diff --git a/.env.template b/.env.template index b91f89540..7fd3ba9e8 100644 --- a/.env.template +++ b/.env.template @@ -16,7 +16,7 @@ STRUCTURED_OUTPUT_FRAMEWORK="instructor" LLM_API_KEY="your_api_key" -LLM_MODEL="openai/gpt-4o-mini" +LLM_MODEL="openai/gpt-5-mini" LLM_PROVIDER="openai" LLM_ENDPOINT="" LLM_API_VERSION="" @@ -30,10 +30,13 @@ EMBEDDING_DIMENSIONS=3072 EMBEDDING_MAX_TOKENS=8191 # If embedding key is not provided same key set for LLM_API_KEY will be used #EMBEDDING_API_KEY="your_api_key" +# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch, +# Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models +#EMBEDDING_BATCH_SIZE=2048 # If using BAML structured output these env variables will be used BAML_LLM_PROVIDER=openai -BAML_LLM_MODEL="gpt-4o-mini" +BAML_LLM_MODEL="gpt-5-mini" BAML_LLM_ENDPOINT="" BAML_LLM_API_KEY="your_api_key" BAML_LLM_API_VERSION="" @@ -52,18 +55,18 @@ BAML_LLM_API_VERSION="" ################################################################################ # Configure storage backend (local filesystem or S3) # STORAGE_BACKEND="local" # Default: uses local filesystem -# +# # -- To switch to S3 storage, uncomment and fill these: --------------------- # STORAGE_BACKEND="s3" # STORAGE_BUCKET_NAME="your-bucket-name" # AWS_REGION="us-east-1" # AWS_ACCESS_KEY_ID="your-access-key" # AWS_SECRET_ACCESS_KEY="your-secret-key" -# +# # -- S3 Root Directories (optional) ----------------------------------------- # DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data" # SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system" -# +# # -- Cache Directory (auto-configured for S3) ------------------------------- # When STORAGE_BACKEND=s3, cache automatically uses S3: s3://BUCKET/cognee/cache # To override the automatic S3 cache location, uncomment: diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 7705a51eb..5a6d7b124 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -58,7 +58,7 @@ body: - Python version: [e.g. 3.9.0] - Cognee version: [e.g. 0.1.0] - LLM Provider: [e.g. OpenAI, Ollama] - - Database: [e.g. Neo4j, FalkorDB] + - Database: [e.g. Neo4j] validations: required: true diff --git a/.github/actions/cognee_setup/action.yml b/.github/actions/cognee_setup/action.yml index e46a42edb..1326f2d81 100644 --- a/.github/actions/cognee_setup/action.yml +++ b/.github/actions/cognee_setup/action.yml @@ -41,4 +41,4 @@ runs: EXTRA_ARGS="$EXTRA_ARGS --extra $extra" done fi - uv sync --extra api --extra docs --extra evals --extra gemini --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS + uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS diff --git a/.github/workflows/db_examples_tests.yml b/.github/workflows/db_examples_tests.yml index a1a81b4d4..51ac9a82a 100644 --- a/.github/workflows/db_examples_tests.yml +++ b/.github/workflows/db_examples_tests.yml @@ -54,6 +54,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Run Neo4j Example env: ENV: dev @@ -66,9 +70,9 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_USERNAME: "neo4j" - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: | uv run python examples/database_examples/neo4j_example.py diff --git a/.github/workflows/distributed_test.yml b/.github/workflows/distributed_test.yml new file mode 100644 index 000000000..57bbb7459 --- /dev/null +++ b/.github/workflows/distributed_test.yml @@ -0,0 +1,73 @@ +name: Distributed Cognee test with modal +permissions: + contents: read +on: + workflow_call: + inputs: + python-version: + required: false + type: string + default: '3.11.x' + secrets: + LLM_MODEL: + required: true + LLM_ENDPOINT: + required: true + LLM_API_KEY: + required: true + LLM_API_VERSION: + required: true + EMBEDDING_MODEL: + required: true + EMBEDDING_ENDPOINT: + required: true + EMBEDDING_API_KEY: + required: true + EMBEDDING_API_VERSION: + required: true + OPENAI_API_KEY: + required: true + +jobs: + run-server-start-test: + name: Distributed Cognee test (Modal) + runs-on: ubuntu-22.04 + steps: + - name: Check out + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: "distributed postgres" + + - name: Run Distributed Cognee (Modal) + env: + ENV: 'dev' + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + MODAL_TOKEN_ID: ${{ secrets.MODAL_TOKEN_ID }} + MODAL_TOKEN_SECRET: ${{ secrets.MODAL_TOKEN_SECRET }} + MODAL_SECRET_NAME: ${{ secrets.MODAL_SECRET_NAME }} + GRAPH_DATABASE_PROVIDER: "neo4j" + GRAPH_DATABASE_URL: ${{ secrets.AZURE_NEO4j_URL }} + GRAPH_DATABASE_USERNAME: ${{ secrets.AZURE_NEO4J_USERNAME }} + GRAPH_DATABASE_PASSWORD: ${{ secrets.AZURE_NEO4J_PW }} + DB_PROVIDER: "postgres" + DB_NAME: ${{ secrets.AZURE_POSTGRES_DB_NAME }} + DB_HOST: ${{ secrets.AZURE_POSTGRES_HOST }} + DB_PORT: ${{ secrets.AZURE_POSTGRES_PORT }} + DB_USERNAME: ${{ secrets.AZURE_POSTGRES_USERNAME }} + DB_PASSWORD: ${{ secrets.AZURE_POSTGRES_PW }} + VECTOR_DB_PROVIDER: "pgvector" + COGNEE_DISTRIBUTED: "true" + run: uv run modal run ./distributed/entrypoint.py diff --git a/.github/workflows/examples_tests.yml b/.github/workflows/examples_tests.yml index df007a576..57bc88157 100644 --- a/.github/workflows/examples_tests.yml +++ b/.github/workflows/examples_tests.yml @@ -234,3 +234,28 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} run: uv run python ./examples/python/permissions_example.py + test_docling_add: + name: Run Add with Docling Test + runs-on: macos-15 + steps: + - name: Check out repository + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: '3.11.x' + extra-dependencies: 'docling' + + - name: Run Docling Test + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + LLM_MODEL: ${{ secrets.LLM_MODEL }} + LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }} + LLM_API_KEY: ${{ secrets.LLM_API_KEY }} + LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }} + EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }} + EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }} + EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} + EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} + run: uv run python ./cognee/tests/test_add_docling_document.py diff --git a/.github/workflows/graph_db_tests.yml b/.github/workflows/graph_db_tests.yml index e7e5a11b8..b07f6232f 100644 --- a/.github/workflows/graph_db_tests.yml +++ b/.github/workflows/graph_db_tests.yml @@ -71,6 +71,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Run default Neo4j env: ENV: 'dev' @@ -83,9 +87,9 @@ jobs: EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }} EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }} GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} - GRAPH_DATABASE_USERNAME: "neo4j" + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_neo4j.py - name: Run Weighted Edges Tests with Neo4j diff --git a/.github/workflows/relational_db_migration_tests.yml b/.github/workflows/relational_db_migration_tests.yml index 26fd7e150..6a37e308e 100644 --- a/.github/workflows/relational_db_migration_tests.yml +++ b/.github/workflows/relational_db_migration_tests.yml @@ -186,6 +186,10 @@ jobs: python-version: '3.11.x' extra-dependencies: "postgres" + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Install specific db dependency run: echo "Dependencies already installed in setup" @@ -206,9 +210,9 @@ jobs: env: ENV: 'dev' GRAPH_DATABASE_PROVIDER: "neo4j" - GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} - GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} - GRAPH_DATABASE_USERNAME: "neo4j" + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} LLM_PROVIDER: openai LLM_MODEL: ${{ secrets.LLM_MODEL }} diff --git a/.github/workflows/search_db_tests.yml b/.github/workflows/search_db_tests.yml index 0b749526d..e3e46dd97 100644 --- a/.github/workflows/search_db_tests.yml +++ b/.github/workflows/search_db_tests.yml @@ -51,20 +51,6 @@ jobs: name: Search test for Neo4j/LanceDB/Sqlite runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }} - services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 steps: - name: Check out @@ -77,6 +63,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -94,9 +84,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'lancedb' DB_PROVIDER: 'sqlite' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_search_db.py run-kuzu-pgvector-postgres-search-tests: @@ -158,19 +148,6 @@ jobs: runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/pgvector/postgres') }} services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 postgres: image: pgvector/pgvector:pg17 env: @@ -196,6 +173,10 @@ jobs: python-version: ${{ inputs.python-version }} extra-dependencies: "postgres" + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -213,9 +194,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'pgvector' DB_PROVIDER: 'postgres' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} DB_NAME: cognee_db DB_HOST: 127.0.0.1 DB_PORT: 5432 diff --git a/.github/workflows/temporal_graph_tests.yml b/.github/workflows/temporal_graph_tests.yml index 4156dffff..66f7d60ee 100644 --- a/.github/workflows/temporal_graph_tests.yml +++ b/.github/workflows/temporal_graph_tests.yml @@ -51,20 +51,6 @@ jobs: name: Temporal Graph test Neo4j (lancedb + sqlite) runs-on: ubuntu-22.04 if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }} - services: - neo4j: - image: neo4j:5.11 - env: - NEO4J_AUTH: neo4j/pleaseletmein - NEO4J_PLUGINS: '["apoc","graph-data-science"]' - ports: - - 7474:7474 - - 7687:7687 - options: >- - --health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'" - --health-interval=10s - --health-timeout=5s - --health-retries=5 steps: - name: Check out @@ -77,6 +63,10 @@ jobs: with: python-version: ${{ inputs.python-version }} + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" @@ -94,9 +84,9 @@ jobs: GRAPH_DATABASE_PROVIDER: 'neo4j' VECTOR_DB_PROVIDER: 'lancedb' DB_PROVIDER: 'sqlite' - GRAPH_DATABASE_URL: bolt://localhost:7687 - GRAPH_DATABASE_USERNAME: neo4j - GRAPH_DATABASE_PASSWORD: pleaseletmein + GRAPH_DATABASE_URL: ${{ steps.neo4j.outputs.neo4j-url }} + GRAPH_DATABASE_USERNAME: ${{ steps.neo4j.outputs.neo4j-username }} + GRAPH_DATABASE_PASSWORD: ${{ steps.neo4j.outputs.neo4j-password }} run: uv run python ./cognee/tests/test_temporal_graph.py run_temporal_graph_kuzu_postgres_pgvector: diff --git a/.github/workflows/test_llms.yml b/.github/workflows/test_llms.yml index 5a0f947c9..6b0221309 100644 --- a/.github/workflows/test_llms.yml +++ b/.github/workflows/test_llms.yml @@ -27,7 +27,7 @@ jobs: env: LLM_PROVIDER: "gemini" LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }} - LLM_MODEL: "gemini/gemini-1.5-flash" + LLM_MODEL: "gemini/gemini-2.0-flash" EMBEDDING_PROVIDER: "gemini" EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }} EMBEDDING_MODEL: "gemini/text-embedding-004" @@ -83,4 +83,4 @@ jobs: EMBEDDING_MODEL: "openai/text-embedding-3-large" EMBEDDING_DIMENSIONS: "3072" EMBEDDING_MAX_TOKENS: "8191" - run: uv run python ./examples/python/simple_example.py \ No newline at end of file + run: uv run python ./examples/python/simple_example.py diff --git a/.github/workflows/test_s3_file_storage.yml b/.github/workflows/test_s3_file_storage.yml index a477d8933..11c808a2d 100644 --- a/.github/workflows/test_s3_file_storage.yml +++ b/.github/workflows/test_s3_file_storage.yml @@ -6,8 +6,12 @@ on: permissions: contents: read +env: + RUNTIME__LOG_LEVEL: ERROR + ENV: 'dev' + jobs: - test-gemini: + test-s3-storage: name: Run S3 File Storage Test runs-on: ubuntu-22.04 steps: diff --git a/.github/workflows/test_suites.yml b/.github/workflows/test_suites.yml index ff18f2962..2f1bdebf0 100644 --- a/.github/workflows/test_suites.yml +++ b/.github/workflows/test_suites.yml @@ -27,6 +27,12 @@ jobs: uses: ./.github/workflows/e2e_tests.yml secrets: inherit + distributed-tests: + name: Distributed Cognee Test + needs: [ basic-tests, e2e-tests, graph-db-tests ] + uses: ./.github/workflows/distributed_test.yml + secrets: inherit + cli-tests: name: CLI Tests uses: ./.github/workflows/cli_tests.yml @@ -104,7 +110,7 @@ jobs: db-examples-tests: name: DB Examples Tests - needs: [vector-db-tests, graph-db-tests, relational-db-migration-tests] + needs: [vector-db-tests, graph-db-tests, relational-db-migration-tests, distributed-tests] uses: ./.github/workflows/db_examples_tests.yml secrets: inherit diff --git a/.github/workflows/weighted_edges_tests.yml b/.github/workflows/weighted_edges_tests.yml index df5e048f2..874ef6ea4 100644 --- a/.github/workflows/weighted_edges_tests.yml +++ b/.github/workflows/weighted_edges_tests.yml @@ -86,12 +86,19 @@ jobs: with: python-version: '3.11' + - name: Setup Neo4j with GDS + uses: ./.github/actions/setup_neo4j + id: neo4j + - name: Dependencies already installed run: echo "Dependencies already installed in setup" - name: Run Weighted Edges Tests env: GRAPH_DATABASE_PROVIDER: ${{ matrix.graph_db_provider }} + GRAPH_DATABASE_URL: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-url || '' }} + GRAPH_DATABASE_USERNAME: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-username || '' }} + GRAPH_DATABASE_PASSWORD: ${{ matrix.graph_db_provider == 'neo4j' && steps.neo4j.outputs.neo4j-password || '' }} run: | uv run pytest cognee/tests/unit/interfaces/graph/test_weighted_edges.py -v --tb=short diff --git a/Dockerfile b/Dockerfile index be29f359a..9b9a34d41 100644 --- a/Dockerfile +++ b/Dockerfile @@ -22,6 +22,7 @@ RUN apt-get update && apt-get install -y \ libpq-dev \ git \ curl \ + cmake \ clang \ build-essential \ && rm -rf /var/lib/apt/lists/* @@ -31,7 +32,7 @@ COPY README.md pyproject.toml uv.lock entrypoint.sh ./ # Install the project's dependencies using the lockfile and settings RUN --mount=type=cache,target=/root/.cache/uv \ - uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable + uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable # Copy Alembic configuration COPY alembic.ini /app/alembic.ini @@ -42,7 +43,7 @@ COPY alembic/ /app/alembic COPY ./cognee /app/cognee COPY ./distributed /app/distributed RUN --mount=type=cache,target=/root/.cache/uv \ -uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable +uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable FROM python:3.12-slim-bookworm diff --git a/README.md b/README.md index 25eb2d519..a1eebae73 100644 --- a/README.md +++ b/README.md @@ -76,6 +76,9 @@ Get started quickly with a Google Colab boolean + ) => { + if (!graphRef.current) { + console.warn("GraphVisualization: graphRef not ready yet"); + return undefined as any; + } + + return graphRef.current.zoomToFit?.(durationMs, padding, nodeFilter); + }; + useImperativeHandle(ref, () => ({ - zoomToFit: graphRef.current!.zoomToFit, - setGraphShape: setGraphShape, + zoomToFit, + setGraphShape, })); + return (
diff --git a/cognee-frontend/src/modules/chat/hooks/useChat.ts b/cognee-frontend/src/modules/chat/hooks/useChat.ts index 613f9134f..ed5bc4d79 100644 --- a/cognee-frontend/src/modules/chat/hooks/useChat.ts +++ b/cognee-frontend/src/modules/chat/hooks/useChat.ts @@ -89,15 +89,6 @@ export default function useChat(dataset: Dataset) { } -interface Node { - name: string; -} - -interface Relationship { - relationship_name: string; -} - -type InsightMessage = [Node, Relationship, Node]; // eslint-disable-next-line @typescript-eslint/no-explicit-any function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: string): string { @@ -106,14 +97,6 @@ function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: strin } switch (searchType) { - case "INSIGHTS": - return systemMessage.map((message: InsightMessage) => { - const [node1, relationship, node2] = message; - if (node1.name && node2.name) { - return `${node1.name} ${relationship.relationship_name} ${node2.name}.`; - } - return ""; - }).join("\n"); case "SUMMARIES": return systemMessage.map((message: { text: string }) => message.text).join("\n"); case "CHUNKS": diff --git a/cognee-mcp/Dockerfile b/cognee-mcp/Dockerfile index 68dce1844..6608102c8 100644 --- a/cognee-mcp/Dockerfile +++ b/cognee-mcp/Dockerfile @@ -65,6 +65,9 @@ ENV PYTHONUNBUFFERED=1 ENV MCP_LOG_LEVEL=DEBUG ENV PYTHONPATH=/app +# Add labels for API mode usage +LABEL org.opencontainers.image.description="Cognee MCP Server with API mode support" + # Use the application name from pyproject.toml for normal operation # For testing, we'll override this with a direct command ENTRYPOINT ["/app/entrypoint.sh"] diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index 8d973725b..d14bc9fa1 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -38,7 +38,8 @@ Build memory for Agents and query from any client that speaks MCP – in your t ## ✨ Features - Multiple transports – choose Streamable HTTP --transport http (recommended for web deployments), SSE --transport sse (real‑time streaming), or stdio (classic pipe, default) -- Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev +- **API Mode** – connect to an already running Cognee FastAPI server instead of using cognee directly (see [API Mode](#-api-mode) below) +- Integrated logging – all actions written to a rotating file (see get_log_file_location()) and mirrored to console in dev - Local file ingestion – feed .md, source files, Cursor rule‑sets, etc. straight from disk - Background pipelines – long‑running cognify & codify jobs spawn off‑thread; check progress with status tools - Developer rules bootstrap – one call indexes .cursorrules, .cursor/rules, AGENT.md, and friends into the developer_rules nodeset @@ -91,7 +92,7 @@ To use different LLM providers / database configurations, and for more info chec ## 🐳 Docker Usage -If you’d rather run cognee-mcp in a container, you have two options: +If you'd rather run cognee-mcp in a container, you have two options: 1. **Build locally** 1. Make sure you are in /cognee root directory and have a fresh `.env` containing only your `LLM_API_KEY` (and your chosen settings). @@ -128,6 +129,64 @@ If you’d rather run cognee-mcp in a container, you have two options: - ✅ Direct: `python src/server.py --transport http` - ❌ Direct: `-e TRANSPORT_MODE=http` (won't work) +### **Docker API Mode** + +To connect the MCP Docker container to a Cognee API server running on your host machine: + +#### **Simple Usage (Automatic localhost handling):** +```bash +# Start your Cognee API server on the host +python -m cognee.api.client + +# Run MCP container in API mode - localhost is automatically converted! +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` +**Note:** The container will automatically convert `localhost` to `host.docker.internal` on Mac/Windows/Docker Desktop. You'll see a message in the logs showing the conversion. + +#### **Explicit host.docker.internal (Mac/Windows):** +```bash +# Or explicitly use host.docker.internal +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://host.docker.internal:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` + +#### **On Linux (use host network or container IP):** +```bash +# Option 1: Use host network (simplest) +docker run \ + --network host \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=your_auth_token \ + --rm -it cognee/cognee-mcp:main + +# Option 2: Use host IP address +# First, get your host IP: ip addr show docker0 +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://172.17.0.1:8000 \ + -e API_TOKEN=your_auth_token \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main +``` + +**Environment variables for API mode:** +- `API_URL`: URL of the running Cognee API server +- `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) + +**Note:** When running in API mode: +- Database migrations are automatically skipped (API server handles its own DB) +- Some features are limited (see [API Mode Limitations](#-api-mode)) + ## 🔗 MCP Client Configuration @@ -255,6 +314,76 @@ You can configure both transports simultaneously for testing: **Note:** Only enable the server you're actually running to avoid connection errors. +## 🌐 API Mode + +The MCP server can operate in two modes: + +### **Direct Mode** (Default) +The MCP server directly imports and uses the cognee library. This is the default mode with full feature support. + +### **API Mode** +The MCP server connects to an already running Cognee FastAPI server via HTTP requests. This is useful when: +- You have a centralized Cognee API server running +- You want to separate the MCP server from the knowledge graph backend +- You need multiple MCP servers to share the same knowledge graph + +**Starting the MCP server in API mode:** +```bash +# Start your Cognee FastAPI server first (default port 8000) +cd /path/to/cognee +python -m cognee.api.client + +# Then start the MCP server in API mode +cd cognee-mcp +python src/server.py --api-url http://localhost:8000 --api-token YOUR_AUTH_TOKEN +``` + +**API Mode with different transports:** +```bash +# With SSE transport +python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN + +# With HTTP transport +python src/server.py --transport http --api-url http://localhost:8000 --api-token YOUR_TOKEN +``` + +**API Mode with Docker:** +```bash +# On Mac/Windows (use host.docker.internal to access host) +docker run \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://host.docker.internal:8000 \ + -e API_TOKEN=YOUR_TOKEN \ + -p 8001:8000 \ + --rm -it cognee/cognee-mcp:main + +# On Linux (use host network) +docker run \ + --network host \ + -e TRANSPORT_MODE=sse \ + -e API_URL=http://localhost:8000 \ + -e API_TOKEN=YOUR_TOKEN \ + --rm -it cognee/cognee-mcp:main +``` + +**Command-line arguments for API mode:** +- `--api-url`: Base URL of the running Cognee FastAPI server (e.g., `http://localhost:8000`) +- `--api-token`: Authentication token for the API (optional, required if API has authentication enabled) + +**Docker environment variables for API mode:** +- `API_URL`: Base URL of the running Cognee FastAPI server +- `API_TOKEN`: Authentication token (optional, required if API has authentication enabled) + +**API Mode limitations:** +Some features are only available in direct mode: +- `codify` (code graph pipeline) +- `cognify_status` / `codify_status` (pipeline status tracking) +- `prune` (data reset) +- `get_developer_rules` (developer rules retrieval) +- `list_data` with specific dataset_id (detailed data listing) + +Basic operations like `cognify`, `search`, `delete`, and `list_data` (all datasets) work in both modes. + ## 💻 Basic Usage The MCP server exposes its functionality through tools. Call them from any MCP client (Cursor, Claude Desktop, Cline, Roo and more). @@ -266,7 +395,7 @@ The MCP server exposes its functionality through tools. Call them from any MCP c - **codify**: Analyse a code repository, build a code graph, stores it in memory -- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, INSIGHTS +- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS - **list_data**: List all datasets and their data items with IDs for deletion operations diff --git a/cognee-mcp/entrypoint.sh b/cognee-mcp/entrypoint.sh index e3ff849e0..2f122bbfd 100644 --- a/cognee-mcp/entrypoint.sh +++ b/cognee-mcp/entrypoint.sh @@ -14,61 +14,94 @@ HTTP_PORT=${HTTP_PORT:-8000} echo "Debug port: $DEBUG_PORT" echo "HTTP port: $HTTP_PORT" -# Run Alembic migrations with proper error handling. -# Note on UserAlreadyExists error handling: -# During database migrations, we attempt to create a default user. If this user -# already exists (e.g., from a previous deployment or migration), it's not a -# critical error and shouldn't prevent the application from starting. This is -# different from other migration errors which could indicate database schema -# inconsistencies and should cause the startup to fail. This check allows for -# smooth redeployments and container restarts while maintaining data integrity. -echo "Running database migrations..." +# Check if API mode is enabled +if [ -n "$API_URL" ]; then + echo "API mode enabled: $API_URL" + echo "Skipping database migrations (API server handles its own database)" +else + echo "Direct mode: Using local cognee instance" + # Run Alembic migrations with proper error handling. + # Note on UserAlreadyExists error handling: + # During database migrations, we attempt to create a default user. If this user + # already exists (e.g., from a previous deployment or migration), it's not a + # critical error and shouldn't prevent the application from starting. This is + # different from other migration errors which could indicate database schema + # inconsistencies and should cause the startup to fail. This check allows for + # smooth redeployments and container restarts while maintaining data integrity. + echo "Running database migrations..." -MIGRATION_OUTPUT=$(alembic upgrade head) -MIGRATION_EXIT_CODE=$? + MIGRATION_OUTPUT=$(alembic upgrade head) + MIGRATION_EXIT_CODE=$? -if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then - if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then - echo "Warning: Default user already exists, continuing startup..." - else - echo "Migration failed with unexpected error." - exit 1 + if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then + if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then + echo "Warning: Default user already exists, continuing startup..." + else + echo "Migration failed with unexpected error." + exit 1 + fi fi -fi -echo "Database migrations done." + echo "Database migrations done." +fi echo "Starting Cognee MCP Server with transport mode: $TRANSPORT_MODE" # Add startup delay to ensure DB is ready sleep 2 +# Build API arguments if API_URL is set +API_ARGS="" +if [ -n "$API_URL" ]; then + # Handle localhost in API_URL - convert to host-accessible address + if echo "$API_URL" | grep -q "localhost" || echo "$API_URL" | grep -q "127.0.0.1"; then + echo "⚠️ Warning: API_URL contains localhost/127.0.0.1" + echo " Original: $API_URL" + + # Try to use host.docker.internal (works on Mac/Windows and recent Linux with Docker Desktop) + FIXED_API_URL=$(echo "$API_URL" | sed 's/localhost/host.docker.internal/g' | sed 's/127\.0\.0\.1/host.docker.internal/g') + + echo " Converted to: $FIXED_API_URL" + echo " This will work on Mac/Windows/Docker Desktop." + echo " On Linux without Docker Desktop, you may need to:" + echo " - Use --network host, OR" + echo " - Set API_URL=http://172.17.0.1:8000 (Docker bridge IP)" + + API_URL="$FIXED_API_URL" + fi + + API_ARGS="--api-url $API_URL" + if [ -n "$API_TOKEN" ]; then + API_ARGS="$API_ARGS --api-token $API_TOKEN" + fi +fi + # Modified startup with transport mode selection and error handling if [ "$ENVIRONMENT" = "dev" ] || [ "$ENVIRONMENT" = "local" ]; then if [ "$DEBUG" = "true" ]; then echo "Waiting for the debugger to attach..." if [ "$TRANSPORT_MODE" = "sse" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport stdio --no-migration + exec python -m debugpy --wait-for-client --listen 0.0.0.0:$DEBUG_PORT -m cognee-mcp --transport stdio --no-migration $API_ARGS fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec cognee-mcp --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration $API_ARGS fi fi else if [ "$TRANSPORT_MODE" = "sse" ]; then - exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport sse --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS elif [ "$TRANSPORT_MODE" = "http" ]; then - exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration + exec cognee-mcp --transport http --host 0.0.0.0 --port $HTTP_PORT --no-migration $API_ARGS else - exec cognee-mcp --transport stdio --no-migration + exec cognee-mcp --transport stdio --no-migration $API_ARGS fi fi diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index e1a3a092c..c5a21a1a7 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -8,10 +8,12 @@ requires-python = ">=3.10" dependencies = [ # For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes. #"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee", + # TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4 "cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.3.4", "fastmcp>=2.10.0,<3.0.0", "mcp>=1.12.0,<2.0.0", "uv>=0.6.3,<1.0.0", + "httpx>=0.27.0,<1.0.0", ] authors = [ @@ -36,4 +38,5 @@ dev = [ allow-direct-references = true [project.scripts] -cognee-mcp = "src:main" +cognee = "src:main" +cognee-mcp = "src:main_mcp" diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py index 1939efe9f..f1c3706c6 100644 --- a/cognee-mcp/src/__init__.py +++ b/cognee-mcp/src/__init__.py @@ -1,8 +1,60 @@ -from .server import main as server_main +try: + from .server import main as server_main +except ImportError: + from server import main as server_main +import warnings +import sys def main(): - """Main entry point for the package.""" + """Deprecated main entry point for the package.""" + import asyncio + + deprecation_notice = """ +DEPRECATION NOTICE +The CLI entry-point used to start the Cognee MCP service has been renamed from +"cognee" to "cognee-mcp". Calling the old entry-point will stop working in a +future release. + +WHAT YOU NEED TO DO: +Locate every place where you launch the MCP process and replace the final +argument cognee → cognee-mcp. + +For the example mcpServers block from Cursor shown below the change is: +{ + "mcpServers": { + "Cognee": { + "command": "uv", + "args": [ + "--directory", + "/path/to/cognee-mcp", + "run", + "cognee" // <-- CHANGE THIS to "cognee-mcp" + ] + } + } +} + +Continuing to use the old "cognee" entry-point will result in failures once it +is removed, so please update your configuration and any shell scripts as soon +as possible. +""" + + warnings.warn( + "The 'cognee' command for cognee-mcp is deprecated and will be removed in a future version. " + "Please use 'cognee-mcp' instead to avoid conflicts with the main cognee library.", + DeprecationWarning, + stacklevel=2, + ) + + print("⚠️ DEPRECATION WARNING", file=sys.stderr) + print(deprecation_notice, file=sys.stderr) + + asyncio.run(server_main()) + + +def main_mcp(): + """Clean main entry point for cognee-mcp command.""" import asyncio asyncio.run(server_main()) diff --git a/cognee-mcp/src/codingagents/coding_rule_associations.py b/cognee-mcp/src/codingagents/coding_rule_associations.py index a34c9233b..c860cb9f1 100644 --- a/cognee-mcp/src/codingagents/coding_rule_associations.py +++ b/cognee-mcp/src/codingagents/coding_rule_associations.py @@ -117,5 +117,4 @@ async def add_rule_associations(data: str, rules_nodeset_name: str): if len(edges_to_save) > 0: await graph_engine.add_edges(edges_to_save) - - await index_graph_edges() + await index_graph_edges(edges_to_save) diff --git a/cognee-mcp/src/cognee_client.py b/cognee-mcp/src/cognee_client.py new file mode 100644 index 000000000..a2fd3345f --- /dev/null +++ b/cognee-mcp/src/cognee_client.py @@ -0,0 +1,338 @@ +""" +Cognee Client abstraction that supports both direct function calls and HTTP API calls. + +This module provides a unified interface for interacting with Cognee, supporting: +- Direct mode: Directly imports and calls cognee functions (default behavior) +- API mode: Makes HTTP requests to a running Cognee FastAPI server +""" + +import sys +from typing import Optional, Any, List, Dict +from uuid import UUID +from contextlib import redirect_stdout +import httpx +from cognee.shared.logging_utils import get_logger +import json + +logger = get_logger() + + +class CogneeClient: + """ + Unified client for interacting with Cognee via direct calls or HTTP API. + + Parameters + ---------- + api_url : str, optional + Base URL of the Cognee API server (e.g., "http://localhost:8000"). + If None, uses direct cognee function calls. + api_token : str, optional + Authentication token for the API (optional, required if API has authentication enabled). + """ + + def __init__(self, api_url: Optional[str] = None, api_token: Optional[str] = None): + self.api_url = api_url.rstrip("/") if api_url else None + self.api_token = api_token + self.use_api = bool(api_url) + + if self.use_api: + logger.info(f"Cognee client initialized in API mode: {self.api_url}") + self.client = httpx.AsyncClient(timeout=300.0) # 5 minute timeout for long operations + else: + logger.info("Cognee client initialized in direct mode") + # Import cognee only if we're using direct mode + import cognee as _cognee + + self.cognee = _cognee + + def _get_headers(self) -> Dict[str, str]: + """Get headers for API requests.""" + headers = {"Content-Type": "application/json"} + if self.api_token: + headers["Authorization"] = f"Bearer {self.api_token}" + return headers + + async def add( + self, data: Any, dataset_name: str = "main_dataset", node_set: Optional[List[str]] = None + ) -> Dict[str, Any]: + """ + Add data to Cognee for processing. + + Parameters + ---------- + data : Any + Data to add (text, file path, etc.) + dataset_name : str + Name of the dataset to add data to + node_set : List[str], optional + List of node identifiers for graph organization + + Returns + ------- + Dict[str, Any] + Result of the add operation + """ + if self.use_api: + endpoint = f"{self.api_url}/api/v1/add" + + files = {"data": ("data.txt", str(data), "text/plain")} + form_data = { + "datasetName": dataset_name, + } + if node_set is not None: + form_data["node_set"] = json.dumps(node_set) + + response = await self.client.post( + endpoint, + files=files, + data=form_data, + headers={"Authorization": f"Bearer {self.api_token}"} if self.api_token else {}, + ) + response.raise_for_status() + return response.json() + else: + with redirect_stdout(sys.stderr): + await self.cognee.add(data, dataset_name=dataset_name, node_set=node_set) + return {"status": "success", "message": "Data added successfully"} + + async def cognify( + self, + datasets: Optional[List[str]] = None, + custom_prompt: Optional[str] = None, + graph_model: Any = None, + ) -> Dict[str, Any]: + """ + Transform data into a knowledge graph. + + Parameters + ---------- + datasets : List[str], optional + List of dataset names to process + custom_prompt : str, optional + Custom prompt for entity extraction + graph_model : Any, optional + Custom graph model (only used in direct mode) + + Returns + ------- + Dict[str, Any] + Result of the cognify operation + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/cognify" + payload = { + "datasets": datasets or ["main_dataset"], + "run_in_background": False, + } + if custom_prompt: + payload["custom_prompt"] = custom_prompt + + response = await self.client.post(endpoint, json=payload, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + kwargs = {} + if datasets: + kwargs["datasets"] = datasets + if custom_prompt: + kwargs["custom_prompt"] = custom_prompt + if graph_model: + kwargs["graph_model"] = graph_model + + await self.cognee.cognify(**kwargs) + return {"status": "success", "message": "Cognify completed successfully"} + + async def search( + self, + query_text: str, + query_type: str, + datasets: Optional[List[str]] = None, + system_prompt: Optional[str] = None, + top_k: int = 10, + ) -> Any: + """ + Search the knowledge graph. + + Parameters + ---------- + query_text : str + The search query + query_type : str + Type of search (e.g., "GRAPH_COMPLETION", "INSIGHTS", etc.) + datasets : List[str], optional + List of datasets to search + system_prompt : str, optional + System prompt for completion searches + top_k : int + Maximum number of results + + Returns + ------- + Any + Search results + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/search" + payload = {"query": query_text, "search_type": query_type.upper(), "top_k": top_k} + if datasets: + payload["datasets"] = datasets + if system_prompt: + payload["system_prompt"] = system_prompt + + response = await self.client.post(endpoint, json=payload, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.search.types import SearchType + + with redirect_stdout(sys.stderr): + results = await self.cognee.search( + query_type=SearchType[query_type.upper()], query_text=query_text + ) + return results + + async def delete(self, data_id: UUID, dataset_id: UUID, mode: str = "soft") -> Dict[str, Any]: + """ + Delete data from a dataset. + + Parameters + ---------- + data_id : UUID + ID of the data to delete + dataset_id : UUID + ID of the dataset containing the data + mode : str + Deletion mode ("soft" or "hard") + + Returns + ------- + Dict[str, Any] + Result of the deletion + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/delete" + params = {"data_id": str(data_id), "dataset_id": str(dataset_id), "mode": mode} + + response = await self.client.delete( + endpoint, params=params, headers=self._get_headers() + ) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.users.methods import get_default_user + + with redirect_stdout(sys.stderr): + user = await get_default_user() + result = await self.cognee.delete( + data_id=data_id, dataset_id=dataset_id, mode=mode, user=user + ) + return result + + async def prune_data(self) -> Dict[str, Any]: + """ + Prune all data from the knowledge graph. + + Returns + ------- + Dict[str, Any] + Result of the prune operation + """ + if self.use_api: + # Note: The API doesn't expose a prune endpoint, so we'll need to handle this + # For now, raise an error + raise NotImplementedError("Prune operation is not available via API") + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + await self.cognee.prune.prune_data() + return {"status": "success", "message": "Data pruned successfully"} + + async def prune_system(self, metadata: bool = True) -> Dict[str, Any]: + """ + Prune system data from the knowledge graph. + + Parameters + ---------- + metadata : bool + Whether to prune metadata + + Returns + ------- + Dict[str, Any] + Result of the prune operation + """ + if self.use_api: + # Note: The API doesn't expose a prune endpoint + raise NotImplementedError("Prune system operation is not available via API") + else: + # Direct mode: Call cognee directly + with redirect_stdout(sys.stderr): + await self.cognee.prune.prune_system(metadata=metadata) + return {"status": "success", "message": "System pruned successfully"} + + async def get_pipeline_status(self, dataset_ids: List[UUID], pipeline_name: str) -> str: + """ + Get the status of a pipeline run. + + Parameters + ---------- + dataset_ids : List[UUID] + List of dataset IDs + pipeline_name : str + Name of the pipeline + + Returns + ------- + str + Status information + """ + if self.use_api: + # Note: This would need a custom endpoint on the API side + raise NotImplementedError("Pipeline status is not available via API") + else: + # Direct mode: Call cognee directly + from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status + + with redirect_stdout(sys.stderr): + status = await get_pipeline_status(dataset_ids, pipeline_name) + return str(status) + + async def list_datasets(self) -> List[Dict[str, Any]]: + """ + List all datasets. + + Returns + ------- + List[Dict[str, Any]] + List of datasets + """ + if self.use_api: + # API mode: Make HTTP request + endpoint = f"{self.api_url}/api/v1/datasets" + response = await self.client.get(endpoint, headers=self._get_headers()) + response.raise_for_status() + return response.json() + else: + # Direct mode: Call cognee directly + from cognee.modules.users.methods import get_default_user + from cognee.modules.data.methods import get_datasets + + with redirect_stdout(sys.stderr): + user = await get_default_user() + datasets = await get_datasets(user.id) + return [ + {"id": str(d.id), "name": d.name, "created_at": str(d.created_at)} + for d in datasets + ] + + async def close(self): + """Close the HTTP client if in API mode.""" + if self.use_api and hasattr(self, "client"): + await self.client.aclose() diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index cc6eac09e..ce6dad88a 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -2,28 +2,27 @@ import json import os import sys import argparse -import cognee import asyncio import subprocess from pathlib import Path +from typing import Optional from cognee.shared.logging_utils import get_logger, setup_logging, get_log_file_location import importlib.util from contextlib import redirect_stdout import mcp.types as types from mcp.server import FastMCP -from cognee.modules.pipelines.operations.get_pipeline_status import get_pipeline_status -from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id -from cognee.modules.users.methods import get_default_user -from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline -from cognee.modules.search.types import SearchType -from cognee.shared.data_models import KnowledgeGraph from cognee.modules.storage.utils import JSONEncoder from starlette.responses import JSONResponse from starlette.middleware import Middleware from starlette.middleware.cors import CORSMiddleware import uvicorn +try: + from .cognee_client import CogneeClient +except ImportError: + from cognee_client import CogneeClient + try: from cognee.tasks.codingagents.coding_rule_associations import ( @@ -41,6 +40,8 @@ mcp = FastMCP("Cognee") logger = get_logger() +cognee_client: Optional[CogneeClient] = None + async def run_sse_with_cors(): """Custom SSE transport with CORS middleware.""" @@ -141,11 +142,20 @@ async def cognee_add_developer_rules( with redirect_stdout(sys.stderr): logger.info(f"Starting cognify for: {file_path}") try: - await cognee.add(file_path, node_set=["developer_rules"]) - model = KnowledgeGraph + await cognee_client.add(file_path, node_set=["developer_rules"]) + + model = None if graph_model_file and graph_model_name: - model = load_class(graph_model_file, graph_model_name) - await cognee.cognify(graph_model=model) + if cognee_client.use_api: + logger.warning( + "Custom graph models are not supported in API mode, ignoring." + ) + else: + from cognee.shared.data_models import KnowledgeGraph + + model = load_class(graph_model_file, graph_model_name) + + await cognee_client.cognify(graph_model=model) logger.info(f"Cognify finished for: {file_path}") except Exception as e: logger.error(f"Cognify failed for {file_path}: {str(e)}") @@ -255,7 +265,7 @@ async def cognify( # 2. Get entity relationships and connections relationships = await cognee.search( "connections between concepts", - query_type=SearchType.INSIGHTS + query_type=SearchType.GRAPH_COMPLETION ) # 3. Find relevant document chunks @@ -293,15 +303,20 @@ async def cognify( # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): logger.info("Cognify process starting.") - if graph_model_file and graph_model_name: - graph_model = load_class(graph_model_file, graph_model_name) - else: - graph_model = KnowledgeGraph - await cognee.add(data) + graph_model = None + if graph_model_file and graph_model_name: + if cognee_client.use_api: + logger.warning("Custom graph models are not supported in API mode, ignoring.") + else: + from cognee.shared.data_models import KnowledgeGraph + + graph_model = load_class(graph_model_file, graph_model_name) + + await cognee_client.add(data) try: - await cognee.cognify(graph_model=graph_model, custom_prompt=custom_prompt) + await cognee_client.cognify(custom_prompt=custom_prompt, graph_model=graph_model) logger.info("Cognify process finished.") except Exception as e: logger.error("Cognify process failed.") @@ -354,16 +369,19 @@ async def save_interaction(data: str) -> list: with redirect_stdout(sys.stderr): logger.info("Save interaction process starting.") - await cognee.add(data, node_set=["user_agent_interaction"]) + await cognee_client.add(data, node_set=["user_agent_interaction"]) try: - await cognee.cognify() + await cognee_client.cognify() logger.info("Save interaction process finished.") - logger.info("Generating associated rules from interaction data.") - await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules") - - logger.info("Associated rules generated from interaction data.") + # Rule associations only work in direct mode + if not cognee_client.use_api: + logger.info("Generating associated rules from interaction data.") + await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules") + logger.info("Associated rules generated from interaction data.") + else: + logger.warning("Rule associations are not available in API mode, skipping.") except Exception as e: logger.error("Save interaction process failed.") @@ -420,11 +438,18 @@ async def codify(repo_path: str) -> list: - All stdout is redirected to stderr to maintain MCP communication integrity """ + if cognee_client.use_api: + error_msg = "❌ Codify operation is not available in API mode. Please use direct mode for code graph pipeline." + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + async def codify_task(repo_path: str): # NOTE: MCP uses stdout to communicate, we must redirect all output # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): logger.info("Codify process starting.") + from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline + results = [] async for result in run_code_graph_pipeline(repo_path, False): results.append(result) @@ -478,11 +503,6 @@ async def search(search_query: str, search_type: str) -> list: Best for: Direct document retrieval, specific fact-finding. Returns: LLM responses based on relevant text chunks. - **INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - Returns: Formatted relationship data and entity connections. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. @@ -524,7 +544,6 @@ async def search(search_query: str, search_type: str) -> list: - "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data - "CODE": Returns code-related knowledge in JSON format - "CHUNKS": Returns raw text chunks from the knowledge graph - - "INSIGHTS": Returns relationships between nodes in readable format - "SUMMARIES": Returns pre-generated hierarchical summaries - "CYPHER": Direct graph database queries - "FEELING_LUCKY": Automatically selects best search type @@ -537,7 +556,6 @@ async def search(search_query: str, search_type: str) -> list: A list containing a single TextContent object with the search results. The format of the result depends on the search_type: - **GRAPH_COMPLETION/RAG_COMPLETION**: Conversational AI response strings - - **INSIGHTS**: Formatted relationship descriptions and entity connections - **CHUNKS**: Relevant text passages with source metadata - **SUMMARIES**: Hierarchical summaries from general to specific - **CODE**: Structured code information with context @@ -547,7 +565,6 @@ async def search(search_query: str, search_type: str) -> list: Performance & Optimization: - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal) - - **INSIGHTS**: Fast, returns structured relationships without LLM processing - **CHUNKS**: Fastest, pure vector similarity search without LLM - **SUMMARIES**: Fast, returns pre-computed summaries - **CODE**: Medium speed, specialized for code understanding @@ -574,23 +591,40 @@ async def search(search_query: str, search_type: str) -> list: # NOTE: MCP uses stdout to communicate, we must redirect all output # going to stdout ( like the print function ) to stderr. with redirect_stdout(sys.stderr): - search_results = await cognee.search( - query_type=SearchType[search_type.upper()], query_text=search_query + search_results = await cognee_client.search( + query_text=search_query, query_type=search_type ) - if search_type.upper() == "CODE": - return json.dumps(search_results, cls=JSONEncoder) - elif ( - search_type.upper() == "GRAPH_COMPLETION" or search_type.upper() == "RAG_COMPLETION" - ): - return str(search_results[0]) - elif search_type.upper() == "CHUNKS": - return str(search_results) - elif search_type.upper() == "INSIGHTS": - results = retrieved_edges_to_string(search_results) - return results + # Handle different result formats based on API vs direct mode + if cognee_client.use_api: + # API mode returns JSON-serialized results + if isinstance(search_results, str): + return search_results + elif isinstance(search_results, list): + if ( + search_type.upper() in ["GRAPH_COMPLETION", "RAG_COMPLETION"] + and len(search_results) > 0 + ): + return str(search_results[0]) + return str(search_results) + else: + return json.dumps(search_results, cls=JSONEncoder) else: - return str(search_results) + # Direct mode processing + if search_type.upper() == "CODE": + return json.dumps(search_results, cls=JSONEncoder) + elif ( + search_type.upper() == "GRAPH_COMPLETION" + or search_type.upper() == "RAG_COMPLETION" + ): + return str(search_results[0]) + elif search_type.upper() == "CHUNKS": + return str(search_results) + elif search_type.upper() == "INSIGHTS": + results = retrieved_edges_to_string(search_results) + return results + else: + return str(search_results) search_results = await search_task(search_query, search_type) return [types.TextContent(type="text", text=search_results)] @@ -623,6 +657,10 @@ async def get_developer_rules() -> list: async def fetch_rules_from_cognee() -> str: """Collect all developer rules from Cognee""" with redirect_stdout(sys.stderr): + if cognee_client.use_api: + logger.warning("Developer rules retrieval is not available in API mode") + return "Developer rules retrieval is not available in API mode" + developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules") return developer_rules @@ -662,16 +700,24 @@ async def list_data(dataset_id: str = None) -> list: with redirect_stdout(sys.stderr): try: - user = await get_default_user() output_lines = [] if dataset_id: - # List data for specific dataset + # Detailed data listing for specific dataset is only available in direct mode + if cognee_client.use_api: + return [ + types.TextContent( + type="text", + text="❌ Detailed data listing for specific datasets is not available in API mode.\nPlease use the API directly or use direct mode.", + ) + ] + + from cognee.modules.users.methods import get_default_user + from cognee.modules.data.methods import get_dataset, get_dataset_data + logger.info(f"Listing data for dataset: {dataset_id}") dataset_uuid = UUID(dataset_id) - - # Get the dataset information - from cognee.modules.data.methods import get_dataset, get_dataset_data + user = await get_default_user() dataset = await get_dataset(user.id, dataset_uuid) @@ -700,11 +746,9 @@ async def list_data(dataset_id: str = None) -> list: output_lines.append(" (No data items in this dataset)") else: - # List all datasets + # List all datasets - works in both modes logger.info("Listing all datasets") - from cognee.modules.data.methods import get_datasets - - datasets = await get_datasets(user.id) + datasets = await cognee_client.list_datasets() if not datasets: return [ @@ -719,20 +763,21 @@ async def list_data(dataset_id: str = None) -> list: output_lines.append("") for i, dataset in enumerate(datasets, 1): - # Get data count for each dataset - from cognee.modules.data.methods import get_dataset_data - - data_items = await get_dataset_data(dataset.id) - - output_lines.append(f"{i}. 📁 {dataset.name}") - output_lines.append(f" Dataset ID: {dataset.id}") - output_lines.append(f" Created: {dataset.created_at}") - output_lines.append(f" Data items: {len(data_items)}") + # In API mode, dataset is a dict; in direct mode, it's formatted as dict + if isinstance(dataset, dict): + output_lines.append(f"{i}. 📁 {dataset.get('name', 'Unnamed')}") + output_lines.append(f" Dataset ID: {dataset.get('id')}") + output_lines.append(f" Created: {dataset.get('created_at', 'N/A')}") + else: + output_lines.append(f"{i}. 📁 {dataset.name}") + output_lines.append(f" Dataset ID: {dataset.id}") + output_lines.append(f" Created: {dataset.created_at}") output_lines.append("") - output_lines.append("💡 To see data items in a specific dataset, use:") - output_lines.append(' list_data(dataset_id="your-dataset-id-here")') - output_lines.append("") + if not cognee_client.use_api: + output_lines.append("💡 To see data items in a specific dataset, use:") + output_lines.append(' list_data(dataset_id="your-dataset-id-here")') + output_lines.append("") output_lines.append("🗑️ To delete specific data, use:") output_lines.append(' delete(data_id="data-id", dataset_id="dataset-id")') @@ -801,12 +846,9 @@ async def delete(data_id: str, dataset_id: str, mode: str = "soft") -> list: data_uuid = UUID(data_id) dataset_uuid = UUID(dataset_id) - # Get default user for the operation - user = await get_default_user() - - # Call the cognee delete function - result = await cognee.delete( - data_id=data_uuid, dataset_id=dataset_uuid, mode=mode, user=user + # Call the cognee delete function via client + result = await cognee_client.delete( + data_id=data_uuid, dataset_id=dataset_uuid, mode=mode ) logger.info(f"Delete operation completed successfully: {result}") @@ -853,11 +895,21 @@ async def prune(): ----- - This operation cannot be undone. All memory data will be permanently deleted. - The function prunes both data content (using prune_data) and system metadata (using prune_system) + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - return [types.TextContent(type="text", text="Pruned")] + try: + await cognee_client.prune_data() + await cognee_client.prune_system(metadata=True) + return [types.TextContent(type="text", text="Pruned")] + except NotImplementedError: + error_msg = "❌ Prune operation is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Prune operation failed: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] @mcp.tool() @@ -880,13 +932,26 @@ async def cognify_status(): - The function retrieves pipeline status specifically for the "cognify_pipeline" on the "main_dataset" - Status information includes job progress, execution time, and completion status - The status is returned in string format for easy reading + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - user = await get_default_user() - status = await get_pipeline_status( - [await get_unique_dataset_id("main_dataset", user)], "cognify_pipeline" - ) - return [types.TextContent(type="text", text=str(status))] + try: + from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id + from cognee.modules.users.methods import get_default_user + + user = await get_default_user() + status = await cognee_client.get_pipeline_status( + [await get_unique_dataset_id("main_dataset", user)], "cognify_pipeline" + ) + return [types.TextContent(type="text", text=str(status))] + except NotImplementedError: + error_msg = "❌ Pipeline status is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Failed to get cognify status: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] @mcp.tool() @@ -909,13 +974,26 @@ async def codify_status(): - The function retrieves pipeline status specifically for the "cognify_code_pipeline" on the "codebase" dataset - Status information includes job progress, execution time, and completion status - The status is returned in string format for easy reading + - This operation is not available in API mode """ with redirect_stdout(sys.stderr): - user = await get_default_user() - status = await get_pipeline_status( - [await get_unique_dataset_id("codebase", user)], "cognify_code_pipeline" - ) - return [types.TextContent(type="text", text=str(status))] + try: + from cognee.modules.data.methods.get_unique_dataset_id import get_unique_dataset_id + from cognee.modules.users.methods import get_default_user + + user = await get_default_user() + status = await cognee_client.get_pipeline_status( + [await get_unique_dataset_id("codebase", user)], "cognify_code_pipeline" + ) + return [types.TextContent(type="text", text=str(status))] + except NotImplementedError: + error_msg = "❌ Pipeline status is not available in API mode" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] + except Exception as e: + error_msg = f"❌ Failed to get codify status: {str(e)}" + logger.error(error_msg) + return [types.TextContent(type="text", text=error_msg)] def node_to_string(node): @@ -949,6 +1027,8 @@ def load_class(model_file, model_name): async def main(): + global cognee_client + parser = argparse.ArgumentParser() parser.add_argument( @@ -992,12 +1072,30 @@ async def main(): help="Argument stops database migration from being attempted", ) + # Cognee API connection options + parser.add_argument( + "--api-url", + default=None, + help="Base URL of a running Cognee FastAPI server (e.g., http://localhost:8000). " + "If provided, the MCP server will connect to the API instead of using cognee directly.", + ) + + parser.add_argument( + "--api-token", + default=None, + help="Authentication token for the API (optional, required if API has authentication enabled).", + ) + args = parser.parse_args() + # Initialize the global CogneeClient + cognee_client = CogneeClient(api_url=args.api_url, api_token=args.api_token) + mcp.settings.host = args.host mcp.settings.port = args.port - if not args.no_migration: + # Skip migrations when in API mode (the API server handles its own database) + if not args.no_migration and not args.api_url: # Run Alembic migrations from the main cognee directory where alembic.ini is located logger.info("Running database migrations...") migration_result = subprocess.run( @@ -1020,6 +1118,8 @@ async def main(): sys.exit(1) logger.info("Database migrations done.") + elif args.api_url: + logger.info("Skipping database migrations (using API mode)") logger.info(f"Starting MCP server with transport: {args.transport}") if args.transport == "stdio": diff --git a/cognee-mcp/uv.lock b/cognee-mcp/uv.lock index e31741613..ca3592bc4 100644 --- a/cognee-mcp/uv.lock +++ b/cognee-mcp/uv.lock @@ -21,7 +21,7 @@ resolution-markers = [ [[package]] name = "aiobotocore" -version = "2.24.2" +version = "2.25.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohttp" }, @@ -32,9 +32,9 @@ dependencies = [ { name = "python-dateutil" }, { name = "wrapt" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/05/93/9f5243c2fd2fc22cff92f8d8a7e98d3080171be60778d49aeabb555a463d/aiobotocore-2.24.2.tar.gz", hash = "sha256:dfb21bdb2610e8de4d22f401e91a24d50f1330a302d03c62c485757becd439a9", size = 119837, upload-time = "2025-09-05T12:13:46.963Z" } +sdist = { url = "https://files.pythonhosted.org/packages/29/89/b1ae494cfd12520c5d3b19704a14ffa19153634be47d48052e45223eee86/aiobotocore-2.25.0.tar.gz", hash = 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a/cognee/__init__.py +++ b/cognee/__init__.py @@ -19,6 +19,7 @@ from .api.v1.add import add from .api.v1.delete import delete from .api.v1.cognify import cognify from .modules.memify import memify +from .api.v1.update import update from .api.v1.config.config import config from .api.v1.datasets.datasets import datasets from .api.v1.prune import prune diff --git a/cognee/api/health.py b/cognee/api/health.py index 1b1e45efa..ac30f3bf4 100644 --- a/cognee/api/health.py +++ b/cognee/api/health.py @@ -189,12 +189,12 @@ class HealthChecker: start_time = time.time() try: from cognee.infrastructure.llm.config import get_llm_config - from cognee.infrastructure.llm import LLMGateway config = get_llm_config() - # Test actual API connection with minimal request - LLMGateway.show_prompt("test", "test.txt") + from cognee.infrastructure.llm.utils import test_llm_connection + + await test_llm_connection() response_time = int((time.time() - start_time) * 1000) return ComponentHealth( @@ -217,13 +217,9 @@ class HealthChecker: """Check embedding service health (non-critical).""" start_time = time.time() try: - from cognee.infrastructure.databases.vector.embeddings.get_embedding_engine import ( - get_embedding_engine, - ) + from cognee.infrastructure.llm.utils import test_embedding_connection - # Test actual embedding generation with minimal text - engine = get_embedding_engine() - await engine.embed_text(["test"]) + await test_embedding_connection() response_time = int((time.time() - start_time) * 1000) return ComponentHealth( @@ -245,16 +241,6 @@ class HealthChecker: """Get comprehensive health status.""" components = {} - # Critical services - critical_components = [ - "relational_db", - "vector_db", - "graph_db", - "file_storage", - "llm_provider", - "embedding_service", - ] - critical_checks = [ ("relational_db", self.check_relational_db()), ("vector_db", self.check_vector_db()), @@ -300,11 +286,11 @@ class HealthChecker: else: components[name] = result + critical_comps = [check[0] for check in critical_checks] # Determine overall status critical_unhealthy = any( - comp.status == HealthStatus.UNHEALTHY + comp.status == HealthStatus.UNHEALTHY and name in critical_comps for name, comp in components.items() - if name in critical_components ) has_degraded = any(comp.status == HealthStatus.DEGRADED for comp in components.values()) diff --git a/cognee/api/v1/add/add.py b/cognee/api/v1/add/add.py index eeb867984..65394f1ec 100644 --- a/cognee/api/v1/add/add.py +++ b/cognee/api/v1/add/add.py @@ -1,6 +1,8 @@ from uuid import UUID -from typing import Union, BinaryIO, List, Optional - +import os +from typing import Union, BinaryIO, List, Optional, Dict, Any +from pydantic import BaseModel +from urllib.parse import urlparse from cognee.modules.users.models import User from cognee.modules.pipelines import Task, run_pipeline from cognee.modules.pipelines.layers.resolve_authorized_user_dataset import ( @@ -11,6 +13,19 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import ( ) from cognee.modules.engine.operations.setup import setup from cognee.tasks.ingestion import ingest_data, resolve_data_directories +from cognee.shared.logging_utils import get_logger + +logger = get_logger() + +try: + from cognee.tasks.web_scraper.config import TavilyConfig, SoupCrawlerConfig + from cognee.context_global_variables import ( + tavily_config as tavily, + soup_crawler_config as soup_crawler, + ) +except ImportError: + logger.debug(f"Unable to import {str(ImportError)}") + pass async def add( @@ -23,12 +38,15 @@ async def add( dataset_id: Optional[UUID] = None, preferred_loaders: List[str] = None, incremental_loading: bool = True, + extraction_rules: Optional[Dict[str, Any]] = None, + tavily_config: Optional[BaseModel] = None, + soup_crawler_config: Optional[BaseModel] = None, ): """ Add data to Cognee for knowledge graph processing. This is the first step in the Cognee workflow - it ingests raw data and prepares it - for processing. The function accepts various data formats including text, files, and + for processing. The function accepts various data formats including text, files, urls and binary streams, then stores them in a specified dataset for further processing. Prerequisites: @@ -68,6 +86,7 @@ async def add( - S3 path: "s3://my-bucket/documents/file.pdf" - List of mixed types: ["text content", "/path/file.pdf", "file://doc.txt", file_handle] - Binary file object: open("file.txt", "rb") + - url: A web link url (https or http) dataset_name: Name of the dataset to store data in. Defaults to "main_dataset". Create separate datasets to organize different knowledge domains. user: User object for authentication and permissions. Uses default user if None. @@ -78,6 +97,9 @@ async def add( vector_db_config: Optional configuration for vector database (for custom setups). graph_db_config: Optional configuration for graph database (for custom setups). dataset_id: Optional specific dataset UUID to use instead of dataset_name. + extraction_rules: Optional dictionary of rules (e.g., CSS selectors, XPath) for extracting specific content from web pages using BeautifulSoup + tavily_config: Optional configuration for Tavily API, including API key and extraction settings + soup_crawler_config: Optional configuration for BeautifulSoup crawler, specifying concurrency, crawl delay, and extraction rules. Returns: PipelineRunInfo: Information about the ingestion pipeline execution including: @@ -126,6 +148,21 @@ async def add( # Add a single file await cognee.add("/home/user/documents/analysis.pdf") + + # Add a single url and bs4 extract ingestion method + extraction_rules = { + "title": "h1", + "description": "p", + "more_info": "a[href*='more-info']" + } + await cognee.add("https://example.com",extraction_rules=extraction_rules) + + # Add a single url and tavily extract ingestion method + Make sure to set TAVILY_API_KEY = YOUR_TAVILY_API_KEY as a environment variable + await cognee.add("https://example.com") + + # Add multiple urls + await cognee.add(["https://example.com","https://books.toscrape.com"]) ``` Environment Variables: @@ -133,22 +170,55 @@ async def add( - LLM_API_KEY: API key for your LLM provider (OpenAI, Anthropic, etc.) Optional: - - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama" + - LLM_PROVIDER: "openai" (default), "anthropic", "gemini", "ollama", "mistral" - LLM_MODEL: Model name (default: "gpt-5-mini") - DEFAULT_USER_EMAIL: Custom default user email - DEFAULT_USER_PASSWORD: Custom default user password - VECTOR_DB_PROVIDER: "lancedb" (default), "chromadb", "pgvector" - GRAPH_DATABASE_PROVIDER: "kuzu" (default), "neo4j" + - TAVILY_API_KEY: YOUR_TAVILY_API_KEY """ + + try: + if not soup_crawler_config and extraction_rules: + soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) + if not tavily_config and os.getenv("TAVILY_API_KEY"): + tavily_config = TavilyConfig(api_key=os.getenv("TAVILY_API_KEY")) + + soup_crawler.set(soup_crawler_config) + tavily.set(tavily_config) + + http_schemes = {"http", "https"} + + def _is_http_url(item: Union[str, BinaryIO]) -> bool: + return isinstance(item, str) and urlparse(item).scheme in http_schemes + + if _is_http_url(data): + node_set = ["web_content"] if not node_set else node_set + ["web_content"] + elif isinstance(data, list) and any(_is_http_url(item) for item in data): + node_set = ["web_content"] if not node_set else node_set + ["web_content"] + except NameError: + logger.debug(f"Unable to import {str(ImportError)}") + pass + tasks = [ Task(resolve_data_directories, include_subdirectories=True), - Task(ingest_data, dataset_name, user, node_set, dataset_id, preferred_loaders), + Task( + ingest_data, + dataset_name, + user, + node_set, + dataset_id, + preferred_loaders, + ), ] await setup() - user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user) + user, authorized_dataset = await resolve_authorized_user_dataset( + dataset_name=dataset_name, dataset_id=dataset_id, user=user + ) await reset_dataset_pipeline_run_status( authorized_dataset.id, user, pipeline_names=["add_pipeline", "cognify_pipeline"] diff --git a/cognee/api/v1/add/routers/get_add_router.py b/cognee/api/v1/add/routers/get_add_router.py index dfa7d275b..4d0063cc9 100644 --- a/cognee/api/v1/add/routers/get_add_router.py +++ b/cognee/api/v1/add/routers/get_add_router.py @@ -73,7 +73,11 @@ def get_add_router() -> APIRouter: try: add_run = await cognee_add( - data, datasetName, user=user, dataset_id=datasetId, node_set=node_set + data, + datasetName, + user=user, + dataset_id=datasetId, + node_set=node_set if node_set else None, ) if isinstance(add_run, PipelineRunErrored): diff --git a/cognee/api/v1/cognify/cognify.py b/cognee/api/v1/cognify/cognify.py index 1292d243a..c3045f00a 100644 --- a/cognee/api/v1/cognify/cognify.py +++ b/cognee/api/v1/cognify/cognify.py @@ -148,7 +148,7 @@ async def cognify( # 2. Get entity relationships and connections relationships = await cognee.search( "connections between concepts", - query_type=SearchType.INSIGHTS + query_type=SearchType.GRAPH_COMPLETION ) # 3. Find relevant document chunks diff --git a/cognee/api/v1/responses/default_tools.py b/cognee/api/v1/responses/default_tools.py index 295d132f1..f3d643df2 100644 --- a/cognee/api/v1/responses/default_tools.py +++ b/cognee/api/v1/responses/default_tools.py @@ -14,7 +14,6 @@ DEFAULT_TOOLS = [ "type": "string", "description": "Type of search to perform", "enum": [ - "INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE", diff --git a/cognee/api/v1/responses/dispatch_function.py b/cognee/api/v1/responses/dispatch_function.py index 85388b564..aea37c350 100644 --- a/cognee/api/v1/responses/dispatch_function.py +++ b/cognee/api/v1/responses/dispatch_function.py @@ -59,7 +59,7 @@ async def handle_search(arguments: Dict[str, Any], user) -> list: valid_search_types = ( search_tool["parameters"]["properties"]["search_type"]["enum"] if search_tool - else ["INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"] + else ["CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"] ) if search_type_str not in valid_search_types: diff --git a/cognee/api/v1/responses/routers/default_tools.py b/cognee/api/v1/responses/routers/default_tools.py index e43620a4b..4194e3376 100644 --- a/cognee/api/v1/responses/routers/default_tools.py +++ b/cognee/api/v1/responses/routers/default_tools.py @@ -14,7 +14,6 @@ DEFAULT_TOOLS = [ "type": "string", "description": "Type of search to perform", "enum": [ - "INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE", diff --git a/cognee/api/v1/search/search.py b/cognee/api/v1/search/search.py index 7209c6036..0a9e76e96 100644 --- a/cognee/api/v1/search/search.py +++ b/cognee/api/v1/search/search.py @@ -52,11 +52,6 @@ async def search( Best for: Direct document retrieval, specific fact-finding. Returns: LLM responses based on relevant text chunks. - **INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - Returns: Formatted relationship data and entity connections. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. @@ -124,9 +119,6 @@ async def search( **GRAPH_COMPLETION/RAG_COMPLETION**: [List of conversational AI response strings] - **INSIGHTS**: - [List of formatted relationship descriptions and entity connections] - **CHUNKS**: [List of relevant text passages with source metadata] @@ -146,7 +138,6 @@ async def search( Performance & Optimization: - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal) - - **INSIGHTS**: Fast, returns structured relationships without LLM processing - **CHUNKS**: Fastest, pure vector similarity search without LLM - **SUMMARIES**: Fast, returns pre-computed summaries - **CODE**: Medium speed, specialized for code understanding diff --git a/cognee/api/v1/settings/routers/get_settings_router.py b/cognee/api/v1/settings/routers/get_settings_router.py index c85352746..c64ce2072 100644 --- a/cognee/api/v1/settings/routers/get_settings_router.py +++ b/cognee/api/v1/settings/routers/get_settings_router.py @@ -21,7 +21,13 @@ class SettingsDTO(OutDTO): class LLMConfigInputDTO(InDTO): - provider: Union[Literal["openai"], Literal["ollama"], Literal["anthropic"], Literal["gemini"]] + provider: Union[ + Literal["openai"], + Literal["ollama"], + Literal["anthropic"], + Literal["gemini"], + Literal["mistral"], + ] model: str api_key: str diff --git a/cognee/api/v1/ui/ui.py b/cognee/api/v1/ui/ui.py index f22a1f00b..51088c3e1 100644 --- a/cognee/api/v1/ui/ui.py +++ b/cognee/api/v1/ui/ui.py @@ -502,22 +502,48 @@ def start_ui( if start_mcp: logger.info("Starting Cognee MCP server with Docker...") - cwd = os.getcwd() - env_file = os.path.join(cwd, ".env") try: + image = "cognee/cognee-mcp:feature-standalone-mcp" # TODO: change to "cognee/cognee-mcp:main" right before merging into main + subprocess.run(["docker", "pull", image], check=True) + + import uuid + + container_name = f"cognee-mcp-{uuid.uuid4().hex[:8]}" + + docker_cmd = [ + "docker", + "run", + "--name", + container_name, + "-p", + f"{mcp_port}:8000", + "--rm", + "-e", + "TRANSPORT_MODE=sse", + ] + + if start_backend: + docker_cmd.extend( + [ + "-e", + f"API_URL=http://localhost:{backend_port}", + ] + ) + logger.info( + f"Configuring MCP to connect to backend API at http://localhost:{backend_port}" + ) + logger.info("(localhost will be auto-converted to host.docker.internal)") + else: + cwd = os.getcwd() + env_file = os.path.join(cwd, ".env") + docker_cmd.extend(["--env-file", env_file]) + + docker_cmd.append( + image + ) # TODO: change to "cognee/cognee-mcp:main" right before merging into main + mcp_process = subprocess.Popen( - [ - "docker", - "run", - "-p", - f"{mcp_port}:8000", - "--rm", - "--env-file", - env_file, - "-e", - "TRANSPORT_MODE=sse", - "cognee/cognee-mcp:daulet-dev", - ], + docker_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, preexec_fn=os.setsid if hasattr(os, "setsid") else None, @@ -526,8 +552,13 @@ def start_ui( _stream_process_output(mcp_process, "stdout", "[MCP]", "\033[34m") # Blue _stream_process_output(mcp_process, "stderr", "[MCP]", "\033[34m") # Blue - pid_callback(mcp_process.pid) - logger.info(f"✓ Cognee MCP server starting on http://127.0.0.1:{mcp_port}/sse") + # Pass both PID and container name using a tuple + pid_callback((mcp_process.pid, container_name)) + + mode_info = "API mode" if start_backend else "direct mode" + logger.info( + f"✓ Cognee MCP server starting on http://127.0.0.1:{mcp_port}/sse ({mode_info})" + ) except Exception as e: logger.error(f"Failed to start MCP server with Docker: {str(e)}") # Start backend server if requested @@ -627,7 +658,6 @@ def start_ui( env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - text=True, shell=True, ) else: @@ -637,7 +667,6 @@ def start_ui( env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, - text=True, preexec_fn=os.setsid if hasattr(os, "setsid") else None, ) diff --git a/cognee/api/v1/update/routers/get_update_router.py b/cognee/api/v1/update/routers/get_update_router.py index 144bdd9e6..4101e1e31 100644 --- a/cognee/api/v1/update/routers/get_update_router.py +++ b/cognee/api/v1/update/routers/get_update_router.py @@ -75,7 +75,7 @@ def get_update_router() -> APIRouter: data=data, dataset_id=dataset_id, user=user, - node_set=node_set, + node_set=node_set if node_set else None, ) # If any cognify run errored return JSONResponse with proper error status code diff --git a/cognee/api/v1/update/update.py b/cognee/api/v1/update/update.py index b4b1f5e5a..a421b3dc0 100644 --- a/cognee/api/v1/update/update.py +++ b/cognee/api/v1/update/update.py @@ -10,9 +10,9 @@ from cognee.api.v1.cognify import cognify async def update( data_id: UUID, data: Union[BinaryIO, list[BinaryIO], str, list[str]], + dataset_id: UUID, user: User = None, node_set: Optional[List[str]] = None, - dataset_id: Optional[UUID] = None, vector_db_config: dict = None, graph_db_config: dict = None, preferred_loaders: List[str] = None, diff --git a/cognee/cli/_cognee.py b/cognee/cli/_cognee.py index b257d37de..1539d1acf 100644 --- a/cognee/cli/_cognee.py +++ b/cognee/cli/_cognee.py @@ -175,19 +175,59 @@ def main() -> int: # Handle UI flag if hasattr(args, "start_ui") and args.start_ui: spawned_pids = [] + docker_container = None def signal_handler(signum, frame): """Handle Ctrl+C and other termination signals""" - nonlocal spawned_pids - fmt.echo("\nShutting down UI server...") + nonlocal spawned_pids, docker_container + try: + fmt.echo("\nShutting down UI server...") + except (BrokenPipeError, OSError): + pass + + # First, stop Docker container if running + if docker_container: + try: + result = subprocess.run( + ["docker", "stop", docker_container], + capture_output=True, + timeout=10, + check=False, + ) + try: + if result.returncode == 0: + fmt.success(f"✓ Docker container {docker_container} stopped.") + else: + fmt.warning( + f"Could not stop container {docker_container}: {result.stderr.decode()}" + ) + except (BrokenPipeError, OSError): + pass + except subprocess.TimeoutExpired: + try: + fmt.warning( + f"Timeout stopping container {docker_container}, forcing removal..." + ) + except (BrokenPipeError, OSError): + pass + subprocess.run( + ["docker", "rm", "-f", docker_container], capture_output=True, check=False + ) + except Exception: + pass + + # Then, stop regular processes for pid in spawned_pids: try: if hasattr(os, "killpg"): # Unix-like systems: Use process groups pgid = os.getpgid(pid) os.killpg(pgid, signal.SIGTERM) - fmt.success(f"✓ Process group {pgid} (PID {pid}) terminated.") + try: + fmt.success(f"✓ Process group {pgid} (PID {pid}) terminated.") + except (BrokenPipeError, OSError): + pass else: # Windows: Use taskkill to terminate process and its children subprocess.run( @@ -195,24 +235,35 @@ def main() -> int: capture_output=True, check=False, ) - fmt.success(f"✓ Process {pid} and its children terminated.") - except (OSError, ProcessLookupError, subprocess.SubprocessError) as e: - fmt.warning(f"Could not terminate process {pid}: {e}") + try: + fmt.success(f"✓ Process {pid} and its children terminated.") + except (BrokenPipeError, OSError): + pass + except (OSError, ProcessLookupError, subprocess.SubprocessError): + pass sys.exit(0) signal.signal(signal.SIGINT, signal_handler) # Ctrl+C signal.signal(signal.SIGTERM, signal_handler) # Termination request + if hasattr(signal, "SIGHUP"): + signal.signal(signal.SIGHUP, signal_handler) try: from cognee import start_ui fmt.echo("Starting cognee UI...") - # Callback to capture PIDs of all spawned processes - def pid_callback(pid): - nonlocal spawned_pids - spawned_pids.append(pid) + # Callback to capture PIDs and Docker container of all spawned processes + def pid_callback(pid_or_tuple): + nonlocal spawned_pids, docker_container + # Handle both regular PIDs and (PID, container_name) tuples + if isinstance(pid_or_tuple, tuple): + pid, container_name = pid_or_tuple + spawned_pids.append(pid) + docker_container = container_name + else: + spawned_pids.append(pid_or_tuple) frontend_port = 3000 start_backend, backend_port = True, 8000 diff --git a/cognee/cli/commands/add_command.py b/cognee/cli/commands/add_command.py index 4260b52bc..e05998ec5 100644 --- a/cognee/cli/commands/add_command.py +++ b/cognee/cli/commands/add_command.py @@ -70,11 +70,11 @@ After adding data, use `cognee cognify` to process it into knowledge graphs. await cognee.add(data=data_to_add, dataset_name=args.dataset_name) fmt.success(f"Successfully added data to dataset '{args.dataset_name}'") except Exception as e: - raise CliCommandInnerException(f"Failed to add data: {str(e)}") + raise CliCommandInnerException(f"Failed to add data: {str(e)}") from e asyncio.run(run_add()) except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error adding data: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Failed to add data: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/cognify_command.py b/cognee/cli/commands/cognify_command.py index 894bfcd37..16eaf0454 100644 --- a/cognee/cli/commands/cognify_command.py +++ b/cognee/cli/commands/cognify_command.py @@ -107,7 +107,7 @@ After successful cognify processing, use `cognee search` to query the knowledge ) return result except Exception as e: - raise CliCommandInnerException(f"Failed to cognify: {str(e)}") + raise CliCommandInnerException(f"Failed to cognify: {str(e)}") from e result = asyncio.run(run_cognify()) @@ -124,5 +124,5 @@ After successful cognify processing, use `cognee search` to query the knowledge except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error during cognification: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error during cognification: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/config_command.py b/cognee/cli/commands/config_command.py index 25333e77a..752db6403 100644 --- a/cognee/cli/commands/config_command.py +++ b/cognee/cli/commands/config_command.py @@ -79,8 +79,10 @@ Configuration changes will affect how cognee processes and stores data. except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error managing configuration: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException( + f"Error managing configuration: {str(e)}", error_code=1 + ) from e def _handle_get(self, args: argparse.Namespace) -> None: try: @@ -122,7 +124,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.note("Configuration viewing not fully implemented yet") except Exception as e: - raise CliCommandInnerException(f"Failed to get configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to get configuration: {str(e)}") from e def _handle_set(self, args: argparse.Namespace) -> None: try: @@ -141,7 +143,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.error(f"Failed to set configuration key '{args.key}'") except Exception as e: - raise CliCommandInnerException(f"Failed to set configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to set configuration: {str(e)}") from e def _handle_unset(self, args: argparse.Namespace) -> None: try: @@ -189,7 +191,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.note("Use 'cognee config list' to see all available configuration options") except Exception as e: - raise CliCommandInnerException(f"Failed to unset configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to unset configuration: {str(e)}") from e def _handle_list(self, args: argparse.Namespace) -> None: try: @@ -209,7 +211,7 @@ Configuration changes will affect how cognee processes and stores data. fmt.echo(" cognee config reset - Reset all to defaults") except Exception as e: - raise CliCommandInnerException(f"Failed to list configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to list configuration: {str(e)}") from e def _handle_reset(self, args: argparse.Namespace) -> None: try: @@ -222,4 +224,4 @@ Configuration changes will affect how cognee processes and stores data. fmt.echo("This would reset all settings to their default values") except Exception as e: - raise CliCommandInnerException(f"Failed to reset configuration: {str(e)}") + raise CliCommandInnerException(f"Failed to reset configuration: {str(e)}") from e diff --git a/cognee/cli/commands/delete_command.py b/cognee/cli/commands/delete_command.py index 43e8b8e13..8400d3b0f 100644 --- a/cognee/cli/commands/delete_command.py +++ b/cognee/cli/commands/delete_command.py @@ -6,6 +6,7 @@ from cognee.cli.reference import SupportsCliCommand from cognee.cli import DEFAULT_DOCS_URL import cognee.cli.echo as fmt from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.modules.data.methods.get_deletion_counts import get_deletion_counts class DeleteCommand(SupportsCliCommand): @@ -41,7 +42,34 @@ Be careful with deletion operations as they are irreversible. fmt.error("Please specify what to delete: --dataset-name, --user-id, or --all") return - # Build confirmation message + # If --force is used, skip the preview and go straight to deletion + if not args.force: + # --- START PREVIEW LOGIC --- + fmt.echo("Gathering data for preview...") + try: + preview_data = asyncio.run( + get_deletion_counts( + dataset_name=args.dataset_name, + user_id=args.user_id, + all_data=args.all, + ) + ) + except CliCommandException as e: + fmt.error(f"Error occured when fetching preview data: {str(e)}") + return + + if not preview_data: + fmt.success("No data found to delete.") + return + + fmt.echo("You are about to delete:") + fmt.echo( + f"Datasets: {preview_data.datasets}\nEntries: {preview_data.entries}\nUsers: {preview_data.users}" + ) + fmt.echo("-" * 20) + # --- END PREVIEW LOGIC --- + + # Build operation message for success/failure logging if args.all: confirm_msg = "Delete ALL data from cognee?" operation = "all data" @@ -51,8 +79,9 @@ Be careful with deletion operations as they are irreversible. elif args.user_id: confirm_msg = f"Delete all data for user '{args.user_id}'?" operation = f"data for user '{args.user_id}'" + else: + operation = "data" - # Confirm deletion unless forced if not args.force: fmt.warning("This operation is irreversible!") if not fmt.confirm(confirm_msg): @@ -64,17 +93,20 @@ Be careful with deletion operations as they are irreversible. # Run the async delete function async def run_delete(): try: + # NOTE: The underlying cognee.delete() function is currently not working as expected. + # This is a separate bug that this preview feature helps to expose. if args.all: await cognee.delete(dataset_name=None, user_id=args.user_id) else: await cognee.delete(dataset_name=args.dataset_name, user_id=args.user_id) except Exception as e: - raise CliCommandInnerException(f"Failed to delete: {str(e)}") + raise CliCommandInnerException(f"Failed to delete: {str(e)}") from e asyncio.run(run_delete()) + # This success message may be inaccurate due to the underlying bug, but we leave it for now. fmt.success(f"Successfully deleted {operation}") except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error deleting data: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error deleting data: {str(e)}", error_code=1) from e diff --git a/cognee/cli/commands/search_command.py b/cognee/cli/commands/search_command.py index 3540a833c..b1715b1c6 100644 --- a/cognee/cli/commands/search_command.py +++ b/cognee/cli/commands/search_command.py @@ -31,10 +31,6 @@ Search Types & Use Cases: Traditional RAG using document chunks without graph structure. Best for: Direct document retrieval, specific fact-finding. -**INSIGHTS**: - Structured entity relationships and semantic connections. - Best for: Understanding concept relationships, knowledge mapping. - **CHUNKS**: Raw text segments that match the query semantically. Best for: Finding specific passages, citations, exact content. @@ -108,7 +104,7 @@ Search Types & Use Cases: ) return results except Exception as e: - raise CliCommandInnerException(f"Failed to search: {str(e)}") + raise CliCommandInnerException(f"Failed to search: {str(e)}") from e results = asyncio.run(run_search()) @@ -145,5 +141,5 @@ Search Types & Use Cases: except Exception as e: if isinstance(e, CliCommandInnerException): - raise CliCommandException(str(e), error_code=1) - raise CliCommandException(f"Error searching: {str(e)}", error_code=1) + raise CliCommandException(str(e), error_code=1) from e + raise CliCommandException(f"Error searching: {str(e)}", error_code=1) from e diff --git a/cognee/cli/config.py b/cognee/cli/config.py index 31e8693c7..d016608c1 100644 --- a/cognee/cli/config.py +++ b/cognee/cli/config.py @@ -19,7 +19,6 @@ COMMAND_DESCRIPTIONS = { SEARCH_TYPE_CHOICES = [ "GRAPH_COMPLETION", "RAG_COMPLETION", - "INSIGHTS", "CHUNKS", "SUMMARIES", "CODE", diff --git a/cognee/context_global_variables.py b/cognee/context_global_variables.py index f6642e3dc..500846e27 100644 --- a/cognee/context_global_variables.py +++ b/cognee/context_global_variables.py @@ -12,6 +12,8 @@ from cognee.modules.users.methods import get_user # for different async tasks, threads and processes vector_db_config = ContextVar("vector_db_config", default=None) graph_db_config = ContextVar("graph_db_config", default=None) +soup_crawler_config = ContextVar("soup_crawler_config", default=None) +tavily_config = ContextVar("tavily_config", default=None) async def set_database_global_context_variables(dataset: Union[str, UUID], user_id: UUID): diff --git a/cognee/infrastructure/databases/graph/config.py b/cognee/infrastructure/databases/graph/config.py index d96de4520..b7907313c 100644 --- a/cognee/infrastructure/databases/graph/config.py +++ b/cognee/infrastructure/databases/graph/config.py @@ -50,26 +50,26 @@ class GraphConfig(BaseSettings): # Model validator updates graph_filename and path dynamically after class creation based on current database provider # If no specific graph_filename or path are provided @pydantic.model_validator(mode="after") - def fill_derived(cls, values): - provider = values.graph_database_provider.lower() + def fill_derived(self): + provider = self.graph_database_provider.lower() base_config = get_base_config() # Set default filename if no filename is provided - if not values.graph_filename: - values.graph_filename = f"cognee_graph_{provider}" + if not self.graph_filename: + self.graph_filename = f"cognee_graph_{provider}" # Handle graph file path - if values.graph_file_path: + if self.graph_file_path: # Check if absolute path is provided - values.graph_file_path = ensure_absolute_path( - os.path.join(values.graph_file_path, values.graph_filename) + self.graph_file_path = ensure_absolute_path( + os.path.join(self.graph_file_path, self.graph_filename) ) else: # Default path databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.graph_file_path = os.path.join(databases_directory_path, values.graph_filename) + self.graph_file_path = os.path.join(databases_directory_path, self.graph_filename) - return values + return self def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index 4ec0eb483..1861aa15c 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -44,16 +44,14 @@ def create_graph_engine( Parameters: ----------- - - graph_database_provider: The type of graph database provider to use (e.g., neo4j, - falkordb, kuzu). - - graph_database_url: The URL for the graph database instance. Required for neo4j - and falkordb providers. + - graph_database_provider: The type of graph database provider to use (e.g., neo4j, falkor, kuzu). + - graph_database_url: The URL for the graph database instance. Required for neo4j and falkordb providers. - graph_database_username: The username for authentication with the graph database. Required for neo4j provider. - graph_database_password: The password for authentication with the graph database. Required for neo4j provider. - graph_database_port: The port number for the graph database connection. Required - for the falkordb provider. + for the falkordb provider - graph_file_path: The filesystem path to the graph file. Required for the kuzu provider. @@ -86,21 +84,6 @@ def create_graph_engine( graph_database_name=graph_database_name or None, ) - elif graph_database_provider == "falkordb": - if not (graph_database_url and graph_database_port): - raise EnvironmentError("Missing required FalkorDB credentials.") - - from cognee.infrastructure.databases.vector.embeddings import get_embedding_engine - from cognee.infrastructure.databases.hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter - - embedding_engine = get_embedding_engine() - - return FalkorDBAdapter( - database_url=graph_database_url, - database_port=graph_database_port, - embedding_engine=embedding_engine, - ) - elif graph_database_provider == "kuzu": if not graph_file_path: raise EnvironmentError("Missing required Kuzu database path.") @@ -179,5 +162,5 @@ def create_graph_engine( raise EnvironmentError( f"Unsupported graph database provider: {graph_database_provider}. " - f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'falkordb', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}" + f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}" ) diff --git a/cognee/infrastructure/databases/graph/kuzu/adapter.py b/cognee/infrastructure/databases/graph/kuzu/adapter.py index 95f534788..7b772097f 100644 --- a/cognee/infrastructure/databases/graph/kuzu/adapter.py +++ b/cognee/infrastructure/databases/graph/kuzu/adapter.py @@ -48,6 +48,29 @@ class KuzuAdapter(GraphDBInterface): def _initialize_connection(self) -> None: """Initialize the Kuzu database connection and schema.""" + + def _install_json_extension(): + """ + Function handles installing of the json extension for the current Kuzu version. + This has to be done with an empty graph db before connecting to an existing database otherwise + missing json extension errors will be raised. + """ + try: + with tempfile.NamedTemporaryFile(mode="w", delete=True) as temp_file: + temp_graph_file = temp_file.name + tmp_db = Database( + temp_graph_file, + buffer_pool_size=2048 * 1024 * 1024, # 2048MB buffer pool + max_db_size=4096 * 1024 * 1024, + ) + tmp_db.init_database() + connection = Connection(tmp_db) + connection.execute("INSTALL JSON;") + except Exception as e: + logger.info(f"JSON extension already installed or not needed: {e}") + + _install_json_extension() + try: if "s3://" in self.db_path: with tempfile.NamedTemporaryFile(mode="w", delete=False) as temp_file: @@ -109,11 +132,6 @@ class KuzuAdapter(GraphDBInterface): self.db.init_database() self.connection = Connection(self.db) - try: - self.connection.execute("INSTALL JSON;") - except Exception as e: - logger.info(f"JSON extension already installed or not needed: {e}") - try: self.connection.execute("LOAD EXTENSION JSON;") logger.info("Loaded JSON extension") diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py index 03b16eb33..520295ed2 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py @@ -68,6 +68,7 @@ class Neo4jAdapter(GraphDBInterface): auth=auth, max_connection_lifetime=120, notifications_min_severity="OFF", + keep_alive=True, ) async def initialize(self) -> None: @@ -205,7 +206,7 @@ class Neo4jAdapter(GraphDBInterface): { "node_id": str(node.id), "label": type(node).__name__, - "properties": self.serialize_properties(node.model_dump()), + "properties": self.serialize_properties(dict(node)), } for node in nodes ] diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py b/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py index 35d37383f..ae9e1219b 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/deadlock_retry.py @@ -8,7 +8,7 @@ from cognee.infrastructure.utils.calculate_backoff import calculate_backoff logger = get_logger("deadlock_retry") -def deadlock_retry(max_retries=5): +def deadlock_retry(max_retries=10): """ Decorator that automatically retries an asynchronous function when rate limit errors occur. diff --git a/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py b/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py index 0f71bd4e9..e3b4c0a6a 100644 --- a/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py +++ b/cognee/infrastructure/databases/graph/neptune_driver/neptune_utils.py @@ -53,7 +53,7 @@ def parse_neptune_url(url: str) -> Tuple[str, str]: return graph_id, region except Exception as e: - raise ValueError(f"Failed to parse Neptune Analytics URL '{url}': {str(e)}") + raise ValueError(f"Failed to parse Neptune Analytics URL '{url}': {str(e)}") from e def validate_graph_id(graph_id: str) -> bool: diff --git a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py deleted file mode 100644 index cb6899925..000000000 --- a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py +++ /dev/null @@ -1,1227 +0,0 @@ -import asyncio - -# from datetime import datetime -import json -from textwrap import dedent -from uuid import UUID -from webbrowser import Error -from typing import List, Dict, Any, Optional, Tuple, Type, Union - -from falkordb import FalkorDB - -from cognee.infrastructure.databases.exceptions import MissingQueryParameterError -from cognee.infrastructure.databases.graph.graph_db_interface import ( - GraphDBInterface, - record_graph_changes, - NodeData, - EdgeData, - Node, -) -from cognee.infrastructure.databases.vector.embeddings import EmbeddingEngine -from cognee.infrastructure.databases.vector.vector_db_interface import VectorDBInterface -from cognee.infrastructure.engine import DataPoint - - -class IndexSchema(DataPoint): - """ - Define a schema for indexing that includes text data and associated metadata. - - This class inherits from the DataPoint class. It contains a string attribute 'text' and - a dictionary 'metadata' that specifies the index fields for this schema. - """ - - text: str - - metadata: dict = {"index_fields": ["text"]} - - -class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): - """ - Manage and interact with a graph database using vector embeddings. - - Public methods include: - - query - - embed_data - - stringify_properties - - create_data_point_query - - create_edge_query - - create_collection - - has_collection - - create_data_points - - create_vector_index - - has_vector_index - - index_data_points - - add_node - - add_nodes - - add_edge - - add_edges - - has_edges - - retrieve - - extract_node - - extract_nodes - - get_connections - - search - - batch_search - - get_graph_data - - delete_data_points - - delete_node - - delete_nodes - - delete_graph - - prune - - get_node - - get_nodes - - get_neighbors - - get_graph_metrics - - get_document_subgraph - - get_degree_one_nodes - """ - - def __init__( - self, - database_url: str, - database_port: int, - embedding_engine=EmbeddingEngine, - ): - self.driver = FalkorDB( - host=database_url, - port=database_port, - ) - self.embedding_engine = embedding_engine - self.graph_name = "cognee_graph" - - def query(self, query: str, params: dict = {}): - """ - Execute a query against the graph database. - - Handles exceptions during the query execution by logging errors and re-raising the - exception. - - The method can be called only if a valid query string and parameters are provided. - - Parameters: - ----------- - - - query (str): The query string to be executed against the graph database. - - params (dict): A dictionary of parameters to be used in the query. (default {}) - - Returns: - -------- - - The result of the query execution, returned by the graph database. - """ - graph = self.driver.select_graph(self.graph_name) - - try: - result = graph.query(query, params) - return result - except Exception as e: - print(f"Error executing query: {e}") - raise e - - async def embed_data(self, data: list[str]) -> list[list[float]]: - """ - Embed a list of text data into vector representations using the embedding engine. - - Parameters: - ----------- - - - data (list[str]): A list of strings that should be embedded into vectors. - - Returns: - -------- - - - list[list[float]]: A list of lists, where each inner list contains float values - representing the embedded vectors. - """ - return await self.embedding_engine.embed_text(data) - - async def stringify_properties(self, properties: dict) -> str: - """ - Convert properties dictionary to a string format suitable for database queries. - - Parameters: - ----------- - - - properties (dict): A dictionary containing properties to be converted to string - format. - - Returns: - -------- - - - str: A string representation of the properties in the appropriate format. - """ - - def parse_value(value): - """ - Convert a value to its string representation based on type for database queries. - - Parameters: - ----------- - - - value: The value to parse into a string representation. - - Returns: - -------- - - Returns the string representation of the value in the appropriate format. - """ - if type(value) is UUID: - return f"'{str(value)}'" - if type(value) is int or type(value) is float: - return value - if ( - type(value) is list - and len(value) > 0 - and type(value[0]) is float - and len(value) == self.embedding_engine.get_vector_size() - ): - return f"'vecf32({value})'" - # if type(value) is datetime: - # return datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%f%z") - if type(value) is dict: - return f"'{json.dumps(value).replace(chr(39), chr(34))}'" - if type(value) is str: - # Escape single quotes and handle special characters - escaped_value = ( - str(value) - .replace("'", "\\'") - .replace('"', '\\"') - .replace("\n", "\\n") - .replace("\r", "\\r") - .replace("\t", "\\t") - ) - return f"'{escaped_value}'" - return f"'{str(value)}'" - - return ",".join([f"{key}:{parse_value(value)}" for key, value in properties.items()]) - - async def create_data_point_query(self, data_point: DataPoint, vectorized_values: dict): - """ - Compose a query to create or update a data point in the database. - - Parameters: - ----------- - - - data_point (DataPoint): An instance of DataPoint containing information about the - entity. - - vectorized_values (dict): A dictionary of vectorized values related to the data - point. - - Returns: - -------- - - A tuple containing the query string and parameters dictionary. - """ - node_label = type(data_point).__name__ - property_names = DataPoint.get_embeddable_property_names(data_point) - - properties = { - **data_point.model_dump(), - **( - { - property_names[index]: ( - vectorized_values[index] - if index < len(vectorized_values) - else getattr(data_point, property_name, None) - ) - for index, property_name in enumerate(property_names) - } - ), - } - - # Clean the properties - remove None values and handle special types - clean_properties = {} - for key, value in properties.items(): - if value is not None: - if isinstance(value, UUID): - clean_properties[key] = str(value) - elif isinstance(value, dict): - clean_properties[key] = json.dumps(value) - elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float): - # This is likely a vector - convert to string representation - clean_properties[key] = f"vecf32({value})" - else: - clean_properties[key] = value - - query = dedent( - f""" - MERGE (node:{node_label} {{id: $node_id}}) - SET node += $properties, node.updated_at = timestamp() - """ - ).strip() - - params = {"node_id": str(data_point.id), "properties": clean_properties} - - return query, params - - def sanitize_relationship_name(self, relationship_name: str) -> str: - """ - Sanitize relationship name to be valid for Cypher queries. - - Parameters: - ----------- - - relationship_name (str): The original relationship name - - Returns: - -------- - - str: A sanitized relationship name valid for Cypher - """ - # Replace hyphens, spaces, and other special characters with underscores - import re - - sanitized = re.sub(r"[^\w]", "_", relationship_name) - # Remove consecutive underscores - sanitized = re.sub(r"_+", "_", sanitized) - # Remove leading/trailing underscores - sanitized = sanitized.strip("_") - # Ensure it starts with a letter or underscore - if sanitized and not sanitized[0].isalpha() and sanitized[0] != "_": - sanitized = "_" + sanitized - return sanitized or "RELATIONSHIP" - - async def create_edge_query(self, edge: tuple[str, str, str, dict]) -> str: - """ - Generate a query to create or update an edge between two nodes in the graph. - - Parameters: - ----------- - - - edge (tuple[str, str, str, dict]): A tuple consisting of source and target node - IDs, edge type, and edge properties. - - Returns: - -------- - - - str: A string containing the query to be executed for creating the edge. - """ - # Sanitize the relationship name for Cypher compatibility - sanitized_relationship = self.sanitize_relationship_name(edge[2]) - - # Add the original relationship name to properties - edge_properties = {**edge[3], "relationship_name": edge[2]} - properties = await self.stringify_properties(edge_properties) - properties = f"{{{properties}}}" - - return dedent( - f""" - MERGE (source {{id:'{edge[0]}'}}) - MERGE (target {{id: '{edge[1]}'}}) - MERGE (source)-[edge:{sanitized_relationship} {properties}]->(target) - ON MATCH SET edge.updated_at = timestamp() - ON CREATE SET edge.updated_at = timestamp() - """ - ).strip() - - async def create_collection(self, collection_name: str): - """ - Create a collection in the graph database with the specified name. - - Parameters: - ----------- - - - collection_name (str): The name of the collection to be created. - """ - pass - - async def has_collection(self, collection_name: str) -> bool: - """ - Check if a collection with the specified name exists in the graph database. - - Parameters: - ----------- - - - collection_name (str): The name of the collection to check for existence. - - Returns: - -------- - - - bool: Returns true if the collection exists, otherwise false. - """ - collections = self.driver.list_graphs() - - return collection_name in collections - - async def create_data_points(self, data_points: list[DataPoint]): - """ - Add a list of data points to the graph database via batching. - - Can raise exceptions if there are issues during the database operations. - - Parameters: - ----------- - - - data_points (list[DataPoint]): A list of DataPoint instances to be inserted into - the database. - """ - embeddable_values = [] - vector_map = {} - - for data_point in data_points: - property_names = DataPoint.get_embeddable_property_names(data_point) - key = str(data_point.id) - vector_map[key] = {} - - for property_name in property_names: - property_value = getattr(data_point, property_name, None) - - if property_value is not None: - vector_map[key][property_name] = len(embeddable_values) - embeddable_values.append(property_value) - else: - vector_map[key][property_name] = None - - vectorized_values = await self.embed_data(embeddable_values) - - for data_point in data_points: - vectorized_data = [ - vectorized_values[vector_map[str(data_point.id)][property_name]] - if vector_map[str(data_point.id)][property_name] is not None - else None - for property_name in DataPoint.get_embeddable_property_names(data_point) - ] - - query, params = await self.create_data_point_query(data_point, vectorized_data) - self.query(query, params) - - async def create_vector_index(self, index_name: str, index_property_name: str): - """ - Create a vector index in the specified graph for a given property if it does not already - exist. - - Parameters: - ----------- - - - index_name (str): The name of the vector index to be created. - - index_property_name (str): The name of the property on which the vector index will - be created. - """ - graph = self.driver.select_graph(self.graph_name) - - if not self.has_vector_index(graph, index_name, index_property_name): - graph.create_node_vector_index( - index_name, index_property_name, dim=self.embedding_engine.get_vector_size() - ) - - def has_vector_index(self, graph, index_name: str, index_property_name: str) -> bool: - """ - Determine if a vector index exists on the specified property of the given graph. - - Parameters: - ----------- - - - graph: The graph instance to check for the vector index. - - index_name (str): The name of the index to check for existence. - - index_property_name (str): The property name associated with the index. - - Returns: - -------- - - - bool: Returns true if the vector index exists, otherwise false. - """ - try: - indices = graph.list_indices() - - return any( - [ - (index[0] == index_name and index_property_name in index[1]) - for index in indices.result_set - ] - ) - except Error as e: - print(e) - return False - - async def index_data_points( - self, index_name: str, index_property_name: str, data_points: list[DataPoint] - ): - """ - Index a list of data points in the specified graph database based on properties. - - To be implemented: does not yet have a defined behavior. - - Parameters: - ----------- - - - index_name (str): The name of the index to be created for the data points. - - index_property_name (str): The property name on which to index the data points. - - data_points (list[DataPoint]): A list of DataPoint instances to be indexed. - """ - pass - - async def add_node(self, node_id: str, properties: Dict[str, Any]) -> None: - """ - Add a single node with specified properties to the graph. - - Parameters: - ----------- - - - node_id (str): Unique identifier for the node being added. - - properties (Dict[str, Any]): A dictionary of properties associated with the node. - """ - # Clean the properties - remove None values and handle special types - clean_properties = {"id": node_id} - for key, value in properties.items(): - if value is not None: - if isinstance(value, UUID): - clean_properties[key] = str(value) - elif isinstance(value, dict): - clean_properties[key] = json.dumps(value) - elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], float): - # This is likely a vector - convert to string representation - clean_properties[key] = f"vecf32({value})" - else: - clean_properties[key] = value - - query = "MERGE (node {id: $node_id}) SET node += $properties, node.updated_at = timestamp()" - params = {"node_id": node_id, "properties": clean_properties} - - self.query(query, params) - - # Helper methods for DataPoint compatibility - async def add_data_point_node(self, node: DataPoint): - """ - Add a single data point as a node in the graph. - - Parameters: - ----------- - - - node (DataPoint): An instance of DataPoint to be added to the graph. - """ - await self.create_data_points([node]) - - async def add_data_point_nodes(self, nodes: list[DataPoint]): - """ - Add multiple data points as nodes in the graph. - - Parameters: - ----------- - - - nodes (list[DataPoint]): A list of DataPoint instances to be added to the graph. - """ - await self.create_data_points(nodes) - - @record_graph_changes - async def add_nodes(self, nodes: Union[List[Node], List[DataPoint]]) -> None: - """ - Add multiple nodes to the graph in a single operation. - - Parameters: - ----------- - - - nodes (Union[List[Node], List[DataPoint]]): A list of Node tuples or DataPoint objects to be added to the graph. - """ - for node in nodes: - if isinstance(node, tuple) and len(node) == 2: - # Node is in (node_id, properties) format - node_id, properties = node - await self.add_node(node_id, properties) - elif hasattr(node, "id") and hasattr(node, "model_dump"): - # Node is a DataPoint object - await self.add_node(str(node.id), node.model_dump()) - else: - raise ValueError( - f"Invalid node format: {node}. Expected tuple (node_id, properties) or DataPoint object." - ) - - async def add_edge( - self, - source_id: str, - target_id: str, - relationship_name: str, - properties: Optional[Dict[str, Any]] = None, - ) -> None: - """ - Create a new edge between two nodes in the graph. - - Parameters: - ----------- - - - source_id (str): The unique identifier of the source node. - - target_id (str): The unique identifier of the target node. - - relationship_name (str): The name of the relationship to be established by the - edge. - - properties (Optional[Dict[str, Any]]): Optional dictionary of properties - associated with the edge. (default None) - """ - if properties is None: - properties = {} - - edge_tuple = (source_id, target_id, relationship_name, properties) - query = await self.create_edge_query(edge_tuple) - self.query(query) - - @record_graph_changes - async def add_edges(self, edges: List[EdgeData]) -> None: - """ - Add multiple edges to the graph in a single operation. - - Parameters: - ----------- - - - edges (List[EdgeData]): A list of EdgeData objects representing edges to be added. - """ - for edge in edges: - if isinstance(edge, tuple) and len(edge) == 4: - # Edge is in (source_id, target_id, relationship_name, properties) format - source_id, target_id, relationship_name, properties = edge - await self.add_edge(source_id, target_id, relationship_name, properties) - else: - raise ValueError( - f"Invalid edge format: {edge}. Expected tuple (source_id, target_id, relationship_name, properties)." - ) - - async def has_edges(self, edges): - """ - Check if the specified edges exist in the graph based on their attributes. - - Parameters: - ----------- - - - edges: A list of edges to check for existence in the graph. - - Returns: - -------- - - Returns a list of edge tuples that exist in the graph. - """ - existing_edges = [] - for edge in edges: - exists = await self.has_edge(str(edge[0]), str(edge[1]), edge[2]) - if exists: - existing_edges.append(edge) - return existing_edges - - async def retrieve(self, data_point_ids: list[UUID]): - """ - Retrieve data points from the graph based on their IDs. - - Parameters: - ----------- - - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - retrieve. - - Returns: - -------- - - Returns the result set containing the retrieved nodes or an empty list if not found. - """ - result = self.query( - "MATCH (node) WHERE node.id IN $node_ids RETURN node", - { - "node_ids": [str(data_point) for data_point in data_point_ids], - }, - ) - return result.result_set - - async def extract_node(self, data_point_id: UUID): - """ - Extract the properties of a single node identified by its data point ID. - - Parameters: - ----------- - - - data_point_id (UUID): The UUID of the data point to extract. - - Returns: - -------- - - Returns the properties of the node if found, otherwise None. - """ - result = await self.retrieve([data_point_id]) - result = result[0][0] if len(result[0]) > 0 else None - return result.properties if result else None - - async def extract_nodes(self, data_point_ids: list[UUID]): - """ - Extract properties of multiple nodes identified by their data point IDs. - - Parameters: - ----------- - - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - extract. - - Returns: - -------- - - Returns the properties of the nodes in a list. - """ - return await self.retrieve(data_point_ids) - - async def get_connections(self, node_id: UUID) -> list: - """ - Retrieve connection details (predecessors and successors) for a given node ID. - - Parameters: - ----------- - - - node_id (UUID): The UUID of the node whose connections are to be retrieved. - - Returns: - -------- - - - list: Returns a list of tuples representing the connections of the node. - """ - predecessors_query = """ - MATCH (node)<-[relation]-(neighbour) - WHERE node.id = $node_id - RETURN neighbour, relation, node - """ - successors_query = """ - MATCH (node)-[relation]->(neighbour) - WHERE node.id = $node_id - RETURN node, relation, neighbour - """ - - predecessors, successors = await asyncio.gather( - self.query(predecessors_query, dict(node_id=node_id)), - self.query(successors_query, dict(node_id=node_id)), - ) - - connections = [] - - for neighbour in predecessors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - for neighbour in successors: - neighbour = neighbour["relation"] - connections.append((neighbour[0], {"relationship_name": neighbour[1]}, neighbour[2])) - - return connections - - async def search( - self, - collection_name: str, - query_text: str = None, - query_vector: list[float] = None, - limit: int = 10, - with_vector: bool = False, - ): - """ - Search for nodes in a collection based on text or vector query, with optional limitation - on results. - - Parameters: - ----------- - - - collection_name (str): The name of the collection in which to search. - - query_text (str): The text to search for (if using text-based query). (default - None) - - query_vector (list[float]): The vector representation of the query if using - vector-based search. (default None) - - limit (int): Maximum number of results to return from the search. (default 10) - - with_vector (bool): Flag indicating whether to return vectors with the search - results. (default False) - - Returns: - -------- - - Returns the search results as a result set from the graph database. - """ - if query_text is None and query_vector is None: - raise MissingQueryParameterError() - - if query_text and not query_vector: - query_vector = (await self.embed_data([query_text]))[0] - - # For FalkorDB, let's do a simple property-based search instead of vector search for now - # since the vector index might not be set up correctly - if "." in collection_name: - [label, attribute_name] = collection_name.split(".") - else: - # If no dot, treat the whole thing as a property search - label = "" - attribute_name = collection_name - - # Simple text-based search if we have query_text - if query_text: - if label: - query = f""" - MATCH (n:{label}) - WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text) - RETURN n, 1.0 as score - LIMIT $limit - """ - else: - query = f""" - MATCH (n) - WHERE toLower(toString(n.{attribute_name})) CONTAINS toLower($query_text) - RETURN n, 1.0 as score - LIMIT $limit - """ - - params = {"query_text": query_text, "limit": limit} - result = self.query(query, params) - return result.result_set - else: - # For vector search, return empty for now since vector indexing needs proper setup - return [] - - async def batch_search( - self, - collection_name: str, - query_texts: list[str], - limit: int = None, - with_vectors: bool = False, - ): - """ - Perform batch search across multiple queries based on text inputs and return results - asynchronously. - - Parameters: - ----------- - - - collection_name (str): The name of the collection in which to perform the - searches. - - query_texts (list[str]): A list of text queries to search for. - - limit (int): Optional limit for the search results for each query. (default None) - - with_vectors (bool): Flag indicating whether to return vectors with the results. - (default False) - - Returns: - -------- - - Returns a list of results for each search query executed in parallel. - """ - query_vectors = await self.embedding_engine.embed_text(query_texts) - - return await asyncio.gather( - *[ - self.search( - collection_name=collection_name, - query_vector=query_vector, - limit=limit, - with_vector=with_vectors, - ) - for query_vector in query_vectors - ] - ) - - async def get_graph_data(self): - """ - Retrieve all nodes and edges from the graph along with their properties. - - Returns: - -------- - - Returns a tuple containing lists of nodes and edges data retrieved from the graph. - """ - query = "MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties" - - result = self.query(query) - - nodes = [ - ( - record[2]["id"], - record[2], - ) - for record in result.result_set - ] - - query = """ - MATCH (n)-[r]->(m) - RETURN ID(n) AS source, ID(m) AS target, TYPE(r) AS type, properties(r) AS properties - """ - result = self.query(query) - edges = [ - ( - record[3]["source_node_id"], - record[3]["target_node_id"], - record[2], - record[3], - ) - for record in result.result_set - ] - - return (nodes, edges) - - async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]): - """ - Remove specified data points from the graph database based on their IDs. - - Parameters: - ----------- - - - collection_name (str): The name of the collection from which to delete the data - points. - - data_point_ids (list[UUID]): A list of UUIDs representing the data points to - delete. - - Returns: - -------- - - Returns the result of the deletion operation from the database. - """ - return self.query( - "MATCH (node) WHERE node.id IN $node_ids DETACH DELETE node", - { - "node_ids": [str(data_point) for data_point in data_point_ids], - }, - ) - - async def delete_node(self, node_id: str) -> None: - """ - Delete a specified node from the graph by its ID. - - Parameters: - ----------- - - - node_id (str): Unique identifier for the node to delete. - """ - query = f"MATCH (node {{id: '{node_id}'}}) DETACH DELETE node" - self.query(query) - - async def delete_nodes(self, node_ids: List[str]) -> None: - """ - Delete multiple nodes from the graph by their identifiers. - - Parameters: - ----------- - - - node_ids (List[str]): A list of unique identifiers for the nodes to delete. - """ - for node_id in node_ids: - await self.delete_node(node_id) - - async def delete_graph(self): - """ - Delete the entire graph along with all its indices and nodes. - """ - try: - graph = self.driver.select_graph(self.graph_name) - - indices = graph.list_indices() - for index in indices.result_set: - for field in index[1]: - graph.drop_node_vector_index(index[0], field) - - graph.delete() - except Exception as e: - print(f"Error deleting graph: {e}") - - async def get_node(self, node_id: str) -> Optional[NodeData]: - """ - Retrieve a single node from the graph using its ID. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node to retrieve. - """ - result = self.query( - "MATCH (node) WHERE node.id = $node_id RETURN node", - {"node_id": node_id}, - ) - - if result.result_set and len(result.result_set) > 0: - # FalkorDB returns node objects as first element in the result list - return result.result_set[0][0].properties - return None - - async def get_nodes(self, node_ids: List[str]) -> List[NodeData]: - """ - Retrieve multiple nodes from the graph using their IDs. - - Parameters: - ----------- - - - node_ids (List[str]): A list of unique identifiers for the nodes to retrieve. - """ - result = self.query( - "MATCH (node) WHERE node.id IN $node_ids RETURN node", - {"node_ids": node_ids}, - ) - - nodes = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns node objects as first element in each record - nodes.append(record[0].properties) - return nodes - - async def get_neighbors(self, node_id: str) -> List[NodeData]: - """ - Get all neighboring nodes connected to the specified node. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node for which to retrieve neighbors. - """ - result = self.query( - "MATCH (node)-[]-(neighbor) WHERE node.id = $node_id RETURN DISTINCT neighbor", - {"node_id": node_id}, - ) - - neighbors = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns neighbor objects as first element in each record - neighbors.append(record[0].properties) - return neighbors - - async def get_edges(self, node_id: str) -> List[EdgeData]: - """ - Retrieve all edges that are connected to the specified node. - - Parameters: - ----------- - - - node_id (str): Unique identifier of the node whose edges are to be retrieved. - """ - result = self.query( - """ - MATCH (n)-[r]-(m) - WHERE n.id = $node_id - RETURN n.id AS source_id, m.id AS target_id, type(r) AS relationship_name, properties(r) AS properties - """, - {"node_id": node_id}, - ) - - edges = [] - if result.result_set: - for record in result.result_set: - # FalkorDB returns values by index: source_id, target_id, relationship_name, properties - edges.append( - ( - record[0], # source_id - record[1], # target_id - record[2], # relationship_name - record[3], # properties - ) - ) - return edges - - async def has_edge(self, source_id: str, target_id: str, relationship_name: str) -> bool: - """ - Verify if an edge exists between two specified nodes. - - Parameters: - ----------- - - - source_id (str): Unique identifier of the source node. - - target_id (str): Unique identifier of the target node. - - relationship_name (str): Name of the relationship to verify. - """ - # Check both the sanitized relationship type and the original name in properties - sanitized_relationship = self.sanitize_relationship_name(relationship_name) - - result = self.query( - f""" - MATCH (source)-[r:{sanitized_relationship}]->(target) - WHERE source.id = $source_id AND target.id = $target_id - AND (r.relationship_name = $relationship_name OR NOT EXISTS(r.relationship_name)) - RETURN COUNT(r) > 0 AS edge_exists - """, - { - "source_id": source_id, - "target_id": target_id, - "relationship_name": relationship_name, - }, - ) - - if result.result_set and len(result.result_set) > 0: - # FalkorDB returns scalar results as a list, access by index instead of key - return result.result_set[0][0] - return False - - async def get_graph_metrics(self, include_optional: bool = False) -> Dict[str, Any]: - """ - Fetch metrics and statistics of the graph, possibly including optional details. - - Parameters: - ----------- - - - include_optional (bool): Flag indicating whether to include optional metrics or - not. (default False) - """ - # Get basic node and edge counts - node_result = self.query("MATCH (n) RETURN count(n) AS node_count") - edge_result = self.query("MATCH ()-[r]->() RETURN count(r) AS edge_count") - - # FalkorDB returns scalar results as a list, access by index instead of key - num_nodes = node_result.result_set[0][0] if node_result.result_set else 0 - num_edges = edge_result.result_set[0][0] if edge_result.result_set else 0 - - metrics = { - "num_nodes": num_nodes, - "num_edges": num_edges, - "mean_degree": (2 * num_edges) / num_nodes if num_nodes > 0 else 0, - "edge_density": num_edges / (num_nodes * (num_nodes - 1)) if num_nodes > 1 else 0, - "num_connected_components": 1, # Simplified for now - "sizes_of_connected_components": [num_nodes] if num_nodes > 0 else [], - } - - if include_optional: - # Add optional metrics - simplified implementation - metrics.update( - { - "num_selfloops": 0, # Simplified - "diameter": -1, # Not implemented - "avg_shortest_path_length": -1, # Not implemented - "avg_clustering": -1, # Not implemented - } - ) - else: - metrics.update( - { - "num_selfloops": -1, - "diameter": -1, - "avg_shortest_path_length": -1, - "avg_clustering": -1, - } - ) - - return metrics - - async def get_document_subgraph(self, content_hash: str): - """ - Get a subgraph related to a specific document by content hash. - - Parameters: - ----------- - - - content_hash (str): The content hash of the document to find. - """ - query = """ - MATCH (d) WHERE d.id CONTAINS $content_hash - OPTIONAL MATCH (d)<-[:CHUNK_OF]-(c) - OPTIONAL MATCH (c)-[:HAS_ENTITY]->(e) - OPTIONAL MATCH (e)-[:IS_INSTANCE_OF]->(et) - RETURN d AS document, - COLLECT(DISTINCT c) AS chunks, - COLLECT(DISTINCT e) AS orphan_entities, - COLLECT(DISTINCT c) AS made_from_nodes, - COLLECT(DISTINCT et) AS orphan_types - """ - - result = self.query(query, {"content_hash": f"text_{content_hash}"}) - - if not result.result_set or not result.result_set[0]: - return None - - # Convert result to dictionary format - # FalkorDB returns values by index: document, chunks, orphan_entities, made_from_nodes, orphan_types - record = result.result_set[0] - return { - "document": record[0], - "chunks": record[1], - "orphan_entities": record[2], - "made_from_nodes": record[3], - "orphan_types": record[4], - } - - async def get_degree_one_nodes(self, node_type: str): - """ - Get all nodes that have only one connection. - - Parameters: - ----------- - - - node_type (str): The type of nodes to filter by, must be 'Entity' or 'EntityType'. - """ - if not node_type or node_type not in ["Entity", "EntityType"]: - raise ValueError("node_type must be either 'Entity' or 'EntityType'") - - result = self.query( - f""" - MATCH (n:{node_type}) - WITH n, COUNT {{ MATCH (n)--() }} as degree - WHERE degree = 1 - RETURN n - """ - ) - - # FalkorDB returns node objects as first element in each record - return [record[0] for record in result.result_set] if result.result_set else [] - - async def get_nodeset_subgraph( - self, node_type: Type[Any], node_name: List[str] - ) -> Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]]: - """ - Fetch a subgraph consisting of a specific set of nodes and their relationships. - - Parameters: - ----------- - - - node_type (Type[Any]): The type of nodes to include in the subgraph. - - node_name (List[str]): A list of names of the nodes to include in the subgraph. - """ - label = node_type.__name__ - - # Find primary nodes of the specified type and names - primary_query = f""" - UNWIND $names AS wantedName - MATCH (n:{label}) - WHERE n.name = wantedName - RETURN DISTINCT n.id, properties(n) AS properties - """ - - primary_result = self.query(primary_query, {"names": node_name}) - if not primary_result.result_set: - return [], [] - - # FalkorDB returns values by index: id, properties - primary_ids = [record[0] for record in primary_result.result_set] - - # Find neighbors of primary nodes - neighbor_query = """ - MATCH (n)-[]-(neighbor) - WHERE n.id IN $ids - RETURN DISTINCT neighbor.id, properties(neighbor) AS properties - """ - - neighbor_result = self.query(neighbor_query, {"ids": primary_ids}) - # FalkorDB returns values by index: id, properties - neighbor_ids = ( - [record[0] for record in neighbor_result.result_set] - if neighbor_result.result_set - else [] - ) - - all_ids = list(set(primary_ids + neighbor_ids)) - - # Get all nodes in the subgraph - nodes_query = """ - MATCH (n) - WHERE n.id IN $ids - RETURN n.id, properties(n) AS properties - """ - - nodes_result = self.query(nodes_query, {"ids": all_ids}) - nodes = [] - if nodes_result.result_set: - for record in nodes_result.result_set: - # FalkorDB returns values by index: id, properties - nodes.append((record[0], record[1])) - - # Get edges between these nodes - edges_query = """ - MATCH (a)-[r]->(b) - WHERE a.id IN $ids AND b.id IN $ids - RETURN a.id AS source_id, b.id AS target_id, type(r) AS relationship_name, properties(r) AS properties - """ - - edges_result = self.query(edges_query, {"ids": all_ids}) - edges = [] - if edges_result.result_set: - for record in edges_result.result_set: - # FalkorDB returns values by index: source_id, target_id, relationship_name, properties - edges.append( - ( - record[0], # source_id - record[1], # target_id - record[2], # relationship_name - record[3], # properties - ) - ) - - return nodes, edges - - async def prune(self): - """ - Prune the graph by deleting the entire graph structure. - """ - await self.delete_graph() diff --git a/cognee/infrastructure/databases/relational/config.py b/cognee/infrastructure/databases/relational/config.py index 4e3c26042..ff7c410a1 100644 --- a/cognee/infrastructure/databases/relational/config.py +++ b/cognee/infrastructure/databases/relational/config.py @@ -23,14 +23,14 @@ class RelationalConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") @pydantic.model_validator(mode="after") - def fill_derived(cls, values): + def fill_derived(self): # Set file path based on graph database provider if no file path is provided - if not values.db_path: + if not self.db_path: base_config = get_base_config() databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.db_path = databases_directory_path + self.db_path = databases_directory_path - return values + return self def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py index 88d2abc7e..380ce9917 100644 --- a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py +++ b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py @@ -283,7 +283,7 @@ class SQLAlchemyAdapter: try: data_entity = (await session.scalars(select(Data).where(Data.id == data_id))).one() except (ValueError, NoResultFound) as e: - raise EntityNotFoundError(message=f"Entity not found: {str(e)}") + raise EntityNotFoundError(message=f"Entity not found: {str(e)}") from e # Check if other data objects point to the same raw data location raw_data_location_entities = ( diff --git a/cognee/infrastructure/databases/vector/config.py b/cognee/infrastructure/databases/vector/config.py index 23678cca4..b6d3ae644 100644 --- a/cognee/infrastructure/databases/vector/config.py +++ b/cognee/infrastructure/databases/vector/config.py @@ -30,21 +30,21 @@ class VectorConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") @pydantic.model_validator(mode="after") - def validate_paths(cls, values): + def validate_paths(self): base_config = get_base_config() # If vector_db_url is provided and is not a path skip checking if path is absolute (as it can also be a url) - if values.vector_db_url and Path(values.vector_db_url).exists(): + if self.vector_db_url and Path(self.vector_db_url).exists(): # Relative path to absolute - values.vector_db_url = ensure_absolute_path( - values.vector_db_url, + self.vector_db_url = ensure_absolute_path( + self.vector_db_url, ) - elif not values.vector_db_url: + elif not self.vector_db_url: # Default path databases_directory_path = os.path.join(base_config.system_root_directory, "databases") - values.vector_db_url = os.path.join(databases_directory_path, "cognee.lancedb") + self.vector_db_url = os.path.join(databases_directory_path, "cognee.lancedb") - return values + return self def to_dict(self) -> dict: """ diff --git a/cognee/infrastructure/databases/vector/create_vector_engine.py b/cognee/infrastructure/databases/vector/create_vector_engine.py index 5c4e93359..639bbb9f6 100644 --- a/cognee/infrastructure/databases/vector/create_vector_engine.py +++ b/cognee/infrastructure/databases/vector/create_vector_engine.py @@ -19,8 +19,7 @@ def create_vector_engine( for each provider, raising an EnvironmentError if any are missing, or ImportError if the ChromaDB package is not installed. - Supported providers include: pgvector, FalkorDB, ChromaDB, and - LanceDB. + Supported providers include: pgvector, ChromaDB, and LanceDB. Parameters: ----------- @@ -79,18 +78,6 @@ def create_vector_engine( embedding_engine, ) - elif vector_db_provider == "falkordb": - if not (vector_db_url and vector_db_port): - raise EnvironmentError("Missing requred FalkorDB credentials!") - - from ..hybrid.falkordb.FalkorDBAdapter import FalkorDBAdapter - - return FalkorDBAdapter( - database_url=vector_db_url, - database_port=vector_db_port, - embedding_engine=embedding_engine, - ) - elif vector_db_provider == "chromadb": try: import chromadb diff --git a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py index afa180c67..c410d99dc 100644 --- a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py @@ -34,3 +34,12 @@ class EmbeddingEngine(Protocol): - int: An integer representing the number of dimensions in the embedding vector. """ raise NotImplementedError() + + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + raise NotImplementedError() diff --git a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py index acb041e76..e34ab5d9d 100644 --- a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py @@ -42,11 +42,13 @@ class FastembedEmbeddingEngine(EmbeddingEngine): model: Optional[str] = "openai/text-embedding-3-large", dimensions: Optional[int] = 3072, max_completion_tokens: int = 512, + batch_size: int = 100, ): self.model = model self.dimensions = dimensions self.max_completion_tokens = max_completion_tokens self.tokenizer = self.get_tokenizer() + self.batch_size = batch_size # self.retry_count = 0 self.embedding_model = TextEmbedding(model_name=model) @@ -88,7 +90,9 @@ class FastembedEmbeddingEngine(EmbeddingEngine): except Exception as error: logger.error(f"Embedding error in FastembedEmbeddingEngine: {str(error)}") - raise EmbeddingException(f"Failed to index data points using model {self.model}") + raise EmbeddingException( + f"Failed to index data points using model {self.model}" + ) from error def get_vector_size(self) -> int: """ @@ -101,6 +105,15 @@ class FastembedEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Instantiate and return the tokenizer used for preparing text for embedding. diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py index 0cb8286fc..ccef7bfcd 100644 --- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py @@ -58,6 +58,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): endpoint: str = None, api_version: str = None, max_completion_tokens: int = 512, + batch_size: int = 100, ): self.api_key = api_key self.endpoint = endpoint @@ -68,6 +69,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): self.max_completion_tokens = max_completion_tokens self.tokenizer = self.get_tokenizer() self.retry_count = 0 + self.batch_size = batch_size enable_mocking = os.getenv("MOCK_EMBEDDING", "false") if isinstance(enable_mocking, bool): @@ -148,7 +150,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): litellm.exceptions.NotFoundError, ) as e: logger.error(f"Embedding error with model {self.model}: {str(e)}") - raise EmbeddingException(f"Failed to index data points using model {self.model}") + raise EmbeddingException(f"Failed to index data points using model {self.model}") from e except Exception as error: logger.error("Error embedding text: %s", str(error)) @@ -165,6 +167,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Load and return the appropriate tokenizer for the specified model based on the provider. @@ -183,9 +194,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): model=model, max_completion_tokens=self.max_completion_tokens ) elif "gemini" in self.provider.lower(): - tokenizer = GeminiTokenizer( - model=model, max_completion_tokens=self.max_completion_tokens + # Since Gemini tokenization needs to send an API request to get the token count we will use TikToken to + # count tokens as we calculate tokens word by word + tokenizer = TikTokenTokenizer( + model=None, max_completion_tokens=self.max_completion_tokens ) + # Note: Gemini Tokenizer expects an LLM model as input and not the embedding model + # tokenizer = GeminiTokenizer( + # llm_model=llm_model, max_completion_tokens=self.max_completion_tokens + # ) elif "mistral" in self.provider.lower(): tokenizer = MistralTokenizer( model=model, max_completion_tokens=self.max_completion_tokens diff --git a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py index e6e590597..e79ba3f6a 100644 --- a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py @@ -54,12 +54,14 @@ class OllamaEmbeddingEngine(EmbeddingEngine): max_completion_tokens: int = 512, endpoint: Optional[str] = "http://localhost:11434/api/embeddings", huggingface_tokenizer: str = "Salesforce/SFR-Embedding-Mistral", + batch_size: int = 100, ): self.model = model self.dimensions = dimensions self.max_completion_tokens = max_completion_tokens self.endpoint = endpoint self.huggingface_tokenizer_name = huggingface_tokenizer + self.batch_size = batch_size self.tokenizer = self.get_tokenizer() enable_mocking = os.getenv("MOCK_EMBEDDING", "false") @@ -122,6 +124,15 @@ class OllamaEmbeddingEngine(EmbeddingEngine): """ return self.dimensions + def get_batch_size(self) -> int: + """ + Return the desired batch size for embedding calls + + Returns: + + """ + return self.batch_size + def get_tokenizer(self): """ Load and return a HuggingFace tokenizer for the embedding engine. diff --git a/cognee/infrastructure/databases/vector/embeddings/config.py b/cognee/infrastructure/databases/vector/embeddings/config.py index 04a1f18f2..24f724151 100644 --- a/cognee/infrastructure/databases/vector/embeddings/config.py +++ b/cognee/infrastructure/databases/vector/embeddings/config.py @@ -19,9 +19,17 @@ class EmbeddingConfig(BaseSettings): embedding_api_key: Optional[str] = None embedding_api_version: Optional[str] = None embedding_max_completion_tokens: Optional[int] = 8191 + embedding_batch_size: Optional[int] = None huggingface_tokenizer: Optional[str] = None model_config = SettingsConfigDict(env_file=".env", extra="allow") + def model_post_init(self, __context) -> None: + # If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models + if not self.embedding_batch_size and self.embedding_provider.lower() == "openai": + self.embedding_batch_size = 2048 + elif not self.embedding_batch_size: + self.embedding_batch_size = 100 + def to_dict(self) -> dict: """ Serialize all embedding configuration settings to a dictionary. diff --git a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py index e7fcf4e94..de56c8971 100644 --- a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py +++ b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py @@ -31,6 +31,7 @@ def get_embedding_engine() -> EmbeddingEngine: config.embedding_endpoint, config.embedding_api_key, config.embedding_api_version, + config.embedding_batch_size, config.huggingface_tokenizer, llm_config.llm_api_key, llm_config.llm_provider, @@ -46,6 +47,7 @@ def create_embedding_engine( embedding_endpoint, embedding_api_key, embedding_api_version, + embedding_batch_size, huggingface_tokenizer, llm_api_key, llm_provider, @@ -84,6 +86,7 @@ def create_embedding_engine( model=embedding_model, dimensions=embedding_dimensions, max_completion_tokens=embedding_max_completion_tokens, + batch_size=embedding_batch_size, ) if embedding_provider == "ollama": @@ -95,6 +98,7 @@ def create_embedding_engine( max_completion_tokens=embedding_max_completion_tokens, endpoint=embedding_endpoint, huggingface_tokenizer=huggingface_tokenizer, + batch_size=embedding_batch_size, ) from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine @@ -108,4 +112,5 @@ def create_embedding_engine( model=embedding_model, dimensions=embedding_dimensions, max_completion_tokens=embedding_max_completion_tokens, + batch_size=embedding_batch_size, ) diff --git a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py index 5ddb656a3..1986fae48 100644 --- a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py +++ b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py @@ -125,41 +125,42 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): data_point_types = get_type_hints(DataPoint) vector_size = self.embedding_engine.get_vector_size() - async with self.VECTOR_DB_LOCK: - if not await self.has_collection(collection_name): + if not await self.has_collection(collection_name): + async with self.VECTOR_DB_LOCK: + if not await self.has_collection(collection_name): - class PGVectorDataPoint(Base): - """ - Represent a point in a vector data space with associated data and vector representation. + class PGVectorDataPoint(Base): + """ + Represent a point in a vector data space with associated data and vector representation. - This class inherits from Base and is associated with a database table defined by - __tablename__. It maintains the following public methods and instance variables: + This class inherits from Base and is associated with a database table defined by + __tablename__. It maintains the following public methods and instance variables: - - __init__(self, id, payload, vector): Initializes a new PGVectorDataPoint instance. + - __init__(self, id, payload, vector): Initializes a new PGVectorDataPoint instance. - Instance variables: - - id: Identifier for the data point, defined by data_point_types. - - payload: JSON data associated with the data point. - - vector: Vector representation of the data point, with size defined by vector_size. - """ + Instance variables: + - id: Identifier for the data point, defined by data_point_types. + - payload: JSON data associated with the data point. + - vector: Vector representation of the data point, with size defined by vector_size. + """ - __tablename__ = collection_name - __table_args__ = {"extend_existing": True} - # PGVector requires one column to be the primary key - id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True) - payload = Column(JSON) - vector = Column(self.Vector(vector_size)) + __tablename__ = collection_name + __table_args__ = {"extend_existing": True} + # PGVector requires one column to be the primary key + id: Mapped[data_point_types["id"]] = mapped_column(primary_key=True) + payload = Column(JSON) + vector = Column(self.Vector(vector_size)) - def __init__(self, id, payload, vector): - self.id = id - self.payload = payload - self.vector = vector + def __init__(self, id, payload, vector): + self.id = id + self.payload = payload + self.vector = vector - async with self.engine.begin() as connection: - if len(Base.metadata.tables.keys()) > 0: - await connection.run_sync( - Base.metadata.create_all, tables=[PGVectorDataPoint.__table__] - ) + async with self.engine.begin() as connection: + if len(Base.metadata.tables.keys()) > 0: + await connection.run_sync( + Base.metadata.create_all, tables=[PGVectorDataPoint.__table__] + ) @retry( retry=retry_if_exception_type(DeadlockDetectedError), diff --git a/cognee/infrastructure/llm/config.py b/cognee/infrastructure/llm/config.py index 6658a6251..8fd196eaf 100644 --- a/cognee/infrastructure/llm/config.py +++ b/cognee/infrastructure/llm/config.py @@ -39,7 +39,7 @@ class LLMConfig(BaseSettings): structured_output_framework: str = "instructor" llm_provider: str = "openai" - llm_model: str = "openai/gpt-4o-mini" + llm_model: str = "openai/gpt-5-mini" llm_endpoint: str = "" llm_api_key: Optional[str] = None llm_api_version: Optional[str] = None @@ -48,7 +48,7 @@ class LLMConfig(BaseSettings): llm_max_completion_tokens: int = 16384 baml_llm_provider: str = "openai" - baml_llm_model: str = "gpt-4o-mini" + baml_llm_model: str = "gpt-5-mini" baml_llm_endpoint: str = "" baml_llm_api_key: Optional[str] = None baml_llm_temperature: float = 0.0 diff --git a/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt b/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt index 7ed2e72fc..1a00bce7e 100644 --- a/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt +++ b/cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt @@ -10,8 +10,6 @@ Here are the available `SearchType` tools and their specific functions: - Summarizing large amounts of information - Quick understanding of complex subjects -* **`INSIGHTS`**: The `INSIGHTS` search type discovers connections and relationships between entities in the knowledge graph. - **Best for:** - Discovering how entities are connected @@ -95,9 +93,6 @@ Here are the available `SearchType` tools and their specific functions: Query: "Summarize the key findings from these research papers" Response: `SUMMARIES` -Query: "What is the relationship between the methodologies used in these papers?" -Response: `INSIGHTS` - Query: "When was Einstein born?" Response: `CHUNKS` diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py index 2c7b73eef..510d29ce8 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/gemini/adapter.py @@ -1,115 +1,155 @@ -import litellm -from pydantic import BaseModel -from typing import Type, Optional -from litellm import acompletion, JSONSchemaValidationError +"""Adapter for Generic API LLM provider API""" -from cognee.shared.logging_utils import get_logger -from cognee.modules.observability.get_observe import get_observe -from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError +import litellm +import instructor +from typing import Type +from pydantic import BaseModel +from openai import ContentFilterFinishReasonError +from litellm.exceptions import ContentPolicyViolationError +from instructor.core import InstructorRetryException + +from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( rate_limit_async, sleep_and_retry_async, ) -logger = get_logger() -observe = get_observe() - class GeminiAdapter(LLMInterface): """ - Handles interactions with a language model API. + Adapter for Gemini API LLM provider. - Public methods include: - - acreate_structured_output - - show_prompt + This class initializes the API adapter with necessary credentials and configurations for + interacting with the gemini LLM models. It provides methods for creating structured outputs + based on user input and system prompts. + + Public methods: + - acreate_structured_output(text_input: str, system_prompt: str, response_model: + Type[BaseModel]) -> BaseModel """ - MAX_RETRIES = 5 + name: str + model: str + api_key: str def __init__( self, + endpoint, api_key: str, model: str, + api_version: str, max_completion_tokens: int, - endpoint: Optional[str] = None, - api_version: Optional[str] = None, - streaming: bool = False, - ) -> None: - self.api_key = api_key + fallback_model: str = None, + fallback_api_key: str = None, + fallback_endpoint: str = None, + ): self.model = model + self.api_key = api_key self.endpoint = endpoint self.api_version = api_version - self.streaming = streaming self.max_completion_tokens = max_completion_tokens - @observe(as_type="generation") + self.fallback_model = fallback_model + self.fallback_api_key = fallback_api_key + self.fallback_endpoint = fallback_endpoint + + self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) + @sleep_and_retry_async() @rate_limit_async async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: """ - Generate structured output from the language model based on the provided input and - system prompt. + Generate a response from a user query. - This method handles retries and raises a ValueError if the request fails or the response - does not conform to the expected schema, logging errors accordingly. + This asynchronous method sends a user query and a system prompt to a language model and + retrieves the generated response. It handles API communication and retries up to a + specified limit in case of request failures. Parameters: ----------- - - text_input (str): The user input text to generate a response for. - - system_prompt (str): The system's prompt or context to influence the language - model's generation. - - response_model (Type[BaseModel]): A model type indicating the expected format of - the response. + - text_input (str): The input text from the user to generate a response for. + - system_prompt (str): A prompt that provides context or instructions for the + response generation. + - response_model (Type[BaseModel]): A Pydantic model that defines the structure of + the expected response. Returns: -------- - - BaseModel: Returns the generated response as an instance of the specified response - model. + - BaseModel: An instance of the specified response model containing the structured + output from the language model. """ + try: - if response_model is str: - response_schema = {"type": "string"} - else: - response_schema = response_model + return await self.aclient.chat.completions.create( + model=self.model, + messages=[ + { + "role": "user", + "content": f"""{text_input}""", + }, + { + "role": "system", + "content": system_prompt, + }, + ], + api_key=self.api_key, + max_retries=5, + api_base=self.endpoint, + api_version=self.api_version, + response_model=response_model, + ) + except ( + ContentFilterFinishReasonError, + ContentPolicyViolationError, + InstructorRetryException, + ) as error: + if ( + isinstance(error, InstructorRetryException) + and "content management policy" not in str(error).lower() + ): + raise error - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": text_input}, - ] - - try: - response = await acompletion( - model=f"{self.model}", - messages=messages, - api_key=self.api_key, - max_completion_tokens=self.max_completion_tokens, - temperature=0.1, - response_format=response_schema, - timeout=100, - num_retries=self.MAX_RETRIES, + if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint): + raise ContentPolicyFilterError( + f"The provided input contains content that is not aligned with our content policy: {text_input}" ) - if response.choices and response.choices[0].message.content: - content = response.choices[0].message.content - if response_model is str: - return content - return response_model.model_validate_json(content) - - except litellm.exceptions.BadRequestError as e: - logger.error(f"Bad request error: {str(e)}") - raise ValueError(f"Invalid request: {str(e)}") - - raise ValueError("Failed to get valid response after retries") - - except JSONSchemaValidationError as e: - logger.error(f"Schema validation failed: {str(e)}") - logger.debug(f"Raw response: {e.raw_response}") - raise ValueError(f"Response failed schema validation: {str(e)}") + try: + return await self.aclient.chat.completions.create( + model=self.fallback_model, + messages=[ + { + "role": "user", + "content": f"""{text_input}""", + }, + { + "role": "system", + "content": system_prompt, + }, + ], + max_retries=5, + api_key=self.fallback_api_key, + api_base=self.fallback_endpoint, + response_model=response_model, + ) + except ( + ContentFilterFinishReasonError, + ContentPolicyViolationError, + InstructorRetryException, + ) as error: + if ( + isinstance(error, InstructorRetryException) + and "content management policy" not in str(error).lower() + ): + raise error + else: + raise ContentPolicyFilterError( + f"The provided input contains content that is not aligned with our content policy: {text_input}" + ) diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py index 86adac25a..917599d4d 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/generic_llm_api/adapter.py @@ -6,7 +6,7 @@ from typing import Type from pydantic import BaseModel from openai import ContentFilterFinishReasonError from litellm.exceptions import ContentPolicyViolationError -from instructor.exceptions import InstructorRetryException +from instructor.core import InstructorRetryException from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( @@ -56,9 +56,7 @@ class GenericAPIAdapter(LLMInterface): self.fallback_api_key = fallback_api_key self.fallback_endpoint = fallback_endpoint - self.aclient = instructor.from_litellm( - litellm.acompletion, mode=instructor.Mode.JSON, api_key=api_key - ) + self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON) @sleep_and_retry_async() @rate_limit_async @@ -102,6 +100,7 @@ class GenericAPIAdapter(LLMInterface): }, ], max_retries=5, + api_key=self.api_key, api_base=self.endpoint, response_model=response_model, ) @@ -119,7 +118,7 @@ class GenericAPIAdapter(LLMInterface): if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint): raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error try: return await self.aclient.chat.completions.create( @@ -152,4 +151,4 @@ class GenericAPIAdapter(LLMInterface): else: raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py index 049b27e04..c7dcecc56 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py @@ -23,6 +23,7 @@ class LLMProvider(Enum): - ANTHROPIC: Represents the Anthropic provider. - CUSTOM: Represents a custom provider option. - GEMINI: Represents the Gemini provider. + - MISTRAL: Represents the Mistral AI provider. """ OPENAI = "openai" @@ -30,6 +31,7 @@ class LLMProvider(Enum): ANTHROPIC = "anthropic" CUSTOM = "custom" GEMINI = "gemini" + MISTRAL = "mistral" def get_llm_client(raise_api_key_error: bool = True): @@ -143,7 +145,36 @@ def get_llm_client(raise_api_key_error: bool = True): max_completion_tokens=max_completion_tokens, endpoint=llm_config.llm_endpoint, api_version=llm_config.llm_api_version, - streaming=llm_config.llm_streaming, + ) + + elif provider == LLMProvider.MISTRAL: + if llm_config.llm_api_key is None: + raise LLMAPIKeyNotSetError() + + from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import ( + MistralAdapter, + ) + + return MistralAdapter( + api_key=llm_config.llm_api_key, + model=llm_config.llm_model, + max_completion_tokens=max_completion_tokens, + endpoint=llm_config.llm_endpoint, + ) + + elif provider == LLMProvider.MISTRAL: + if llm_config.llm_api_key is None: + raise LLMAPIKeyNotSetError() + + from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.mistral.adapter import ( + MistralAdapter, + ) + + return MistralAdapter( + api_key=llm_config.llm_api_key, + model=llm_config.llm_model, + max_completion_tokens=max_completion_tokens, + endpoint=llm_config.llm_endpoint, ) else: diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py new file mode 100644 index 000000000..c4e51b70b --- /dev/null +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/mistral/adapter.py @@ -0,0 +1,129 @@ +import litellm +import instructor +from pydantic import BaseModel +from typing import Type, Optional +from litellm import acompletion, JSONSchemaValidationError + +from cognee.shared.logging_utils import get_logger +from cognee.modules.observability.get_observe import get_observe +from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( + LLMInterface, +) +from cognee.infrastructure.llm.LLMGateway import LLMGateway +from cognee.infrastructure.llm.config import get_llm_config +from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( + rate_limit_async, + sleep_and_retry_async, +) + +logger = get_logger() +observe = get_observe() + + +class MistralAdapter(LLMInterface): + """ + Adapter for Mistral AI API, for structured output generation and prompt display. + + Public methods: + - acreate_structured_output + - show_prompt + """ + + name = "Mistral" + model: str + api_key: str + max_completion_tokens: int + + def __init__(self, api_key: str, model: str, max_completion_tokens: int, endpoint: str = None): + from mistralai import Mistral + + self.model = model + self.max_completion_tokens = max_completion_tokens + + self.aclient = instructor.from_litellm( + litellm.acompletion, + mode=instructor.Mode.MISTRAL_TOOLS, + api_key=get_llm_config().llm_api_key, + ) + + @sleep_and_retry_async() + @rate_limit_async + async def acreate_structured_output( + self, text_input: str, system_prompt: str, response_model: Type[BaseModel] + ) -> BaseModel: + """ + Generate a response from the user query. + + Parameters: + ----------- + - text_input (str): The input text from the user to be processed. + - system_prompt (str): A prompt that sets the context for the query. + - response_model (Type[BaseModel]): The model to structure the response according to + its format. + + Returns: + -------- + - BaseModel: An instance of BaseModel containing the structured response. + """ + try: + messages = [ + { + "role": "system", + "content": system_prompt, + }, + { + "role": "user", + "content": f"""Use the given format to extract information + from the following input: {text_input}""", + }, + ] + try: + response = await self.aclient.chat.completions.create( + model=self.model, + max_tokens=self.max_completion_tokens, + max_retries=5, + messages=messages, + response_model=response_model, + ) + if response.choices and response.choices[0].message.content: + content = response.choices[0].message.content + return response_model.model_validate_json(content) + else: + raise ValueError("Failed to get valid response after retries") + except litellm.exceptions.BadRequestError as e: + logger.error(f"Bad request error: {str(e)}") + raise ValueError(f"Invalid request: {str(e)}") + + except JSONSchemaValidationError as e: + logger.error(f"Schema validation failed: {str(e)}") + logger.debug(f"Raw response: {e.raw_response}") + raise ValueError(f"Response failed schema validation: {str(e)}") + + def show_prompt(self, text_input: str, system_prompt: str) -> str: + """ + Format and display the prompt for a user query. + + Parameters: + ----------- + - text_input (str): Input text from the user to be included in the prompt. + - system_prompt (str): The system prompt that will be shown alongside the user input. + + Returns: + -------- + - str: The formatted prompt string combining system prompt and user input. + """ + if not text_input: + text_input = "No user input provided." + if not system_prompt: + raise MissingSystemPromptPathError() + + system_prompt = LLMGateway.read_query_prompt(system_prompt) + + formatted_prompt = ( + f"""System Prompt:\n{system_prompt}\n\nUser Input:\n{text_input}\n""" + if system_prompt + else None + ) + + return formatted_prompt diff --git a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py index 7273de976..527f64d75 100644 --- a/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py +++ b/cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/openai/adapter.py @@ -5,15 +5,13 @@ from typing import Type from pydantic import BaseModel from openai import ContentFilterFinishReasonError from litellm.exceptions import ContentPolicyViolationError -from instructor.exceptions import InstructorRetryException +from instructor.core import InstructorRetryException -from cognee.infrastructure.llm.LLMGateway import LLMGateway from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import ( LLMInterface, ) from cognee.infrastructure.llm.exceptions import ( ContentPolicyFilterError, - MissingSystemPromptPathError, ) from cognee.infrastructure.files.utils.open_data_file import open_data_file from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import ( @@ -148,11 +146,11 @@ class OpenAIAdapter(LLMInterface): ContentFilterFinishReasonError, ContentPolicyViolationError, InstructorRetryException, - ): + ) as e: if not (self.fallback_model and self.fallback_api_key): raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from e try: return await self.aclient.chat.completions.create( @@ -185,7 +183,7 @@ class OpenAIAdapter(LLMInterface): else: raise ContentPolicyFilterError( f"The provided input contains content that is not aligned with our content policy: {text_input}" - ) + ) from error @observe @sleep_and_retry_sync() diff --git a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py index a57cff3f7..d595fba2d 100644 --- a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py @@ -3,6 +3,7 @@ from typing import List, Any from ..tokenizer_interface import TokenizerInterface +# NOTE: DEPRECATED as to count tokens you need to send an API request to Google it is too slow to use with Cognee class GeminiTokenizer(TokenizerInterface): """ Implements a tokenizer interface for the Gemini model, managing token extraction and @@ -16,10 +17,10 @@ class GeminiTokenizer(TokenizerInterface): def __init__( self, - model: str, + llm_model: str, max_completion_tokens: int = 3072, ): - self.model = model + self.llm_model = llm_model self.max_completion_tokens = max_completion_tokens # Get LLM API key from config @@ -28,12 +29,11 @@ class GeminiTokenizer(TokenizerInterface): get_llm_config, ) - config = get_embedding_config() llm_config = get_llm_config() - import google.generativeai as genai + from google import genai - genai.configure(api_key=config.embedding_api_key or llm_config.llm_api_key) + self.client = genai.Client(api_key=llm_config.llm_api_key) def extract_tokens(self, text: str) -> List[Any]: """ @@ -77,6 +77,7 @@ class GeminiTokenizer(TokenizerInterface): - int: The number of tokens in the given text. """ - import google.generativeai as genai - return len(genai.embed_content(model=f"models/{self.model}", content=text)) + tokens_response = self.client.models.count_tokens(model=self.llm_model, contents=text) + + return tokens_response.total_tokens diff --git a/cognee/infrastructure/loaders/LoaderEngine.py b/cognee/infrastructure/loaders/LoaderEngine.py index d4be2aab5..87a008660 100644 --- a/cognee/infrastructure/loaders/LoaderEngine.py +++ b/cognee/infrastructure/loaders/LoaderEngine.py @@ -27,11 +27,11 @@ class LoaderEngine: self.default_loader_priority = [ "text_loader", - "advanced_pdf_loader", "pypdf_loader", "image_loader", "audio_loader", "unstructured_loader", + "advanced_pdf_loader", ] def register_loader(self, loader: LoaderInterface) -> bool: diff --git a/cognee/modules/data/methods/get_dataset_data.py b/cognee/modules/data/methods/get_dataset_data.py index afe61ddc5..5d06e9067 100644 --- a/cognee/modules/data/methods/get_dataset_data.py +++ b/cognee/modules/data/methods/get_dataset_data.py @@ -9,7 +9,10 @@ async def get_dataset_data(dataset_id: UUID) -> list[Data]: async with db_engine.get_async_session() as session: result = await session.execute( - select(Data).join(Data.datasets).filter((Dataset.id == dataset_id)) + select(Data) + .join(Data.datasets) + .filter((Dataset.id == dataset_id)) + .order_by(Data.data_size.desc()) ) data = list(result.scalars().all()) diff --git a/cognee/modules/data/methods/get_deletion_counts.py b/cognee/modules/data/methods/get_deletion_counts.py new file mode 100644 index 000000000..9237a8b85 --- /dev/null +++ b/cognee/modules/data/methods/get_deletion_counts.py @@ -0,0 +1,92 @@ +from uuid import UUID +from cognee.cli.exceptions import CliCommandException +from cognee.infrastructure.databases.exceptions.exceptions import EntityNotFoundError +from sqlalchemy import select +from sqlalchemy.sql import func +from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.modules.data.models import Dataset, Data, DatasetData +from cognee.modules.users.models import User +from cognee.modules.users.methods import get_user +from dataclasses import dataclass + + +@dataclass +class DeletionCountsPreview: + datasets: int = 0 + data_entries: int = 0 + users: int = 0 + + +async def get_deletion_counts( + dataset_name: str = None, user_id: str = None, all_data: bool = False +) -> DeletionCountsPreview: + """ + Calculates the number of items that will be deleted based on the provided arguments. + """ + counts = DeletionCountsPreview() + relational_engine = get_relational_engine() + async with relational_engine.get_async_session() as session: + if dataset_name: + # Find the dataset by name + dataset_result = await session.execute( + select(Dataset).where(Dataset.name == dataset_name) + ) + dataset = dataset_result.scalar_one_or_none() + + if dataset is None: + raise CliCommandException( + f"No Dataset exists with the name {dataset_name}", error_code=1 + ) + + # Count data entries linked to this dataset + count_query = ( + select(func.count()) + .select_from(DatasetData) + .where(DatasetData.dataset_id == dataset.id) + ) + data_entry_count = (await session.execute(count_query)).scalar_one() + counts.users = 1 + counts.datasets = 1 + counts.entries = data_entry_count + return counts + + elif all_data: + # Simplified logic: Get total counts directly from the tables. + counts.datasets = ( + await session.execute(select(func.count()).select_from(Dataset)) + ).scalar_one() + counts.entries = ( + await session.execute(select(func.count()).select_from(Data)) + ).scalar_one() + counts.users = ( + await session.execute(select(func.count()).select_from(User)) + ).scalar_one() + return counts + + # Placeholder for user_id logic + elif user_id: + user = None + try: + user_uuid = UUID(user_id) + user = await get_user(user_uuid) + except (ValueError, EntityNotFoundError): + raise CliCommandException(f"No User exists with ID {user_id}", error_code=1) + counts.users = 1 + # Find all datasets owned by this user + datasets_query = select(Dataset).where(Dataset.owner_id == user.id) + user_datasets = (await session.execute(datasets_query)).scalars().all() + dataset_count = len(user_datasets) + counts.datasets = dataset_count + if dataset_count > 0: + dataset_ids = [d.id for d in user_datasets] + # Count all data entries across all of the user's datasets + data_count_query = ( + select(func.count()) + .select_from(DatasetData) + .where(DatasetData.dataset_id.in_(dataset_ids)) + ) + data_entry_count = (await session.execute(data_count_query)).scalar_one() + counts.entries = data_entry_count + else: + counts.entries = 0 + return counts diff --git a/cognee/modules/engine/models/TableRow.py b/cognee/modules/engine/models/TableRow.py index dd07612fb..ad82d68e3 100644 --- a/cognee/modules/engine/models/TableRow.py +++ b/cognee/modules/engine/models/TableRow.py @@ -5,7 +5,6 @@ from typing import Optional class TableRow(DataPoint): name: str - is_a: Optional[TableType] = None description: str properties: str diff --git a/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py b/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py index 7e3d1c124..981c02112 100644 --- a/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py +++ b/cognee/modules/pipelines/layers/resolve_authorized_user_dataset.py @@ -1,47 +1,31 @@ from uuid import UUID +from typing import Optional -from cognee.api.v1.exceptions import DatasetNotFoundError from cognee.modules.users.models import User -from cognee.modules.users.methods import get_default_user -from cognee.modules.data.methods import ( - create_authorized_dataset, - get_authorized_dataset, - get_authorized_dataset_by_name, +from cognee.modules.pipelines.layers.resolve_authorized_user_datasets import ( + resolve_authorized_user_datasets, ) -async def resolve_authorized_user_dataset(dataset_id: UUID, dataset_name: str, user: User): +async def resolve_authorized_user_dataset( + dataset_name: str, dataset_id: Optional[UUID] = None, user: Optional[User] = None +): """ Function handles creation and dataset authorization if dataset already exist for Cognee. Verifies that provided user has necessary permission for provided Dataset. If Dataset does not exist creates the Dataset and gives permission for the user creating the dataset. Args: - dataset_id: Id of the dataset. dataset_name: Name of the dataset. + dataset_id: Id of the dataset. user: Cognee User request is being processed for, if None default user will be used. Returns: Tuple[User, Dataset]: A tuple containing the user and the authorized dataset. """ - if not user: - user = await get_default_user() - if dataset_id: - authorized_dataset = await get_authorized_dataset(user, dataset_id, "write") - elif dataset_name: - authorized_dataset = await get_authorized_dataset_by_name(dataset_name, user, "write") + user, authorized_datasets = await resolve_authorized_user_datasets( + datasets=dataset_id if dataset_id else dataset_name, user=user + ) - if not authorized_dataset: - authorized_dataset = await create_authorized_dataset( - dataset_name=dataset_name, user=user - ) - else: - raise ValueError("Either dataset_id or dataset_name must be provided.") - - if not authorized_dataset: - raise DatasetNotFoundError( - message=f"Dataset ({str(dataset_id) or dataset_name}) not found." - ) - - return user, authorized_dataset + return user, authorized_datasets[0] diff --git a/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py b/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py index f91064995..cda39d7bb 100644 --- a/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py +++ b/cognee/modules/pipelines/layers/resolve_authorized_user_datasets.py @@ -1,5 +1,5 @@ from uuid import UUID -from typing import Union, Tuple, List +from typing import Union, Tuple, List, Optional from cognee.modules.users.methods import get_default_user from cognee.modules.users.models import User @@ -13,7 +13,7 @@ from cognee.modules.data.methods import ( async def resolve_authorized_user_datasets( - datasets: Union[str, UUID, list[str], list[UUID]], user: User = None + datasets: Union[str, UUID, list[str], list[UUID]], user: Optional[User] = None ) -> Tuple[User, List[Dataset]]: """ Function handles creation and dataset authorization if datasets already exist for Cognee. diff --git a/cognee/modules/pipelines/operations/run_tasks.py b/cognee/modules/pipelines/operations/run_tasks.py index 62d4972ad..4a0c77309 100644 --- a/cognee/modules/pipelines/operations/run_tasks.py +++ b/cognee/modules/pipelines/operations/run_tasks.py @@ -4,35 +4,28 @@ import asyncio from uuid import UUID from typing import Any, List from functools import wraps -from sqlalchemy import select -import cognee.modules.ingestion as ingestion from cognee.infrastructure.databases.graph import get_graph_engine from cognee.infrastructure.databases.relational import get_relational_engine from cognee.modules.pipelines.operations.run_tasks_distributed import run_tasks_distributed from cognee.modules.users.models import User -from cognee.modules.data.models import Data -from cognee.infrastructure.files.utils.open_data_file import open_data_file from cognee.shared.logging_utils import get_logger from cognee.modules.users.methods import get_default_user from cognee.modules.pipelines.utils import generate_pipeline_id from cognee.modules.pipelines.exceptions import PipelineRunFailedError -from cognee.tasks.ingestion import save_data_item_to_storage, resolve_data_directories +from cognee.tasks.ingestion import resolve_data_directories from cognee.modules.pipelines.models.PipelineRunInfo import ( PipelineRunCompleted, PipelineRunErrored, PipelineRunStarted, - PipelineRunYield, - PipelineRunAlreadyCompleted, ) -from cognee.modules.pipelines.models.DataItemStatus import DataItemStatus - from cognee.modules.pipelines.operations import ( log_pipeline_run_start, log_pipeline_run_complete, log_pipeline_run_error, ) from .run_tasks_with_telemetry import run_tasks_with_telemetry +from .run_tasks_data_item import run_tasks_data_item from ..tasks.task import Task @@ -68,176 +61,6 @@ async def run_tasks( context: dict = None, incremental_loading: bool = False, ): - async def _run_tasks_data_item_incremental( - data_item, - dataset, - tasks, - pipeline_name, - pipeline_id, - pipeline_run_id, - context, - user, - ): - db_engine = get_relational_engine() - # If incremental_loading of data is set to True don't process documents already processed by pipeline - # If data is being added to Cognee for the first time calculate the id of the data - if not isinstance(data_item, Data): - file_path = await save_data_item_to_storage(data_item) - # Ingest data and add metadata - async with open_data_file(file_path) as file: - classified_data = ingestion.classify(file) - # data_id is the hash of file contents + owner id to avoid duplicate data - data_id = ingestion.identify(classified_data, user) - else: - # If data was already processed by Cognee get data id - data_id = data_item.id - - # Check pipeline status, if Data already processed for pipeline before skip current processing - async with db_engine.get_async_session() as session: - data_point = ( - await session.execute(select(Data).filter(Data.id == data_id)) - ).scalar_one_or_none() - if data_point: - if ( - data_point.pipeline_status.get(pipeline_name, {}).get(str(dataset.id)) - == DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED - ): - yield { - "run_info": PipelineRunAlreadyCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - return - - try: - # Process data based on data_item and list of tasks - async for result in run_tasks_with_telemetry( - tasks=tasks, - data=[data_item], - user=user, - pipeline_name=pipeline_id, - context=context, - ): - yield PipelineRunYield( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - payload=result, - ) - - # Update pipeline status for Data element - async with db_engine.get_async_session() as session: - data_point = ( - await session.execute(select(Data).filter(Data.id == data_id)) - ).scalar_one_or_none() - data_point.pipeline_status[pipeline_name] = { - str(dataset.id): DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED - } - await session.merge(data_point) - await session.commit() - - yield { - "run_info": PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - - except Exception as error: - # Temporarily swallow error and try to process rest of documents first, then re-raise error at end of data ingestion pipeline - logger.error( - f"Exception caught while processing data: {error}.\n Data processing failed for data item: {data_item}." - ) - yield { - "run_info": PipelineRunErrored( - pipeline_run_id=pipeline_run_id, - payload=repr(error), - dataset_id=dataset.id, - dataset_name=dataset.name, - ), - "data_id": data_id, - } - - if os.getenv("RAISE_INCREMENTAL_LOADING_ERRORS", "true").lower() == "true": - raise error - - async def _run_tasks_data_item_regular( - data_item, - dataset, - tasks, - pipeline_id, - pipeline_run_id, - context, - user, - ): - # Process data based on data_item and list of tasks - async for result in run_tasks_with_telemetry( - tasks=tasks, - data=[data_item], - user=user, - pipeline_name=pipeline_id, - context=context, - ): - yield PipelineRunYield( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - payload=result, - ) - - yield { - "run_info": PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ) - } - - async def _run_tasks_data_item( - data_item, - dataset, - tasks, - pipeline_name, - pipeline_id, - pipeline_run_id, - context, - user, - incremental_loading, - ): - # Go through async generator and return data item processing result. Result can be PipelineRunAlreadyCompleted when data item is skipped, - # PipelineRunCompleted when processing was successful and PipelineRunErrored if there were issues - result = None - if incremental_loading: - async for result in _run_tasks_data_item_incremental( - data_item=data_item, - dataset=dataset, - tasks=tasks, - pipeline_name=pipeline_name, - pipeline_id=pipeline_id, - pipeline_run_id=pipeline_run_id, - context=context, - user=user, - ): - pass - else: - async for result in _run_tasks_data_item_regular( - data_item=data_item, - dataset=dataset, - tasks=tasks, - pipeline_id=pipeline_id, - pipeline_run_id=pipeline_run_id, - context=context, - user=user, - ): - pass - - return result - if not user: user = await get_default_user() @@ -269,7 +92,7 @@ async def run_tasks( # Create async tasks per data item that will run the pipeline for the data item data_item_tasks = [ asyncio.create_task( - _run_tasks_data_item( + run_tasks_data_item( data_item, dataset, tasks, diff --git a/cognee/modules/pipelines/operations/run_tasks_data_item.py b/cognee/modules/pipelines/operations/run_tasks_data_item.py new file mode 100644 index 000000000..94fc631a8 --- /dev/null +++ b/cognee/modules/pipelines/operations/run_tasks_data_item.py @@ -0,0 +1,261 @@ +""" +Data item processing functions for pipeline operations. + +This module contains reusable functions for processing individual data items +within pipeline operations, supporting both incremental and regular processing modes. +""" + +import os +from typing import Any, Dict, AsyncGenerator, Optional +from sqlalchemy import select + +import cognee.modules.ingestion as ingestion +from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.infrastructure.files.utils.open_data_file import open_data_file +from cognee.shared.logging_utils import get_logger +from cognee.modules.users.models import User +from cognee.modules.data.models import Data, Dataset +from cognee.tasks.ingestion import save_data_item_to_storage +from cognee.modules.pipelines.models.PipelineRunInfo import ( + PipelineRunCompleted, + PipelineRunErrored, + PipelineRunYield, + PipelineRunAlreadyCompleted, +) +from cognee.modules.pipelines.models.DataItemStatus import DataItemStatus +from cognee.modules.pipelines.operations.run_tasks_with_telemetry import run_tasks_with_telemetry +from ..tasks.task import Task + +logger = get_logger("run_tasks_data_item") + + +async def run_tasks_data_item_incremental( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, +) -> AsyncGenerator[Dict[str, Any], None]: + """ + Process a single data item with incremental loading support. + + This function handles incremental processing by checking if the data item + has already been processed for the given pipeline and dataset. If it has, + it skips processing and returns a completion status. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_name: Name of the pipeline + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + + Yields: + Dict containing run_info and data_id for each processing step + """ + db_engine = get_relational_engine() + + # If incremental_loading of data is set to True don't process documents already processed by pipeline + # If data is being added to Cognee for the first time calculate the id of the data + if not isinstance(data_item, Data): + file_path = await save_data_item_to_storage(data_item) + # Ingest data and add metadata + async with open_data_file(file_path) as file: + classified_data = ingestion.classify(file) + # data_id is the hash of file contents + owner id to avoid duplicate data + data_id = ingestion.identify(classified_data, user) + else: + # If data was already processed by Cognee get data id + data_id = data_item.id + + # Check pipeline status, if Data already processed for pipeline before skip current processing + async with db_engine.get_async_session() as session: + data_point = ( + await session.execute(select(Data).filter(Data.id == data_id)) + ).scalar_one_or_none() + if data_point: + if ( + data_point.pipeline_status.get(pipeline_name, {}).get(str(dataset.id)) + == DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED + ): + yield { + "run_info": PipelineRunAlreadyCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + return + + try: + # Process data based on data_item and list of tasks + async for result in run_tasks_with_telemetry( + tasks=tasks, + data=[data_item], + user=user, + pipeline_name=pipeline_id, + context=context, + ): + yield PipelineRunYield( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + payload=result, + ) + + # Update pipeline status for Data element + async with db_engine.get_async_session() as session: + data_point = ( + await session.execute(select(Data).filter(Data.id == data_id)) + ).scalar_one_or_none() + data_point.pipeline_status[pipeline_name] = { + str(dataset.id): DataItemStatus.DATA_ITEM_PROCESSING_COMPLETED + } + await session.merge(data_point) + await session.commit() + + yield { + "run_info": PipelineRunCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + + except Exception as error: + # Temporarily swallow error and try to process rest of documents first, then re-raise error at end of data ingestion pipeline + logger.error( + f"Exception caught while processing data: {error}.\n Data processing failed for data item: {data_item}." + ) + yield { + "run_info": PipelineRunErrored( + pipeline_run_id=pipeline_run_id, + payload=repr(error), + dataset_id=dataset.id, + dataset_name=dataset.name, + ), + "data_id": data_id, + } + + if os.getenv("RAISE_INCREMENTAL_LOADING_ERRORS", "true").lower() == "true": + raise error + + +async def run_tasks_data_item_regular( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, +) -> AsyncGenerator[Dict[str, Any], None]: + """ + Process a single data item in regular (non-incremental) mode. + + This function processes a data item without checking for previous processing + status, executing all tasks on the data item. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + + Yields: + Dict containing run_info for each processing step + """ + # Process data based on data_item and list of tasks + async for result in run_tasks_with_telemetry( + tasks=tasks, + data=[data_item], + user=user, + pipeline_name=pipeline_id, + context=context, + ): + yield PipelineRunYield( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + payload=result, + ) + + yield { + "run_info": PipelineRunCompleted( + pipeline_run_id=pipeline_run_id, + dataset_id=dataset.id, + dataset_name=dataset.name, + ) + } + + +async def run_tasks_data_item( + data_item: Any, + dataset: Dataset, + tasks: list[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[Dict[str, Any]], + user: User, + incremental_loading: bool, +) -> Optional[Dict[str, Any]]: + """ + Process a single data item, choosing between incremental and regular processing. + + This is the main entry point for data item processing that delegates to either + incremental or regular processing based on the incremental_loading flag. + + Args: + data_item: The data item to process + dataset: The dataset containing the data item + tasks: List of tasks to execute on the data item + pipeline_name: Name of the pipeline + pipeline_id: Unique identifier for the pipeline + pipeline_run_id: Unique identifier for this pipeline run + context: Optional context dictionary + user: User performing the operation + incremental_loading: Whether to use incremental processing + + Returns: + Dict containing the final processing result, or None if processing was skipped + """ + # Go through async generator and return data item processing result. Result can be PipelineRunAlreadyCompleted when data item is skipped, + # PipelineRunCompleted when processing was successful and PipelineRunErrored if there were issues + result = None + if incremental_loading: + async for result in run_tasks_data_item_incremental( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_name=pipeline_name, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + ): + pass + else: + async for result in run_tasks_data_item_regular( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + ): + pass + + return result diff --git a/cognee/modules/pipelines/operations/run_tasks_distributed.py b/cognee/modules/pipelines/operations/run_tasks_distributed.py index d7dc2a403..95cdb0266 100644 --- a/cognee/modules/pipelines/operations/run_tasks_distributed.py +++ b/cognee/modules/pipelines/operations/run_tasks_distributed.py @@ -3,49 +3,96 @@ try: except ModuleNotFoundError: modal = None +from typing import Any, List, Optional +from uuid import UUID + +from cognee.modules.pipelines.tasks.task import Task from cognee.infrastructure.databases.relational import get_relational_engine +from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.pipelines.models import ( PipelineRunStarted, - PipelineRunYield, PipelineRunCompleted, + PipelineRunErrored, ) -from cognee.modules.pipelines.operations import log_pipeline_run_start, log_pipeline_run_complete -from cognee.modules.pipelines.utils.generate_pipeline_id import generate_pipeline_id +from cognee.modules.pipelines.operations import ( + log_pipeline_run_start, + log_pipeline_run_complete, + log_pipeline_run_error, +) +from cognee.modules.pipelines.utils import generate_pipeline_id from cognee.modules.users.methods import get_default_user from cognee.shared.logging_utils import get_logger - -from .run_tasks_with_telemetry import run_tasks_with_telemetry - +from cognee.modules.users.models import User +from cognee.modules.pipelines.exceptions import PipelineRunFailedError +from cognee.tasks.ingestion import resolve_data_directories +from .run_tasks_data_item import run_tasks_data_item logger = get_logger("run_tasks_distributed()") - if modal: + import os from distributed.app import app from distributed.modal_image import image + secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + @app.function( retries=3, image=image, timeout=86400, max_containers=50, - secrets=[modal.Secret.from_name("distributed_cognee")], + secrets=[modal.Secret.from_name(secret_name)], ) - async def run_tasks_on_modal(tasks, data_item, user, pipeline_name, context): - pipeline_run = run_tasks_with_telemetry(tasks, data_item, user, pipeline_name, context) + async def run_tasks_on_modal( + data_item, + dataset_id: UUID, + tasks: List[Task], + pipeline_name: str, + pipeline_id: str, + pipeline_run_id: str, + context: Optional[dict], + user: User, + incremental_loading: bool, + ): + """ + Wrapper that runs the run_tasks_data_item function. + This is the function/code that runs on modal executor and produces the graph/vector db objects + """ + from cognee.infrastructure.databases.relational import get_relational_engine - run_info = None + async with get_relational_engine().get_async_session() as session: + from cognee.modules.data.models import Dataset - async for pipeline_run_info in pipeline_run: - run_info = pipeline_run_info + dataset = await session.get(Dataset, dataset_id) - return run_info + result = await run_tasks_data_item( + data_item=data_item, + dataset=dataset, + tasks=tasks, + pipeline_name=pipeline_name, + pipeline_id=pipeline_id, + pipeline_run_id=pipeline_run_id, + context=context, + user=user, + incremental_loading=incremental_loading, + ) + + return result -async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, context): +async def run_tasks_distributed( + tasks: List[Task], + dataset_id: UUID, + data: List[Any] = None, + user: User = None, + pipeline_name: str = "unknown_pipeline", + context: dict = None, + incremental_loading: bool = False, +): if not user: user = await get_default_user() + # Get dataset object db_engine = get_relational_engine() async with db_engine.get_async_session() as session: from cognee.modules.data.models import Dataset @@ -53,9 +100,7 @@ async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, co dataset = await session.get(Dataset, dataset_id) pipeline_id = generate_pipeline_id(user.id, dataset.id, pipeline_name) - pipeline_run = await log_pipeline_run_start(pipeline_id, pipeline_name, dataset_id, data) - pipeline_run_id = pipeline_run.pipeline_run_id yield PipelineRunStarted( @@ -65,30 +110,67 @@ async def run_tasks_distributed(tasks, dataset_id, data, user, pipeline_name, co payload=data, ) - data_count = len(data) if isinstance(data, list) else 1 + try: + if not isinstance(data, list): + data = [data] - arguments = [ - [tasks] * data_count, - [[data_item] for data_item in data[:data_count]] if data_count > 1 else [data], - [user] * data_count, - [pipeline_name] * data_count, - [context] * data_count, - ] + data = await resolve_data_directories(data) - async for result in run_tasks_on_modal.map.aio(*arguments): - logger.info(f"Received result: {result}") + number_of_data_items = len(data) if isinstance(data, list) else 1 - yield PipelineRunYield( + data_item_tasks = [ + data, + [dataset.id] * number_of_data_items, + [tasks] * number_of_data_items, + [pipeline_name] * number_of_data_items, + [pipeline_id] * number_of_data_items, + [pipeline_run_id] * number_of_data_items, + [context] * number_of_data_items, + [user] * number_of_data_items, + [incremental_loading] * number_of_data_items, + ] + + results = [] + async for result in run_tasks_on_modal.map.aio(*data_item_tasks): + if not result: + continue + results.append(result) + + # Remove skipped results + results = [r for r in results if r] + + # If any data item failed, raise PipelineRunFailedError + errored = [ + r + for r in results + if r and r.get("run_info") and isinstance(r["run_info"], PipelineRunErrored) + ] + if errored: + raise PipelineRunFailedError("Pipeline run failed. Data item could not be processed.") + + await log_pipeline_run_complete( + pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data + ) + + yield PipelineRunCompleted( pipeline_run_id=pipeline_run_id, dataset_id=dataset.id, dataset_name=dataset.name, - payload=result, + data_ingestion_info=results, ) - await log_pipeline_run_complete(pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data) + except Exception as error: + await log_pipeline_run_error( + pipeline_run_id, pipeline_id, pipeline_name, dataset_id, data, error + ) - yield PipelineRunCompleted( - pipeline_run_id=pipeline_run_id, - dataset_id=dataset.id, - dataset_name=dataset.name, - ) + yield PipelineRunErrored( + pipeline_run_id=pipeline_run_id, + payload=repr(error), + dataset_id=dataset.id, + dataset_name=dataset.name, + data_ingestion_info=locals().get("results"), + ) + + if not isinstance(error, PipelineRunFailedError): + raise diff --git a/cognee/modules/retrieval/graph_completion_retriever.py b/cognee/modules/retrieval/graph_completion_retriever.py index 1772c6300..80815a42a 100644 --- a/cognee/modules/retrieval/graph_completion_retriever.py +++ b/cognee/modules/retrieval/graph_completion_retriever.py @@ -194,7 +194,7 @@ class GraphCompletionRetriever(BaseGraphRetriever): belongs_to_set=interactions_node_set, ) - await add_data_points(data_points=[cognee_user_interaction], update_edge_collection=False) + await add_data_points(data_points=[cognee_user_interaction]) relationships = [] relationship_name = "used_graph_element_to_answer" diff --git a/cognee/modules/retrieval/insights_retriever.py b/cognee/modules/retrieval/insights_retriever.py deleted file mode 100644 index 10aa288e6..000000000 --- a/cognee/modules/retrieval/insights_retriever.py +++ /dev/null @@ -1,133 +0,0 @@ -import asyncio -from typing import Any, Optional - -from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge, Node -from cognee.modules.retrieval.base_graph_retriever import BaseGraphRetriever -from cognee.shared.logging_utils import get_logger -from cognee.infrastructure.databases.graph import get_graph_engine -from cognee.infrastructure.databases.vector import get_vector_engine -from cognee.modules.retrieval.exceptions.exceptions import NoDataError -from cognee.infrastructure.databases.vector.exceptions.exceptions import CollectionNotFoundError - -logger = get_logger("InsightsRetriever") - - -class InsightsRetriever(BaseGraphRetriever): - """ - Retriever for handling graph connection-based insights. - - Public methods include: - - get_context - - get_completion - - Instance variables include: - - exploration_levels - - top_k - """ - - def __init__(self, exploration_levels: int = 1, top_k: Optional[int] = 5): - """Initialize retriever with exploration levels and search parameters.""" - self.exploration_levels = exploration_levels - self.top_k = top_k - - async def get_context(self, query: str) -> list: - """ - Find neighbours of a given node in the graph. - - If the provided query does not correspond to an existing node, - search for similar entities and retrieve their connections. - Reraises NoDataError if there is no data found in the system. - - Parameters: - ----------- - - - query (str): A string identifier for the node whose neighbours are to be - retrieved. - - Returns: - -------- - - - list: A list of unique connections found for the queried node. - """ - if query is None: - return [] - - node_id = query - graph_engine = await get_graph_engine() - exact_node = await graph_engine.extract_node(node_id) - - if exact_node is not None and "id" in exact_node: - node_connections = await graph_engine.get_connections(str(exact_node["id"])) - else: - vector_engine = get_vector_engine() - - try: - results = await asyncio.gather( - vector_engine.search("Entity_name", query_text=query, limit=self.top_k), - vector_engine.search("EntityType_name", query_text=query, limit=self.top_k), - ) - except CollectionNotFoundError as error: - logger.error("Entity collections not found") - raise NoDataError("No data found in the system, please add data first.") from error - - results = [*results[0], *results[1]] - relevant_results = [result for result in results if result.score < 0.5][: self.top_k] - - if len(relevant_results) == 0: - return [] - - node_connections_results = await asyncio.gather( - *[graph_engine.get_connections(result.id) for result in relevant_results] - ) - - node_connections = [] - for neighbours in node_connections_results: - node_connections.extend(neighbours) - - unique_node_connections_map = {} - unique_node_connections = [] - - for node_connection in node_connections: - if "id" not in node_connection[0] or "id" not in node_connection[2]: - continue - - unique_id = f"{node_connection[0]['id']} {node_connection[1]['relationship_name']} {node_connection[2]['id']}" - if unique_id not in unique_node_connections_map: - unique_node_connections_map[unique_id] = True - unique_node_connections.append(node_connection) - - return unique_node_connections - # return [ - # Edge( - # node1=Node(node_id=connection[0]["id"], attributes=connection[0]), - # node2=Node(node_id=connection[2]["id"], attributes=connection[2]), - # attributes={ - # **connection[1], - # "relationship_type": connection[1]["relationship_name"], - # }, - # ) - # for connection in unique_node_connections - # ] - - async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """ - Returns the graph connections context. - - If a context is not provided, it fetches the context using the query provided. - - Parameters: - ----------- - - - query (str): A string identifier used to fetch the context. - - context (Optional[Any]): An optional context to use for the completion; if None, - it fetches the context based on the query. (default None) - - Returns: - -------- - - - Any: The context used for the completion, which is either provided or fetched - based on the query. - """ - if context is None: - context = await self.get_context(query) - return context diff --git a/cognee/modules/retrieval/user_qa_feedback.py b/cognee/modules/retrieval/user_qa_feedback.py index 21e3d8e59..6f6c4ed8e 100644 --- a/cognee/modules/retrieval/user_qa_feedback.py +++ b/cognee/modules/retrieval/user_qa_feedback.py @@ -8,7 +8,7 @@ from cognee.shared.logging_utils import get_logger from cognee.modules.retrieval.base_feedback import BaseFeedback from cognee.modules.retrieval.utils.models import CogneeUserFeedback from cognee.modules.retrieval.utils.models import UserFeedbackEvaluation -from cognee.tasks.storage import add_data_points +from cognee.tasks.storage import add_data_points, index_graph_edges logger = get_logger("CompletionRetriever") @@ -47,7 +47,7 @@ class UserQAFeedback(BaseFeedback): belongs_to_set=feedbacks_node_set, ) - await add_data_points(data_points=[cognee_user_feedback], update_edge_collection=False) + await add_data_points(data_points=[cognee_user_feedback]) relationships = [] relationship_name = "gives_feedback_to" @@ -76,6 +76,7 @@ class UserQAFeedback(BaseFeedback): if len(relationships) > 0: graph_engine = await get_graph_engine() await graph_engine.add_edges(relationships) + await index_graph_edges(relationships) await graph_engine.apply_feedback_weight( node_ids=to_node_ids, weight=feedback_sentiment.score ) diff --git a/cognee/modules/retrieval/utils/description_to_codepart_search.py b/cognee/modules/retrieval/utils/description_to_codepart_search.py index a61feb574..649edb6ed 100644 --- a/cognee/modules/retrieval/utils/description_to_codepart_search.py +++ b/cognee/modules/retrieval/utils/description_to_codepart_search.py @@ -62,7 +62,7 @@ async def code_description_to_code_part( try: if include_docs: - search_results = await search(query_text=query, query_type="INSIGHTS") + search_results = await search(query_text=query, query_type="GRAPH_COMPLETION") concatenated_descriptions = " ".join( obj["description"] diff --git a/cognee/modules/search/methods/get_search_type_tools.py b/cognee/modules/search/methods/get_search_type_tools.py index 9cf67785e..72e2db89a 100644 --- a/cognee/modules/search/methods/get_search_type_tools.py +++ b/cognee/modules/search/methods/get_search_type_tools.py @@ -9,7 +9,6 @@ from cognee.modules.search.exceptions import UnsupportedSearchTypeError # Retrievers from cognee.modules.retrieval.user_qa_feedback import UserQAFeedback from cognee.modules.retrieval.chunks_retriever import ChunksRetriever -from cognee.modules.retrieval.insights_retriever import InsightsRetriever from cognee.modules.retrieval.summaries_retriever import SummariesRetriever from cognee.modules.retrieval.completion_retriever import CompletionRetriever from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever @@ -44,10 +43,6 @@ async def get_search_type_tools( SummariesRetriever(top_k=top_k).get_completion, SummariesRetriever(top_k=top_k).get_context, ], - SearchType.INSIGHTS: [ - InsightsRetriever(top_k=top_k).get_completion, - InsightsRetriever(top_k=top_k).get_context, - ], SearchType.CHUNKS: [ ChunksRetriever(top_k=top_k).get_completion, ChunksRetriever(top_k=top_k).get_context, diff --git a/cognee/modules/search/methods/search.py b/cognee/modules/search/methods/search.py index 8bcef815f..958af6444 100644 --- a/cognee/modules/search/methods/search.py +++ b/cognee/modules/search/methods/search.py @@ -19,7 +19,9 @@ from cognee.modules.search.types import ( from cognee.modules.search.operations import log_query, log_result from cognee.modules.users.models import User from cognee.modules.data.models import Dataset -from cognee.modules.users.permissions.methods import get_specific_user_permission_datasets +from cognee.modules.data.methods.get_authorized_existing_datasets import ( + get_authorized_existing_datasets, +) from .get_search_type_tools import get_search_type_tools from .no_access_control_search import no_access_control_search @@ -202,7 +204,9 @@ async def authorized_search( Not to be used outside of active access control mode. """ # Find datasets user has read access for (if datasets are provided only return them. Provided user has read access) - search_datasets = await get_specific_user_permission_datasets(user.id, "read", dataset_ids) + search_datasets = await get_authorized_existing_datasets( + datasets=dataset_ids, permission_type="read", user=user + ) if use_combined_context: search_responses = await search_in_datasets_context( diff --git a/cognee/modules/search/types/SearchType.py b/cognee/modules/search/types/SearchType.py index 418aec0b5..4536c0e01 100644 --- a/cognee/modules/search/types/SearchType.py +++ b/cognee/modules/search/types/SearchType.py @@ -3,7 +3,6 @@ from enum import Enum class SearchType(Enum): SUMMARIES = "SUMMARIES" - INSIGHTS = "INSIGHTS" CHUNKS = "CHUNKS" RAG_COMPLETION = "RAG_COMPLETION" GRAPH_COMPLETION = "GRAPH_COMPLETION" diff --git a/cognee/modules/settings/get_settings.py b/cognee/modules/settings/get_settings.py index fa92c8043..071bcca36 100644 --- a/cognee/modules/settings/get_settings.py +++ b/cognee/modules/settings/get_settings.py @@ -15,6 +15,7 @@ class ModelName(Enum): ollama = "ollama" anthropic = "anthropic" gemini = "gemini" + mistral = "mistral" class LLMConfig(BaseModel): @@ -72,6 +73,10 @@ def get_settings() -> SettingsDict: "value": "gemini", "label": "Gemini", }, + { + "value": "mistral", + "label": "Mistral", + }, ] return SettingsDict.model_validate( @@ -134,6 +139,24 @@ def get_settings() -> SettingsDict: "label": "Gemini 2.0 Flash", }, ], + "mistral": [ + { + "value": "mistral-medium-2508", + "label": "Mistral Medium 3.1", + }, + { + "value": "magistral-medium-2509", + "label": "Magistral Medium 1.2", + }, + { + "value": "magistral-medium-2507", + "label": "Magistral Medium 1.1", + }, + { + "value": "mistral-large-2411", + "label": "Mistral Large 2.1", + }, + ], }, }, vector_db={ diff --git a/cognee/modules/users/methods/get_authenticated_user.py b/cognee/modules/users/methods/get_authenticated_user.py index a2dd2330e..d78215892 100644 --- a/cognee/modules/users/methods/get_authenticated_user.py +++ b/cognee/modules/users/methods/get_authenticated_user.py @@ -37,6 +37,8 @@ async def get_authenticated_user( except Exception as e: # Convert any get_default_user failure into a proper HTTP 500 error logger.error(f"Failed to create default user: {str(e)}") - raise HTTPException(status_code=500, detail=f"Failed to create default user: {str(e)}") + raise HTTPException( + status_code=500, detail=f"Failed to create default user: {str(e)}" + ) from e return user diff --git a/cognee/modules/users/roles/methods/create_role.py b/cognee/modules/users/roles/methods/create_role.py index a57f000fe..aa7eda91d 100644 --- a/cognee/modules/users/roles/methods/create_role.py +++ b/cognee/modules/users/roles/methods/create_role.py @@ -40,8 +40,8 @@ async def create_role( # Add association directly to the association table role = Role(name=role_name, tenant_id=tenant.id) session.add(role) - except IntegrityError: - raise EntityAlreadyExistsError(message="Role already exists for tenant.") + except IntegrityError as e: + raise EntityAlreadyExistsError(message="Role already exists for tenant.") from e await session.commit() await session.refresh(role) diff --git a/cognee/modules/users/tenants/methods/create_tenant.py b/cognee/modules/users/tenants/methods/create_tenant.py index 09dfc8855..bfd23e08f 100644 --- a/cognee/modules/users/tenants/methods/create_tenant.py +++ b/cognee/modules/users/tenants/methods/create_tenant.py @@ -35,5 +35,5 @@ async def create_tenant(tenant_name: str, user_id: UUID) -> UUID: await session.merge(user) await session.commit() return tenant.id - except IntegrityError: - raise EntityAlreadyExistsError(message="Tenant already exists.") + except IntegrityError as e: + raise EntityAlreadyExistsError(message="Tenant already exists.") from e diff --git a/cognee/shared/data_models.py b/cognee/shared/data_models.py index 4e80ed81a..dcf036b09 100644 --- a/cognee/shared/data_models.py +++ b/cognee/shared/data_models.py @@ -288,7 +288,6 @@ class SummarizedCode(BaseModel): class GraphDBType(Enum): NETWORKX = auto() NEO4J = auto() - FALKORDB = auto() KUZU = auto() diff --git a/cognee/tasks/codingagents/coding_rule_associations.py b/cognee/tasks/codingagents/coding_rule_associations.py index 191204df3..d8d29ab91 100644 --- a/cognee/tasks/codingagents/coding_rule_associations.py +++ b/cognee/tasks/codingagents/coding_rule_associations.py @@ -124,5 +124,4 @@ async def add_rule_associations( if len(edges_to_save) > 0: await graph_engine.add_edges(edges_to_save) - - await index_graph_edges() + await index_graph_edges(edges_to_save) diff --git a/cognee/tasks/graph/extract_graph_from_data.py b/cognee/tasks/graph/extract_graph_from_data.py index ea26eaae0..49b51af2d 100644 --- a/cognee/tasks/graph/extract_graph_from_data.py +++ b/cognee/tasks/graph/extract_graph_from_data.py @@ -4,6 +4,7 @@ from pydantic import BaseModel from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.ontology.ontology_env_config import get_ontology_env_config +from cognee.tasks.storage import index_graph_edges from cognee.tasks.storage.add_data_points import add_data_points from cognee.modules.ontology.ontology_config import Config from cognee.modules.ontology.get_default_ontology_resolver import ( @@ -88,6 +89,7 @@ async def integrate_chunk_graphs( if len(graph_edges) > 0: await graph_engine.add_edges(graph_edges) + await index_graph_edges(graph_edges) return data_chunks diff --git a/cognee/tasks/ingestion/save_data_item_to_storage.py b/cognee/tasks/ingestion/save_data_item_to_storage.py index 34f70e917..b6e1f7d00 100644 --- a/cognee/tasks/ingestion/save_data_item_to_storage.py +++ b/cognee/tasks/ingestion/save_data_item_to_storage.py @@ -8,6 +8,7 @@ from cognee.modules.ingestion import save_data_to_file from cognee.shared.logging_utils import get_logger from pydantic_settings import BaseSettings, SettingsConfigDict + logger = get_logger() @@ -17,6 +18,13 @@ class SaveDataSettings(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") +class HTMLContent(str): + def __new__(cls, value: str): + if not ("<" in value and ">" in value): + raise ValueError("Not valid HTML-like content") + return super().__new__(cls, value) + + settings = SaveDataSettings() @@ -27,6 +35,12 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str return await get_data_from_llama_index(data_item) + if "docling" in str(type(data_item)): + from docling_core.types import DoclingDocument + + if isinstance(data_item, DoclingDocument): + data_item = data_item.export_to_text() + # data is a file object coming from upload. if hasattr(data_item, "file"): return await save_data_to_file(data_item.file, filename=data_item.filename) @@ -48,6 +62,40 @@ async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any]) -> str # data is s3 file path if parsed_url.scheme == "s3": return data_item + elif parsed_url.scheme == "http" or parsed_url.scheme == "https": + # Validate URL by sending a HEAD request + try: + from cognee.context_global_variables import tavily_config, soup_crawler_config + from cognee.tasks.web_scraper import fetch_page_content + + tavily = tavily_config.get() + soup_crawler = soup_crawler_config.get() + preferred_tool = "beautifulsoup" if soup_crawler else "tavily" + if preferred_tool == "tavily" and tavily is None: + raise IngestionError( + message="TavilyConfig must be set on the ingestion context when fetching HTTP URLs without a SoupCrawlerConfig." + ) + if preferred_tool == "beautifulsoup" and soup_crawler is None: + raise IngestionError( + message="SoupCrawlerConfig must be set on the ingestion context when using the BeautifulSoup scraper." + ) + + data = await fetch_page_content( + data_item, + preferred_tool=preferred_tool, + tavily_config=tavily, + soup_crawler_config=soup_crawler, + ) + content = "" + for key, value in data.items(): + content += f"{key}:\n{value}\n\n" + return await save_data_to_file(content) + except IngestionError: + raise + except Exception as e: + raise IngestionError( + message=f"Error ingesting webpage results of url {data_item}: {str(e)}" + ) # data is local file path elif parsed_url.scheme == "file": diff --git a/cognee/tasks/storage/add_data_points.py b/cognee/tasks/storage/add_data_points.py index 1c7a4c2bc..ad1693e82 100644 --- a/cognee/tasks/storage/add_data_points.py +++ b/cognee/tasks/storage/add_data_points.py @@ -10,9 +10,7 @@ from cognee.tasks.storage.exceptions import ( ) -async def add_data_points( - data_points: List[DataPoint], update_edge_collection: bool = True -) -> List[DataPoint]: +async def add_data_points(data_points: List[DataPoint]) -> List[DataPoint]: """ Add a batch of data points to the graph database by extracting nodes and edges, deduplicating them, and indexing them for retrieval. @@ -25,9 +23,6 @@ async def add_data_points( Args: data_points (List[DataPoint]): A list of data points to process and insert into the graph. - update_edge_collection (bool, optional): - Whether to update the edge index after adding edges. - Defaults to True. Returns: List[DataPoint]: @@ -73,12 +68,10 @@ async def add_data_points( graph_engine = await get_graph_engine() + await graph_engine.add_nodes(nodes) await index_data_points(nodes) - await graph_engine.add_nodes(nodes) await graph_engine.add_edges(edges) - - if update_edge_collection: - await index_graph_edges() + await index_graph_edges(edges) return data_points diff --git a/cognee/tasks/storage/index_data_points.py b/cognee/tasks/storage/index_data_points.py index 9c363c04c..362412657 100644 --- a/cognee/tasks/storage/index_data_points.py +++ b/cognee/tasks/storage/index_data_points.py @@ -39,7 +39,7 @@ async def index_data_points(data_points: list[DataPoint]): field_name = index_name_and_field[first_occurence + 1 :] try: # In case the amount of indexable points is too large we need to send them in batches - batch_size = 100 + batch_size = vector_engine.embedding_engine.get_batch_size() for i in range(0, len(indexable_points), batch_size): batch = indexable_points[i : i + batch_size] await vector_engine.index_data_points(index_name, field_name, batch) diff --git a/cognee/tasks/storage/index_graph_edges.py b/cognee/tasks/storage/index_graph_edges.py index 2233ab99f..b7bf7a2b9 100644 --- a/cognee/tasks/storage/index_graph_edges.py +++ b/cognee/tasks/storage/index_graph_edges.py @@ -1,15 +1,18 @@ from cognee.modules.engine.utils.generate_edge_id import generate_edge_id -from cognee.shared.logging_utils import get_logger, ERROR +from cognee.shared.logging_utils import get_logger from collections import Counter - +from typing import Optional, Dict, Any, List, Tuple, Union from cognee.infrastructure.databases.vector import get_vector_engine from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.graph.models.EdgeType import EdgeType +from cognee.infrastructure.databases.graph.graph_db_interface import EdgeData -logger = get_logger(level=ERROR) +logger = get_logger() -async def index_graph_edges(batch_size: int = 1024): +async def index_graph_edges( + edges_data: Union[List[EdgeData], List[Tuple[str, str, str, Optional[Dict[str, Any]]]]] = None, +): """ Indexes graph edges by creating and managing vector indexes for relationship types. @@ -35,13 +38,17 @@ async def index_graph_edges(batch_size: int = 1024): index_points = {} vector_engine = get_vector_engine() - graph_engine = await get_graph_engine() + + if edges_data is None: + graph_engine = await get_graph_engine() + _, edges_data = await graph_engine.get_graph_data() + logger.warning( + "Your graph edge embedding is deprecated, please pass edges to the index_graph_edges directly." + ) except Exception as e: logger.error("Failed to initialize engines: %s", e) raise RuntimeError("Initialization error") from e - _, edges_data = await graph_engine.get_graph_data() - edge_types = Counter( item.get("relationship_name") for edge in edges_data @@ -72,6 +79,8 @@ async def index_graph_edges(batch_size: int = 1024): for index_name, indexable_points in index_points.items(): index_name, field_name = index_name.split(".") + # Get maximum batch size for embedding model + batch_size = vector_engine.embedding_engine.get_batch_size() # We save the data in batches of {batch_size} to not put a lot of pressure on the database for start in range(0, len(indexable_points), batch_size): batch = indexable_points[start : start + batch_size] diff --git a/cognee/tasks/web_scraper/__init__.py b/cognee/tasks/web_scraper/__init__.py new file mode 100644 index 000000000..d8e580fad --- /dev/null +++ b/cognee/tasks/web_scraper/__init__.py @@ -0,0 +1,18 @@ +"""Web scraping module for cognee. + +This module provides tools for scraping web content, managing scraping jobs, and storing +data in a graph database. It includes classes and functions for crawling web pages using +BeautifulSoup or Tavily, defining data models, and handling scraping configurations. +""" + +from .bs4_crawler import BeautifulSoupCrawler +from .utils import fetch_page_content +from .web_scraper_task import cron_web_scraper_task, web_scraper_task + + +__all__ = [ + "BeautifulSoupCrawler", + "fetch_page_content", + "cron_web_scraper_task", + "web_scraper_task", +] diff --git a/cognee/tasks/web_scraper/bs4_crawler.py b/cognee/tasks/web_scraper/bs4_crawler.py new file mode 100644 index 000000000..0fbff4808 --- /dev/null +++ b/cognee/tasks/web_scraper/bs4_crawler.py @@ -0,0 +1,546 @@ +"""BeautifulSoup-based web crawler for extracting content from web pages. + +This module provides the BeautifulSoupCrawler class for fetching and extracting content +from web pages using BeautifulSoup or Playwright for JavaScript-rendered pages. It +supports robots.txt handling, rate limiting, and custom extraction rules. +""" + +import asyncio +import time +from typing import Union, List, Dict, Any, Optional +from urllib.parse import urlparse +from dataclasses import dataclass, field +from functools import lru_cache +import httpx +from bs4 import BeautifulSoup +from cognee.shared.logging_utils import get_logger + +logger = get_logger(__name__) + +try: + from playwright.async_api import async_playwright +except ImportError: + logger.warning( + "Failed to import playwright, make sure to install using pip install playwright>=1.9.0" + ) + async_playwright = None + +try: + from protego import Protego +except ImportError: + logger.warning("Failed to import protego, make sure to install using pip install protego>=0.1") + Protego = None + + +@dataclass +class ExtractionRule: + """Normalized extraction rule for web content. + + Attributes: + selector: CSS selector for extraction (if any). + xpath: XPath expression for extraction (if any). + attr: HTML attribute to extract (if any). + all: If True, extract all matching elements; otherwise, extract first. + join_with: String to join multiple extracted elements. + """ + + selector: Optional[str] = None + xpath: Optional[str] = None + attr: Optional[str] = None + all: bool = False + join_with: str = " " + + +@dataclass +class RobotsTxtCache: + """Cache for robots.txt data. + + Attributes: + protego: Parsed robots.txt object (Protego instance). + crawl_delay: Delay between requests (in seconds). + timestamp: Time when the cache entry was created. + """ + + protego: Any + crawl_delay: float + timestamp: float = field(default_factory=time.time) + + +class BeautifulSoupCrawler: + """Crawler for fetching and extracting web content using BeautifulSoup. + + Supports asynchronous HTTP requests, Playwright for JavaScript rendering, robots.txt + compliance, and rate limiting. Extracts content using CSS selectors or XPath rules. + + Attributes: + concurrency: Number of concurrent requests allowed. + crawl_delay: Minimum seconds between requests to the same domain. + timeout: Per-request timeout in seconds. + max_retries: Number of retries for failed requests. + retry_delay_factor: Multiplier for exponential backoff on retries. + headers: HTTP headers for requests (e.g., User-Agent). + robots_cache_ttl: Time-to-live for robots.txt cache in seconds. + """ + + def __init__( + self, + *, + concurrency: int = 5, + crawl_delay: float = 0.5, + timeout: float = 15.0, + max_retries: int = 2, + retry_delay_factor: float = 0.5, + headers: Optional[Dict[str, str]] = None, + robots_cache_ttl: float = 3600.0, + ): + """Initialize the BeautifulSoupCrawler. + + Args: + concurrency: Number of concurrent requests allowed. + crawl_delay: Minimum seconds between requests to the same domain. + timeout: Per-request timeout in seconds. + max_retries: Number of retries for failed requests. + retry_delay_factor: Multiplier for exponential backoff on retries. + headers: HTTP headers for requests (defaults to User-Agent: Cognee-Scraper/1.0). + robots_cache_ttl: Time-to-live for robots.txt cache in seconds. + """ + self.concurrency = concurrency + self._sem = asyncio.Semaphore(concurrency) + self.crawl_delay = crawl_delay + self.timeout = timeout + self.max_retries = max_retries + self.retry_delay_factor = retry_delay_factor + self.headers = headers or {"User-Agent": "Cognee-Scraper/1.0"} + self.robots_cache_ttl = robots_cache_ttl + self._last_request_time_per_domain: Dict[str, float] = {} + self._robots_cache: Dict[str, RobotsTxtCache] = {} + self._client: Optional[httpx.AsyncClient] = None + self._robots_lock = asyncio.Lock() + + async def _ensure_client(self): + """Initialize the HTTP client if not already created.""" + if self._client is None: + self._client = httpx.AsyncClient(timeout=self.timeout, headers=self.headers) + + async def close(self): + """Close the HTTP client.""" + if self._client: + await self._client.aclose() + self._client = None + + async def __aenter__(self): + """Enter the context manager, initializing the HTTP client.""" + await self._ensure_client() + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """Exit the context manager, closing the HTTP client.""" + await self.close() + + @lru_cache(maxsize=1024) + def _domain_from_url(self, url: str) -> str: + """Extract the domain (netloc) from a URL. + + Args: + url: The URL to parse. + + Returns: + str: The domain (netloc) of the URL. + """ + try: + return urlparse(url).netloc + except Exception: + return url + + @lru_cache(maxsize=1024) + def _get_domain_root(self, url: str) -> str: + """Get the root URL (scheme and netloc) from a URL. + + Args: + url: The URL to parse. + + Returns: + str: The root URL (e.g., "https://example.com"). + """ + parsed = urlparse(url) + return f"{parsed.scheme}://{parsed.netloc}" + + async def _respect_rate_limit(self, url: str, crawl_delay: Optional[float] = None): + """Enforce rate limiting for requests to the same domain. + + Args: + url: The URL to check. + crawl_delay: Custom crawl delay in seconds (if any). + """ + domain = self._domain_from_url(url) + last = self._last_request_time_per_domain.get(domain) + delay = crawl_delay if crawl_delay is not None else self.crawl_delay + + if last is None: + self._last_request_time_per_domain[domain] = time.time() + return + + elapsed = time.time() - last + wait_for = delay - elapsed + if wait_for > 0: + await asyncio.sleep(wait_for) + self._last_request_time_per_domain[domain] = time.time() + + async def _get_robots_cache(self, domain_root: str) -> Optional[RobotsTxtCache]: + """Get cached robots.txt data if valid. + + Args: + domain_root: The root URL (e.g., "https://example.com"). + + Returns: + Optional[RobotsTxtCache]: Cached robots.txt data or None if expired or not found. + """ + if Protego is None: + return None + + cached = self._robots_cache.get(domain_root) + if cached and (time.time() - cached.timestamp) < self.robots_cache_ttl: + return cached + return None + + async def _fetch_and_cache_robots(self, domain_root: str) -> RobotsTxtCache: + """Fetch and cache robots.txt data. + + Args: + domain_root: The root URL (e.g., "https://example.com"). + + Returns: + RobotsTxtCache: Cached robots.txt data with crawl delay. + + Raises: + Exception: If fetching robots.txt fails. + """ + async with self._robots_lock: + cached = await self._get_robots_cache(domain_root) + if cached: + return cached + + robots_url = f"{domain_root}/robots.txt" + try: + await self._ensure_client() + await self._respect_rate_limit(robots_url, self.crawl_delay) + resp = await self._client.get(robots_url, timeout=5.0) + content = resp.text if resp.status_code == 200 else "" + except Exception as e: + logger.debug(f"Failed to fetch robots.txt from {domain_root}: {e}") + content = "" + + protego = Protego.parse(content) if content.strip() else None + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + + crawl_delay = self.crawl_delay + if protego: + delay = protego.crawl_delay(agent) or protego.crawl_delay("*") + crawl_delay = delay if delay else self.crawl_delay + + cache_entry = RobotsTxtCache(protego=protego, crawl_delay=crawl_delay) + self._robots_cache[domain_root] = cache_entry + return cache_entry + + async def _is_url_allowed(self, url: str) -> bool: + """Check if a URL is allowed by robots.txt. + + Args: + url: The URL to check. + + Returns: + bool: True if the URL is allowed, False otherwise. + """ + if Protego is None: + return True + + try: + domain_root = self._get_domain_root(url) + cache = await self._get_robots_cache(domain_root) + if cache is None: + cache = await self._fetch_and_cache_robots(domain_root) + + if cache.protego is None: + return True + + agent = next((v for k, v in self.headers.items() if k.lower() == "user-agent"), "*") + return cache.protego.can_fetch(agent, url) or cache.protego.can_fetch("*", url) + except Exception as e: + logger.debug(f"Error checking robots.txt for {url}: {e}") + return True + + async def _get_crawl_delay(self, url: str) -> float: + """Get the crawl delay for a URL from robots.txt. + + Args: + url: The URL to check. + + Returns: + float: Crawl delay in seconds. + """ + if Protego is None: + return self.crawl_delay + + try: + domain_root = self._get_domain_root(url) + cache = await self._get_robots_cache(domain_root) + if cache is None: + cache = await self._fetch_and_cache_robots(domain_root) + return cache.crawl_delay + except Exception: + return self.crawl_delay + + async def _fetch_httpx(self, url: str) -> str: + """Fetch a URL using HTTPX with retries. + + Args: + url: The URL to fetch. + + Returns: + str: The HTML content of the page. + + Raises: + Exception: If all retry attempts fail. + """ + await self._ensure_client() + assert self._client is not None, "HTTP client not initialized" + + attempt = 0 + crawl_delay = await self._get_crawl_delay(url) + + while True: + try: + await self._respect_rate_limit(url, crawl_delay) + resp = await self._client.get(url) + resp.raise_for_status() + return resp.text + except Exception as exc: + attempt += 1 + if attempt > self.max_retries: + logger.error(f"Fetch failed for {url} after {attempt} attempts: {exc}") + raise + + delay = self.retry_delay_factor * (2 ** (attempt - 1)) + logger.warning( + f"Retrying {url} after {delay:.2f}s (attempt {attempt}) due to {exc}" + ) + await asyncio.sleep(delay) + + async def _render_with_playwright( + self, url: str, js_wait: float = 1.0, timeout: Optional[float] = None + ) -> str: + """Fetch and render a URL using Playwright for JavaScript content. + + Args: + url: The URL to fetch. + js_wait: Seconds to wait for JavaScript to load. + timeout: Timeout for the request (in seconds, defaults to instance timeout). + + Returns: + str: The rendered HTML content. + + Raises: + RuntimeError: If Playwright is not installed. + Exception: If all retry attempts fail. + """ + if async_playwright is None: + raise RuntimeError( + "Playwright is not installed. Install with `pip install playwright` and run `playwright install`." + ) + attempt = 0 + while True: + try: + async with async_playwright() as p: + browser = await p.chromium.launch(headless=True) + try: + context = await browser.new_context() + page = await context.new_page() + await page.goto( + url, + wait_until="networkidle", + timeout=int((timeout or self.timeout) * 1000), + ) + if js_wait: + await asyncio.sleep(js_wait) + return await page.content() + finally: + await browser.close() + except Exception as exc: + attempt += 1 + if attempt > self.max_retries: + logger.error(f"Playwright fetch failed for {url}: {exc}") + raise + backoff = self.retry_delay_factor * (2 ** (attempt - 1)) + logger.warning( + f"Retrying playwright fetch {url} after {backoff:.2f}s (attempt {attempt})" + ) + await asyncio.sleep(backoff) + + def _normalize_rule(self, rule: Union[str, Dict[str, Any]]) -> ExtractionRule: + """Normalize an extraction rule to an ExtractionRule dataclass. + + Args: + rule: A string (CSS selector) or dict with extraction parameters. + + Returns: + ExtractionRule: Normalized extraction rule. + + Raises: + ValueError: If the rule is invalid. + """ + if isinstance(rule, str): + return ExtractionRule(selector=rule) + if isinstance(rule, dict): + return ExtractionRule( + selector=rule.get("selector"), + xpath=rule.get("xpath"), + attr=rule.get("attr"), + all=bool(rule.get("all", False)), + join_with=rule.get("join_with", " "), + ) + raise ValueError(f"Invalid extraction rule: {rule}") + + def _extract_with_bs4(self, html: str, rule: ExtractionRule) -> str: + """Extract content from HTML using BeautifulSoup or lxml XPath. + + Args: + html: The HTML content to extract from. + rule: The extraction rule to apply. + + Returns: + str: The extracted content. + + Raises: + RuntimeError: If XPath is used but lxml is not installed. + """ + soup = BeautifulSoup(html, "html.parser") + + if rule.xpath: + try: + from lxml import html as lxml_html + except ImportError: + raise RuntimeError( + "XPath requested but lxml is not available. Install lxml or use CSS selectors." + ) + doc = lxml_html.fromstring(html) + nodes = doc.xpath(rule.xpath) + texts = [] + for n in nodes: + if hasattr(n, "text_content"): + texts.append(n.text_content().strip()) + else: + texts.append(str(n).strip()) + return rule.join_with.join(t for t in texts if t) + + if not rule.selector: + return "" + + if rule.all: + nodes = soup.select(rule.selector) + pieces = [] + for el in nodes: + if rule.attr: + val = el.get(rule.attr) + if val: + pieces.append(val.strip()) + else: + text = el.get_text(strip=True) + if text: + pieces.append(text) + return rule.join_with.join(pieces).strip() + else: + el = soup.select_one(rule.selector) + if el is None: + return "" + if rule.attr: + val = el.get(rule.attr) + return (val or "").strip() + return el.get_text(strip=True) + + async def fetch_with_bs4( + self, + urls: Union[str, List[str], Dict[str, Dict[str, Any]]], + extraction_rules: Optional[Dict[str, Any]] = None, + *, + use_playwright: bool = False, + playwright_js_wait: float = 0.8, + join_all_matches: bool = False, + ) -> Dict[str, str]: + """Fetch and extract content from URLs using BeautifulSoup or Playwright. + + Args: + urls: A single URL, list of URLs, or dict mapping URLs to extraction rules. + extraction_rules: Default extraction rules for string or list URLs. + use_playwright: If True, use Playwright for JavaScript rendering. + playwright_js_wait: Seconds to wait for JavaScript to load. + join_all_matches: If True, extract all matching elements for each rule. + + Returns: + Dict[str, str]: A dictionary mapping URLs to their extracted content. + + Raises: + ValueError: If extraction_rules are missing when required or if urls is invalid. + Exception: If fetching or extraction fails. + """ + url_rules_map: Dict[str, Dict[str, Any]] = {} + + if isinstance(urls, str): + if not extraction_rules: + raise ValueError("extraction_rules required when urls is a string") + url_rules_map[urls] = extraction_rules + elif isinstance(urls, list): + if not extraction_rules: + raise ValueError("extraction_rules required when urls is a list") + for url in urls: + url_rules_map[url] = extraction_rules + elif isinstance(urls, dict): + url_rules_map = urls + else: + raise ValueError(f"Invalid urls type: {type(urls)}") + + normalized_url_rules: Dict[str, List[ExtractionRule]] = {} + for url, rules in url_rules_map.items(): + normalized_rules = [] + for _, rule in rules.items(): + r = self._normalize_rule(rule) + if join_all_matches: + r.all = True + normalized_rules.append(r) + normalized_url_rules[url] = normalized_rules + + async def _task(url: str): + async with self._sem: + try: + allowed = await self._is_url_allowed(url) + if not allowed: + logger.warning(f"URL disallowed by robots.txt: {url}") + return url, "" + + if use_playwright: + html = await self._render_with_playwright( + url, js_wait=playwright_js_wait, timeout=self.timeout + ) + else: + html = await self._fetch_httpx(url) + + pieces = [] + for rule in normalized_url_rules[url]: + text = self._extract_with_bs4(html, rule) + if text: + pieces.append(text) + + concatenated = " ".join(pieces).strip() + return url, concatenated + + except Exception as e: + logger.error(f"Error processing {url}: {e}") + return url, "" + + tasks = [asyncio.create_task(_task(u)) for u in url_rules_map.keys()] + results = {} + + for coro in asyncio.as_completed(tasks): + url, text = await coro + results[url] = text + + return results diff --git a/cognee/tasks/web_scraper/config.py b/cognee/tasks/web_scraper/config.py new file mode 100644 index 000000000..2ee43ed32 --- /dev/null +++ b/cognee/tasks/web_scraper/config.py @@ -0,0 +1,24 @@ +from pydantic import BaseModel, Field +from typing import Any, Dict, Optional, Literal +import os + + +class TavilyConfig(BaseModel): + api_key: Optional[str] = os.getenv("TAVILY_API_KEY") + extract_depth: Literal["basic", "advanced"] = "basic" + proxies: Optional[Dict[str, str]] = None + timeout: Optional[int] = Field(default=10, ge=1, le=60) + + +class SoupCrawlerConfig(BaseModel): + concurrency: int = 5 + crawl_delay: float = 0.5 + timeout: float = 15.0 + max_retries: int = 2 + retry_delay_factor: float = 0.5 + headers: Optional[Dict[str, str]] = None + extraction_rules: Dict[str, Any] + use_playwright: bool = False + playwright_js_wait: float = 0.8 + robots_cache_ttl: float = 3600.0 + join_all_matches: bool = False diff --git a/cognee/tasks/web_scraper/models.py b/cognee/tasks/web_scraper/models.py new file mode 100644 index 000000000..aed2db0ed --- /dev/null +++ b/cognee/tasks/web_scraper/models.py @@ -0,0 +1,46 @@ +from cognee.infrastructure.engine import DataPoint +from typing import Optional, Dict, Any, List +from datetime import datetime + + +class WebPage(DataPoint): + """Represents a scraped web page with metadata""" + + name: Optional[str] + content: str + content_hash: str + scraped_at: datetime + last_modified: Optional[datetime] + status_code: int + content_type: str + page_size: int + extraction_rules: Dict[str, Any] # CSS selectors, XPath rules used + description: str + metadata: dict = {"index_fields": ["name", "description", "content"]} + + +class WebSite(DataPoint): + """Represents a website or domain being scraped""" + + name: str + base_url: str + robots_txt: Optional[str] + crawl_delay: float + last_crawled: datetime + page_count: int + scraping_config: Dict[str, Any] + description: str + metadata: dict = {"index_fields": ["name", "description"]} + + +class ScrapingJob(DataPoint): + """Represents a scraping job configuration""" + + name: str + urls: List[str] + schedule: Optional[str] # Cron-like schedule for recurring scrapes + status: str # "active", "paused", "completed", "failed" + last_run: Optional[datetime] + next_run: Optional[datetime] + description: str + metadata: dict = {"index_fields": ["name", "description"]} diff --git a/cognee/tasks/web_scraper/utils.py b/cognee/tasks/web_scraper/utils.py new file mode 100644 index 000000000..6d094f423 --- /dev/null +++ b/cognee/tasks/web_scraper/utils.py @@ -0,0 +1,126 @@ +"""Utilities for fetching web content using BeautifulSoup or Tavily. + +This module provides functions to fetch and extract content from web pages, supporting +both BeautifulSoup for custom extraction rules and Tavily for API-based scraping. +""" + +from typing import Dict, List, Union, Optional, Literal +from cognee.shared.logging_utils import get_logger +from .bs4_crawler import BeautifulSoupCrawler +from .config import TavilyConfig, SoupCrawlerConfig + +logger = get_logger(__name__) + + +async def fetch_page_content( + urls: Union[str, List[str]], + *, + preferred_tool: Optional[Literal["tavily", "beautifulsoup"]] = "beautifulsoup", + tavily_config: Optional[TavilyConfig] = None, + soup_crawler_config: Optional[SoupCrawlerConfig] = None, +) -> Dict[str, str]: + """Fetch content from one or more URLs using the specified tool. + + This function retrieves web page content using either BeautifulSoup (with custom + extraction rules) or Tavily (API-based scraping). It handles single URLs or lists of + URLs and returns a dictionary mapping URLs to their extracted content. + + Args: + urls: A single URL (str) or a list of URLs (List[str]) to scrape. + preferred_tool: The scraping tool to use ("tavily" or "beautifulsoup"). + Defaults to "beautifulsoup". + tavily_config: Configuration for Tavily API, including API key. + Required if preferred_tool is "tavily". + soup_crawler_config: Configuration for BeautifulSoup crawler, including + extraction rules. Required if preferred_tool is "beautifulsoup" and + extraction_rules are needed. + + Returns: + Dict[str, str]: A dictionary mapping each URL to its + extracted content (as a string for BeautifulSoup or a dict for Tavily). + + Raises: + ValueError: If Tavily API key is missing when using Tavily, or if + extraction_rules are not provided when using BeautifulSoup. + ImportError: If required dependencies (beautifulsoup4 or tavily-python) are not + installed. + """ + if preferred_tool == "tavily": + if not tavily_config or tavily_config.api_key is None: + raise ValueError("TAVILY_API_KEY must be set in TavilyConfig to use Tavily") + return await fetch_with_tavily(urls, tavily_config) + + if preferred_tool == "beautifulsoup": + try: + from bs4 import BeautifulSoup as _ # noqa: F401 + except ImportError: + logger.error( + "Failed to import bs4, make sure to install using pip install beautifulsoup4>=4.13.1" + ) + raise ImportError + if not soup_crawler_config or soup_crawler_config.extraction_rules is None: + raise ValueError("extraction_rules must be provided when not using Tavily") + extraction_rules = soup_crawler_config.extraction_rules + crawler = BeautifulSoupCrawler( + concurrency=soup_crawler_config.concurrency, + crawl_delay=soup_crawler_config.crawl_delay, + timeout=soup_crawler_config.timeout, + max_retries=soup_crawler_config.max_retries, + retry_delay_factor=soup_crawler_config.retry_delay_factor, + headers=soup_crawler_config.headers, + robots_cache_ttl=soup_crawler_config.robots_cache_ttl, + ) + try: + results = await crawler.fetch_with_bs4( + urls, + extraction_rules, + use_playwright=soup_crawler_config.use_playwright, + playwright_js_wait=soup_crawler_config.playwright_js_wait, + join_all_matches=soup_crawler_config.join_all_matches, + ) + return results + except Exception as e: + logger.error(f"Error fetching page content: {str(e)}") + raise + finally: + await crawler.close() + + +async def fetch_with_tavily( + urls: Union[str, List[str]], tavily_config: Optional[TavilyConfig] = None +) -> Dict[str, str]: + """Fetch content from URLs using the Tavily API. + + Args: + urls: A single URL (str) or a list of URLs (List[str]) to scrape. + + Returns: + Dict[str, str]: A dictionary mapping each URL to its raw content as a string. + + Raises: + ImportError: If tavily-python is not installed. + Exception: If the Tavily API request fails. + """ + try: + from tavily import AsyncTavilyClient + except ImportError: + logger.error( + "Failed to import tavily, make sure to install using pip install tavily-python>=0.7.0" + ) + raise + client = AsyncTavilyClient( + api_key=tavily_config.api_key if tavily_config else None, + proxies=tavily_config.proxies if tavily_config else None, + ) + results = await client.extract( + urls, + format="text", + extract_depth=tavily_config.extract_depth if tavily_config else "basic", + timeout=tavily_config.timeout if tavily_config else 10, + ) + for failed_result in results.get("failed_results", []): + logger.warning(f"Failed to fetch {failed_result}") + return_results = {} + for result in results.get("results", []): + return_results[result["url"]] = result["raw_content"] + return return_results diff --git a/cognee/tasks/web_scraper/web_scraper_task.py b/cognee/tasks/web_scraper/web_scraper_task.py new file mode 100644 index 000000000..52154c6ef --- /dev/null +++ b/cognee/tasks/web_scraper/web_scraper_task.py @@ -0,0 +1,396 @@ +"""Web scraping tasks for storing scraped data in a graph database. + +This module provides functions to scrape web content, create or update WebPage, WebSite, +and ScrapingJob data points, and store them in a Kuzu graph database. It supports +scheduled scraping tasks and ensures that node updates preserve existing graph edges. +""" + +import os +import hashlib +from datetime import datetime +from typing import Union, List +from urllib.parse import urlparse +from uuid import uuid5, NAMESPACE_OID + +from cognee.infrastructure.databases.graph import get_graph_engine +from cognee.shared.logging_utils import get_logger +from cognee.tasks.storage.index_data_points import index_data_points +from cognee.tasks.storage.index_graph_edges import index_graph_edges +from cognee.modules.engine.operations.setup import setup + +from .models import WebPage, WebSite, ScrapingJob +from .config import SoupCrawlerConfig, TavilyConfig +from .utils import fetch_page_content + +try: + from apscheduler.triggers.cron import CronTrigger + from apscheduler.schedulers.asyncio import AsyncIOScheduler +except ImportError: + raise ImportError("Please install apscheduler by pip install APScheduler>=3.10") + +logger = get_logger(__name__) + + +_scheduler = None + + +def get_scheduler(): + global _scheduler + if _scheduler is None: + _scheduler = AsyncIOScheduler() + return _scheduler + + +async def cron_web_scraper_task( + url: Union[str, List[str]], + *, + schedule: str = None, + extraction_rules: dict = None, + tavily_api_key: str = os.getenv("TAVILY_API_KEY"), + soup_crawler_config: SoupCrawlerConfig = None, + tavily_config: TavilyConfig = None, + job_name: str = "scraping", +): + """Schedule or run a web scraping task. + + This function schedules a recurring web scraping task using APScheduler or runs it + immediately if no schedule is provided. It delegates to web_scraper_task for actual + scraping and graph storage. + + Args: + url: A single URL or list of URLs to scrape. + schedule: A cron expression for scheduling (e.g., "0 0 * * *"). If None, runs immediately. + extraction_rules: Dictionary of extraction rules for BeautifulSoup (e.g., CSS selectors). + tavily_api_key: API key for Tavily. Defaults to TAVILY_API_KEY environment variable. + soup_crawler_config: Configuration for BeautifulSoup crawler. + tavily_config: Configuration for Tavily API. + job_name: Name of the scraping job. Defaults to "scraping". + + Returns: + Any: The result of web_scraper_task if run immediately, or None if scheduled. + + Raises: + ValueError: If the schedule is an invalid cron expression. + ImportError: If APScheduler is not installed. + """ + now = datetime.now() + job_name = job_name or f"scrape_{now.strftime('%Y%m%d_%H%M%S')}" + if schedule: + try: + trigger = CronTrigger.from_crontab(schedule) + except ValueError as e: + raise ValueError(f"Invalid cron string '{schedule}': {e}") + + scheduler = get_scheduler() + scheduler.add_job( + web_scraper_task, + kwargs={ + "url": url, + "schedule": schedule, + "extraction_rules": extraction_rules, + "tavily_api_key": tavily_api_key, + "soup_crawler_config": soup_crawler_config, + "tavily_config": tavily_config, + "job_name": job_name, + }, + trigger=trigger, + id=job_name, + name=job_name or f"WebScraper_{uuid5(NAMESPACE_OID, name=job_name)}", + replace_existing=True, + ) + if not scheduler.running: + scheduler.start() + return + + # If no schedule, run immediately + logger.info(f"[{datetime.now()}] Running web scraper task immediately...") + return await web_scraper_task( + url=url, + schedule=schedule, + extraction_rules=extraction_rules, + tavily_api_key=tavily_api_key, + soup_crawler_config=soup_crawler_config, + tavily_config=tavily_config, + job_name=job_name, + ) + + +async def web_scraper_task( + url: Union[str, List[str]], + *, + schedule: str = None, + extraction_rules: dict = None, + tavily_api_key: str = os.getenv("TAVILY_API_KEY"), + soup_crawler_config: SoupCrawlerConfig = None, + tavily_config: TavilyConfig = None, + job_name: str = None, +): + """Scrape URLs and store data points in a Graph database. + + This function scrapes content from the provided URLs, creates or updates WebPage, + WebSite, and ScrapingJob data points, and stores them in a Graph database. + Each data point includes a description field summarizing its attributes. It creates + 'is_scraping' (ScrapingJob to WebSite) and 'is_part_of' (WebPage to WebSite) + relationships, preserving existing edges during node updates. + + Args: + url: A single URL or list of URLs to scrape. + schedule: A cron expression for scheduling (e.g., "0 0 * * *"). If None, runs once. + extraction_rules: Dictionary of extraction rules for BeautifulSoup (e.g., CSS selectors). + tavily_api_key: API key for Tavily. Defaults to TAVILY_API_KEY environment variable. + soup_crawler_config: Configuration for BeautifulSoup crawler. + tavily_config: Configuration for Tavily API. + job_name: Name of the scraping job. Defaults to a timestamp-based name. + + Returns: + Any: The graph data returned by the graph database. + + Raises: + TypeError: If neither tavily_config nor soup_crawler_config is provided. + Exception: If fetching content or database operations fail. + """ + await setup() + graph_db = await get_graph_engine() + + if isinstance(url, str): + url = [url] + + soup_crawler_config, tavily_config, preferred_tool = check_arguments( + tavily_api_key, extraction_rules, tavily_config, soup_crawler_config + ) + now = datetime.now() + job_name = job_name or f"scrape_{now.strftime('%Y%m%d_%H%M%S')}" + status = "active" + trigger = CronTrigger.from_crontab(schedule) if schedule else None + next_run = trigger.get_next_fire_time(None, now) if trigger else None + scraping_job_created = await graph_db.get_node(uuid5(NAMESPACE_OID, name=job_name)) + + # Create description for ScrapingJob + scraping_job_description = ( + f"Scraping job: {job_name}\n" + f"URLs: {', '.join(url)}\n" + f"Status: {status}\n" + f"Schedule: {schedule}\n" + f"Last run: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Next run: {next_run.strftime('%Y-%m-%d %H:%M:%S') if next_run else 'Not scheduled'}" + ) + + scraping_job = ScrapingJob( + id=uuid5(NAMESPACE_OID, name=job_name), + name=job_name, + urls=url, + status=status, + schedule=schedule, + last_run=now, + next_run=next_run, + description=scraping_job_description, + ) + + if scraping_job_created: + await graph_db.add_node(scraping_job) # Update existing scraping job + websites_dict = {} + webpages = [] + + # Fetch content + results = await fetch_page_content( + urls=url, + preferred_tool=preferred_tool, + tavily_config=tavily_config, + soup_crawler_config=soup_crawler_config, + ) + for page_url, content in results.items(): + parsed_url = urlparse(page_url) + domain = parsed_url.netloc + base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" + + # Create or update WebSite + if base_url not in websites_dict: + # Create description for WebSite + website_description = ( + f"Website: {domain}\n" + f"Base URL: {base_url}\n" + f"Last crawled: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Page count: 1\n" + f"Scraping tool: {preferred_tool}\n" + f"Robots.txt: {'Available' if websites_dict.get(base_url, {}).get('robots_txt') else 'Not set'}\n" + f"Crawl delay: 0.5 seconds" + ) + + websites_dict[base_url] = WebSite( + id=uuid5(NAMESPACE_OID, name=domain), + name=domain, + base_url=base_url, + robots_txt=None, + crawl_delay=0.5, + last_crawled=now, + page_count=1, + scraping_config={ + "extraction_rules": extraction_rules or {}, + "tool": preferred_tool, + }, + description=website_description, + ) + if scraping_job_created: + await graph_db.add_node(websites_dict[base_url]) + else: + websites_dict[base_url].page_count += 1 + # Update description for existing WebSite + websites_dict[base_url].description = ( + f"Website: {domain}\n" + f"Base URL: {base_url}\n" + f"Last crawled: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Page count: {websites_dict[base_url].page_count}\n" + f"Scraping tool: {preferred_tool}\n" + f"Robots.txt: {'Available' if websites_dict[base_url].robots_txt else 'Not set'}\n" + f"Crawl delay: {websites_dict[base_url].crawl_delay} seconds" + ) + if scraping_job_created: + await graph_db.add_node(websites_dict[base_url]) + + # Create WebPage + content_str = content if isinstance(content, str) else str(content) + content_hash = hashlib.sha256(content_str.encode("utf-8")).hexdigest() + content_preview = content_str[:500] + ("..." if len(content_str) > 500 else "") + # Create description for WebPage + webpage_description = ( + f"Webpage: {parsed_url.path.lstrip('/') or 'Home'}\n" + f"URL: {page_url}\n" + f"Scraped at: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Content: {content_preview}\n" + f"Content type: text\n" + f"Page size: {len(content_str)} bytes\n" + f"Status code: 200" + ) + page_extraction_rules = extraction_rules + webpage = WebPage( + id=uuid5(NAMESPACE_OID, name=page_url), + name=page_url, + content=content_str, + content_hash=content_hash, + scraped_at=now, + last_modified=None, + status_code=200, + content_type="text/html", + page_size=len(content_str), + extraction_rules=page_extraction_rules or {}, + description=webpage_description, + ) + webpages.append(webpage) + + scraping_job.status = "completed" if webpages else "failed" + # Update ScrapingJob description with final status + scraping_job.description = ( + f"Scraping job: {job_name}\n" + f"URLs: {', '.join(url)}\n" + f"Status: {scraping_job.status}\n" + f"Last run: {now.strftime('%Y-%m-%d %H:%M:%S')}\n" + f"Next run: {next_run.strftime('%Y-%m-%d %H:%M:%S') if next_run else 'Not scheduled'}" + ) + + websites = list(websites_dict.values()) + # Adding Nodes and Edges + node_mapping = {scraping_job.id: scraping_job} + edge_mapping = [] + + for website in websites: + node_mapping[website.id] = website + edge_mapping.append( + ( + scraping_job.id, + website.id, + "is_scraping", + { + "source_node_id": scraping_job.id, + "target_node_id": website.id, + "relationship_name": "is_scraping", + }, + ) + ) + for webpage in webpages: + node_mapping[webpage.id] = webpage + parsed_url = urlparse(webpage.name) + base_url = f"{parsed_url.scheme}://{parsed_url.netloc}" + edge_mapping.append( + ( + webpage.id, + websites_dict[base_url].id, + "is_part_of", + { + "source_node_id": webpage.id, + "target_node_id": websites_dict[base_url].id, + "relationship_name": "is_part_of", + }, + ) + ) + + await graph_db.add_nodes(list(node_mapping.values())) + await graph_db.add_edges(edge_mapping) + await index_data_points(list(node_mapping.values())) + await index_graph_edges() + + return await graph_db.get_graph_data() + + +def check_arguments(tavily_api_key, extraction_rules, tavily_config, soup_crawler_config): + """Validate and configure arguments for web_scraper_task. + + Args: + tavily_api_key: API key for Tavily. + extraction_rules: Extraction rules for BeautifulSoup. + tavily_config: Configuration for Tavily API. + soup_crawler_config: Configuration for BeautifulSoup crawler. + + Returns: + Tuple[SoupCrawlerConfig, TavilyConfig, str]: Configured soup_crawler_config, + tavily_config, and preferred_tool ("tavily" or "beautifulsoup"). + + Raises: + TypeError: If neither tavily_config nor soup_crawler_config is provided. + """ + preferred_tool = "beautifulsoup" + + if extraction_rules and not soup_crawler_config: + soup_crawler_config = SoupCrawlerConfig(extraction_rules=extraction_rules) + + if tavily_api_key: + if not tavily_config: + tavily_config = TavilyConfig(api_key=tavily_api_key) + else: + tavily_config.api_key = tavily_api_key + if not extraction_rules and not soup_crawler_config: + preferred_tool = "tavily" + + if not tavily_config and not soup_crawler_config: + raise TypeError("Make sure you pass arguments for web_scraper_task") + + return soup_crawler_config, tavily_config, preferred_tool + + +def get_path_after_base(base_url: str, url: str) -> str: + """Extract the path after the base URL. + + Args: + base_url: The base URL (e.g., "https://example.com"). + url: The full URL to extract the path from. + + Returns: + str: The path after the base URL, with leading slashes removed. + + Raises: + ValueError: If the base URL and target URL are from different domains. + """ + parsed_base = urlparse(base_url) + parsed_url = urlparse(url) + + # Ensure they have the same netloc (domain) + if parsed_base.netloc != parsed_url.netloc: + raise ValueError("Base URL and target URL are from different domains") + + # Return everything after base_url path + base_path = parsed_base.path.rstrip("/") + full_path = parsed_url.path + + if full_path.startswith(base_path): + return full_path[len(base_path) :].lstrip("/") + else: + return full_path.lstrip("/") diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py index e5d6a0c64..7654a781a 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_commands.py @@ -12,7 +12,8 @@ from cognee.cli.commands.search_command import SearchCommand from cognee.cli.commands.cognify_command import CognifyCommand from cognee.cli.commands.delete_command import DeleteCommand from cognee.cli.commands.config_command import ConfigCommand -from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.cli.exceptions import CliCommandException +from cognee.modules.data.methods.get_deletion_counts import DeletionCountsPreview # Mock asyncio.run to properly handle coroutines @@ -282,13 +283,18 @@ class TestDeleteCommand: assert "all" in actions assert "force" in actions + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") @patch("cognee.cli.commands.delete_command.asyncio.run", side_effect=_mock_run) - def test_execute_delete_dataset_with_confirmation(self, mock_asyncio_run, mock_confirm): + def test_execute_delete_dataset_with_confirmation( + self, mock_asyncio_run, mock_confirm, mock_get_deletion_counts + ): """Test execute delete dataset with user confirmation""" # Mock the cognee module mock_cognee = MagicMock() mock_cognee.delete = AsyncMock() + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() with patch.dict(sys.modules, {"cognee": mock_cognee}): command = DeleteCommand() @@ -301,13 +307,16 @@ class TestDeleteCommand: command.execute(args) mock_confirm.assert_called_once_with(f"Delete dataset '{args.dataset_name}'?") - mock_asyncio_run.assert_called_once() + assert mock_asyncio_run.call_count == 2 assert asyncio.iscoroutine(mock_asyncio_run.call_args[0][0]) mock_cognee.delete.assert_awaited_once_with(dataset_name="test_dataset", user_id=None) + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") - def test_execute_delete_cancelled(self, mock_confirm): + def test_execute_delete_cancelled(self, mock_confirm, mock_get_deletion_counts): """Test execute when user cancels deletion""" + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() command = DeleteCommand() args = argparse.Namespace(dataset_name="test_dataset", user_id=None, all=False, force=False) diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py index d4e4b0ee5..ca27c0f67 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_edge_cases.py @@ -13,6 +13,7 @@ from cognee.cli.commands.cognify_command import CognifyCommand from cognee.cli.commands.delete_command import DeleteCommand from cognee.cli.commands.config_command import ConfigCommand from cognee.cli.exceptions import CliCommandException, CliCommandInnerException +from cognee.modules.data.methods.get_deletion_counts import DeletionCountsPreview # Mock asyncio.run to properly handle coroutines @@ -378,13 +379,18 @@ class TestCognifyCommandEdgeCases: class TestDeleteCommandEdgeCases: """Test edge cases for DeleteCommand""" + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") @patch("cognee.cli.commands.delete_command.asyncio.run", side_effect=_mock_run) - def test_delete_all_with_user_id(self, mock_asyncio_run, mock_confirm): + def test_delete_all_with_user_id( + self, mock_asyncio_run, mock_confirm, mock_get_deletion_counts + ): """Test delete command with both --all and --user-id""" # Mock the cognee module mock_cognee = MagicMock() mock_cognee.delete = AsyncMock() + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() with patch.dict(sys.modules, {"cognee": mock_cognee}): command = DeleteCommand() @@ -396,13 +402,17 @@ class TestDeleteCommandEdgeCases: command.execute(args) mock_confirm.assert_called_once_with("Delete ALL data from cognee?") - mock_asyncio_run.assert_called_once() + assert mock_asyncio_run.call_count == 2 assert asyncio.iscoroutine(mock_asyncio_run.call_args[0][0]) mock_cognee.delete.assert_awaited_once_with(dataset_name=None, user_id="test_user") + @patch("cognee.cli.commands.delete_command.get_deletion_counts") @patch("cognee.cli.commands.delete_command.fmt.confirm") - def test_delete_confirmation_keyboard_interrupt(self, mock_confirm): + def test_delete_confirmation_keyboard_interrupt(self, mock_confirm, mock_get_deletion_counts): """Test delete command when user interrupts confirmation""" + mock_get_deletion_counts = AsyncMock() + mock_get_deletion_counts.return_value = DeletionCountsPreview() + command = DeleteCommand() args = argparse.Namespace(dataset_name="test_dataset", user_id=None, all=False, force=False) diff --git a/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py b/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py index 00ee5f442..05de0c70b 100644 --- a/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py +++ b/cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py @@ -53,7 +53,6 @@ class TestCliConfig: expected_types = [ "GRAPH_COMPLETION", "RAG_COMPLETION", - "INSIGHTS", "CHUNKS", "SUMMARIES", "CODE", diff --git a/cognee/tests/tasks/web_scraping/web_scraping_test.py b/cognee/tests/tasks/web_scraping/web_scraping_test.py new file mode 100644 index 000000000..bf66b5155 --- /dev/null +++ b/cognee/tests/tasks/web_scraping/web_scraping_test.py @@ -0,0 +1,172 @@ +import asyncio +import cognee +from cognee.tasks.web_scraper.config import SoupCrawlerConfig +from cognee.tasks.web_scraper import cron_web_scraper_task + + +async def test_web_scraping_using_bs4(): + await cognee.prune.prune_data() + await cognee.prune.prune_system() + + url = "https://quotes.toscrape.com/" + rules = { + "quotes": {"selector": ".quote span.text", "all": True}, + "authors": {"selector": ".quote small", "all": True}, + } + + soup_config = SoupCrawlerConfig( + concurrency=5, + crawl_delay=0.5, + timeout=15.0, + max_retries=2, + retry_delay_factor=0.5, + extraction_rules=rules, + use_playwright=False, + ) + + await cognee.add( + data=url, + soup_crawler_config=soup_config, + incremental_loading=False, + ) + + await cognee.cognify() + + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0] + print("Test passed! Found Albert Einstein in scraped data.") + + +async def test_web_scraping_using_bs4_and_incremental_loading(): + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + url = "https://books.toscrape.com/" + rules = {"titles": "article.product_pod h3 a", "prices": "article.product_pod p.price_color"} + + soup_config = SoupCrawlerConfig( + concurrency=1, + crawl_delay=0.1, + timeout=10.0, + max_retries=1, + retry_delay_factor=0.5, + extraction_rules=rules, + use_playwright=False, + structured=True, + ) + + await cognee.add( + data=url, + soup_crawler_config=soup_config, + incremental_loading=True, + ) + + await cognee.cognify() + + results = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "51.77" in results[0] + print("Test passed! Found 'A Light in the Attic' in scraped data.") + + +async def test_web_scraping_using_tavily(): + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + url = "https://quotes.toscrape.com/" + + await cognee.add( + data=url, + incremental_loading=False, + ) + + await cognee.cognify() + + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0] + print("Test passed! Found Albert Einstein in scraped data.") + + +async def test_web_scraping_using_tavily_and_incremental_loading(): + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + url = "https://quotes.toscrape.com/" + + await cognee.add( + data=url, + incremental_loading=True, + ) + + await cognee.cognify() + + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + assert "Albert Einstein" in results[0] + print("Test passed! Found Albert Einstein in scraped data.") + + +# ---------- cron job tests ---------- +async def test_cron_web_scraper(): + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + urls = ["https://quotes.toscrape.com/", "https://books.toscrape.com/"] + extraction_rules = { + "quotes": ".quote .text", + "authors": ".quote .author", + "titles": "article.product_pod h3 a", + "prices": "article.product_pod p.price_color", + } + + # Run cron_web_scraper_task + await cron_web_scraper_task( + url=urls, + job_name="cron_scraping_job", + extraction_rules=extraction_rules, + ) + results = await cognee.search( + "Who said 'The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking'?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + + assert "Albert Einstein" in results[0] + + results_books = await cognee.search( + "What is the price of 'A Light in the Attic' book?", + query_type=cognee.SearchType.GRAPH_COMPLETION, + ) + + assert "51.77" in results_books[0] + + print("Cron job web_scraping test passed!") + + +async def main(): + print("Starting BS4 incremental loading test...") + await test_web_scraping_using_bs4_and_incremental_loading() + + print("Starting BS4 normal test...") + await test_web_scraping_using_bs4() + + print("Starting Tavily incremental loading test...") + await test_web_scraping_using_tavily_and_incremental_loading() + + print("Starting Tavily normal test...") + await test_web_scraping_using_tavily() + + print("Starting cron job test...") + await test_cron_web_scraper() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cognee/tests/test_add_docling_document.py b/cognee/tests/test_add_docling_document.py new file mode 100644 index 000000000..2c82af66f --- /dev/null +++ b/cognee/tests/test_add_docling_document.py @@ -0,0 +1,56 @@ +import asyncio +import cognee + +import os + + +async def main(): + # Get file path to document to process + from pathlib import Path + + current_directory = Path(__file__).resolve().parent + file_path_artificial = os.path.join( + current_directory, "test_data", "artificial-intelligence.pdf" + ) + file_path_png = os.path.join(current_directory, "test_data", "example_copy.png") + file_path_pptx = os.path.join(current_directory, "test_data", "example.pptx") + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + # Import necessary converter, and convert file to DoclingDocument format + from docling.document_converter import DocumentConverter + + converter = DocumentConverter() + + result = converter.convert(file_path_artificial) + await cognee.add(result.document) + + result = converter.convert(file_path_png) + await cognee.add(result.document) + + result = converter.convert(file_path_pptx) + await cognee.add(result.document) + + await cognee.cognify() + + answer = await cognee.search("Tell me about Artificial Intelligence.") + assert len(answer) != 0 + + answer = await cognee.search("Do programmers change light bulbs?") + assert len(answer) != 0 + lowercase_answer = answer[0].lower() + assert ("no" in lowercase_answer) or ("none" in lowercase_answer) + + answer = await cognee.search("What colours are there in the presentation table?") + assert len(answer) != 0 + lowercase_answer = answer[0].lower() + assert ( + ("red" in lowercase_answer) + and ("blue" in lowercase_answer) + and ("green" in lowercase_answer) + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/cognee/tests/test_chromadb.py b/cognee/tests/test_chromadb.py index 01e1938d7..767edf3dc 100644 --- a/cognee/tests/test_chromadb.py +++ b/cognee/tests/test_chromadb.py @@ -133,20 +133,16 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name_2) + await cognee.add([explanation_file_path_quantum], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) @@ -159,7 +155,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_falkordb.py b/cognee/tests/test_falkordb.py deleted file mode 100755 index a42470e54..000000000 --- a/cognee/tests/test_falkordb.py +++ /dev/null @@ -1,174 +0,0 @@ -import os -import cognee -import pathlib -from cognee.infrastructure.files.storage import get_storage_config -from cognee.modules.search.operations import get_history -from cognee.modules.users.methods import get_default_user -from cognee.shared.logging_utils import get_logger -from cognee.modules.search.types import SearchType - -logger = get_logger() - - -async def check_falkordb_connection(): - """Check if FalkorDB is available at localhost:6379""" - try: - from falkordb import FalkorDB - - client = FalkorDB(host="localhost", port=6379) - # Try to list graphs to check connection - client.list_graphs() - return True - except Exception as e: - logger.warning(f"FalkorDB not available at localhost:6379: {e}") - return False - - -async def main(): - # Check if FalkorDB is available - if not await check_falkordb_connection(): - print("⚠️ FalkorDB is not available at localhost:6379") - print(" To run this test, start FalkorDB server:") - print(" docker run -p 6379:6379 falkordb/falkordb:latest") - print(" Skipping FalkorDB test...") - return - - print("✅ FalkorDB connection successful, running test...") - - # Configure FalkorDB as the graph database provider - cognee.config.set_graph_db_config( - { - "graph_database_url": "localhost", # FalkorDB URL (using Redis protocol) - "graph_database_port": 6379, - "graph_database_provider": "falkordb", - } - ) - - # Configure FalkorDB as the vector database provider too since it's a hybrid adapter - cognee.config.set_vector_db_config( - { - "vector_db_url": "localhost", - "vector_db_port": 6379, - "vector_db_provider": "falkordb", - } - ) - - data_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_falkordb") - ).resolve() - ) - cognee.config.data_root_directory(data_directory_path) - cognee_directory_path = str( - pathlib.Path( - os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_falkordb") - ).resolve() - ) - cognee.config.system_root_directory(cognee_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - dataset_name = "artificial_intelligence" - - ai_text_file_path = os.path.join( - pathlib.Path(__file__).parent, "test_data/artificial-intelligence.pdf" - ) - await cognee.add([ai_text_file_path], dataset_name) - - text = """A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification. LLMs acquire these abilities by learning statistical relationships from text documents during a computationally intensive self-supervised and semi-supervised training process. LLMs can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word. - LLMs are artificial neural networks. The largest and most capable, as of March 2024, are built with a decoder-only transformer-based architecture while some recent implementations are based on other architectures, such as recurrent neural network variants and Mamba (a state space model). - Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results.[6] They are thought to acquire knowledge about syntax, semantics and "ontology" inherent in human language corpora, but also inaccuracies and biases present in the corpora. - Some notable LLMs are OpenAI's GPT series of models (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's PaLM and Gemini (the latter of which is currently used in the chatbot of the same name), xAI's Grok, Meta's LLaMA family of open-source models, Anthropic's Claude models, Mistral AI's open source models, and Databricks' open source DBRX. - """ - - await cognee.add([text], dataset_name) - - await cognee.cognify([dataset_name]) - - from cognee.infrastructure.databases.vector import get_vector_engine - - vector_engine = get_vector_engine() - random_node = (await vector_engine.search("entity.name", "AI"))[0] - random_node_name = random_node.payload["text"] - - search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name - ) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted sentences are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=random_node_name) - assert len(search_results) != 0, "The search results list is empty." - print("\n\nExtracted chunks are:\n") - for result in search_results: - print(f"{result}\n") - - search_results = await cognee.search( - query_type=SearchType.SUMMARIES, query_text=random_node_name - ) - assert len(search_results) != 0, "Query related summaries don't exist." - print("\nExtracted summaries are:\n") - for result in search_results: - print(f"{result}\n") - - user = await get_default_user() - history = await get_history(user.id) - - assert len(history) == 6, "Search history is not correct." - - # Assert local data files are cleaned properly - await cognee.prune.prune_data() - data_root_directory = get_storage_config()["data_root_directory"] - assert not os.path.isdir(data_root_directory), "Local data files are not deleted" - - # Assert relational, vector and graph databases have been cleaned properly - await cognee.prune.prune_system(metadata=True) - - # For FalkorDB vector engine, check if collections are empty - # Since FalkorDB is a hybrid adapter, we can check if the graph is empty - # as the vector data is stored in the same graph - if hasattr(vector_engine, "driver"): - # This is FalkorDB - check if graphs exist - collections = vector_engine.driver.list_graphs() - # The graph should be deleted, so either no graphs or empty graph - if vector_engine.graph_name in collections: - # Graph exists but should be empty - vector_graph_data = await vector_engine.get_graph_data() - vector_nodes, vector_edges = vector_graph_data - assert len(vector_nodes) == 0 and len(vector_edges) == 0, ( - "FalkorDB vector database is not empty" - ) - else: - # Fallback for other vector engines like LanceDB - connection = await vector_engine.get_connection() - collection_names = await connection.table_names() - assert len(collection_names) == 0, "Vector database is not empty" - - from cognee.infrastructure.databases.relational import get_relational_engine - - assert not os.path.exists(get_relational_engine().db_path), ( - "SQLite relational database is not empty" - ) - - # For FalkorDB, check if the graph database is empty - from cognee.infrastructure.databases.graph import get_graph_engine - - graph_engine = get_graph_engine() - graph_data = await graph_engine.get_graph_data() - nodes, edges = graph_data - assert len(nodes) == 0 and len(edges) == 0, "FalkorDB graph database is not empty" - - print("🎉 FalkorDB test completed successfully!") - print(" ✓ Data ingestion worked") - print(" ✓ Cognify processing worked") - print(" ✓ Search operations worked") - print(" ✓ Cleanup worked") - - -if __name__ == "__main__": - import asyncio - - asyncio.run(main(), debug=True) diff --git a/cognee/tests/test_kuzu.py b/cognee/tests/test_kuzu.py index 6afd4540a..8749e42d0 100644 --- a/cognee/tests/test_kuzu.py +++ b/cognee/tests/test_kuzu.py @@ -38,19 +38,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ - await cognee.add([text], dataset_name) + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) + + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) @@ -61,7 +58,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_lancedb.py b/cognee/tests/test_lancedb.py index ea3b455cd..115ba99fd 100644 --- a/cognee/tests/test_lancedb.py +++ b/cognee/tests/test_lancedb.py @@ -131,20 +131,16 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name_2) + await cognee.add([explanation_file_path_quantum], dataset_name_2) await cognee.cognify([dataset_name_2, dataset_name_1]) @@ -157,7 +153,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_library.py b/cognee/tests/test_library.py index c5e6cc64b..81f81ee61 100755 --- a/cognee/tests/test_library.py +++ b/cognee/tests/test_library.py @@ -6,6 +6,7 @@ from cognee.modules.search.operations import get_history from cognee.modules.users.methods import get_default_user from cognee.shared.logging_utils import get_logger from cognee.modules.search.types import SearchType +from cognee import update logger = get_logger() @@ -42,7 +43,7 @@ async def main(): await cognee.add([text], dataset_name) - await cognee.cognify([dataset_name]) + cognify_run_info = await cognee.cognify([dataset_name]) from cognee.infrastructure.databases.vector import get_vector_engine @@ -51,7 +52,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") @@ -77,6 +78,35 @@ async def main(): assert len(history) == 6, "Search history is not correct." + # Test updating of documents + # Get Pipeline Run object + pipeline_run_obj = list(cognify_run_info.values())[0] + for data_item in pipeline_run_obj.data_ingestion_info: + # Update all documents in dataset to only contain Mark and Cindy information + await update( + dataset_id=pipeline_run_obj.dataset_id, + data_id=data_item["data_id"], + data="Mark met with Cindy at a cafe.", + ) + + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="What information do you contain?" + ) + assert "Mark" in search_results[0], ( + "Failed to update document, no mention of Mark in search results" + ) + assert "Cindy" in search_results[0], ( + "Failed to update document, no mention of Cindy in search results" + ) + assert "Artificial intelligence" not in search_results[0], ( + "Failed to update document, Artificial intelligence still mentioned in search results" + ) + + # Test visualization + from cognee import visualize_graph + + await visualize_graph() + # Assert local data files are cleaned properly await cognee.prune.prune_data() data_root_directory = get_storage_config()["data_root_directory"] diff --git a/cognee/tests/test_memgraph.py b/cognee/tests/test_memgraph.py index b21cf3735..d0d968fc4 100644 --- a/cognee/tests/test_memgraph.py +++ b/cognee/tests/test_memgraph.py @@ -32,20 +32,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) @@ -56,7 +52,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_neo4j.py b/cognee/tests/test_neo4j.py index 7f24e8418..c74b4ab65 100644 --- a/cognee/tests/test_neo4j.py +++ b/cognee/tests/test_neo4j.py @@ -32,20 +32,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) @@ -56,7 +52,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_neptune_analytics_vector.py b/cognee/tests/test_neptune_analytics_vector.py index eececacdd..99c4d94b4 100644 --- a/cognee/tests/test_neptune_analytics_vector.py +++ b/cognee/tests/test_neptune_analytics_vector.py @@ -38,20 +38,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) @@ -60,7 +56,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_permissions.py b/cognee/tests/test_permissions.py index 95f769263..3c8a4606a 100644 --- a/cognee/tests/test_permissions.py +++ b/cognee/tests/test_permissions.py @@ -34,25 +34,21 @@ async def main(): await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) # Add document for default user - await cognee.add([explanation_file_path], dataset_name="NLP") + await cognee.add([explanation_file_path_nlp], dataset_name="NLP") default_user = await get_default_user() - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) # Add document for test user test_user = await create_user("user@example.com", "example") - await cognee.add([text], dataset_name="QUANTUM", user=test_user) + await cognee.add([explanation_file_path_quantum], dataset_name="QUANTUM", user=test_user) nlp_cognify_result = await cognee.cognify(["NLP"], user=default_user) quantum_cognify_result = await cognee.cognify(["QUANTUM"], user=test_user) @@ -101,7 +97,7 @@ async def main(): add_error = False try: await cognee.add( - [explanation_file_path], + [explanation_file_path_nlp], dataset_name="QUANTUM", dataset_id=test_user_dataset_id, user=default_user, @@ -143,7 +139,7 @@ async def main(): # Add new data to test_users dataset from default_user await cognee.add( - [explanation_file_path], + [explanation_file_path_nlp], dataset_name="QUANTUM", dataset_id=test_user_dataset_id, user=default_user, @@ -216,7 +212,7 @@ async def main(): ) # Try deleting data from test_user dataset with default_user after getting delete permission - # Get the dataset data to find the ID of the remaining data item (explanation_file_path) + # Get the dataset data to find the ID of the remaining data item (explanation_file_path_nlp) test_user_dataset_data = await get_dataset_data(test_user_dataset_id) explanation_file_data_id = test_user_dataset_data[0].id diff --git a/cognee/tests/test_pgvector.py b/cognee/tests/test_pgvector.py index 059cf691a..240f9e9bb 100644 --- a/cognee/tests/test_pgvector.py +++ b/cognee/tests/test_pgvector.py @@ -141,10 +141,10 @@ async def main(): dataset_name_1 = "natural_language" dataset_name_2 = "quantum" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name_1) + await cognee.add([explanation_file_path_nlp], dataset_name_1) text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. @@ -167,7 +167,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") @@ -202,7 +202,7 @@ async def main(): history = await get_history(user.id) assert len(history) == 8, "Search history is not correct." - await test_local_file_deletion(text, explanation_file_path) + await test_local_file_deletion(text, explanation_file_path_nlp) await cognee.prune.prune_data() data_root_directory = get_storage_config()["data_root_directory"] diff --git a/cognee/tests/test_remote_kuzu.py b/cognee/tests/test_remote_kuzu.py index bbff48096..1c619719c 100644 --- a/cognee/tests/test_remote_kuzu.py +++ b/cognee/tests/test_remote_kuzu.py @@ -42,19 +42,16 @@ async def main(): dataset_name = "cs_explanations" - explanation_file_path = os.path.join( + explanation_file_path_nlp = os.path.join( pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt" ) - await cognee.add([explanation_file_path], dataset_name) + await cognee.add([explanation_file_path_nlp], dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ - await cognee.add([text], dataset_name) + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) + + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) @@ -65,7 +62,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_s3_file_storage.py b/cognee/tests/test_s3_file_storage.py index 3a20b232c..c7fc62cf2 100755 --- a/cognee/tests/test_s3_file_storage.py +++ b/cognee/tests/test_s3_file_storage.py @@ -47,7 +47,7 @@ async def main(): random_node_name = random_node.payload["text"] search_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text=random_node_name + query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name ) assert len(search_results) != 0, "The search results list is empty." print("\n\nExtracted sentences are:\n") diff --git a/cognee/tests/test_search_db.py b/cognee/tests/test_search_db.py index cb4636470..e24abd0f5 100644 --- a/cognee/tests/test_search_db.py +++ b/cognee/tests/test_search_db.py @@ -1,3 +1,5 @@ +import pathlib +import os import cognee from cognee.infrastructure.databases.graph import get_graph_engine from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge @@ -27,15 +29,11 @@ async def main(): text_1 = """Germany is located in europe right next to the Netherlands""" await cognee.add(text_1, dataset_name) - text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena. - At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states. - Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible. - The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly. - Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate. - In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited. - """ + explanation_file_path_quantum = os.path.join( + pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt" + ) - await cognee.add([text], dataset_name) + await cognee.add([explanation_file_path_quantum], dataset_name) await cognee.cognify([dataset_name]) diff --git a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py index 9cd96f5b9..48bbc53e3 100644 --- a/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py +++ b/cognee/tests/unit/infrastructure/databases/test_index_graph_edges.py @@ -1,5 +1,5 @@ import pytest -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, patch, MagicMock from cognee.tasks.storage.index_graph_edges import index_graph_edges @@ -16,6 +16,7 @@ async def test_index_graph_edges_success(): ], ) mock_vector_engine = AsyncMock() + mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100) # Patch the globals of the function so that when it does: # vector_engine = get_vector_engine() diff --git a/cognee/tests/unit/modules/retrieval/insights_retriever_test.py b/cognee/tests/unit/modules/retrieval/insights_retriever_test.py deleted file mode 100644 index 21dbc98dd..000000000 --- a/cognee/tests/unit/modules/retrieval/insights_retriever_test.py +++ /dev/null @@ -1,251 +0,0 @@ -import os -import pytest -import pathlib - -import cognee -from cognee.low_level import setup -from cognee.tasks.storage import add_data_points -from cognee.modules.engine.models import Entity, EntityType -from cognee.infrastructure.databases.graph import get_graph_engine -from cognee.infrastructure.databases.vector import get_vector_engine -from cognee.modules.retrieval.exceptions.exceptions import NoDataError -from cognee.modules.retrieval.insights_retriever import InsightsRetriever - - -class TestInsightsRetriever: - @pytest.mark.asyncio - async def test_insights_context_simple(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_simple" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_simple" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - await setup() - - entityTypePerson = EntityType( - name="Person", - description="An individual", - ) - - person1 = Entity( - name="Steve Rodger", - is_a=entityTypePerson, - description="An American actor, comedian, and filmmaker", - ) - - person2 = Entity( - name="Mike Broski", - is_a=entityTypePerson, - description="Financial advisor and philanthropist", - ) - - person3 = Entity( - name="Christina Mayer", - is_a=entityTypePerson, - description="Maker of next generation of iconic American music videos", - ) - - entityTypeCompany = EntityType( - name="Company", - description="An organization that operates on an annual basis", - ) - - company1 = Entity( - name="Apple", - is_a=entityTypeCompany, - description="An American multinational technology company headquartered in Cupertino, California", - ) - - company2 = Entity( - name="Google", - is_a=entityTypeCompany, - description="An American multinational technology company that specializes in Internet-related services and products", - ) - - company3 = Entity( - name="Facebook", - is_a=entityTypeCompany, - description="An American social media, messaging, and online platform", - ) - - entities = [person1, person2, person3, company1, company2, company3] - - await add_data_points(entities) - - retriever = InsightsRetriever() - - context = await retriever.get_context("Mike") - - assert context[0][0]["name"] == "Mike Broski", "Failed to get Mike Broski" - - @pytest.mark.asyncio - async def test_insights_context_complex(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_complex" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_complex" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - await setup() - - entityTypePerson = EntityType( - name="Person", - description="An individual", - ) - - person1 = Entity( - name="Steve Rodger", - is_a=entityTypePerson, - description="An American actor, comedian, and filmmaker", - ) - - person2 = Entity( - name="Mike Broski", - is_a=entityTypePerson, - description="Financial advisor and philanthropist", - ) - - person3 = Entity( - name="Christina Mayer", - is_a=entityTypePerson, - description="Maker of next generation of iconic American music videos", - ) - - person4 = Entity( - name="Jason Statham", - is_a=entityTypePerson, - description="An American actor", - ) - - person5 = Entity( - name="Mike Tyson", - is_a=entityTypePerson, - description="A former professional boxer from the United States", - ) - - entityTypeCompany = EntityType( - name="Company", - description="An organization that operates on an annual basis", - ) - - company1 = Entity( - name="Apple", - is_a=entityTypeCompany, - description="An American multinational technology company headquartered in Cupertino, California", - ) - - company2 = Entity( - name="Google", - is_a=entityTypeCompany, - description="An American multinational technology company that specializes in Internet-related services and products", - ) - - company3 = Entity( - name="Facebook", - is_a=entityTypeCompany, - description="An American social media, messaging, and online platform", - ) - - entities = [person1, person2, person3, company1, company2, company3] - - await add_data_points(entities) - - graph_engine = await get_graph_engine() - - await graph_engine.add_edges( - [ - ( - (str)(person1.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person1.id, - target_node_id=company1.id, - ), - ), - ( - (str)(person2.id), - (str)(company2.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person2.id, - target_node_id=company2.id, - ), - ), - ( - (str)(person3.id), - (str)(company3.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person3.id, - target_node_id=company3.id, - ), - ), - ( - (str)(person4.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person4.id, - target_node_id=company1.id, - ), - ), - ( - (str)(person5.id), - (str)(company1.id), - "works_for", - dict( - relationship_name="works_for", - source_node_id=person5.id, - target_node_id=company1.id, - ), - ), - ] - ) - - retriever = InsightsRetriever(top_k=20) - - context = await retriever.get_context("Christina") - - assert context[0][0]["name"] == "Christina Mayer", "Failed to get Christina Mayer" - - @pytest.mark.asyncio - async def test_insights_context_on_empty_graph(self): - system_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_on_empty_graph" - ) - cognee.config.system_root_directory(system_directory_path) - data_directory_path = os.path.join( - pathlib.Path(__file__).parent, ".data_storage/test_insights_context_on_empty_graph" - ) - cognee.config.data_root_directory(data_directory_path) - - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - retriever = InsightsRetriever() - - with pytest.raises(NoDataError): - await retriever.get_context("Christina Mayer") - - vector_engine = get_vector_engine() - await vector_engine.create_collection("Entity_name", payload_schema=Entity) - await vector_engine.create_collection("EntityType_name", payload_schema=EntityType) - - context = await retriever.get_context("Christina Mayer") - assert context == [], "Returned context should be empty on an empty graph" diff --git a/distributed/Dockerfile b/distributed/Dockerfile index 6ac818d45..99c92cfea 100644 --- a/distributed/Dockerfile +++ b/distributed/Dockerfile @@ -29,6 +29,3 @@ RUN poetry install --extras neo4j --extras postgres --extras aws --extras distri COPY cognee/ /app/cognee COPY distributed/ /app/distributed -RUN chmod +x /app/distributed/entrypoint.sh - -ENTRYPOINT ["/app/distributed/entrypoint.sh"] diff --git a/distributed/entrypoint.py b/distributed/entrypoint.py index 977c92c95..0f1f83d81 100644 --- a/distributed/entrypoint.py +++ b/distributed/entrypoint.py @@ -10,6 +10,7 @@ from distributed.app import app from distributed.queues import add_nodes_and_edges_queue, add_data_points_queue from distributed.workers.graph_saving_worker import graph_saving_worker from distributed.workers.data_point_saving_worker import data_point_saving_worker +from distributed.signal import QueueSignal logger = get_logger() @@ -23,13 +24,14 @@ async def main(): await add_nodes_and_edges_queue.clear.aio() await add_data_points_queue.clear.aio() - number_of_graph_saving_workers = 1 # Total number of graph_saving_worker to spawn - number_of_data_point_saving_workers = 5 # Total number of graph_saving_worker to spawn + number_of_graph_saving_workers = 1 # Total number of graph_saving_worker to spawn (MAX 1) + number_of_data_point_saving_workers = ( + 10 # Total number of graph_saving_worker to spawn (MAX 10) + ) - results = [] consumer_futures = [] - # await prune.prune_data() # We don't want to delete files on s3 + await prune.prune_data() # This prunes the data from the file storage # Delete DBs and saved files from metastore await prune.prune_system(metadata=True) @@ -45,16 +47,28 @@ async def main(): worker_future = data_point_saving_worker.spawn() consumer_futures.append(worker_future) + """ Example: Setting and adding S3 path as input s3_bucket_path = os.getenv("S3_BUCKET_PATH") s3_data_path = "s3://" + s3_bucket_path await cognee.add(s3_data_path, dataset_name="s3-files") + """ + await cognee.add( + [ + "Audi is a German car manufacturer", + "The Netherlands is next to Germany", + "Berlin is the capital of Germany", + "The Rhine is a major European river", + "BMW produces luxury vehicles", + ], + dataset_name="s3-files", + ) await cognee.cognify(datasets=["s3-files"]) - 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-[metadata] -lock-version = "2.1" -python-versions = ">=3.10,<=3.13" -content-hash = "d15e6b5d065016613be0b8c015cccf85e7f63891353c97636d136d14e5c8f62e" diff --git a/distributed/pyproject.toml b/distributed/pyproject.toml deleted file mode 100644 index f61157236..000000000 --- a/distributed/pyproject.toml +++ /dev/null @@ -1,186 +0,0 @@ -[project] -name = "cognee" -version = "0.2.2.dev0" -description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning." -authors = [ - { name = "Vasilije Markovic" }, - { name = "Boris Arzentar" }, -] -requires-python = ">=3.10,<=3.13" -readme = "README.md" -license = "Apache-2.0" -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: Apache Software License", - "Topic :: Software Development :: Libraries", - "Operating System :: MacOS :: MacOS X", - "Operating System :: POSIX :: Linux", - "Operating System :: Microsoft :: Windows", -] -dependencies = [ - "openai>=1.80.1,<2", - "python-dotenv>=1.0.1,<2.0.0", - "pydantic>=2.11.7,<3.0.0", - "pydantic-settings>=2.10.1,<3", - "typing_extensions>=4.12.2,<5.0.0", - "nltk>=3.9.1,<4.0.0", - "numpy>=1.26.4, <=4.0.0", - "pandas>=2.2.2,<3.0.0", - # Note: New s3fs and boto3 versions don't work well together - # Always use comaptible fixed versions of these two dependencies - "s3fs[boto3]==2025.3.2", - "sqlalchemy>=2.0.39,<3.0.0", - "aiosqlite>=0.20.0,<1.0.0", - "tiktoken>=0.8.0,<1.0.0", - "litellm>=1.57.4, <1.71.0", - "instructor>=1.9.1,<2.0.0", - "langfuse>=2.32.0,<3", - "filetype>=1.2.0,<2.0.0", - "aiohttp>=3.11.14,<4.0.0", - "aiofiles>=23.2.1,<24.0.0", - "rdflib>=7.1.4,<7.2.0", - "pypdf>=4.1.0,<7.0.0", - "jinja2>=3.1.3,<4", - "matplotlib>=3.8.3,<4", - "networkx>=3.4.2,<4", - "lancedb>=0.24.0,<1.0.0", - "alembic>=1.13.3,<2", - "pre-commit>=4.0.1,<5", - "scikit-learn>=1.6.1,<2", - "limits>=4.4.1,<5", - "fastapi>=0.115.7,<1.0.0", - "python-multipart>=0.0.20,<1.0.0", - "fastapi-users[sqlalchemy]>=14.0.1,<15.0.0", - 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-[project.urls] -Homepage = "https://www.cognee.ai" -Repository = "https://github.com/topoteretes/cognee" - -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build] -exclude = [ - "/bin", - "/dist", - "/.data", - "/.github", - "/alembic", - "/deployment", - "/cognee-mcp", - "/cognee-frontend", - "/examples", - "/helm", - "/licenses", - "/logs", - "/notebooks", - "/profiling", - "/tests", - "/tools", -] - -[tool.hatch.build.targets.wheel] -packages = ["cognee", "distributed"] - -[tool.ruff] -line-length = 100 -exclude = [ - "migrations/", # Ignore migrations directory - "notebooks/", # Ignore notebook files - "build/", # Ignore build directory - "cognee/pipelines.py", - "cognee/modules/users/models/Group.py", - "cognee/modules/users/models/ACL.py", - "cognee/modules/pipelines/models/Task.py", - "cognee/modules/data/models/Dataset.py" -] - -[tool.ruff.lint] -ignore = ["F401"] diff --git a/distributed/signal.py b/distributed/signal.py new file mode 100644 index 000000000..d4e94d151 --- /dev/null +++ b/distributed/signal.py @@ -0,0 +1,5 @@ +from enum import Enum + + +class QueueSignal(str, Enum): + STOP = "STOP" diff --git a/distributed/workers/data_point_saving_worker.py b/distributed/workers/data_point_saving_worker.py index 178b63041..fe842b5e1 100644 --- a/distributed/workers/data_point_saving_worker.py +++ b/distributed/workers/data_point_saving_worker.py @@ -1,16 +1,17 @@ +import os import modal import asyncio from sqlalchemy.exc import OperationalError, DBAPIError from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential from distributed.app import app +from distributed.signal import QueueSignal from distributed.modal_image import image from distributed.queues import add_data_points_queue from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.vector import get_vector_engine - logger = get_logger("data_point_saving_worker") @@ -39,55 +40,84 @@ def is_deadlock_error(error): return False +secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + + @app.function( retries=3, image=image, timeout=86400, - max_containers=5, - secrets=[modal.Secret.from_name("distributed_cognee")], + max_containers=10, + secrets=[modal.Secret.from_name(secret_name)], ) async def data_point_saving_worker(): print("Started processing of data points; starting vector engine queue.") vector_engine = get_vector_engine() + # Defines how many data packets do we glue together from the modal queue before embedding call and ingestion + BATCH_SIZE = 25 + stop_seen = False while True: + if stop_seen: + print("Finished processing all data points; stopping vector engine queue consumer.") + return True + if await add_data_points_queue.len.aio() != 0: try: - add_data_points_request = await add_data_points_queue.get.aio(block=False) + print("Remaining elements in queue:") + print(await add_data_points_queue.len.aio()) + + # collect batched requests + batched_points = {} + for _ in range(min(BATCH_SIZE, await add_data_points_queue.len.aio())): + add_data_points_request = await add_data_points_queue.get.aio(block=False) + + if not add_data_points_request: + continue + + if add_data_points_request == QueueSignal.STOP: + await add_data_points_queue.put.aio(QueueSignal.STOP) + stop_seen = True + break + + if len(add_data_points_request) == 2: + collection_name, data_points = add_data_points_request + if collection_name not in batched_points: + batched_points[collection_name] = [] + batched_points[collection_name].extend(data_points) + else: + print("NoneType or invalid request detected.") + + if batched_points: + for collection_name, data_points in batched_points.items(): + print( + f"Adding {len(data_points)} data points to '{collection_name}' collection." + ) + + @retry( + retry=retry_if_exception_type(VectorDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def add_data_points(): + try: + await vector_engine.create_data_points( + collection_name, data_points, distributed=False + ) + except DBAPIError as error: + if is_deadlock_error(error): + raise VectorDatabaseDeadlockError() + except OperationalError as error: + if is_deadlock_error(error): + raise VectorDatabaseDeadlockError() + + await add_data_points() + print(f"Finished adding data points to '{collection_name}'.") + except modal.exception.DeserializationError as error: logger.error(f"Deserialization error: {str(error)}") continue - if len(add_data_points_request) == 0: - print("Finished processing all data points; stopping vector engine queue.") - return True - - if len(add_data_points_request) == 2: - (collection_name, data_points) = add_data_points_request - - print(f"Adding {len(data_points)} data points to '{collection_name}' collection.") - - @retry( - retry=retry_if_exception_type(VectorDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def add_data_points(): - try: - await vector_engine.create_data_points( - collection_name, data_points, distributed=False - ) - except DBAPIError as error: - if is_deadlock_error(error): - raise VectorDatabaseDeadlockError() - except OperationalError as error: - if is_deadlock_error(error): - raise VectorDatabaseDeadlockError() - - await add_data_points() - - print("Finished adding data points.") - else: print("No jobs, go to sleep.") await asyncio.sleep(5) diff --git a/distributed/workers/graph_saving_worker.py b/distributed/workers/graph_saving_worker.py index a0202eea5..67c034225 100644 --- a/distributed/workers/graph_saving_worker.py +++ b/distributed/workers/graph_saving_worker.py @@ -1,8 +1,10 @@ +import os import modal import asyncio from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential from distributed.app import app +from distributed.signal import QueueSignal from distributed.modal_image import image from distributed.queues import add_nodes_and_edges_queue @@ -10,7 +12,6 @@ from cognee.shared.logging_utils import get_logger from cognee.infrastructure.databases.graph import get_graph_engine from cognee.infrastructure.databases.graph.config import get_graph_config - logger = get_logger("graph_saving_worker") @@ -37,68 +38,91 @@ def is_deadlock_error(error): return False +secret_name = os.environ.get("MODAL_SECRET_NAME", "distributed_cognee") + + @app.function( retries=3, image=image, timeout=86400, - max_containers=5, - secrets=[modal.Secret.from_name("distributed_cognee")], + max_containers=1, + secrets=[modal.Secret.from_name(secret_name)], ) async def graph_saving_worker(): print("Started processing of nodes and edges; starting graph engine queue.") graph_engine = await get_graph_engine() + # Defines how many data packets do we glue together from the queue before ingesting them into the graph database + BATCH_SIZE = 25 + stop_seen = False while True: + if stop_seen: + print("Finished processing all data points; stopping graph engine queue consumer.") + return True + if await add_nodes_and_edges_queue.len.aio() != 0: try: - nodes_and_edges = await add_nodes_and_edges_queue.get.aio(block=False) + print("Remaining elements in queue:") + print(await add_nodes_and_edges_queue.len.aio()) + + all_nodes, all_edges = [], [] + for _ in range(min(BATCH_SIZE, await add_nodes_and_edges_queue.len.aio())): + nodes_and_edges = await add_nodes_and_edges_queue.get.aio(block=False) + + if not nodes_and_edges: + continue + + if nodes_and_edges == QueueSignal.STOP: + await add_nodes_and_edges_queue.put.aio(QueueSignal.STOP) + stop_seen = True + break + + if len(nodes_and_edges) == 2: + nodes, edges = nodes_and_edges + all_nodes.extend(nodes) + all_edges.extend(edges) + else: + print("None Type detected.") + + if all_nodes or all_edges: + print(f"Adding {len(all_nodes)} nodes and {len(all_edges)} edges.") + + @retry( + retry=retry_if_exception_type(GraphDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def save_graph_nodes(new_nodes): + try: + await graph_engine.add_nodes(new_nodes, distributed=False) + except Exception as error: + if is_deadlock_error(error): + raise GraphDatabaseDeadlockError() + + @retry( + retry=retry_if_exception_type(GraphDatabaseDeadlockError), + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=2, min=1, max=6), + ) + async def save_graph_edges(new_edges): + try: + await graph_engine.add_edges(new_edges, distributed=False) + except Exception as error: + if is_deadlock_error(error): + raise GraphDatabaseDeadlockError() + + if all_nodes: + await save_graph_nodes(all_nodes) + + if all_edges: + await save_graph_edges(all_edges) + + print("Finished adding nodes and edges.") + except modal.exception.DeserializationError as error: logger.error(f"Deserialization error: {str(error)}") continue - if len(nodes_and_edges) == 0: - print("Finished processing all nodes and edges; stopping graph engine queue.") - return True - - if len(nodes_and_edges) == 2: - print( - f"Adding {len(nodes_and_edges[0])} nodes and {len(nodes_and_edges[1])} edges." - ) - nodes = nodes_and_edges[0] - edges = nodes_and_edges[1] - - @retry( - retry=retry_if_exception_type(GraphDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def save_graph_nodes(new_nodes): - try: - await graph_engine.add_nodes(new_nodes, distributed=False) - except Exception as error: - if is_deadlock_error(error): - raise GraphDatabaseDeadlockError() - - @retry( - retry=retry_if_exception_type(GraphDatabaseDeadlockError), - stop=stop_after_attempt(3), - wait=wait_exponential(multiplier=2, min=1, max=6), - ) - async def save_graph_edges(new_edges): - try: - await graph_engine.add_edges(new_edges, distributed=False) - except Exception as error: - if is_deadlock_error(error): - raise GraphDatabaseDeadlockError() - - if nodes: - await save_graph_nodes(nodes) - - if edges: - await save_graph_edges(edges) - - print("Finished adding nodes and edges.") - else: print("No jobs, go to sleep.") await asyncio.sleep(5) diff --git a/docker-compose.yml b/docker-compose.yml index 1c0a7dbd3..9f0e199a0 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -96,17 +96,6 @@ services: networks: - cognee-network - falkordb: - image: falkordb/falkordb:edge - container_name: falkordb - profiles: - - falkordb - ports: - - 6379:6379 - - 3001:3000 - networks: - - cognee-network - chromadb: image: chromadb/chroma:0.6.3 container_name: chromadb diff --git a/evals/src/qa/qa_benchmark_cognee.py b/evals/src/qa/qa_benchmark_cognee.py index 2a09f250d..e4b2faf85 100644 --- a/evals/src/qa/qa_benchmark_cognee.py +++ b/evals/src/qa/qa_benchmark_cognee.py @@ -34,7 +34,7 @@ class CogneeConfig(QABenchmarkConfig): system_prompt_path: str = "answer_simple_question_benchmark2.txt" # Search parameters (fallback if not using eval framework) - search_type: SearchType = SearchType.INSIGHTS + search_type: SearchType = SearchType.GRAPH_COMPLETION # Clean slate on initialization clean_start: bool = True diff --git a/examples/database_examples/chromadb_example.py b/examples/database_examples/chromadb_example.py index defa7f78d..71005fb4f 100644 --- a/examples/database_examples/chromadb_example.py +++ b/examples/database_examples/chromadb_example.py @@ -57,7 +57,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "ChromaDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="ChromaDB") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="ChromaDB" + ) print("\nInsights about ChromaDB:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/falkordb_example.py b/examples/database_examples/falkordb_example.py deleted file mode 100644 index 9e096fd81..000000000 --- a/examples/database_examples/falkordb_example.py +++ /dev/null @@ -1,87 +0,0 @@ -import os -import pathlib -import asyncio -import cognee -from cognee.modules.search.types import SearchType - - -async def main(): - """ - Example script demonstrating how to use Cognee with FalkorDB - - This example: - 1. Configures Cognee to use FalkorDB as graph database - 2. Sets up data directories - 3. Adds sample data to Cognee - 4. Processes (cognifies) the data - 5. Performs different types of searches - """ - # Configure FalkorDB as the graph database provider - cognee.config.set_graph_db_config( - { - "graph_database_url": "localhost", # FalkorDB URL (using Redis protocol) - "graph_database_port": 6379, - "graph_database_provider": "falkordb", - } - ) - - # Set up data directories for storing documents and system files - # You should adjust these paths to your needs - current_dir = pathlib.Path(__file__).parent - data_directory_path = str(current_dir / "data_storage") - cognee.config.data_root_directory(data_directory_path) - - cognee_directory_path = str(current_dir / "cognee_system") - cognee.config.system_root_directory(cognee_directory_path) - - # Clean any existing data (optional) - await cognee.prune.prune_data() - await cognee.prune.prune_system(metadata=True) - - # Create a dataset - dataset_name = "falkordb_example" - - # Add sample text to the dataset - sample_text = """FalkorDB is a graph database that evolved from RedisGraph. - It is focused on providing high-performance graph operations. - FalkorDB uses sparse adjacency matrices to represent the graph data structure. - It supports the Cypher query language for querying graph data. - FalkorDB can be integrated with vector search capabilities for AI applications. - It provides a Redis module, allowing users to leverage Redis's features alongside graph capabilities.""" - - # Add the sample text to the dataset - await cognee.add([sample_text], dataset_name) - - # Process the added document to extract knowledge - await cognee.cognify([dataset_name]) - - # Now let's perform some searches - # 1. Search for insights related to "FalkorDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="FalkorDB") - print("\nInsights about FalkorDB:") - for result in insights_results: - print(f"- {result}") - - # 2. Search for text chunks related to "graph database" - chunks_results = await cognee.search( - query_type=SearchType.CHUNKS, query_text="graph database", datasets=[dataset_name] - ) - print("\nChunks about graph database:") - for result in chunks_results: - print(f"- {result}") - - # 3. Get graph completion related to databases - graph_completion_results = await cognee.search( - query_type=SearchType.GRAPH_COMPLETION, query_text="database" - ) - print("\nGraph completion for databases:") - for result in graph_completion_results: - print(f"- {result}") - - # Clean up (optional) - # await cognee.prune.prune_data() - # await cognee.prune.prune_system(metadata=True) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/database_examples/kuzu_example.py b/examples/database_examples/kuzu_example.py index a31404cbc..ab24a0e07 100644 --- a/examples/database_examples/kuzu_example.py +++ b/examples/database_examples/kuzu_example.py @@ -55,7 +55,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "KuzuDB" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="KuzuDB") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="KuzuDB" + ) print("\nInsights about KuzuDB:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/neo4j_example.py b/examples/database_examples/neo4j_example.py index 45985610f..b5b6bfbbc 100644 --- a/examples/database_examples/neo4j_example.py +++ b/examples/database_examples/neo4j_example.py @@ -64,7 +64,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "Neo4j" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Neo4j") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="Neo4j" + ) print("\nInsights about Neo4j:") for result in insights_results: print(f"- {result}") diff --git a/examples/database_examples/neptune_analytics_example.py b/examples/database_examples/neptune_analytics_example.py index 5d36e2803..d98d1768c 100644 --- a/examples/database_examples/neptune_analytics_example.py +++ b/examples/database_examples/neptune_analytics_example.py @@ -79,7 +79,7 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "Neptune Analytics" insights_results = await cognee.search( - query_type=SearchType.INSIGHTS, query_text="Neptune Analytics" + query_type=SearchType.GRAPH_COMPLETION, query_text="Neptune Analytics" ) print("\n========Insights about Neptune Analytics========:") for result in insights_results: diff --git a/examples/database_examples/pgvector_example.py b/examples/database_examples/pgvector_example.py index 19dbb44e9..026cbfd08 100644 --- a/examples/database_examples/pgvector_example.py +++ b/examples/database_examples/pgvector_example.py @@ -69,7 +69,9 @@ async def main(): # Now let's perform some searches # 1. Search for insights related to "PGVector" - insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="PGVector") + insights_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text="PGVector" + ) print("\nInsights about PGVector:") for result in insights_results: print(f"- {result}") diff --git a/examples/python/relational_database_migration_example.py b/examples/python/relational_database_migration_example.py index 6a5c3b78b..7e87347bc 100644 --- a/examples/python/relational_database_migration_example.py +++ b/examples/python/relational_database_migration_example.py @@ -70,23 +70,23 @@ async def main(): search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What kind of data do you contain?", - top_k=1000, + top_k=200, ) print(f"Search results: {search_results}") # Having a top_k value set to too high might overwhelm the LLM context when specific questions need to be answered. - # For this kind of question we've set the top_k to 30 + # For this kind of question we've set the top_k to 50 search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What invoices are related to Leonie Köhler?", - top_k=30, + top_k=50, ) print(f"Search results: {search_results}") search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What invoices are related to Luís Gonçalves?", - top_k=30, + top_k=50, ) print(f"Search results: {search_results}") diff --git a/examples/python/simple_example.py b/examples/python/simple_example.py index 41be06f25..c13e48f85 100644 --- a/examples/python/simple_example.py +++ b/examples/python/simple_example.py @@ -50,7 +50,9 @@ async def main(): query_text = "Tell me about NLP" print(f"Searching cognee for insights with query: '{query_text}'") # Query cognee for insights on the added text - search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=query_text) + search_results = await cognee.search( + query_type=SearchType.GRAPH_COMPLETION, query_text=query_text + ) print("Search results:") # Display results diff --git a/notebooks/cognee_demo.ipynb b/notebooks/cognee_demo.ipynb index 51eeab560..46c355321 100644 --- a/notebooks/cognee_demo.ipynb +++ b/notebooks/cognee_demo.ipynb @@ -68,30 +68,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": 2, - "id": "8aba58c24daec519", - "metadata": { - "ExecuteTime": { - "end_time": "2025-06-30T11:38:36.328621Z", - "start_time": "2025-06-30T11:38:36.068313Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.2.4-local\n" - ] - } - ], - "source": [ - "import cognee\n", - "print(cognee.__version__)" - ] - }, { "cell_type": "markdown", "id": "074f0ea8-c659-4736-be26-be4b0e5ac665", @@ -110,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "df16431d0f48b006", "metadata": { "ExecuteTime": { @@ -419,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 1, "id": "9f1a1dbd", "metadata": { "ExecuteTime": { @@ -433,9 +409,32 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:01:17.533854\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:36:22.969905\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-15-58.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:18.755266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:36:23.577073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:36:23.577696\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "\n", + "\u001b[2m2025-10-07T20:36:28.324602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:36:28.362499\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:36:30.889776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n", + "\n", + "\u001b[1mDeleting cache... \u001b[0m\n", + "\n", + "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" ] } ], @@ -445,7 +444,8 @@ "import cognee\n", "\n", "await cognee.prune.prune_data()\n", - "await cognee.prune.prune_system(metadata=True)" + "await cognee.prune.prune_system(metadata=True)\n", + "print(cognee.__version__)" ] }, { @@ -458,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "904df61ba484a8e5", "metadata": { "ExecuteTime": { @@ -466,7 +466,117 @@ "start_time": "2025-06-30T11:39:32.388973Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User 17d4a6b1-da2b-46b8-a607-06969eda0881 has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:33:26.264681\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: 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pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('a9cc087f-1fe6-54d9-8626-06fb6bee185e')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('2352a9c1-c822-59e1-a38a-a62fa0009143')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('8175b7ba-1eeb-512e-9c9e-9ac7859cc505')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('fb1b835a-d9ae-5599-ba17-3dcfb767f297')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('9ce0cfb9-a3f7-5b95-b7e2-c8824363fa8a')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('7957d938-8b11-5dbb-8044-bd39e30da549'), dataset_id=UUID('4cc90c80-59e1-57db-8d4e-28f45f9d1661'), dataset_name='example', payload=None, data_ingestion_info=None), 'data_id': UUID('e64a82b1-6070-53a4-b631-cca67bce7d14')}])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import cognee\n", "\n", @@ -561,881 +671,389 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:01:47.379592\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.364927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.554990\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.365793\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.737751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.366370\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:47.869950\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.367252\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:01:48.007947\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:01:48 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:37.367570\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:37.367948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:37.368650\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.369534\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.282550\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.369944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.320418\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.370804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.371064\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.371413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.321779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science, stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics, university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322397\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.372864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.322732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.373605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.373898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.374241\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.323719\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.392711\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.404807\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist at innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.407830\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.324780\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist at datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.410319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.412354\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325515\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.414016\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325718\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.420301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.325964\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.427979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.432593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326477\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'keras' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.439575\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.326728\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.445716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.327255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hadoop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:37.456232\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.327861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spark' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.370377\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tableau' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.420182\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'matplotlib' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.455440\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328590\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'natural language processing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.456243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dr. emily carter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.328763\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.456685\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329041\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457029\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stanford university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457363\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of california, berkeley' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329454\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.457690\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'innovateai labs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datawave analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.329988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ph.d. in computer science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.330158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:48.458876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:13.330367\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved prediction accuracy by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:50.397043\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:14 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.351674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.358793\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:15 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.360301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.361050\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:51.361631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:16.127289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:16 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:51.362581\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:51.363138\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educational_institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:03:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:51.363844\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:51.364552\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:24.159354\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.365273\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.f.a. in graphic design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.365922\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.366327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.366630\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:27 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:51.367065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:03:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:53.924808\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:55.543740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.025361\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.440785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.170836\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.446845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.306326\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.447421\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.446538\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.447928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.579414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.448478\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.712543\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.449018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.841720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.449488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:28.971284\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.450019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.100235\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.450742\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.233945\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.451246\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programming language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.390158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.451757\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:03:29.533576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:03:29 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:56.452305\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.452770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.453216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.453631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.132445\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical analysis software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.140870\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454425\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.141437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.454815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spss' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.141811\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.455234\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cloud platform' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in data science, carnegie mellon university (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.455609\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'aws' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in computer science, university of michigan (2011)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'azure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.142706\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456536\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.143079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.456927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.143669\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.457388\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.144377\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.501611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.146600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist, alpha analytics (2017 – present)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.505241\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'company' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.148160\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist, techinsights (2013 – 2017)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.505755\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.148944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506086\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job_title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.149419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506420\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.149899\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506687\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2017' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.506949\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'field' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.150911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.151386\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.152026\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.507994\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programming_language' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.152702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'java' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508283\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.508845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.153915\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'xgboost' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509066\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep_learning_framework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.154201\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaborn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509327\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.154668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plotly' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155091\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mysql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.509855\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'academic_qualification' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155395\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mongodb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.510156\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'masters or ph.d. in data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.510369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:50.155892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical modeling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.510617\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.909307\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.912471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.912916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael rodriguez' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:04:53 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.913239\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.913593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alpha analytics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:04:53.183427\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:04:53 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:56.913979\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'techinsights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.914278\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carnegie mellon university' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:56.914655\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of michigan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:56.914927\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:05.322305\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:05:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2011' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.915658\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.916228\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:56.916564\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:05:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:33:57.029740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.034344\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:08.880095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.034871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.011845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.035333\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.139270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.035762\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.287708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.485773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036543\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'educationalinstitution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.625262\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.036875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.763675\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037182\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fieldofstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:09.933624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.078157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.037882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.222889\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.038189\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales manager' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.380248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.038472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales representative' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:05:10.536508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:05:10 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:33:57.038860\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:57.039102\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales strategy and planning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:33:57.039364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team leadership and development' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.039673\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'crm software' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.019994\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040029\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'negotiation and relationship building' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.026138\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027020\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sarah nguyen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.040897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027427\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041191\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.027629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'quantumtech' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041458\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'datacore solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.041729\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015-present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028180\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.519924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028485\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'm.s. in statistics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.623680\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.028892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.s. in applied mathematics' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.029151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of washington' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624457\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.029443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of texas at austin' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.030216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.624985\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.031108\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2014' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.625245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.031843\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:57.625479\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.032486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2016' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.292226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.300823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:58.827732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.033876\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.040051\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.036096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.648684\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.036641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649221\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.037043\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'scikit-learn' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649550\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038085\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.649845\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038562\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spss' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.038903\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'aws' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650723\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'azure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:33:59.650926\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039529\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'position' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:01.349738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data scientist (quantumtech)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.022925\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.039942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'junior data scientist (datacore solutions)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.680974\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.040135\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.976034\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:24.040332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'improved model efficiency by 20%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.976591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:24 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.976959\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977216\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977514\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:03.977819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:03.978068\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:27.001049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:27 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:34:05.183360\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:34:05.183819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:06:34 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", + "\u001b[2m2025-10-07T20:34:05.184132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:05.184426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:34.782530\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.184708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:35 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.185092\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.185378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:36 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:05.323602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:06.166424\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:06:37 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", + "\u001b[2m2025-10-07T20:34:06.166969\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.167301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:37.896055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168035\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.023334\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168356\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.163692\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168621\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.306820\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:06.168897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.437716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.850334\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.566678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851074\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.701049\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.837828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.851921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:38.971017\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.852348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:39.106825\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:07.852612\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:06:39.269402\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:06:39.416821\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:06:39 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.076839\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.089869\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.090647\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david thompson' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'contact' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091481\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'david.thompson@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.091804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 456-7890' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'education' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rhode island school of design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.092896\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093203\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativeworks agency' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093480\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'visual innovations' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.093732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094071\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior graphic designer (creativeworks agency)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094411\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'graphic designer (visual innovations)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.094677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2012' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.095716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096076\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe photoshop' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096539\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe illustrator' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.096817\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adobe indesign' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097309\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'html' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'css' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.097934\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'specialty' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.098206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'branding and identity' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:39.098423\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'typography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:39 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:42.898490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:43 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:07:50 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:50.774170\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:52 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:55 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:07:56 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.710744\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.844417\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:56.974409\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.120036\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.258343\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.389231\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.519095\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.648012\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.777905\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:57.903188\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:58.041288\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:07:58.183908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:07:58 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:09:25 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.506882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.514484\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica miller' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'email' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.515918\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jessica.miller@example.com' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.516371\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phone' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.516680\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '(555) 567-8901' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517023\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'b.a. in business administration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517660\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.517900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'university of southern california' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518556\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2010' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.518816\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'global enterprises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519156\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'market leaders inc.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job title' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.519738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales manager' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales representative' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2015' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'present' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.520908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'achievement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'managed a sales team of 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521489\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '20% increase in annual revenue' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521725\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'expanded customer base by 25%' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.521963\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'award' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522325\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'top salesperson award (2013)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2013' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.522861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sales strategy and planning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523495\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team leadership and development' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.523776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'negotiation and relationship building' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'software' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'salesforce' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:25.524716\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'zoho' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:26 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:28 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:29 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:31 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:31.465804\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:31 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:09:40 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:40.613929\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:41 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:42 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:09:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:46.891337\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:48.115206\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:52.397824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.325055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.712006\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:55.866332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:56.078882\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:56.461802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:58.451988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:58.834324\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:59.456885\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:09:59.835685\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:09:59 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:10:44 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:10:45 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.004741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.013274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.013770\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'technova solutions' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'job' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014545\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'senior data scientist (machine learning)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.014916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.015535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'san francisco, ca' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.015856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'team' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analytics team' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016573\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'skill' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.016923\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'machine learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.017917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data science' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.018571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'python' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.018975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'r' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.019720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sql' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.020396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tensorflow' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021063\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pytorch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'deep learning' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.021983\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'degree' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022306\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'master or phd' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'experience' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.022823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '5+ years experience' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023130\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'problem-solving' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023387\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'attention to detail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mentoring' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.023928\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cross-functional collaboration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024195\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'large, complex datasets' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.024705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'responsibility' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025003\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'predictive models integration' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025196\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stay updated with ml advancements' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025624\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'analyze datasets and extract insights' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:45.025942\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'develop and implement ml algorithms' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\u001b[92m14:10:46 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:47 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:48 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:10:51 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:10:51.648268\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:10:52 - LiteLLM:INFO\u001b[0m: utils.py:2929 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:11:04 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\u001b[92m14:11:05 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2025-08-07\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2025-08-07\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:05.002175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:10 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:11 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:12 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:13 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\u001b[92m14:11:16 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.526730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.672123\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.807143\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:16.938931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.131661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.287410\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:11:17.455194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e29ae05e-6381-5dff-a952-b7f7156a123f`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:07.852868\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `a68fd4c4-46a2-5aca-98d5-46646fa0cbec`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] } ], @@ -1455,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "id": "9dd29caf28c272d1", "metadata": { "ExecuteTime": { @@ -1469,17 +1087,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:14:03.046467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-32-51.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:34:17.282583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:14:03.841002\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:03.841488\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:05.798338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:14:05.799781\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:17.283063\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored at path: /Users/daulet/Desktop/dev/cognee-claude/notebooks/.artifacts/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] } ], @@ -1514,7 +1124,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 15, "id": "e5e7dfc8", "metadata": { "ExecuteTime": { @@ -1523,28 +1133,20 @@ } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709534883499146}\n", - "{'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'payload': {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica.miller@example.com'}, 'score': 0.7205207943916321}\n", - "{'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'payload': {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'david.thompson@example.com'}, 'score': 0.775915801525116}\n", - "{'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'payload': {'id': '5066c251-53a3-5f4c-a650-f291057b4659', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'salesforce'}, 'score': 1.3353958129882812}\n", - "{'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'payload': {'id': 'd510e7a8-85a6-557e-be89-3bef491c190f', 'created_at': 1756300271229, 'updated_at': 1756300271229, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495608568191528}\n", - "{'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'payload': {'id': 'ea51c0df-d8c8-549b-8978-3bc56d2cdacc', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'b.s. in computer science, university of michigan (2011)'}, 'score': 1.3630532026290894}\n", - "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1756299996071, 'updated_at': 1756299996071, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n", - "{'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'payload': {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'created_at': 1756300071900, 'updated_at': 1756300071900, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '(555) 456-7890'}, 'score': 1.3842216730117798}\n", - "{'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'payload': {'id': '3b2935d9-1f76-5e7b-8367-907b26efc873', 'created_at': 1756299906589, 'updated_at': 1756299906589, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in data science, carnegie mellon university (2013)'}, 'score': 1.396019697189331}\n", - "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1756300182609, 'updated_at': 1756300182609, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042032957077026}\n" + "{'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'payload': {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sarah nguyen'}, 'score': 0.5709534883499146}\n", + "{'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'payload': {'id': '198e2ab8-75e9-5931-97ab-da9a5a8e188c', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'san francisco, ca'}, 'score': 1.3495469093322754}\n", + "{'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'payload': {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sas'}, 'score': 1.378708004951477}\n", + "{'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'payload': {'id': '435dbd37-ab20-503c-9e99-ab8b8a3484e5', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'senior data scientist'}, 'score': 1.3935251235961914}\n", + "{'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'payload': {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'jessica miller'}, 'score': 1.4042136669158936}\n", + "{'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'payload': {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'created_at': 1759869246443, 'updated_at': 1759869246443, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'michael rodriguez'}, 'score': 1.4521194696426392}\n", + "{'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'payload': {'id': 'df9b4927-8f23-54ca-8ac9-015ac74e348c', 'created_at': 1759869244531, 'updated_at': 1759869244531, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'sales manager'}, 'score': 1.45354425907135}\n", + "{'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'payload': {'id': '570781f9-ae7c-546b-aa21-88c5754443f4', 'created_at': 1759869242254, 'updated_at': 1759869242254, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '5+ years experience'}, 'score': 1.456857442855835}\n", + "{'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'payload': {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'created_at': 1759869236359, 'updated_at': 1759869236359, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'dr. emily carter'}, 'score': 1.4710627794265747}\n", + "{'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'payload': {'id': 'd82673ff-ff5e-5436-bc8b-2967d4301b4f', 'created_at': 1759869243986, 'updated_at': 1759869243986, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'm.s. in statistics'}, 'score': 1.4715948104858398}\n" ] } ], @@ -1599,7 +1201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "id": "21a3e9a6", "metadata": { "ExecuteTime": { @@ -1613,15 +1215,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:15:00.678844\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:36.551256\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:00.683634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.197175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.048173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.197911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.048720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:37.198373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:01.049052\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:37.198826\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" ] }, { @@ -1631,17 +1233,17 @@ "\n", "\\Extracted summaries are:\n", "\n", - "{'id': 'd63c0626-f304-5b51-81be-b7a42cdb7d88', 'created_at': 1756299804334, 'updated_at': 1756299804334, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist with 8+ years’ experience in machine learning and predictive analytics, specializing in NLP, deep learning, and production model deployment.'}\n", + "{'id': '4c47f1ef-54a5-54c9-bc4f-f23c45477451', 'created_at': 1759869237535, 'updated_at': 1759869237535, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Graphic Designer with over 8 years in visual design and branding, skilled in Adobe Creative Suite, focused on crafting engaging visuals.'}\n", "\n", - "{'id': '9c02f5ca-e629-5930-b178-1c6bee059689', 'created_at': 1756300070938, 'updated_at': 1756300070938, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Graphic designer with 8+ years’ experience specializing in branding and visual design; skilled in Adobe Creative Suite and basic web design.'}\n", + "{'id': 'f99dc79b-af29-5f70-9fcb-5bb62790994a', 'created_at': 1759869241394, 'updated_at': 1759869241394, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist specializing in Machine Learning at TechNova Solutions.'}\n", "\n", - "{'id': '54db4268-2156-5ae2-ba54-8c6c0d9bd3ed', 'created_at': 1756299905496, 'updated_at': 1756299905496, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Data Scientist specializing in machine learning and statistical modeling, skilled at working with large datasets and turning data into actionable business outcomes.'}\n", + "{'id': 'df7d916e-6056-5302-ad95-b5c46d5dac42', 'created_at': 1759869245597, 'updated_at': 1759869245597, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist proficient in machine learning and statistical analysis, experienced in managing extensive datasets and converting data into strategic business insights.'}\n", "\n", - "{'id': '799d1f2a-4c78-517f-b09b-ede659507d4d', 'created_at': 1756300180835, 'updated_at': 1756300180835, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Seasoned sales leader with a strong record of driving revenue growth and building high-performing teams.'}\n", + "{'id': '7144d310-9f4e-5b13-acaa-1f53bdd77cc0', 'created_at': 1759869243709, 'updated_at': 1759869243709, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Sales Manager with a proven history of driving revenue growth and cultivating effective teams. Strong leadership and communication abilities.'}\n", "\n", - "{'id': 'a3534071-8c78-5f37-99ea-0f14ea971422', 'created_at': 1756300269688, 'updated_at': 1756300269688, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'TechNova Solutions is hiring a Senior Data Scientist (Machine Learning) in San Francisco to design, train, and operationalize ML models, analyze large datasets, and guide junior team members.'}\n", + "{'id': '352ab868-e930-5009-a855-14b83894520d', 'created_at': 1759869243053, 'updated_at': 1759869243053, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years of experience in machine learning, focused on utilizing data for business improvements and product optimization.'}\n", "\n", - "{'id': '594514cb-0399-5389-a1d9-4d6873606c70', 'created_at': 1756299994959, 'updated_at': 1756299994959, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Data Scientist with 6 years’ experience in machine learning and statistical modeling, focused on financial forecasting and predictive analytics. Proven track record of boosting model efficiency by 20%; skilled in Python, R, PyTorch, scikit-learn, SAS, SPSS, and cloud platforms (AWS, Azure). Holds an M.S. in Statistics and a B.S. in Applied Mathematics.'}\n", + "{'id': '9b808a69-a954-5e68-af72-045c3e7f652a', 'created_at': 1759869235560, 'updated_at': 1759869235560, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Experienced Senior Data Scientist with 8+ years in machine learning and predictive analytics, proficient in creating advanced algorithms and deploying scalable models in production.'}\n", "\n" ] } @@ -1668,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "id": "c7a8abff", "metadata": { "ExecuteTime": { @@ -1682,15 +1284,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:15:07.822953\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:44.865101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting chunk retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:07.823567\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting chunk retrieval for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.083571\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 chunks from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.131266\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 6 chunks from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.084214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 chunk payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.132088\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 6 chunk payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:34:45.084687\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'sarah nguyen'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:15:08.132522\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:34:45.085107\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 6 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mChunksRetriever\u001b[0m]\u001b[0m\n" ] }, { @@ -1701,17 +1303,17 @@ "\n", "Extracted chunks are:\n", "\n", - "{'id': 'e2a2fff5-44bc-5e49-bbca-901634f33033', 'created_at': 1756299995290, 'updated_at': 1756299995290, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", + "{'id': 'b8730838-071e-542f-9bba-83de86d7a56b', 'created_at': 1759869243385, 'updated_at': 1759869243385, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 3: Relevant\\nName: Sarah Nguyen\\nContact Information:\\n\\nEmail: sarah.nguyen@example.com\\nPhone: (555) 345-6789\\nSummary:\\n\\nData Scientist specializing in machine learning with 6 years of experience. Passionate about leveraging data to drive business solutions and improve product performance.\\n\\nEducation:\\n\\nM.S. in Statistics, University of Washington (2014)\\nB.S. in Applied Mathematics, University of Texas at Austin (2012)\\nExperience:\\n\\nData Scientist, QuantumTech (2016 – Present)\\nDesigned and implemented machine learning algorithms for financial forecasting.\\nImproved model efficiency by 20% through algorithm optimization.\\nJunior Data Scientist, DataCore Solutions (2014 – 2016)\\nAssisted in developing predictive models for supply chain optimization.\\nConducted data cleaning and preprocessing on large datasets.\\nSkills:\\n\\nProgramming Languages: Python, R\\nMachine Learning Frameworks: PyTorch, Scikit-Learn\\nStatistical Analysis: SAS, SPSS\\nCloud Platforms: AWS, Azure\\n'}\n", "\n", - "{'id': '075c882b-ae90-50c0-bc60-71c8da1d898d', 'created_at': 1756300181483, 'updated_at': 1756300181483, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", + "{'id': '51c1e778-f0fe-5cfa-bb0b-8c7a8a28aae5', 'created_at': 1759869243982, 'updated_at': 1759869243982, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"\\nCV 5: Not Relevant\\nName: Jessica Miller\\nContact Information:\\n\\nEmail: jessica.miller@example.com\\nPhone: (555) 567-8901\\nSummary:\\n\\nExperienced Sales Manager with a strong track record in driving sales growth and building high-performing teams. Excellent communication and leadership skills.\\n\\nEducation:\\n\\nB.A. in Business Administration, University of Southern California (2010)\\nExperience:\\n\\nSales Manager, Global Enterprises (2015 – Present)\\nManaged a sales team of 15 members, achieving a 20% increase in annual revenue.\\nDeveloped sales strategies that expanded customer base by 25%.\\nSales Representative, Market Leaders Inc. (2010 – 2015)\\nConsistently exceeded sales targets and received the 'Top Salesperson' award in 2013.\\nSkills:\\n\\nSales Strategy and Planning\\nTeam Leadership and Development\\nCRM Software: Salesforce, Zoho\\nNegotiation and Relationship Building\\n\"}\n", "\n", - "{'id': '0af63ca5-47a0-50f3-b4d8-3463a1d0a749', 'created_at': 1756300071327, 'updated_at': 1756300071327, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", + "{'id': 'e7c43949-a4d6-5cdc-90e0-a7046a528a95', 'created_at': 1759869237781, 'updated_at': 1759869237781, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 4: Not Relevant\\nName: David Thompson\\nContact Information:\\n\\nEmail: david.thompson@example.com\\nPhone: (555) 456-7890\\nSummary:\\n\\nCreative Graphic Designer with over 8 years of experience in visual design and branding. Proficient in Adobe Creative Suite and passionate about creating compelling visuals.\\n\\nEducation:\\n\\nB.F.A. in Graphic Design, Rhode Island School of Design (2012)\\nExperience:\\n\\nSenior Graphic Designer, CreativeWorks Agency (2015 – Present)\\nLed design projects for clients in various industries.\\nCreated branding materials that increased client engagement by 30%.\\nGraphic Designer, Visual Innovations (2012 – 2015)\\nDesigned marketing collateral, including brochures, logos, and websites.\\nCollaborated with the marketing team to develop cohesive brand strategies.\\nSkills:\\n\\nDesign Software: Adobe Photoshop, Illustrator, InDesign\\nWeb Design: HTML, CSS\\nSpecialties: Branding and Identity, Typography\\n'}\n", "\n", - "{'id': '15190e7c-0ec2-5d99-ae68-5d76cb6bb414', 'created_at': 1756299805159, 'updated_at': 1756299805159, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", + "{'id': 'beb4b9bc-703c-5e20-8512-d4468c849277', 'created_at': 1759869235792, 'updated_at': 1759869235792, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 1: Relevant\\nName: Dr. Emily Carter\\nContact Information:\\n\\nEmail: emily.carter@example.com\\nPhone: (555) 123-4567\\nSummary:\\n\\nSenior Data Scientist with over 8 years of experience in machine learning and predictive analytics. Expertise in developing advanced algorithms and deploying scalable models in production environments.\\n\\nEducation:\\n\\nPh.D. in Computer Science, Stanford University (2014)\\nB.S. in Mathematics, University of California, Berkeley (2010)\\nExperience:\\n\\nSenior Data Scientist, InnovateAI Labs (2016 – Present)\\nLed a team in developing machine learning models for natural language processing applications.\\nImplemented deep learning algorithms that improved prediction accuracy by 25%.\\nCollaborated with cross-functional teams to integrate models into cloud-based platforms.\\nData Scientist, DataWave Analytics (2014 – 2016)\\nDeveloped predictive models for customer segmentation and churn analysis.\\nAnalyzed large datasets using Hadoop and Spark frameworks.\\nSkills:\\n\\nProgramming Languages: Python, R, SQL\\nMachine Learning: TensorFlow, Keras, Scikit-Learn\\nBig Data Technologies: Hadoop, Spark\\nData Visualization: Tableau, Matplotlib\\n'}\n", "\n", - "{'id': 'fde5c9b9-ff07-5f7a-ba70-afc421666787', 'created_at': 1756299905819, 'updated_at': 1756299905819, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", + "{'id': '2d052615-b28f-511c-ac15-1bdb7a155ad7', 'created_at': 1759869245851, 'updated_at': 1759869245851, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': '\\nCV 2: Relevant\\nName: Michael Rodriguez\\nContact Information:\\n\\nEmail: michael.rodriguez@example.com\\nPhone: (555) 234-5678\\nSummary:\\n\\nData Scientist with a strong background in machine learning and statistical modeling. Skilled in handling large datasets and translating data into actionable business insights.\\n\\nEducation:\\n\\nM.S. in Data Science, Carnegie Mellon University (2013)\\nB.S. in Computer Science, University of Michigan (2011)\\nExperience:\\n\\nSenior Data Scientist, Alpha Analytics (2017 – Present)\\nDeveloped machine learning models to optimize marketing strategies.\\nReduced customer acquisition cost by 15% through predictive modeling.\\nData Scientist, TechInsights (2013 – 2017)\\nAnalyzed user behavior data to improve product features.\\nImplemented A/B testing frameworks to evaluate product changes.\\nSkills:\\n\\nProgramming Languages: Python, Java, SQL\\nMachine Learning: Scikit-Learn, XGBoost\\nData Visualization: Seaborn, Plotly\\nDatabases: MySQL, MongoDB\\n'}\n", "\n", - "{'id': '15790448-7267-5a84-9dd7-be4997889842', 'created_at': 1756300270349, 'updated_at': 1756300270349, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", + "{'id': '1d3c1a95-e2ec-562a-a326-2eadfa556bf4', 'created_at': 1759869241747, 'updated_at': 1759869241747, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'Senior Data Scientist (Machine Learning)\\n\\nCompany: TechNova Solutions\\nLocation: San Francisco, CA\\n\\nJob Description:\\n\\nTechNova Solutions is seeking a Senior Data Scientist specializing in Machine Learning to join our dynamic analytics team. The ideal candidate will have a strong background in developing and deploying machine learning models, working with large datasets, and translating complex data into actionable insights.\\n\\nResponsibilities:\\n\\nDevelop and implement advanced machine learning algorithms and models.\\nAnalyze large, complex datasets to extract meaningful patterns and insights.\\nCollaborate with cross-functional teams to integrate predictive models into products.\\nStay updated with the latest advancements in machine learning and data science.\\nMentor junior data scientists and provide technical guidance.\\nQualifications:\\n\\nMaster’s or Ph.D. in Data Science, Computer Science, Statistics, or a related field.\\n5+ years of experience in data science and machine learning.\\nProficient in Python, R, and SQL.\\nExperience with deep learning frameworks (e.g., TensorFlow, PyTorch).\\nStrong problem-solving skills and attention to detail.\\nCandidate CVs\\n'}\n", "\n" ] } @@ -1733,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 18, "id": "706a3954", "metadata": {}, "outputs": [ @@ -1745,57 +1347,49 @@ "\n", "Extracted sentences are:\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'ontology_valid': False}, {'id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'name': 'python', 'type': 'Entity', 'created_at': 1756300245018, 'updated_at': 1756300245018, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used for data science and machine learning.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'ontology_valid': False}, {'id': 'c95db510-e2ee-5a00-bded-20bbcb50c492', 'name': 'python', 'type': 'Entity', 'created_at': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'ontology_valid': False}, {'id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'name': 'r', 'type': 'Entity', 'created_at': 1756300245019, 'updated_at': 1756300245019, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language for statistical analysis.'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'ontology_valid': False}, {'id': '39bd9707-8098-52ed-9cbf-bbdd26b963fb', 'name': 'r', 'type': 'Entity', 'created_at': 1759869236452, 'updated_at': 1759869236452, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Programming language used by Sarah Nguyen.'})\n", "\n", - 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"({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'ontology_valid': False}, {'id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'name': 'spss', 'type': 'Entity', 'created_at': 1756299984038, 'updated_at': 1756299984038, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'ontology_valid': False}, {'id': '37eecdcc-fb56-519c-bc18-d0d3afea0c0d', 'name': 'scikit-learn', 'type': 'Entity', 'created_at': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Machine learning framework used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'ontology_valid': False}, {'id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'name': 'aws', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'ontology_valid': False}, {'id': '6b726763-e259-5e91-b505-85284f8ea5ea', 'name': 'sas', 'type': 'Entity', 'created_at': 1759869236454, 'updated_at': 1759869236454, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'has_skill', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'ontology_valid': False}, {'id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'name': 'azure', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': ''})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'ontology_valid': False}, {'id': '4bee5f9c-846b-50f7-96fd-3e5889872b10', 'name': 'spss', 'type': 'Entity', 'created_at': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Statistical analysis software used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'ontology_valid': False}, {'id': '7ba500ab-9375-5eeb-9ada-ec931f3120a1', 'name': 'data scientist (quantumtech)', 'type': 'Entity', 'created_at': 1756299984039, 'updated_at': 1756299984039, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2016\\nend_date: Date_Present\\nresponsibilities: Designed and implemented machine learning algorithms for financial forecasting; Improved model efficiency by 20% through algorithm optimization'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'ontology_valid': False}, {'id': '3edcdf3f-25af-57a3-8878-8008bd7ea05a', 'name': 'aws', 'type': 'Entity', 'created_at': 1759869236455, 'updated_at': 1759869236455, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n", "\n", - "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'}, {'relationship_name': 'held_position', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'ontology_valid': False}, {'id': '7f9f181a-350c-5970-85d4-ca3f1703a089', 'name': 'junior data scientist (datacore solutions)', 'type': 'Entity', 'created_at': 1756299984040, 'updated_at': 1756299984040, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'start_date: Date_2014\\nend_date: Date_2016\\nresponsibilities: Assisted in developing predictive models for supply chain optimization; Conducted data cleaning and preprocessing on large datasets'})\n", + "({'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'}, {'relationship_name': 'uses', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'ontology_valid': False}, {'id': '8b431923-4aa2-5886-a661-b8de0f888a9b', 'name': 'azure', 'type': 'Entity', 'created_at': 1759869236456, 'updated_at': 1759869236456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Cloud platform used by Sarah Nguyen.'})\n", "\n", - "({'id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'name': 'email', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email'}, {'relationship_name': 'is_a', 'source_node_id': 'b6365021-70ae-53ea-83e7-885714b56092', 'target_node_id': 'dfa1b90a-397e-59c3-acf8-071fa2509968', 'updated_at': '2025-08-27 13:09:40'}, {'id': 'b6365021-70ae-53ea-83e7-885714b56092', 'name': 'jessica.miller@example.com', 'type': 'Entity', 'created_at': 1756300165516, 'updated_at': 1756300165516, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Primary contact email'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:33:55'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1759869228456, 'updated_at': 1759869228456, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Senior Data Scientist with expertise in machine learning and predictive analytics.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '29e771c8-4c3f-52de-9511-6b705878e130', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:03:24'}, {'id': '29e771c8-4c3f-52de-9511-6b705878e130', 'name': 'dr. emily carter', 'type': 'Entity', 'created_at': 1756299793321, 'updated_at': 1756299793321, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: emily.carter@example.com; phone: (555) 123-4567; profession: Senior Data Scientist; years_experience: 8; summary: Senior Data Scientist with over 8 years of experience in machine learning and predictive analytics'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:33:57'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1759869231360, 'updated_at': 1759869231360, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Creative Graphic Designer with over 8 years of experience in visual design and branding.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:05:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1756299890141, 'updated_at': 1756299890141, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: michael.rodriguez@example.com, phone: (555) 234-5678, summary: Data Scientist with background in machine learning and statistical modeling'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1759869236447, 'updated_at': 1759869236447, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist specializing in machine learning with 6 years of experience.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:06:34'}, {'id': '4d8dda57-2681-5264-a2bd-e2ddfe66a785', 'name': 'sarah nguyen', 'type': 'Entity', 'created_at': 1756299984027, 'updated_at': 1756299984027, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: sarah.nguyen@example.com\\nphone: (555) 345-6789\\nsummary: Data Scientist specializing in machine learning with 6 years of experience'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:05'}, {'id': '73ae630f-7b09-5dce-8c18-45d0a57b30f9', 'name': 'michael rodriguez', 'type': 'Entity', 'created_at': 1759869236913, 'updated_at': 1759869236913, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Data Scientist with a strong background in machine learning and statistical modeling.'})\n", "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'a4777597-06c7-562c-bc44-56f74571a01a', 'name': 'david thompson', 'type': 'Entity', 'created_at': 1756300059090, 'updated_at': 1756300059090, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'summary: Creative Graphic Designer with over 8 years of experience in visual design and branding'})\n", - "\n", - "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-08-27 13:09:40'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1756300165515, 'updated_at': 1756300165515, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", - "\n", - "({'id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'name': 'team', 'type': 'EntityType', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'team'}, {'relationship_name': 'is_a', 'source_node_id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'target_node_id': 'e308d782-2a18-5ace-b0e8-236c7c3b4db4', 'updated_at': '2025-08-27 13:11:09'}, {'id': '402e2e8b-9150-5ee8-b71b-f9b91f9699e1', 'name': 'analytics team', 'type': 'Entity', 'created_at': 1756300245016, 'updated_at': 1756300245016, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Dynamic analytics team at TechNova Solutions.'})\n", - "\n", - "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': '282e7e92-e3d5-5a27-96fd-f145d6ebf7ec', 'name': 'david.thompson@example.com', 'type': 'Entity', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'email: david.thompson@example.com'})\n", - "\n", - "({'id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'name': 'contact', 'type': 'EntityType', 'created_at': 1756300059091, 'updated_at': 1756300059091, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'contact'}, {'relationship_name': 'is_a', 'source_node_id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'target_node_id': 'ceab6411-7a0b-5c82-a2a2-bbfab015fe03', 'updated_at': '2025-08-27 13:07:50'}, {'id': 'f2daec66-8503-57ae-8ceb-b590a0593eeb', 'name': '(555) 456-7890', 'type': 'Entity', 'created_at': 1756300059092, 'updated_at': 1756300059092, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'phone: (555) 456-7890'})\n", + "({'id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'name': 'person', 'type': 'EntityType', 'created_at': 1759869236912, 'updated_at': 1759869236912, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'person'}, {'relationship_name': 'is_a', 'source_node_id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'target_node_id': 'd072ba0f-e1a9-58bf-9974-e1802adc8134', 'updated_at': '2025-10-07 20:34:03'}, {'id': '36a5e3c8-c5f5-5ab5-8d59-ea69d8b36932', 'name': 'jessica miller', 'type': 'Entity', 'created_at': 1759869237035, 'updated_at': 1759869237035, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'metadata': {'index_fields': ['name']}, 'belongs_to_set': None, 'description': 'Experienced Sales Manager with a strong track record in driving sales growth and building high-performing teams.'})\n", "\n" ] } ], "source": [ - "search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=node_name)\n", + "search_results = await cognee.search(query_type=SearchType.GRAPH_COMPLETION, query_text=node_name)\n", "print(\"\\n\\nExtracted sentences are:\\n\")\n", "for result in search_results:\n", " print(f\"{result}\\n\")" @@ -1806,7 +1400,19 @@ "execution_count": null, "id": "d42b3245", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# Only exit in interactive mode, not during GitHub Actions\n", "import os\n", @@ -1845,7 +1451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/cognee_multimedia_demo.ipynb b/notebooks/cognee_multimedia_demo.ipynb index 72342250b..608e11956 100644 --- a/notebooks/cognee_multimedia_demo.ipynb +++ b/notebooks/cognee_multimedia_demo.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:44.613431Z", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2025-06-30T11:54:46.739157Z", @@ -97,6 +97,38 @@ "# os.environ[\"DB_PASSWORD\"]=\"cognee\"" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:37:13.488510\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-23.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:37:14.172414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:37:14.172932\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + } + ], + "source": [ + "import cognee\n", + "print(cognee.__version__)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -114,31 +146,24 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T14:33:41.256195\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-27_14-00-27.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:37:20.743332\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:42.133224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:20.776490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:42.133667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:23.387773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:43.785214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:44.215920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[1mDeleting cache... \u001b[0m\n", "\n", - "\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\n", - "\u001b[92m15:33:44 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n" + "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "User 37ea34fa-cae7-4bea-8cb3-1ba234688771 has registered.\n" + "User 03f552c1-331f-40b2-a99b-b3b05aa93e0d has registered.\n" ] }, { @@ -148,164 +173,131 @@ "\n", "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.440270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.691142\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.690756\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.691670\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:50.996600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.692087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.287352\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.693388\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.287759\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.693668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.694024\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.708303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:51.288576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:52 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\u001b[2m2025-10-07T20:37:24.708776\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.455447\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.599686\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709654\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:52.806593\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:24.709898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.075106\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.420233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.209912\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.420796\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:53.355890\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:53 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:28.421255\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m15:33:57 - LiteLLM:INFO\u001b[0m: utils.py:1274 - Wrapper: Completed Call, calling success_handler\n", + "\u001b[2m2025-10-07T20:37:28.423491\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1mWrapper: Completed Call, calling success_handler\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.423881\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.407561\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.424259\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.560808\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.434168\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.713507\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `017311b3-90e5-53ce-9974-00c4d9551248`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453069\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.897060\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453489\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:57.938027\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.453823\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.093101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.255165\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.428623\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.454948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:33:58.588682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:33:58 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:28.462413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.466745\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.706892\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.470294\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.707703\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:28.476006\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708083\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.030103\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708440\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.065148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.708802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.065868\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.709129\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.066315\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:19.709475\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'how many programmers does it take to change a light bulb? none, thats a hardware problem.' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.066713\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:24.553989\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:32.067064\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.067410\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:32.883579\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.202761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'profession' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.680233\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.203355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.825933\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.203785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:35.975352\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.126720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204544\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.275404\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:32.204964\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'humor' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.424984\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:34.265785\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.576258\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.003525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.754472\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.952187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:36.912219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:35.970171\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.053036\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.024476\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.220157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.025311\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:34:37.388094\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:34:37 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:38.025564\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.025803\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.010321\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.012394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026413\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.012794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.026663\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013111\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'light bulb' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.680393\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.680986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013598\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hardware problem' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.013914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'joke' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681647\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:00.014215\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'programmer joke' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:38.681917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:35:02.040520\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m15:35:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:37:38.682229\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:11.589828\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.446614\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.622281\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:14.820192\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.004173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.518803\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.756519\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T14:35:15.978364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `9bd0d908-8e9e-5780-b4c2-09fc8d471f1b`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:37:38.682567\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "{UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('17b5c469-a8ce-5347-bea5-ab3dba767d13')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('69b78d3d-4d27-5d9f-918f-57e77b3cb10a'), dataset_id=UUID('a08926db-6319-5cd9-adc9-2cf9dfbc75e0'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('5b1e3c7e-d837-5704-a3b3-53abdda3a84f')}])}" + "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}" ] }, "execution_count": 4, @@ -349,23 +341,29 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T14:36:09.273837\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.668682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", - "\u001b[2m2025-08-27T14:36:09.275355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting summary retrieval for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.933137\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 2 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.691101\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFound 2 summaries from vector search\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.933995\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 2 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.691827\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning 2 summary payloads \u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:37:42.934301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mStarting completion generation for query: 'What is in the multimedia files?'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T14:36:09.692207\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 2 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:37:42.934604\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReturning context with 2 item(s)\u001b[0m [\u001b[0m\u001b[1m\u001b[34mSummariesRetriever\u001b[0m]\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "{'id': 'facab42e-12fc-557e-aaf4-09c02ae1cd4f', 'created_at': 1756305273061, 'updated_at': 1756305273061, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers won't change a light bulb — it's considered a hardware issue.\"}\n", - "{'id': '958f2bc9-060b-5500-b14a-19b300cc99aa', 'created_at': 1756305311791, 'updated_at': 1756305311791, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'One-line programmer joke: changing a light bulb is labeled a hardware issue.'}\n" + "{'id': '766ac5d6-1a81-530e-a934-61e2bf505d9b', 'created_at': 1759869455990, 'updated_at': 1759869455990, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': 'A humorous take on programmers and light bulbs.'}\n", + "{'id': '2862798a-0dfc-5994-a3ca-9f4329f42f06', 'created_at': 1759869455989, 'updated_at': 1759869455989, 'ontology_valid': False, 'version': 1, 'topological_rank': 0, 'type': 'IndexSchema', 'text': \"Programmers won't change a light bulb.\"}\n" ] } ], @@ -429,7 +427,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/cognee_simple_demo.ipynb b/notebooks/cognee_simple_demo.ipynb index 6ca42ada5..161e03d4b 100644 --- a/notebooks/cognee_simple_demo.ipynb +++ b/notebooks/cognee_simple_demo.ipynb @@ -79,580 +79,246 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:34:14.905059\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-08-26_12-44-42.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", + "\u001b[2m2025-10-07T20:38:23.321871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-16-35.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:15.858104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.2.4-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.12.7\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:23.924664\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:15.859638\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[1mLangfuse client is disabled since no public_key was provided as a parameter or environment variable 'LANGFUSE_PUBLIC_KEY'. See our docs: https://langfuse.com/docs/sdk/python/low-level-sdk#initialize-client\u001b[0m\n", - "\u001b[92m14:34:18 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:23.925152\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n", "\u001b[1mEmbeddingRateLimiter initialized: enabled=False, requests_limit=60, interval_seconds=60\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:22.954992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.824653\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.160900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.825175\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.335242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.825559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.516399\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.834754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: pypdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.517356\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.835421\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: text_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.518011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.835697\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: image_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.518486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.835966\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: audio_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.519037\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.836157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: unstructured_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.536911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.836754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mRegistered loader: advanced_pdf_loader\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.LoaderEngine\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.685341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:23.836303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `bb1e12db-8d3f-5e80-8615-2444eda4b32a`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847599\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.058213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.847894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `e16895e4-38f6-5ad7-a969-cd1629861b40`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.084248\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.967450\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.232775\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.980303\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology file 'None' not found. No owl ontology will be attached to the graph.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.378181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.998286\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.555749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:30.998936\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:34:24.803351\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:30.999638\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:31.006879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:31.119544\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.668159\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669164\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669470\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.669777\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.670086\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:34:24 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", + "\u001b[2m2025-10-07T20:38:51.670369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.670667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit-hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.974856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.670972\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.976689\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alice' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671244\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.976989\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671450\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977311\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671650\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977608\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dinah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.671857\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.977842\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672094\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978119\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672398\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978445\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tunnel or well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672607\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dodo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.978688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heap of sticks and dry leaves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.672847\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lory' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'long low hall' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eaglet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979218\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'object' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673399\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duck' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'three legged glass table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673634\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'historical figure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979726\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tiny golden key' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.673871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'william the conqueror' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.979974\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674120\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mary ann' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small passage' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674384\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980378\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lovely garden' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674682\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jar labeled orange marmalade' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.674931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caterpillar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980758\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cupboard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675153\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.980944\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle labeled drink me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675376\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981181\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'small glass box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675611\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cake labeled eat me' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.675837\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mushroom' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981672\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676060\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'father william' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.981921\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves and fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676333\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'character' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982174\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bookchapter' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676573\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pigeon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982434\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter i down the rabbit hole' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.676801\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'baby' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.982656\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chapter ii the pool of tears' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cheshire cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983007\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677242\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hatter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983274\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duck' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677490\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dodo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677694\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'queen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lory' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.677922\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.983917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'eaglet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678148\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magpie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'may' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984380\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canary' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.678911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.984798\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'young crab' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985030\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'animalclass' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985270\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cats' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679587\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dogs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.679917\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the rose tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'event' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.985875\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'w. rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the mad tea party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986089\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.680632\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986292\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'white kid gloves' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681429\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986527\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bottle (drink me)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gryphon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986754\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'thimble' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.681931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soldiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.986962\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'comfits' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682200\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_1' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987297\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'event' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682379\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_2' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caucus-race' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.682699\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gardener_3' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.987735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grew large event' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tortoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988057\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creature' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683560\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizards' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988461\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'magic bottle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.683846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'food' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988654\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size-changing cake' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684025\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tarts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.988869\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pebbles' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dance' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mushroom' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684671\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989289\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caterpillar' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.684888\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'subject' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989498\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hookah' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685135\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989699\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill (lizard)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'arithmetic' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.989916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685715\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mystery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.685998\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990390\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mary ann' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686239\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'court' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990541\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'place' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686484\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990742\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rabbit house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686688\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.990894\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'window' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.686886\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 14' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991048\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'door' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687125\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 15' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991246\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chimney' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687426\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march 16' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991431\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cucumber-frame' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.687861\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'place' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991636\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wood' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688321\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.991856\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'puppy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688615\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'father william' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.688914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992222\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689141\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'child-life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992402\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'size change' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689345\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple_joys' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.992893\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pigeon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:51.689662\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple_sorrows' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993054\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'serpent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:38:55.033467\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993213\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trees' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:03.406344\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993435\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little house' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.113087\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993576\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fish-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.113738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.993834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frog-footman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114015\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994053\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchess' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114579\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994264\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.114971\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'baby' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.115220\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:35:55.994661\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pig' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.994797\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cheshire cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.994960\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'kitchen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995114\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cauldron' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995283\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995444\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995637\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.995924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'frying-pan' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996079\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'invitation letter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996298\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'queen' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996443\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hatter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996602\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996794\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'game' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.996993\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the cat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997651\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the dormouse' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.997898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a mad tea-party' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998205\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'table' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998409\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the hatters watch' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998581\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'house of the march hare' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998819\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'a rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.998980\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'five' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999133\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'two' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999302\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seven' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999465\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'group' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:55.999749\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'soldiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.000078\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'courtiers' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.000273\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'royal children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.001132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'knave of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.002873\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.003453\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the queen of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.004815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'treacle-well' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'elsie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lacie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.005904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tillie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'riddle: why is a raven like a writing-desk?' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006483\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'song: twinkle twinkle little bat' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.006698\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tea-time' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.007096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'the king' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.007449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'executioner' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.008948\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'gryphon' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009396\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009604\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet game' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.009761\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'croquet ground' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hedgehog' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010238\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flamingo' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rose-tree' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.010795\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'flower-pot' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'players' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011425\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mock turtle soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.011802\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old turtle (tortoise)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.012966\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bill the lizard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.014629\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015124\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015383\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'snail' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'porpoise' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.015898\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'owl' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.016462\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'panther' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sea-shore' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.017920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'court of hearts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.018187\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lobster quadrille (dance)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.019379\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'creativework' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.020269\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beautiful soup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.021436\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'whiting song' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'school (sea-school)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022613\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeling and writhing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.022812\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ambition' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023185\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'distraction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023469\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'uglification' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'derision' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.023904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mystery (ancient and modern)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024157\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'seaography' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024376\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'drawling' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024600\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'stretching' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.024992\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fainting in coils' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025219\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laughing' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025502\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grief' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.025949\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'conger eel (drawling-master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026243\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'old crab (classics master)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026692\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tarts' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.026920\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pencil' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.027897\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'slate' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.028631\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'duchesss cook' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.028848\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lizard (bill)' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.029560\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'guinea-pigs' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.030437\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.030900\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031211\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trial of the knave' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031414\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'document' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031563\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'parchment scroll' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031771\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trumpet' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.031975\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'teacup' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032145\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'bread-and-butter' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032348\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pepper-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032520\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'inkstand' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.032767\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'canvas bag' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.033132\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jury-box' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.034834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alices dream' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.036198\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.037172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fourteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.037973\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'fifteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038237\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sixteenth of march' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038606\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'she' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.038879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'little sister' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039022\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039194\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dull reality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039400\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'plant' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039578\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grass' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039741\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pool' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.039913\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'reeds' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040074\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'rattling teacups' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040285\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'tinkling sheep-bells' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040468\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'shepherd boy' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040662\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other queer noises' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.040836\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'busy farm-yard' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.041105\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cattle' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.041323\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'grown woman' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042019\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple and loving heart' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042329\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'other little children' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'strange tale' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.042824\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dream of wonderland' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043010\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple sorrows' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043173\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'simple joys' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043331\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'child-life' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:35:56.043505\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'happy summer days' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:02.674965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\u001b[92m14:36:02 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:16.723969\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.505751\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.656760\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.853916\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:24.999416\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.182065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.379190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", - "\n", - "\u001b[2m2025-08-27T13:36:25.534779\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `52948913-bf39-51ee-a535-e4f140f34c10`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:07.115479\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `453ce944-eb27-567c-9918-0d44d1614f97`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "{UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('692741cd-46e5-5988-85e9-f3901d104b7e')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('899de74a-1bef-5afd-a478-1ea944503514')}, {'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1cde937f-ae7a-5151-a20c-dc3567bee0a9'), dataset_id=UUID('241b64d6-f023-5b87-9a8f-87056f0a442c'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('6a0c7501-47bc-5632-b79c-3343d8a0b2a2')}])}" + "{UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'): PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=[{'run_info': PipelineRunCompleted(status='PipelineRunCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('3ad0b58b-2b39-5bf8-97de-4db67bd2555c')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('56c22102-965d-592e-958c-c1ebebf0008f')}, {'run_info': PipelineRunAlreadyCompleted(status='PipelineRunAlreadyCompleted', pipeline_run_id=UUID('1c237436-d3eb-5408-874d-91647cf2dcef'), dataset_id=UUID('8f486d81-4723-5f3d-b37b-5e27d9967d33'), dataset_name='main_dataset', payload=None, data_ingestion_info=None), 'data_id': UUID('e26acfac-f1c2-5d9d-b95a-e970a75aedde')}])}" ] }, "execution_count": 3, @@ -662,6 +328,7 @@ ], "source": [ "import cognee\n", + "print(cognee.__version__)\n", "await cognee.add(file_path)\n", "await cognee.cognify()" ] @@ -685,20 +352,15 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:36:30.994342\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 239 nodes, 745 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:07.164471\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:36:31.598044\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.04s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", - "\u001b[92m14:36:31 - LiteLLM:INFO\u001b[0m: utils.py:3341 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:07.474073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "['Acknowledged.']" + "['1. Alice \\n2. White Rabbit \\n3. March Hare \\n4. Hatter \\n5. Cheshire Cat \\n6. Queen of Hearts \\n7. Knave of Hearts \\n8. Dormouse']" ] }, "execution_count": 4, @@ -712,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "883ce50d2d9dc584", "metadata": {}, "outputs": [ @@ -720,44 +382,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:00 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:01 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:02 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:03 - 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"\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m" + "\n", + "\u001b[2m2025-10-07T20:39:36.551739\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:39:36.896038\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "[\"Alice ended up in Wonderland by following a curious White Rabbit that she encountered while sitting on a riverbank. The Rabbit muttered about being late and carried a watch, piquing Alice's curiosity. She followed it down a rabbit hole, which led her to a fantastical world.\"]" + "['Alice ended up in Wonderland by following a hurried White Rabbit down a rabbit-hole after feeling bored and drowsy.']" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -768,7 +405,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "677e1bc52aa078b6", "metadata": {}, "outputs": [ @@ -776,44 +413,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - 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"\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:06 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/text-embedding-3-large\n", - "\u001b[1mselected model name for cost calculation: openai/text-embedding-3-large\u001b[0m\u001b[92m20:17:07 - LiteLLM:INFO\u001b[0m: utils.py:3101 - \n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\n", - "\u001b[1m\n", - "LiteLLM completion() model= gpt-5-mini; provider = openai\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m\u001b[92m20:17:08 - LiteLLM:INFO\u001b[0m: cost_calculator.py:655 - selected model name for cost calculation: openai/gpt-5-mini-2024-07-18\n", - "\u001b[1mselected model name for cost calculation: openai/gpt-5-mini-2024-07-18\u001b[0m" + "\n", + "\u001b[2m2025-10-07T20:39:43.171619\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 110 nodes, 292 edges in 0.02s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:39:43.468210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { "data": { "text/plain": [ - "['Alice is portrayed as a curious and adventurous girl who explores Wonderland. She often questions her identity and the world around her, showing a blend of innocence and boldness. Alice displays a willingness to engage with bizarre situations and characters, while also being contemplative about her experiences.']" + "[\"Alice is described as a curious girl who exhibits a desire for adventure and exploration. She is imaginative, pondering various whimsical questions and thoughts as she navigates the oddities of Wonderland. Her personality shows signs of being thoughtful and reflective, often giving herself advice, though she doesn't always follow it. Despite her adventures and the surreal situations she encounters, she maintains a sense of bravery and a degree of confidence in her interactions with the fantastical characters she meets.\"]" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -832,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "6effdae590b795d3", "metadata": {}, "outputs": [ @@ -841,9 +453,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[2m2025-08-27T13:36:40.283583\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\u001b[2m2025-10-07T20:39:50.413314\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", "\n", - "\u001b[2m2025-08-27T13:36:40.284941\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + "\u001b[2m2025-10-07T20:39:50.413846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" ] }, { @@ -861,7 +473,7 @@ "True" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -932,7 +544,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/notebooks/data/graphrag b/notebooks/data/graphrag deleted file mode 160000 index 130b84db9..000000000 --- a/notebooks/data/graphrag +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 130b84db9270734756d16918e5c86034777140fc diff --git a/notebooks/neptune-analytics-example.ipynb b/notebooks/neptune-analytics-example.ipynb index 197918db5..e80ea4dcb 100644 --- a/notebooks/neptune-analytics-example.ipynb +++ b/notebooks/neptune-analytics-example.ipynb @@ -295,7 +295,7 @@ "cell_type": "code", "source": [ "# Search graph insights\n", - "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.INSIGHTS)\n", + "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n", "print(\"\\nInsights about Neptune Analytics:\")\n", "for result in insights_results:\n", " src_node = result[0].get(\"name\", result[0][\"type\"])\n", diff --git a/notebooks/ontology_demo.ipynb b/notebooks/ontology_demo.ipynb index ef4a046b8..17d7feb28 100644 --- a/notebooks/ontology_demo.ipynb +++ b/notebooks/ontology_demo.ipynb @@ -36,36 +36,61 @@ }, { "cell_type": "code", + "execution_count": null, "id": "8cf7ba29f9a150af", "metadata": {}, + "outputs": [], "source": [ "# Install required package\n", "# !pip install cognee" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 1, "id": "abb86851", "metadata": {}, + "outputs": [], "source": [ "import os\n", "\n", "# Set up OpenAI API key (required for Cognee's LLM functionality)\n", "if \"LLM_API_KEY\" not in os.environ:\n", " os.environ[\"LLM_API_KEY\"] = \"your-api-key-here\" # Replace with your API key" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 2, "id": "d825d126b3a0ec26", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:15.192965\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted old log file: /Users/daulet/Desktop/dev/cognee-claude/logs/2025-10-07_21-25-04.log\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:15.894155\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLogging initialized \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m \u001b[36mcognee_version\u001b[0m=\u001b[35m0.3.5-local\u001b[0m \u001b[36mdatabase_path\u001b[0m=\u001b[35m/Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m \u001b[36mgraph_database_name\u001b[0m=\u001b[35m\u001b[0m \u001b[36mos_info\u001b[0m=\u001b[35m'Darwin 24.5.0 (Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:43 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T8132)'\u001b[0m \u001b[36mpython_version\u001b[0m=\u001b[35m3.10.11\u001b[0m \u001b[36mrelational_config\u001b[0m=\u001b[35mcognee_db\u001b[0m \u001b[36mstructlog_version\u001b[0m=\u001b[35m25.4.0\u001b[0m \u001b[36mvector_config\u001b[0m=\u001b[35mlancedb\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:15.894641\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase storage: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "/Users/daulet/Desktop/dev/cognee-claude/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3.5-local\n" + ] + } + ], "source": [ "# Import required libraries\n", "import cognee\n", + "print(cognee.__version__)\n", "from cognee.shared.logging_utils import get_logger\n", "\n", "cognee.config.set_llm_model(\"gpt-4o-mini\")\n", @@ -73,9 +98,7 @@ "from cognee.api.v1.search import SearchType\n", "\n", "logger = get_logger()" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -92,8 +115,10 @@ }, { "cell_type": "code", + "execution_count": 3, "id": "4d0e4a58e4207a7d", "metadata": {}, + "outputs": [], "source": [ "async def run_pipeline(config=None):\n", " # Clean existing data\n", @@ -124,9 +149,7 @@ " )\n", " answers.append(search_results)\n", " return answers" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -140,8 +163,433 @@ }, { "cell_type": "code", + "execution_count": 4, "id": "1363772d2b48f5c0", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:24.236015\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mOntology loaded successfully from file: ../examples/python/ontology_input_example/enriched_medical_ontology_with_classes.owl\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.236931\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLookup built: 4 classes, 50 individuals\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.348030\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mJSON extension already loaded or unavailable: Binder exception: Extension: JSON is already loaded. You can check loaded extensions by `CALL SHOW_LOADED_EXTENSIONS() RETURN *`.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:24.387417\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Results WITH ontology ---\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:26.930731\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mStorage manager absolute path: /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_cache\u001b[0m\n", + "\n", + "\u001b[1mDeleting cache... \u001b[0m\n", + "\n", + "\u001b[1m✓ Cache deleted successfully! 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Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:35.977344\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFalling back to PyPDF loader for /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:35.979542\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReading PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/nutrients-13-01241.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.pypdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.323535\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mProcessing PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf ...\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.627761\u001b[0m [\u001b[33m\u001b[1mwarning \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. Is poppler installed and in PATH?\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.628591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mFalling back to PyPDF loader for /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.629517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mReading PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.pypdf_loader\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.747011\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `ingest_data`\u001b[0m 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"\u001b[2m2025-10-07T20:40:36.774764\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.775056\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.775718\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.783240\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.809815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: No languages specified, defaulting to English.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:40:36.849367\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:36.863627\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.726911\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.757864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.758561\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.759088\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.759456\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.759801\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760065\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760398\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760701\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.760968\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#Hypertension match was found for found for 'hypertension' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.763146\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#HighCholesterol match was found for found for 'cholesterol' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.763622\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#AtrialFibrillation match was found for found for 'atrial fibrillation' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.764459\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#HeartFailure match was found for found for 'heart failure' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.765517\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CoronaryArteryDisease match was found for found for 'coronary heart disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.765914\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766214\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'filtered coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766546\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'boiled coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.766895\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767142\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767407\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2023' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.767784\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768084\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.768618\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769313\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health metric' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769628\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.769899\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.770226\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.770777\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'smoking history' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771177\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771432\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research method' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771696\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.771952\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cohort study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.772202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:55.772565\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#ModerateCoffeeConsumption match was found for found for 'moderate coffee consumption' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:40:58.393172\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.885405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.886605\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura torres-collado' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.887338\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'laura maría compañ-gabucio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.888151\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sandra gonzález-palacios' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.888781\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'leyre notario-barandiaran' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.889249\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.889792\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.890369\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.890717\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.891286\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.891705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.892208\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.892732\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.893359\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.893834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.894316\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.894743\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'methodology' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895096\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cox regression models' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895355\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.895772\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.896129\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrients 2021, 13, 1241' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.896884\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897158\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'dietary habit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897435\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.897846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#CardiovascularDisease match was found for found for 'cardiovascular disease (cvd)' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.899072\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#Cancer match was found for found for 'cancer' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.900268\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcome' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.900642\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all-cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901370\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mediterranean population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.901726\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research finding' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902070\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee death association' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902364\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.902739\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption types' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903068\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903419\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.903728\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemical compound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904073\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904441\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.904846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'trigonelline' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905223\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'melanoidins' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905559\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health benefit' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.905871\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption mortality reduction' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906210\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.906740\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.907285\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.907833\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'adult population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.908178\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'lifestyle' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.908658\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mhttp://example.org/ontology#MediterraneanDiet match was found for found for 'mediterranean lifestyle' node\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909236\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909603\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.909852\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910155\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutritional study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910525\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study participants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.910775\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'institution' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911136\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ethics committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911403\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:00.911738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'data contribution' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:03.240933\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:04.242533\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.771183\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.771815\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772143\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772389\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772610\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.772909\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:05.773236\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:11.284104\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.335459\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336033\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336293\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336526\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.336901\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.337200\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.337497\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `325268f2-318a-5318-8570-3616626129ed`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.351757\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:15.656347\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:17.595983\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:18.045113\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:19.425864\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:19.711301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:21.796373\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 126 nodes, 264 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:22.366335\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.09s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q: What are common risk factors for Type 2 Diabetes?\n", + "A: ['Common risk factors for Type 2 Diabetes include:\\n1. Obesity\\n2. Hypertension (high blood pressure)\\n3. High cholesterol\\n4. Smoking\\n5. Cardiovascular disease\\n6. Heart failure']\n", + "\n", + "Q: What preventive measures reduce the risk of Hypertension?\n", + "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. Low sodium diet\\n2. Moderate coffee consumption\\n3. Regular exercise\\n4. Maintaining a healthy lifestyle']\n", + "\n", + "Q: What symptoms indicate possible Cardiovascular Disease?\n", + "A: ['Symptoms indicating possible cardiovascular disease include:\\n1. Chest pain\\n2. Shortness of breath\\n3. Fatigue']\n", + "\n", + "Q: What diseases are associated with Obesity?\n", + "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, hypertension, high cholesterol, and high blood pressure.']\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:48.134301\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mLoaded JSON extension \u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + } + ], "source": [ "from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver\n", "from cognee.modules.ontology.ontology_config import Config\n", @@ -168,14 +616,411 @@ "answers_with = await query_pipeline(questions)\n", "for q, a in zip(questions, answers_with):\n", " print(f\"Q: {q}\\nA: {a}\\n\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": 5, "id": "3aa18f4cdd5ceff6", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:23.957345\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDeleted Kuzu database files at /Users/daulet/Desktop/dev/cognee-claude/cognee/.cognee_system/databases/cognee_graph_kuzu\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Results WITHOUT ontology ---\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:41:25.980678\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mDatabase deleted successfully.\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[1mDeleting cache... \u001b[0m\n", + "\n", + "\u001b[1m✓ Cache deleted successfully! \u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.100988\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.101412\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.101746\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.102449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run started: `d7e0340c-b6c6-568f-856c-b9f4347628d4`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.102713\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `resolve_data_directories`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.103039\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `ingest_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.115879\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mProcessing PDF: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.infrastructure.loaders.external.advanced_pdf_loader\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "User 215612a9-f107-44d8-9263-d680872182c9 has registered.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1mReading PDF for file: /Users/daulet/Desktop/dev/cognee/examples/data/scientific_papers/TOJ-22-0073_152Mendoza.pdf ...\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:26.605043\u001b[0m [\u001b[33m\u001b[1mwarning \u001b[0m] \u001b[1mFailed to process PDF with AdvancedPdfLoader: Unable to get page count. 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"\u001b[2m2025-10-07T20:41:27.349508\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task started: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:27.387202\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:27.398908\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.152674\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.153506\u001b[0m [\u001b[32m\u001b[1minfo 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[\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'alejandro oncina-cánovas' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.155919\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'jesús vioque' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.156428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'manuela garcía-de la hera' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.156854\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'location' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157224\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spain' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157532\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.157904\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.158267\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'journal' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.158702\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrients' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159018\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159393\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-03-17' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.159738\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-07' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160058\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for '2021-04-09' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160442\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.160809\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161047\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161404\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.161735\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162033\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'license' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162473\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cc by' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.162946\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'valencia nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.163295\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.163722\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164014\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'all cause mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164319\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cvd mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.164705\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cancer mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165120\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165394\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'decaffeinated coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165657\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical measure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.165990\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffees hazard ratio' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166280\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medical condition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166547\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'oxidative stress' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.166889\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'measurement' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167111\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person years' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167340\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'demographic' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167630\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.167945\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrient' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169055\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169486\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.169838\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'population' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170109\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'spanish population' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170432\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.170773\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study design' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171027\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'self-reported data' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171340\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chronic illness' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171649\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'nutrition survey' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.171924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'organization' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172150\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ethical committee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172534\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health outcomes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.172825\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee preparation method' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173190\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'statistical power' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173499\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'informed consent' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:48.173800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'sample size' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.669259\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'person' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.669912\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'michael f. mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.670464\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'ralf martz sulague, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.670996\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'therese posas-mendoza, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.671558\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'carl j. lavie, md' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.671838\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'concept' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672203\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672619\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular health' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.672998\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'hypertension' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673312\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'heart failure' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673566\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'atrial fibrillation' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.673909\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coronary heart disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674199\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee preparation methods' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674587\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'diterpenes' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.674834\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'phenolic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675105\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'caffeine' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675428\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'date' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.675730\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'study period 2000-2021' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676116\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee consumption effects' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676405\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'research type' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676642\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'meta-analysis' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.676924\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'pilot study' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.677362\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'beverage' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.677669\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'coffee' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chemicalcompound' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678326\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'antioxidants' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678644\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medicalcondition' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.678986\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiovascular disease' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679278\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'mortality' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679582\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'behavior' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.679851\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'smoking' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680130\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'chlorogenic acid' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680382\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'moderate coffee consumption' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680667\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'researchstudy' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.680968\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'health studies' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681265\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'researchmethod' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681565\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'medicalprocedure' in category 'classes'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.681841\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mNo close match found for 'cardiac surgery' in category 'individuals'\u001b[0m [\u001b[0m\u001b[1m\u001b[34mOntologyAdapter\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:50.725265\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:53.910668\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:57.080341\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.770067\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task started: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.803614\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804032\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804279\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804531\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.804800\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.805061\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:41:59.805294\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.709736\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `add_data_points`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.710292\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `summarize_text`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.710708\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `extract_graph_from_data`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.711324\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mAsync Generator task completed: `extract_chunks_from_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.711677\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `check_permissions_on_dataset`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.712008\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mCoroutine task completed: `classify_documents`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_base\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.712225\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mPipeline run completed: `8919119c-bee5-5ff1-bc01-794e8a1015ad`\u001b[0m [\u001b[0m\u001b[1m\u001b[34mrun_tasks_with_telemetry()\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:02.725796\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:03.005337\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:05.945308\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.00s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:07.476846\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:11.047320\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:11.394591\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.08s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:14.634449\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph projection completed: 97 nodes, 209 edges in 0.01s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mCogneeGraph\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:14.913245\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mVector collection retrieval completed: Retrieved distances from 6 collections in 0.07s\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Q: What are common risk factors for Type 2 Diabetes?\n", + "A: ['Common risk factors for Type 2 Diabetes include obesity (as indicated by a body mass index of 25 kg/m² or more), sedentary lifestyle, poor dietary habits, smoking habits, and the presence of chronic conditions such as hypertension and high blood cholesterol. Additionally, waist circumference measurement can indicate risk levels, with increased risk seen in men with a waist over 102 cm and women over 88 cm.']\n", + "\n", + "Q: What preventive measures reduce the risk of Hypertension?\n", + "A: ['Preventive measures to reduce the risk of hypertension include:\\n1. **Moderate Coffee Consumption**: Regular moderate coffee consumption (1-4 cups per day) is associated with a decreased risk of developing hypertension.\\n2. **Dietary Antioxidants**: Coffee contains antioxidants, such as chlorogenic acid, which may inhibit inflammation and support cardiovascular health.\\n3. **Healthy Preparation Methods**: Choosing filtered coffee over boiled coffee can reduce cholesterol levels and promote better heart health.\\n4. **Lifestyle Factors**: Maintaining overall cardiovascular health through lifestyle choices can also contribute to lowering the risk of hypertension.']\n", + "\n", + "Q: What symptoms indicate possible Cardiovascular Disease?\n", + "A: ['Possible symptoms indicating cardiovascular disease include:\\n1. Chest pain or discomfort\\n2. Shortness of breath\\n3. Fatigue or weakness\\n4. Irregular heartbeat\\n5. Swelling in the legs, ankles, or feet \\n6. Lightheadedness or dizziness \\n7. Nausea or cold sweat \\n\\nIt is important to consult a healthcare professional for proper diagnosis and treatment.']\n", + "\n", + "Q: What diseases are associated with Obesity?\n", + "A: ['Diseases associated with obesity include cardiovascular disease, diabetes, and hypertension. Obesity is a significant risk factor for these health conditions, often leading to increased mortality and other health complications.']\n", + "\n" + ] + } + ], "source": [ "# Run without ontology\n", "print(\"\\n--- Results WITHOUT ontology ---\\n\")\n", @@ -183,9 +1028,7 @@ "answers_without = await query_pipeline(questions)\n", "for q, a in zip(questions, answers_without):\n", " print(f\"Q: {q}\\nA: {a}\\n\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -199,8 +1042,40 @@ }, { "cell_type": "code", + "execution_count": 6, "id": "36ee2a360f47a054", "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001b[2m2025-10-07T20:42:22.959024\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mGraph visualization saved as /Users/daulet/graph_visualization.html\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n", + "\n", + "\u001b[2m2025-10-07T20:42:22.959720\u001b[0m [\u001b[32m\u001b[1minfo \u001b[0m] \u001b[1mThe HTML file has been stored on your home directory! Navigate there with cd ~\u001b[0m [\u001b[0m\u001b[1m\u001b[34mcognee.shared.logging_utils\u001b[0m]\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'/Users/daulet/graph_visualization.html'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import webbrowser\n", "import os\n", @@ -210,9 +1085,7 @@ "html_file = os.path.join(home_dir, \"graph_visualization.html\")\n", "display(html_file)\n", "webbrowser.open(f\"file://{html_file}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -250,8 +1123,22 @@ }, { "cell_type": "code", + "execution_count": null, "id": "8d2a0fe555a7bc0f", "metadata": {}, + "outputs": [ + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "# Only exit in interactive mode, not during GitHub Actions\n", "import os\n", @@ -262,17 +1149,15 @@ " os._exit(0)\n", "else:\n", " print(\"Skipping kernel exit - running in GitHub Actions\")" - ], - "outputs": [], - "execution_count": null + ] }, { - "metadata": {}, "cell_type": "code", - "source": "", + "execution_count": null, "id": "adb6601890237b6a", + "metadata": {}, "outputs": [], - "execution_count": null + "source": [] } ], "metadata": { @@ -291,7 +1176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index f873ca5a0..120de4f7f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. 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networking framework on top of asyncio or Trio" optional = false python-versions = ">=3.9" groups = ["main"] files = [ - {file = "anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc"}, - {file = "anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4"}, + {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"}, + {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"}, ] [package.dependencies] @@ -339,7 +339,7 @@ sniffio = ">=1.1" typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -trio = ["trio (>=0.31.0)"] +trio = ["trio (>=0.26.1)"] [[package]] name = "appnope" @@ -437,15 +437,15 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock [[package]] name = "asgiref" -version = "3.9.2" 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"networkx>=3.4.2,<4", "uvicorn>=0.34.0,<1.0.0", "gunicorn>=20.1.0,<24", - "websockets>=15.0.1,<16.0.0" + "websockets>=15.0.1,<16.0.0", + "mistralai>=1.9.10", ] [project.optional-dependencies] @@ -63,7 +64,14 @@ api=[] distributed = [ "modal>=1.0.5,<2.0.0", ] - +scraping = [ + "tavily-python>=0.7.0", + "beautifulsoup4>=4.13.1", + "playwright>=1.9.0", + "lxml>=4.9.3,<5.0.0", + "protego>=0.1", + "APScheduler>=3.10.0,<=3.11.0" +] neo4j = ["neo4j>=5.28.0,<6"] neptune = ["langchain_aws>=0.2.22"] postgres = [ @@ -82,14 +90,12 @@ langchain = [ "langchain_text_splitters>=0.3.2,<1.0.0", ] llama-index = ["llama-index-core>=0.12.11,<0.13"] -gemini = ["google-generativeai>=0.8.4,<0.9"] huggingface = ["transformers>=4.46.3,<5"] ollama = ["transformers>=4.46.3,<5"] mistral = ["mistral-common>=1.5.2,<2"] anthropic = ["anthropic>=0.27"] deepeval = ["deepeval>=3.0.1,<4"] posthog = ["posthog>=3.5.0,<4"] -falkordb = ["falkordb>=1.0.9,<2.0.0"] groq = ["groq>=0.8.0,<1.0.0"] chromadb = [ 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