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/39868-40218\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\",\"text\":\" + tech-oriented environment, possibly related to \\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40223-40271\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\",\"text\":\"nology + or innovation. \\n\\n\\n \\n\\n\\n a digital graphic artifact, likely a logo + or an icon. It shows a green geometric shape with three arrows pointing upwards, + set against a white background. Visible objects include the green shapes and + arrows, as well as som\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40275-40528\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\",\"text\":\"e + black elements that could represent additional details or t\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40528-40589\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\",\"text\":\" + The scene is minimalistic, focusing on the geometric shapes and their directional + emphasis. \\n\\n\\n \\n 33\xA9 2025 Progress Software Corporation and/or + its subsidiaries or affiliates. All rights reserved. \\n \\n Progress Data + Platform \\n \\n \xC7\xC7 \\n \\n \u2713 Simplified Licensing Terms \u2013 + Harmonized EULA and clear discounting guidelines. \\n \\n \u2713 Faster Contract + Approvals \u2013 Streamlined deal desk and legal alignme\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40593-40984\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\",\"text\":\"ion + for faster provisioning and order accuracy. \\n \\n \u2713 Unified UI \u2013 + Improved experience across product set to increase efficiency and user engagement. + \\n \\n Customer Experience \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n + \ \\n\\n\\n a graphic illustration featuring three stylized icons: a wave + symbol, a green checkmark icon, and a smiley face icon. The scene shows a + modern digital interface with these icons, sugg\\n\\nDOCUMENT METADATA AT + ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context + and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41337-41738\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\",\"text\":\"esting + a focus on user feedback or\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/41738-41772\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\",\"text\":\"althy + eating or culinary preparation. \\n\\n\\n \\n\\n\\n a digital image of a + landscape scene. Visible objects include trees, grass, rocks, and water. The + scene depicts a serene natural environment with lush greenery and calm waters. + \\n\\n\\n \\n 34\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates. All rights reserved. \\n \\n Intersection of \\n Moment-in-Time + \\n Pain and Long-term \\n Business Impact \\n \\n Long-Term \\n \\n Transformation + \\n Help to solve today's pains \\n \\n repeatedly \\n \\n until long-term + impact \\n \\n becomes instilled. \\n \\n AI-Driven \\n \\n Foundation \\n + Build a robust AI \\n \\n foundation that \\n \\n accelerates insights and + \\n \\n decision-making \\n for long-term success. \\n \\n Customer-Centric + \\n \\n Approach \\n Focus on meeting \\n \\n customers at their \\n \\n current + state to address \\n \\n immediate \\n pain points effectively. \\n \\n https://www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a long-exposure photograph capturing the dynamic + movement of vehic\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/42209-43142\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\",\"text\":\"n + an intricate highway network at night. Visible objects include multiple intersecting + highways with light trails indicating the speed and flow of traffic. The scene + is set in a bustling urban area, likely a city center, with illuminated buildings + and streets in the background. \\n\\n\\n \\n Progress Data Platform: Vertical + Concentration \\n \\n \xC7\xC7 \\n \\n Manufacturing \\n \\n \xC7\xC7 \\n + \\n Healthcare \\n \\n \xC7\xC7 \\n \\n Government \\n \\n \xC7\xC7 \\n \\n + Media & \\n \\n Publishing \\n \\n \xC7\xC7 \\n \\n FinServ/ \\n \\n Insuranc\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43147-43615\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\",\"text\":\"ions + team with \\n advanced tools to better \\n assist clients and drive \\n \\n + revenue generation \\n \\n Continuous monitoring \\n \\n and enhancement of + \\n security measures to \\n \\n safeguard customer data \\n \\n integrity + \\n \\n https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital + artwork depicting a street sc\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54700-54989\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\",\"text\":\"ith + a focus on the intersection of two roads. Visible objects include cars, pedestrians, + and buildings. The scene captures a moment of everyday life, highlighting + urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a digital artwork + depicting a street scene with a focus on the intersection of two roads. Visible + objects include cars, pede\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54999-55330\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\",\"text\":\"ns, + and buildings. The scene captures a moment of everyday life, highlighting + urban dynamics and human activity. \\n\\n\\n \\n\\n\\n a digital artwork + depicting a street scene with a focus on the intersection of two roads. Visible + objects include cars, pedestrians, and buildings. The scene captures \\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55340-55629\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\",\"text\":\"ent + of everyday life, highlighting urban dynamics and human activity. \\n\\n\\n + \ \\n\\n\\n a digital illustration featuring a gear-like blue background with + interconnected lines and nodes, symbolizing technology and data management. + In the foreground, there are two open books: one with a brain diagram on the + left and another\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56177-56491\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\",\"text\":\" + a yellow brain icon on the right. Below the books, there is a graph showing + data trends. The scene suggest\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56501-56608\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\",\"text\":\"ocus + on information t\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/56613-56634\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\",\"text\":\" + alignment \\n \\n World Tour \u2013 Global events \\n showcasing OpenEdge + innovation \\n \\n Community - Collaborative network for \\n learning, sharing, + and innovation \\n \\n Customer Validation Program - Early \\n access to roadmap + features and \\n \\n collaborative feedback. \\n \\n AppMod \u2013 Separating + US from \\n \\n database and business logic and API \\n enablement \\n \\n + Managed Database Administration \\n (MDB\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26541-26922\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\",\"text\":\"Proactive + DB monitoring \\n \\n and expert management \\n \\n Security Assessment \u2013 + Identify \\n \\n vulnerabilities and strengthen \\n protection \\n \\n Cloud + Migration \u2013 Move OpenEdge \\n workloads to cloud \\n \\n https://www.progress.com/ + \\n\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/26932-27153\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\",\"text\":\"\\n\\n + \ \\n\\n\\n a digital artwork depicting a street scene with various elements. + Visible objects include buildings, trees, and pedestrians. The scene captures + the essence of urban life, highlighting the blend of architecture and nature. + \\n\\n\\n \\n\\n\\n a digital artwork depicting a street scene with various + elements.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27158-27462\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\",\"text\":\"or + its subsidiaries or affiliates. All rights reserved. \\n \\n The Solution: + Trusted, Intelligent Insights \\n \\n \xC7\xC7 \\n \\n MCP Server \\n \\n + AI You Can \\n \\n TRUST \\n \u2022 Unified Intelligence \\n \\n \u2022 Faster + Modernization \\n \\n \u2022 Trusted, Governed AI \\n \\n https://\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27669-27911\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\",\"text\":\"www.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a graphic representation of a light bulb with + bright yellow glow and sparkles around it. The scene shows a sunny day with + a clear blue sky, and the light bulb is placed on a surface with a few decorative + elements like stars and flowers. \\n\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/27911-28181\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\",\"text\":\"\\n\\n + \ \\n\\n\\n a digital artwork depicting a street scene with a focus on the + intersection of two roads. Visible objects include cars, pedestrians, and + buildings. The scene captures a moment of everyday life, highlighting urban + dynamics and human activity. \\n\\n\\n \\n\\n\\n a logo, featuring a gear + with arrows pointing downwards. The gear is yellow with black teeth, \\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28181-28534\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\",\"text\":\"and + the arrows are blue. The backg\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/28534-28568\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\",\"text\":\" + is green. Visible objects include the gear and arrows. Context shows a factory + setting with machinery and equipment. \\n\\n\\n \\n\\n\\n a digital artwork + depicting a street scene with a focus on the intersection of two roads. Visible + objects include cars, pedestrians, and buildings. The scene captures a moment + of everyday life, highlighting urban dynamics and human activity. \\n\\n\\n + \ \\n\\n\\n a stylized graphic representation of a motorcyclis\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/30837-31265\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\",\"text\":\"ing + fast, likely on a racetrack. Visible objects include the rider's silhouette, + the motorcycle, and dynamic motion lines indicating speed. The scene captures + the thrill and intensity of racing, emphasizing rapid movement and speed. + \\n\\n\\n \\n\\n\\n a digital artwork that depicts a street scene with various + elements. Visible objects include buildings, trees, and vehicles. The scene + ca\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31275-31654\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\",\"text\":\"s + the essence of urban life, highlighting the contrast between nature and architecture. + \\n\\n\\n \\n 28\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates. All rights reserved. \\n \\n Progress Corticon \\n \\n \xC7\xC7\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31659-31881\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\",\"text\":\"ity + with modern platforms \\n \\n Unified Data + Rules \u2013 Decision logic \\n + \\n with real-time, governed data for more \\n accurate outcomes \\n \\n Accelerated + Compliance \u2013 Regulatory \\n requirements faster and with greater \\n + \\n confidence \\n \\n Future-Ready Architecture for \\n \\n Scalability \u2013 + Modern, scalable \\n founda\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32155-32457\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\",\"text\":\"tion + for advanced analytics and AI \\n \\n Targeted U.S. State\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32457-32515\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\",\"text\":\" + https://www.progress.com/ \\n \\n \\n\\n\\n \\n\\n\\n a digital artwork + depicting a street scene with various elements such as buildings, trees, and + vehicles. Visible objects include a building with a large window, a tree with + green leaves, and a car parked on the side of the road. The scene captures + a typical urban environment with a mix of architectural styles and natural + elements. \\n\\n\\n \\n\\n\\n a graphic\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/32524-32916\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\",\"text\":\" + \xC7\xC7 \\n \\n Pharma \\n \\n \u2022 R&D \u2013 pr\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43620-43649\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\",\"text\":\"t + \\n design, test, \\n \\n pricing, etc. \\n \\n \u2022 Knowledge \\n \\n management + \\n \\n \u2022 Supply chain \\n \\n management and \\n \\n logistics \\n \\n + \u2022 Safety \\n \\n \u2022 Compliance \\n \\n Provider: \\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43653-43816\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\",\"text\":\"Clinical + coding \\n \\n \u2022\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/43820-43841\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\",\"text\":\"nes + \\n \\n document describes the process and the \\n \\n technical details for + deploying a new \\n \\n product/service in PDC (contact Stephen \\n \\n Rice + for details) \\n \\n https://www.progress.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52072-52252\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\",\"text\":\" + \\n \\n \\n\\n\\n Alltools Edit\\n1c)\\n\\n= menu) \xA5y Product Hosting + Guideli. x @ \xA9 # Signin - a x\\nConvert E-Sign Findtextortoo Q 6% a 8\\n| + @\\nProduct Hosting Guidelines\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52256-52411\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\",\"text\":\"t + updated by | Stephen Rice | 3 Dec 2025 at 14:36 GMT i(k)\\n\\nZ.\\n\\n2\\n\\nContents\\n* + Introduction\\n+ Terminology and Overview\\n+ Product Hosting Features\\n+ + Is PDC the correct place to host your service?\\n+ PDC Product Deployment + Process\\n+ Exploration - Need is identified for product to be hosted\\n+ + Preparation - Product and PDC teams create implementati...\\n\\n+ Product + Updates - Product team implements required cha...\\n\\n+ PDC Review - PDC + team reviews design documentation an...\\n+ PDC Integration - PDC deploys + product and performs inte..\\n\\n+ Operations On-boarding - Operations team + is provided wi...\\n+ Retrospective - Revi\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/52415-53028\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\",\"text\":\"rocess + and discuss how any issues..\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53032-53067\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\",\"text\":\"Technical + Requirements\\n\\n+ Reverse Proxy Support\\n\\n+ specif .\\n\\n+ Network Connections + Requirements\\n\\n+ Helm Charts\\n\\n+ Load Bala\\n\\n2 p \\n\\n\\n a screenshot + of a webpage titled \\\"Product Hosting Guidelines\\\". The page contains + a list of contents, including sections on introduction, product deplo\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53071-53354\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\",\"text\":\"r + images present. \\n\\n\\n \\n Product Innovation \\n \\n \\n \\n\\n\\n \\n + 46\xA9 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress \\n Data Cloud \\n \\n https://www.progress.com/ + \\n \\n \\n\\n\\n 3) Progress DataCloud \\n\\n\\n a logo for Progress DataCloud. + It shows a green square with a white \\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53655-53971\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\",\"text\":\"arrow + pointing upwards, indicating progress or growth. Visible objects include the + green square and the white arrow. The s\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/53971-54093\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\",\"text\":\"shows + a modern office environment with a desk, chair, and computer monitor. \\n\\n\\n + \ \\n 47\xA9 2025 Progress Software Corporation and/or its subsidiaries or + affiliates. All rights reserved. \\n \\n Platform \\n \\n Functionality \\n + \\n Support \\n \\n Support for additional \\n \\n PDP products and \\n services + as we\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/54102-54391\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\",\"text\":\"eople + in a garage setting, likely a car repair shop. The woman is seated at a desk + writing on a clipboard, while the man stands behind he\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/10976-11113\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\",\"text\":\"aning + slightly towards her. Visible objects include a vintage telephone, a metal + workbench with various tools and equipment, and a classic car parked in the + background. The context suggests a professional environment focused on automotive + repairs. \\n\\n\\n \\n 15\xA9 2025 Progress Software Corporation and/or its + subsidiaries or affiliates. All rights reserved. \\n \\n http\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11122-11484\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\",\"text\":\"ww.progress.com/ + \\n \\n \\n\\n\\n \\n\\n\\n a vintage photograph showing a woman working + at a computer desk in an automotive workshop. Visible objects\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/11489-11626\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\",\"text\":\" + interface, specifically the Wineria Santine app. The screen displays various + menu options such as \\\"Rolls water transport,\\\" \\\"Faree 310,54\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12075-12212\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\",\"text\":\"04,00,00,\\\" + \\\"Sensitiveness,\\\" \\\"Revenue to market 280 mnx,\\\" \\\"Delivery schedule,\\\" + and \\\"New mascara-intern\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12216-12317\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\",\"text\":\"al + brands 06.\\\" Visible objects include a woman sitting on a bench holding + a smartphone, with storefronts in the background featuring signage in Hindi. + \\n\\n\\n \\n 17\xA9 2025 Progress Software Corporation and/or its subsidiaries + or affiliates. All rights reserved. \\n \\n https://www.progress.com/ \\n + \\n \\n\\n\\n AAPFOINFAE\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12322-12628\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\",\"text\":\"MFROMDE\\n\u201CTUsEBALI + NOW_16 2050 G-\u20ACARAEE\\n[DEWAUS EG TO NELIMAL LK\\nENIOY @0F9 UN JOURTEY. + \\n\\n\\n a digital interface displaying an image of a c\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/12633-12770\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\",\"text\":\"7\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Congrats getting to the next level! \\n \\n Employee + Location Scrum Team New Position \\n Abhishek Tiwari Hyderabad Mangalayan + QA Engineer, Senior 2 \\n \\n Ajoy Soni Hyderabad Avengers Software Engineer, + Senior 1 \\n \\n Amit Anchaliya Hyderabad Atlantis Software Engineer, Principal + 1 \\n \\n Anshuman Singh Hyderabad Conquerors Software Engineer, Senior 1 + \\n \\n Bing Zhu USA Light QA Engineer, Senior Principal \\n \\n Brahmananda + Gouni Hyderabad - Senior Manager, Software Engineering \\n \\n Brian Tang + USA Quicksilver Software Engineer, Principal \\n \\n Hari Krishna Tirunagari + Hyderabad Mangalayan QA Engineer, Principal 3 \\n \\n Hemanandh S Hyderabad + Avengers Software Engineer, Principal 3 \\n \\n Jonathan Miller USA Kalimba + Software Engineer, Senior \\n \\n Kunal Basarkar Hyderabad Avengers Software + Engineer, Senior Principal \\n \\n Nitesh Mehta Hyderabad Harmonica Software + Engineer, Senior Principal \\n \\n Saivenkat Chepuri Hyderabad Conquerors + Software Engineer, Principal 1 \\n \\n Sathyanarayana Gundoji Hyderabad Mangalayan + Software Engineer, Senior 1 \\n \\n Srikar Veeramallu Hyderabad Atlantis Software + Engineer, Principal 1 \\n \\n Stephen Brown USA Apollo Software Engineer, + Principal \\n \\n Sumit Pritmani Hyderabad Transformers Software Engineer, + Senior 2 \\n \\n Vikram Reddy Deva Hyderabad Avengers QA Engineer, Senior + 2 \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4766-6189\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\",\"text\":\"5\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Vamshi Shanigala + \\n Coop/Intern Technical 2 \\n \\n MarkLogic \\n Hyderabad \\n \\n Nathan + Van Gheem \\n Enterprise Architect \\n \\n PDP \\n Raleigh \\n \\n Shivabalu + Thouta \\n Software Engineer, Senior \\n \\n MarkLogic \\n Hyderabad \\n \\n + https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/3054-3424\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\",\"text\":\"24\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress ADP: GTM Motions \\n \\n Expansion: + \\n Cross-sell & Upsell \\n \\n Increase account \\n \\n value through \\n + \\n added ADP \\n \\n products or \\n \\n leveling up tiers \\n \\n \xC7\xC7\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/16815-17085\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\",\"text\":\"6\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Welcome to the Team \\n \\n Aleix Ruiz de Villa + \\n Scientific Advisor \\n \\n PDP \\n Spain \\n \\n Casimiro Pio Carrino + \\n Scientific Advisor \\n \\n PDP \\n Spain\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/4148-4399\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\",\"text\":\"26\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Progress OpenEdge \\n \\n \xC7\xC7 \\n \\n Modernization + Services & Plus Ones Engagement \\n \\n OpenEdge 13 \u2013 Latest version + \\n supporting FIPS compliance \\n \\n 12 and GenAI Workshops \u2013 Free + \\n hands-on training \\n \\n QSM \u2013 Quality and security \\n management + tools \\n \\n MCP Connector for ABL - AI Code \\n Assistant to improve code + quality \\n \\n Agentic RAG \u2013 AI-driven contextual \\n knowledge retrieval + \\n \\n Forrester Total Economic Impact \u2013 \\n Validated ROI and business + value \\n \\n PUG Challenge \u2013 Community- \\n \\n driven learning and + networking \\n \\n Customer Advisory Board \u2013 Customer \\n \\n advisory + for strategic alignment \\n \\n World Tour \u2013 Global events \\n showcasing + OpenEdge innovation \\n \\n Community - Collaborative network for \\n learning, + sharing, and innovation \\n \\n Customer Validation Program - Early \\n access + to roadmap features and \\n \\n collaborative feedback.\\n\\nDOCUMENT METADATA + AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: Business + Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/19109-20048\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\",\"text\":\"New + \\n \\n Acquisition \\n \\n Win net new \\n \\n customers \\n \\n adopting + Progress \\n \\n Data Platform \\n \\n portfolio \\n \\n Customer \\n \\n + Renewal \\n \\n Retain existing \\n \\n customers by \\n \\n reinforcing ongoing + \\n \\n value of current \\n \\n product(s) in use \\n \\n https://www.progress.com/\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/17085-17354\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\",\"text\":\"and + what qualifications staff require to apply it? \\n \\n Submit \\n \\n https://www.progress.com/\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/14158-14258\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\",\"text\":\"Celebrating + Innovation\\nfrom PI 3...\\n\\nBrian Tang - FastTrack w/ MCP\\n\\nVanessa + Zhang - Al diff analysis\\n\\nShivali Tayal - 3rd party\\ndependency update + automation\\n\\nProgress\u2019\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/2193-2360\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\",\"text\":\"2\xA9 + 2022 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Celebrating Q4 \\n \\n Releases \\n \\n ARC, + PostgreSQL, Oracle \\n \\n OpenAccess 9.0 for AIX \\n \\n Corticon 7.3.0 \\n + \\n Corticonjs 2.4.0 \\n \\n MarkLogic 11.3.2/3 \\n \\n MarkLogic IronBank + \\n \\n Operator 1.1.1 \\n \\n Progress Agentic RAG \\n \\n PDC 1.2.2/1.3.0 + \\n \\n Semaphore 5.8.6 \\n \\n Security \\n \\n Trufflehog \\n \\n SAMM Level + 2 \\n \\n Spy Attributes \\n Vulnerability \\n Resolution \\n \\n HDP 4.6.2 + \\n \\n Infrastructure \\n \\n Sonar Code \\n Coverage \\n \\n Innovation + \\n \\n Corticon AI \\n Generated User \\n Interface \\n \\n AI Video Production + \\n \\n Share your additional celebrations in the meeting chat!\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/1388-2043\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\",\"text\":\"48\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n PDC Priorities & Innovation \\n \\n \u2022 Functional + Expansion \u2013 Increase value proposition by on- \\n boarding additional + PDP (and other) services \\n \\n Business Priorities \\n \\n \u2022 MCP OpenEdge + ABL Service \\n \\n \u2022 Hybrid Data Pipeline Service \\n \\n \u2022 EULA + Acceptance Feedback Mechanism \\n \\n \u2022 Data Symphony Service \\n \\n + \u2022 Operational Support Shared Logging (CRIBL) \\n \\n Innovation Areas + \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/46719-47227\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\",\"text\":\"Creation + of a new enterprise \\n \\n architecture function led by \\n \\n James Kerr + with a first \\n \\n dedicated PDP level \\n \\n enterprise architect: Nathan + \\n van Gheem. \\n \\n \u2022 Nuclia \\n \\n \u2022 Reporting line changes + for \\n \\n HDP\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/8847-9067\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\",\"text\":\"\u2022 + Expansion/New Logos - Supporting the AI Journey, Lowering TCO, Supporting + \\n Hybrid Deployment Options \\n \\n Business Priorities \\n \\n \u2022 Graviton + Support \\n \\n \u2022 Monitoring Agent \\n \\n \u2022 Nuclia-MarkLogic Integration + \\n \\n \u2022 MarkLogic Knowledge Hub \\n \\n \u2022 Flux as PDC Service + \\n \\n Innovation \\n \\n \u2022 Property Graphs \\n \\n \u2022 Read from + Replicas \\n \\n \u2022 Index Exclusion \\n \\n \u2022 Vector improvements + \\n \\n \u2022 Object storage improvement \\n \u2022 Support Kendo Theme Builder + in FastTrack \\n \\n https://www.progress.com/\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: + 2025-12-11 15:41:54.970000+00:00\\nfilename: Business Context and Product + Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: + Test folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/49179-49670\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\",\"text\":\"42\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. 42 \\n \\n Getting Started \\n \\n \u2022 Contact the + PDC PM/PO (Stephen Rice) to \\n \\n discuss \\n \\n \u27A2 Provide as many + details as required \\n \\n including timeframes \\n \\n \u27A2 Meeting will + be arranged between key \\n \\n stakeholders \\n \\n https://www.progress.com/\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/40988-41333\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/55634-56168\",\"text\":\"59\xA9 + 2025 Progress Software Corporation and/or its subsidiaries or affiliates. + All rights reserved. \\n \\n Corticon Priorities & Innovation \\n \\n \u2022 + Retention \u2013 Support, CVEs, encourage update to latest LTS \\n \\n \u2022 + Public Sector Expansion - Replicate success at State of Mississippi \\n \\n + \u2022 Platform Modernization - New UI and SaaS to enable more customer wins + \\n \\n Business Priorities \\n \\n \u2022 Modern UI \\n \\n \u2022 AI powered + user experience \\n \\n \u2022 SaaS offering in PDC \\n \\n \u2022 Symphony + rule deployment \\n \\n Innovation \\n \\n https://www.progress.com/\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: 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The man is seated, + looking at the computer screen, while the woman stands behind him, smiling. + Visible objects include a desktop computer with a keyboard and mouse, a pen + holder, and some documents on the desk. The background features a green wall + with charts and graphs.\\n\\nDOCUMENT METADATA AT ORIGIN:\\ncreated: 2025-12-11 + 15:41:54.970000+00:00\\nfilename: Business Context and Product Vision 2026 + 1_self_signed.pdf\\nmodified: 2025-12-11 15:42:05.133000+00:00\\npath: Test + folder/Test folder/Business Context and Product Vision 2026 1_self_signed.pdf\\nsource_id: + sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n auth_provider: + sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n file_id: + fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: ''\\n\",\"parent\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/7044-7374\",\"augmentation_type\":\"metadata_extension\"},\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\":{\"id\":\"866f34bfe00a53e9265df7699d9b264b/f/866f34bfe00a53e9265df7699d9b264b/31881-32155\",\"text\":\"\u2713 + Faster Contract Approvals \u2013 Streamlined deal desk and legal alignment + for quicker turnaround. \\n \\n \u2713 Transparent Pricing Models \u2013 Unified + price list and predictable cost structures. \\n \\n \u2713 Starter Bundles + for Simplicity \u2013 Upcoming PDP SKU for easier entry and adoption.\\n\\nDOCUMENT + METADATA AT ORIGIN:\\ncreated: 2025-12-11 15:41:54.970000+00:00\\nfilename: + Business Context and Product Vision 2026 1_self_signed.pdf\\nmodified: 2025-12-11 + 15:42:05.133000+00:00\\npath: Test folder/Test folder/Business Context and + Product Vision 2026 1_self_signed.pdf\\nsource_id: sync_config_019cade7-c177-77c5-99c2-c8771f85cf91\\nsync_metadata:\\n + \ auth_provider: sharefile_oauth\\n content_hash: 866f34bfe00a53e9265df7699d9b264b\\n + \ file_id: fi3567d8-8bbe-96ac-4e21-132d539a9e53\\nurl: 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Select only information - directly relevant to the question.\n2. Break down compound sentences into simple, - single-idea statements. Preserve original phrasing when possible.\n3. For any - named entity with descriptive details, separate those details into distinct - propositions.\n4. Ensure clarity by replacing pronouns (e.g., \"it\", \"he\", - \"she\", \"they\", \"this\", \"that\") with the full names of the entities they - reference, and add necessary modifiers to clarify meaning.\n5. The context may - be delimited by tags such as and . Treat - everything between these tags as context.\n6. Assess whether the context sufficiently - answers the question. If it answers it partially, provide the answer; if it - does not answer it fully, specify what information is missing to answer the - question.\n7. If the context does not answer the question at all, just return - the original question as the missing information.\n8. 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All - rights reserved. \n \n Welcome to the Team \n \n Vamshi Shanigala \n Coop/Intern - Technical 2 \n \n MarkLogic \n Hyderabad \n \n Nathan Van Gheem \n Enterprise - Architect \n \n PDP \n Raleigh \n \n Shivabalu Thouta \n Software Engineer, - Senior \n \n MarkLogic \n Hyderabad \n \n https://www.progress.com/ \n \n ```\n\n\n---\"\n\n\n**block-AB**\n\n## - Chunk: Business Context and Product Vision 2026 1_self_signed.pdf\n``` \n 6\u00a9 - 2025 Progress Software Corporation and/or its subsidiaries or affiliates. All - rights reserved. \n \n Welcome to the Team \n \n Aleix Ruiz de Villa \n Scientific - Advisor \n \n PDP \n Spain \n \n Casimiro Pio Carrino \n Scientific Advisor - \n \n PDP \n Spain \n \n ```\n\n\n---\"\n\n\n\n\n\n\n"}, "citations": - false, "citation_threshold": null, "generative_model": "chatgpt-azure-4o-mini", - "max_tokens": 8192, "query_context_images": {}, "prefer_markdown": null, "json_schema": - {"title": "validate_or_answer", "description": "Validate or answer", "parameters": - {"type": "object", "properties": {"reason": {"type": "string", "description": - "Reasoning for the answer or validation"}, "answer": {"type": "string", "description": - "Partial or complete answer to the user query from the information in the context."}, - "missing_info_query": {"type": "string", "description": "Query needed to retrieve - the missing information in case the context is not enough to answer the question. - If the context does not answer the question at all, just return the original - question."}, "useful": {"type": "string", "description": "Is the context useful - to answer the question?", "enum": ["yes", "no"]}, "citations": {"type": "array", - "items": {"type": "string", "description": "Block ID cited in the answer, e.g. - block-AB"}, "description": "List of block IDs cited in the answer, if any"}}, - "required": ["reason", "answer", "missing_info_query", "useful", "citations"]}}, - "format_prompt": false, "rerank_context": false, "tools": [], "tool_choice": - {"type": "required"}, "reasoning": false, "seed": null}' - headers: - Accept: - - application/x-ndjson - Accept-Encoding: - - gzip, deflate - Connection: - - keep-alive - Content-Length: - - '4872' - Content-Type: - - application/json - Host: - - europe-1.dp.progress.cloud - User-Agent: - - nuclia.py/4.11.5 - x-origin: - - RAO - x-show-consumption: - - 'true' - x-stf-nuakey: - - DUMMY - method: POST - uri: https://europe-1.dp.progress.cloud/api/v1/predict/chat - response: - body: - string: '{"chunk":{"type":"object","object":{"reason":"The context provides - names and roles of new employees at ADP, including their locations. 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