From b58d7d44f3f424e37fbd5de83ed7c3bea45187a3 Mon Sep 17 00:00:00 2001 From: Vasilije <8619304+Vasilije1990@users.noreply.github.com> Date: Sat, 31 May 2025 02:19:29 +0200 Subject: [PATCH] fix: 0.1.41 Release (#894) ## Description ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin. --------- Signed-off-by: Diego B Theuerkauf Co-authored-by: Igor Ilic <30923996+dexters1@users.noreply.github.com> Co-authored-by: Boris Arzentar Co-authored-by: Boris Co-authored-by: Igor Ilic Co-authored-by: Hande <159312713+hande-k@users.noreply.github.com> Co-authored-by: Matea Pesic <80577904+matea16@users.noreply.github.com> Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com> Co-authored-by: Daniel Molnar Co-authored-by: Diego Baptista Theuerkauf <34717973+diegoabt@users.noreply.github.com> Co-authored-by: Dmitrii Galkin <36552323+dm1tryG@users.noreply.github.com> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: lxobr <122801072+lxobr@users.noreply.github.com> Co-authored-by: github-actions[bot] --- .data/code/example.txt | 28 - .data/short_stories/soldiers-home.pdf | Bin 33733 -> 0 bytes .github/workflows/db_examples_tests.yml | 269 + .github/workflows/test_suites.yml | 8 + CONTRIBUTING.md | 2 +- Dockerfile | 6 + Dockerfile_modal | 11 +- README.md | 28 +- .../1d0bb7fede17_add_pipeline_run_status.py | 33 + {community => assets/community}/README.pt.md | 0 {community => assets/community}/README.ru.md | 0 {community => assets/community}/README.zh.md | 0 .../community}/cognee_benefits_zh.JPG | Bin .../community}/cognee_diagram_zh.JPG | Bin .../community}/graph_visualization_pt.png | Bin .../community}/graph_visualization_ru.png | Bin cognee-mcp/Dockerfile | 4 + cognee-mcp/entrypoint.sh | 17 +- cognee-mcp/pyproject.toml | 2 +- cognee-mcp/src/server.py | 254 +- cognee-mcp/uv.lock | 5087 +++++++++++++++++ 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profiling/util/DummyEmbeddingEngine.py | 10 - profiling/util/DummyLLMAdapter.py | 59 - pyproject.toml | 23 +- uv.lock | 2 +- 213 files changed, 15950 insertions(+), 761 deletions(-) delete mode 100644 .data/code/example.txt delete mode 100644 .data/short_stories/soldiers-home.pdf create mode 100644 .github/workflows/db_examples_tests.yml create mode 100644 alembic/versions/1d0bb7fede17_add_pipeline_run_status.py rename {community => assets/community}/README.pt.md (100%) rename {community => assets/community}/README.ru.md (100%) rename {community => assets/community}/README.zh.md (100%) rename {community => assets/community}/cognee_benefits_zh.JPG (100%) rename {community => assets/community}/cognee_diagram_zh.JPG (100%) rename {community => assets/community}/graph_visualization_pt.png (100%) rename {community => assets/community}/graph_visualization_ru.png (100%) create mode 100644 cognee-mcp/uv.lock create mode 100644 cognee/api/v1/responses/__init__.py create mode 100644 cognee/api/v1/responses/default_tools.py create mode 100644 cognee/api/v1/responses/dispatch_function.py create mode 100644 cognee/api/v1/responses/models.py create mode 100644 cognee/api/v1/responses/routers/__init__.py create mode 100644 cognee/api/v1/responses/routers/default_tools.py create mode 100644 cognee/api/v1/responses/routers/get_responses_router.py create mode 100644 cognee/eval_framework/modal_eval_dashboard.py create mode 100644 cognee/infrastructure/databases/graph/supported_databases.py create mode 100644 cognee/infrastructure/databases/graph/use_graph_adapter.py create mode 100644 cognee/infrastructure/databases/vector/supported_databases.py create mode 100644 cognee/infrastructure/databases/vector/use_vector_adapter.py create mode 100644 cognee/infrastructure/llm/prompts/cot_followup_system_prompt.txt create mode 100644 cognee/infrastructure/llm/prompts/cot_followup_user_prompt.txt create mode 100644 cognee/infrastructure/llm/prompts/cot_validation_system_prompt.txt create mode 100644 cognee/infrastructure/llm/prompts/cot_validation_user_prompt.txt create mode 100644 cognee/modules/engine/models/ColumnValue.py create mode 100644 cognee/modules/retrieval/graph_completion_context_extension_retriever.py create mode 100644 cognee/modules/retrieval/graph_completion_cot_retriever.py create mode 100644 cognee/tests/unit/modules/retrieval/graph_completion_retriever_context_extension_test.py create mode 100644 cognee/tests/unit/modules/retrieval/graph_completion_retriever_cot_test.py create mode 100644 cognee/version.py rename {helm => deployment/helm}/Chart.yaml (100%) rename {helm => deployment/helm}/Dockerfile (100%) rename {helm => deployment/helm}/README.md (100%) rename {helm => deployment/helm}/docker-compose-helm.yml (100%) rename {helm => deployment/helm}/templates/cognee_deployment.yaml (100%) rename {helm => deployment/helm}/templates/cognee_service.yaml (100%) rename {helm => deployment/helm}/templates/postgres_deployment.yaml (100%) rename {helm => deployment/helm}/templates/postgres_pvc.yaml (100%) rename {helm => deployment/helm}/templates/postgres_service.yaml (100%) rename {helm => deployment/helm}/values.yaml (100%) rename {.data => examples/data}/multimedia/example.png (100%) rename {.data => examples/data}/multimedia/text_to_speech.mp3 (100%) create mode 100644 examples/database_examples/chromadb_example.py create mode 100644 examples/database_examples/falkordb_example.py create mode 100644 examples/database_examples/kuzu_example.py create mode 100644 examples/database_examples/milvus_example.py create mode 100644 examples/database_examples/neo4j_example.py create mode 100644 examples/database_examples/pgvector_example.py create mode 100644 examples/database_examples/qdrant_example.py create mode 100644 examples/database_examples/weaviate_example.py create mode 100644 notebooks/cognee_openai_compatable_demo.ipynb delete mode 100644 profiling/graph_pydantic_conversion/benchmark_function.py delete mode 100644 profiling/graph_pydantic_conversion/profile_graph_pydantic_conversion.py delete mode 100644 profiling/util/DummyEmbeddingEngine.py delete mode 100644 profiling/util/DummyLLMAdapter.py diff --git a/.data/code/example.txt b/.data/code/example.txt deleted file mode 100644 index 4596a08eb..000000000 --- a/.data/code/example.txt +++ /dev/null @@ -1,28 +0,0 @@ -''' - Given a string, find the length of the longest substring without repeating characters. - - Examples: - - Given "abcabcbb", the answer is "abc", which the length is 3. - - Given "bbbbb", the answer is "b", with the length of 1. - - Given "pwwkew", the answer is "wke", with the length of 3. Note that the answer must be a substring, "pwke" is a subsequence and not a substring. -''' - -class Solution(object): - def lengthOfLongestSubstring(self, s): - """ - :type s: str - :rtype: int - """ - mapSet = {} - start, result = 0, 0 - - for end in range(len(s)): - if s[end] in mapSet: - start = max(mapSet[s[end]], start) - result = max(result, end-start+1) - mapSet[s[end]] = end+1 - - return result diff --git a/.data/short_stories/soldiers-home.pdf b/.data/short_stories/soldiers-home.pdf deleted file mode 100644 index e453ca4bc64157ac078193238ce2a1f5982b557c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 33733 zcmb@uby!?YlRk{Q1qi_cg9W#lVFq_6xVyV+kU+2mLU0WdAV?r+a0o8J-CcrPa0vbz z@;uomyYGJ2?)AMpf6Sce)7@2F)u-z2u2W5?BreIy$p*rpyN7)+pa2elqlpa$KR*V$ znwOI~fL+Ph(i{W!?PBiW2H=F5GGH#f637IpLl=yJdcAe=w|ln1Df!7k?L;0CMa z3gG&yo{G7vqq~c#xhnwjt3Xf?!`#8_q5p^fzv(EsIGU=Oy8(3BVY~p?)yzHJ0PHgM zFuJ0D|BC(nD+8k;38MjcU{4YV;Qot@BnZIsyZ?VkJn-rqb{ 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name: Check out + uses: actions/checkout@master + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install Neo4j extra + run: | + poetry install -E neo4j + + - name: Run Neo4j Example + 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 }} + GRAPH_DATABASE_PROVIDER: "neo4j" + GRAPH_DATABASE_URL: ${{ secrets.NEO4J_API_URL }} + GRAPH_DATABASE_USERNAME: "neo4j" + GRAPH_DATABASE_PASSWORD: ${{ secrets.NEO4J_API_KEY }} + run: | + poetry run python examples/database_examples/neo4j_example.py + + run-db-example-kuzu: + name: "Kuzu DB Example Test" + runs-on: ubuntu-22.04 + if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu') }} + + steps: + - name: Check out + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install Kuzu extra + run: | + poetry install -E kuzu + + - name: Run Kuzu Example + 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 }} + GRAPH_DATABASE_PROVIDER: "kuzu" + run: | + poetry run python examples/database_examples/kuzu_example.py + + run-db-example-milvus: + name: "Milvus DB Example Test" + runs-on: ubuntu-22.04 + if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'milvus') }} + + steps: + - name: Check out + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install Milvus extra + run: | + poetry install -E milvus + + - name: Run Milvus Example + 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 }} + run: | + poetry run python examples/database_examples/milvus_example.py + + run-db-example-weaviate: + name: "Weaviate DB Example Test" + runs-on: ubuntu-22.04 + if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'weaviate') }} + steps: + - name: Check out + uses: actions/checkout@v4 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install Weaviate extra + run: | + poetry install -E weaviate + + - name: Run Weaviate Example + 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 }} + VECTOR_DB_URL: ${{ secrets.WEAVIATE_API_URL }} + VECTOR_DB_KEY: ${{ secrets.WEAVIATE_API_KEY }} + run: | + poetry run python examples/database_examples/weaviate_example.py + + run-db-example-qdrant: + name: "Qdrant DB Example Test" + runs-on: ubuntu-22.04 + if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'qdrant') }} + defaults: + run: + shell: bash + + steps: + - name: Check out + uses: actions/checkout@master + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install Qdrant extra + run: | + poetry install -E qdrant + + - name: Run Qdrant Example + 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 }} + VECTOR_DB_URL: ${{ secrets.QDRANT_API_URL }} + VECTOR_DB_KEY: ${{ secrets.QDRANT_API_KEY }} + run: | + poetry run python examples/database_examples/qdrant_example.py + + run-db-example-pgvector: + name: "PostgreSQL PGVector DB Example Test" + runs-on: ubuntu-22.04 + if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'postgres') }} + services: + postgres: + image: pgvector/pgvector:pg17 + env: + POSTGRES_USER: cognee + POSTGRES_PASSWORD: ${{ secrets.POSTGRES_PASSWORD }} + POSTGRES_DB: cognee_db + options: >- + --health-cmd pg_isready + --health-interval 10s + --health-timeout 5s + --health-retries 5 + ports: + - 5432:5432 + + steps: + - name: Check out + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Cognee Setup + uses: ./.github/actions/cognee_setup + with: + python-version: ${{ inputs.python-version }} + + - name: Install PGVector extra + run: | + poetry install -E postgres + + - name: Run PGVector Example + 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: ${{ secrets.EMBEDDING_API_VERSION }} + run: | + poetry run python examples/database_examples/pgvector_example.py + + \ No newline at end of file diff --git a/.github/workflows/test_suites.yml b/.github/workflows/test_suites.yml index 05e6d1f80..d0bd1ffcb 100644 --- a/.github/workflows/test_suites.yml +++ b/.github/workflows/test_suites.yml @@ -77,6 +77,12 @@ jobs: uses: ./.github/workflows/examples_tests.yml secrets: inherit + db-examples-tests: + name: DB Examples Tests + needs: [vector-db-tests] + uses: ./.github/workflows/db_examples_tests.yml + secrets: inherit + # Additional LLM tests gemini-tests: name: Gemini Tests @@ -113,6 +119,7 @@ jobs: python-version-tests, vector-db-tests, example-tests, + db-examples-tests, gemini-tests, ollama-tests, relational-db-migration-tests, @@ -131,6 +138,7 @@ jobs: "${{ needs.python-version-tests.result }}" == "success" && "${{ needs.vector-db-tests.result }}" == "success" && "${{ needs.example-tests.result }}" == "success" && + "${{ needs.db-examples-tests.result }}" == "success" && "${{ needs.relational-db-migration-tests.result }}" == "success" && "${{ needs.gemini-tests.result }}" == "success" && "${{ needs.docker-compose-test.result }}" == "success" && diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9e97d0d23..ed52658c1 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -97,7 +97,7 @@ git push origin feature/your-feature-name 2. Create a Pull Request: - Go to the [**cognee** repository](https://github.com/topoteretes/cognee) - - Click "Compare & Pull Request" + - Click "Compare & Pull Request" and open a PR against dev branch - Fill in the PR template with details about your changes ## 5. 📜 Developer Certificate of Origin (DCO) diff --git a/Dockerfile b/Dockerfile index a3ea68bf6..9783dcb95 100644 --- a/Dockerfile +++ b/Dockerfile @@ -45,6 +45,10 @@ uv sync --extra debug --extra api --extra postgres --extra weaviate --extra qdra FROM python:3.12-slim-bookworm +RUN apt-get update && apt-get install -y \ + libpq5 \ + && rm -rf /var/lib/apt/lists/* + WORKDIR /app COPY --from=uv /app /app @@ -57,5 +61,7 @@ RUN chmod +x /app/entrypoint.sh ENV PATH="/app/.venv/bin:$PATH" ENV PYTHONPATH=/app +# ENV LOG_LEVEL=ERROR +ENV PYTHONUNBUFFERED=1 ENTRYPOINT ["/app/entrypoint.sh"] diff --git a/Dockerfile_modal b/Dockerfile_modal index f8ca663a8..c3f45c6b8 100644 --- a/Dockerfile_modal +++ b/Dockerfile_modal @@ -18,15 +18,12 @@ RUN apt-get update && apt-get install -y \ WORKDIR /app - -ENV PYTHONPATH=/app -WORKDIR /app -COPY pyproject.toml poetry.lock /app/ - +COPY pyproject.toml poetry.lock README.md /app/ RUN pip install poetry -RUN poetry install --all-extras --no-root --without dev +RUN poetry config virtualenvs.create false + +RUN poetry install --extras neo4j --extras qdrant --no-root COPY cognee/ /app/cognee -COPY README.md /app/README.md diff --git a/README.md b/README.md index ad46fe716..832ffaa3a 100644 --- a/README.md +++ b/README.md @@ -30,18 +30,18 @@ Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load) pipelines. -More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github.com/topoteretes/cognee/tree/main/evals) +More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github.com/topoteretes/cognee/tree/main/evals)

🌐 Available Languages : - 🇵🇹 Português + 🇵🇹 Português · - 🇨🇳 [中文] + 🇨🇳 [中文] · - 🇷🇺 Русский + 🇷🇺 Русский

- +
Why cognee? @@ -73,7 +73,7 @@ Your contributions are at the core of making this a true open source project. An ## 📦 Installation -You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager. +You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager. Cognee supports Python 3.8 to 3.12 ### With pip @@ -82,6 +82,17 @@ Cognee supports Python 3.8 to 3.12 pip install cognee ``` +## Local Cognee installation + +You can install the local Cognee repo using **pip**, **poetry** and **uv**. +For local pip installation please make sure your pip version is above version 21.3. + +### with UV with all optional dependencies + +```bash +uv sync --all-extras +``` + ## 💻 Basic Usage ### Setup @@ -127,12 +138,12 @@ if __name__ == '__main__': Example output: ``` Natural Language Processing (NLP) is a cross-disciplinary and interdisciplinary field that involves computer science and information retrieval. It focuses on the interaction between computers and human language, enabling machines to understand and process natural language. - + ``` ### cognee UI -You can also cognify your files and query using cognee UI. +You can also cognify your files and query using cognee UI. Cognee UI 2 @@ -175,4 +186,3 @@ We are committed to making open source an enjoyable and respectful experience fo ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=topoteretes/cognee&type=Date)](https://star-history.com/#topoteretes/cognee&Date) - diff --git a/alembic/versions/1d0bb7fede17_add_pipeline_run_status.py b/alembic/versions/1d0bb7fede17_add_pipeline_run_status.py new file mode 100644 index 000000000..5e0e99dba --- /dev/null +++ b/alembic/versions/1d0bb7fede17_add_pipeline_run_status.py @@ -0,0 +1,33 @@ +"""Add pipeline run status + +Revision ID: 1d0bb7fede17 +Revises: 482cd6517ce4 +Create Date: 2025-05-19 10:58:15.993314 +""" + +from typing import Sequence, Union + +from alembic import op + +from cognee.infrastructure.databases.relational.get_relational_engine import get_relational_engine +from cognee.modules.pipelines.models.PipelineRun import PipelineRun, PipelineRunStatus + + +# revision identifiers, used by Alembic. +revision: str = "1d0bb7fede17" +down_revision: Union[str, None] = "482cd6517ce4" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = "482cd6517ce4" + + +def upgrade() -> None: + db_engine = get_relational_engine() + + if db_engine.engine.dialect.name == "postgresql": + op.execute( + "ALTER TYPE pipelinerunstatus ADD VALUE IF NOT EXISTS 'DATASET_PROCESSING_INITIATED'" + ) + + +def downgrade() -> None: + pass diff --git a/community/README.pt.md b/assets/community/README.pt.md similarity index 100% rename from community/README.pt.md rename to assets/community/README.pt.md diff --git a/community/README.ru.md b/assets/community/README.ru.md similarity index 100% rename from community/README.ru.md rename to assets/community/README.ru.md diff --git a/community/README.zh.md b/assets/community/README.zh.md similarity index 100% rename from community/README.zh.md rename to assets/community/README.zh.md diff --git a/community/cognee_benefits_zh.JPG b/assets/community/cognee_benefits_zh.JPG similarity index 100% rename from community/cognee_benefits_zh.JPG rename to assets/community/cognee_benefits_zh.JPG diff --git a/community/cognee_diagram_zh.JPG b/assets/community/cognee_diagram_zh.JPG similarity index 100% rename from community/cognee_diagram_zh.JPG rename to assets/community/cognee_diagram_zh.JPG diff --git a/community/graph_visualization_pt.png b/assets/community/graph_visualization_pt.png similarity index 100% rename from community/graph_visualization_pt.png rename to assets/community/graph_visualization_pt.png diff --git a/community/graph_visualization_ru.png b/assets/community/graph_visualization_ru.png similarity index 100% rename from community/graph_visualization_ru.png rename to assets/community/graph_visualization_ru.png diff --git a/cognee-mcp/Dockerfile b/cognee-mcp/Dockerfile index 13183a0f2..a3f06c749 100644 --- a/cognee-mcp/Dockerfile +++ b/cognee-mcp/Dockerfile @@ -48,6 +48,10 @@ RUN --mount=type=cache,target=/root/.cache/uv \ FROM python:3.12-slim-bookworm +RUN apt-get update && apt-get install -y \ + libpq5 \ + && rm -rf /var/lib/apt/lists/* + WORKDIR /app COPY --from=uv /root/.local /root/.local diff --git a/cognee-mcp/entrypoint.sh b/cognee-mcp/entrypoint.sh index 2c14518fc..0403393e1 100644 --- a/cognee-mcp/entrypoint.sh +++ b/cognee-mcp/entrypoint.sh @@ -13,17 +13,22 @@ echo "Environment: $ENVIRONMENT" # inconsistencies and should cause the startup to fail. This check allows for # smooth redeployments and container restarts while maintaining data integrity. echo "Running database migrations..." -MIGRATION_OUTPUT=$(uv run alembic upgrade head 2>&1) || { - if [[ $MIGRATION_OUTPUT == *"UserAlreadyExists"* ]] || [[ $MIGRATION_OUTPUT == *"User default_user@example.com already exists"* ]]; then + +MIGRATION_OUTPUT=$(alembic upgrade head 2>&1) +MIGRATION_EXIT_CODE=$? + +if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then + if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then echo "Warning: Default user already exists, continuing startup..." else - echo "Migration failed with unexpected error:" - echo "$MIGRATION_OUTPUT" + echo "Migration failed with unexpected error." exit 1 fi -} +fi -echo "Starting Cognee MCP Server" +echo "Database migrations done." + +echo "Starting Cognee MCP Server..." # Add startup delay to ensure DB is ready sleep 2 diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index a2190dfda..0f8c8684d 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -8,7 +8,7 @@ requires-python = ">=3.10" dependencies = [ # For local cognee repo usage remove comment bellow and add absolute path to cognee #"cognee[postgres,codegraph,gemini,huggingface] @ file:/Users//Desktop/cognee", - "cognee[postgres,codegraph,gemini,huggingface]==0.1.40", + "cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.1.40", "fastmcp>=1.0", "mcp==1.5.0", "uv>=0.6.3", diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py index f8c65bc7c..fffbaf223 100755 --- a/cognee-mcp/src/server.py +++ b/cognee-mcp/src/server.py @@ -24,9 +24,127 @@ log_file = get_log_file_location() @mcp.tool() -async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> list: +async def cognee_add_developer_rules( + base_path: str = ".", graph_model_file: str = None, graph_model_name: str = None +) -> list: + """ + Ingest core developer rule files into Cognee's memory layer. + + This function loads a predefined set of developer-related configuration, + rule, and documentation files from the base repository and assigns them + to the special 'developer_rules' node set in Cognee. It ensures these + foundational files are always part of the structured memory graph. + + Parameters + ---------- + base_path : str + Root path to resolve relative file paths. Defaults to current directory. + + graph_model_file : str, optional + Optional path to a custom schema file for knowledge graph generation. + + graph_model_name : str, optional + Optional class name to use from the graph_model_file schema. + + Returns + ------- + list + A message indicating how many rule files were scheduled for ingestion, + and how to check their processing status. + + Notes + ----- + - Each file is processed asynchronously in the background. + - Files are attached to the 'developer_rules' node set. + - Missing files are skipped with a logged warning. + """ + + developer_rule_paths = [ + ".cursorrules", + ".cursor/rules", + ".same/todos.md", + ".windsurfrules", + ".clinerules", + "CLAUDE.md", + ".sourcegraph/memory.md", + "AGENT.md", + "AGENTS.md", + ] + + async def cognify_task(file_path: str) -> None: + with redirect_stdout(sys.stderr): + logger.info(f"Starting cognify for: {file_path}") + try: + await cognee.add(file_path, nodeset="developer_rules") + model = KnowledgeGraph + if graph_model_file and graph_model_name: + model = load_class(graph_model_file, graph_model_name) + await cognee.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)}") + + tasks = [] + for rel_path in developer_rule_paths: + abs_path = os.path.join(base_path, rel_path) + if os.path.isfile(abs_path): + tasks.append(asyncio.create_task(cognify_task(abs_path))) + else: + logger.warning(f"Skipped missing developer rule file: {abs_path}") + + return [ + types.TextContent( + type="text", + text=( + f"Started cognify for {len(tasks)} developer rule files in background.\n" + f"All are added to the `developer_rules` node set.\n" + f"Use `cognify_status` or check logs at {log_file} to monitor progress." + ), + ) + ] + + +@mcp.tool() +async def cognify(data: str, graph_model_file: str = None, graph_model_name: str = None) -> list: + """ + Transform data into a structured knowledge graph in Cognee's memory layer. + + This function launches a background task that processes the provided text/file location and + generates a knowledge graph representation. The function returns immediately while + the processing continues in the background due to MCP timeout constraints. + + Parameters + ---------- + data : str + The data to be processed and transformed into structured knowledge. + This can include natural language, file location, or any text-based information + that should become part of the agent's memory. + + graph_model_file : str, optional + Path to a custom schema file that defines the structure of the generated knowledge graph. + If provided, this file will be loaded using importlib to create a custom graph model. + Default is None, which uses Cognee's built-in KnowledgeGraph model. + + graph_model_name : str, optional + Name of the class within the graph_model_file to instantiate as the graph model. + Required if graph_model_file is specified. + Default is None, which uses the default KnowledgeGraph class. + + Returns + ------- + list + A list containing a single TextContent object with information about the + background task launch and how to check its status. + + Notes + ----- + - The function launches a background task and returns immediately + - The actual cognify process may take significant time depending on text length + - Use the cognify_status tool to check the progress of the operation + """ + async def cognify_task( - text: str, graph_model_file: str = None, graph_model_name: str = None + data: str, graph_model_file: str = None, graph_model_name: str = None ) -> str: """Build knowledge graph from the input text""" # NOTE: MCP uses stdout to communicate, we must redirect all output @@ -38,7 +156,7 @@ async def cognify(text: str, graph_model_file: str = None, graph_model_name: str else: graph_model = KnowledgeGraph - await cognee.add(text) + await cognee.add(data) try: await cognee.cognify(graph_model=graph_model) @@ -49,7 +167,7 @@ async def cognify(text: str, graph_model_file: str = None, graph_model_name: str asyncio.create_task( cognify_task( - text=text, + data=data, graph_model_file=graph_model_file, graph_model_name=graph_model_name, ) @@ -71,6 +189,35 @@ async def cognify(text: str, graph_model_file: str = None, graph_model_name: str @mcp.tool() async def codify(repo_path: str) -> list: + """ + Analyze and generate a code-specific knowledge graph from a software repository. + + This function launches a background task that processes the provided repository + and builds a code knowledge graph. The function returns immediately while + the processing continues in the background due to MCP timeout constraints. + + Parameters + ---------- + repo_path : str + Path to the code repository to analyze. This can be a local file path or a + relative path to a repository. The path should point to the root of the + repository or a specific directory within it. + + Returns + ------- + list + A list containing a single TextContent object with information about the + background task launch and how to check its status. + + Notes + ----- + - The function launches a background task and returns immediately + - The code graph generation may take significant time for larger repositories + - Use the codify_status tool to check the progress of the operation + - Process results are logged to the standard Cognee log file + - All stdout is redirected to stderr to maintain MCP communication integrity + """ + 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. @@ -103,6 +250,46 @@ async def codify(repo_path: str) -> list: @mcp.tool() async def search(search_query: str, search_type: str) -> list: + """ + Search the Cognee knowledge graph for information relevant to the query. + + This function executes a search against the Cognee knowledge graph using the + specified query and search type. It returns formatted results based on the + search type selected. + + Parameters + ---------- + search_query : str + The search query in natural language. This can be a question, instruction, or + any text that expresses what information is needed from the knowledge graph. + + search_type : str + The type of search to perform. Valid options include: + - "GRAPH_COMPLETION": Returns an LLM response based on the search query and Cognee's memory + - "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 + + The search_type is case-insensitive and will be converted to uppercase. + + Returns + ------- + list + A list containing a single TextContent object with the search results. + The format of the result depends on the search_type: + - For CODE: JSON-formatted search results + - For GRAPH_COMPLETION/RAG_COMPLETION: A single text completion + - For CHUNKS: String representation of the raw chunks + - For INSIGHTS: Formatted string showing node relationships + - For other types: String representation of the search results + + Notes + ----- + - Different search types produce different output formats + - The function handles the conversion between Cognee's internal result format and MCP's output format + """ + async def search_task(search_query: str, search_type: str) -> str: """Search the knowledge graph""" # NOTE: MCP uses stdout to communicate, we must redirect all output @@ -132,7 +319,24 @@ async def search(search_query: str, search_type: str) -> list: @mcp.tool() async def prune(): - """Reset the knowledge graph""" + """ + Reset the Cognee knowledge graph by removing all stored information. + + This function performs a complete reset of both the data layer and system layer + of the Cognee knowledge graph, removing all nodes, edges, and associated metadata. + It is typically used during development or when needing to start fresh with a new + knowledge base. + + Returns + ------- + list + A list containing a single TextContent object with confirmation of the prune operation. + + Notes + ----- + - 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) + """ with redirect_stdout(sys.stderr): await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) @@ -141,7 +345,25 @@ async def prune(): @mcp.tool() async def cognify_status(): - """Get status of cognify pipeline""" + """ + Get the current status of the cognify pipeline. + + This function retrieves information about current and recently completed cognify operations + in the main_dataset. It provides details on progress, success/failure status, and statistics + about the processed data. + + Returns + ------- + list + A list containing a single TextContent object with the status information as a string. + The status includes information about active and completed jobs for the cognify_pipeline. + + Notes + ----- + - 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 + """ with redirect_stdout(sys.stderr): user = await get_default_user() status = await get_pipeline_status( @@ -152,7 +374,25 @@ async def cognify_status(): @mcp.tool() async def codify_status(): - """Get status of codify pipeline""" + """ + Get the current status of the codify pipeline. + + This function retrieves information about current and recently completed codify operations + in the codebase dataset. It provides details on progress, success/failure status, and statistics + about the processed code repositories. + + Returns + ------- + list + A list containing a single TextContent object with the status information as a string. + The status includes information about active and completed jobs for the cognify_code_pipeline. + + Notes + ----- + - 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 + """ with redirect_stdout(sys.stderr): user = await get_default_user() status = await get_pipeline_status( diff --git a/cognee-mcp/uv.lock b/cognee-mcp/uv.lock new file mode 100644 index 000000000..cf91b6562 --- /dev/null +++ b/cognee-mcp/uv.lock @@ -0,0 +1,5087 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.13'", + 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.api.v1.cognify import cognify diff --git a/cognee/api/client.py b/cognee/api/client.py index b91e149c1..8d40b5fce 100644 --- a/cognee/api/client.py +++ b/cognee/api/client.py @@ -14,6 +14,7 @@ from cognee.api.v1.cognify.routers import get_code_pipeline_router, get_cognify_ from cognee.api.v1.search.routers import get_search_router from cognee.api.v1.add.routers import get_add_router from cognee.api.v1.delete.routers import get_delete_router +from cognee.api.v1.responses.routers import get_responses_router from fastapi import Request from fastapi.encoders import jsonable_encoder from fastapi.exceptions import RequestValidationError @@ -167,6 +168,8 @@ app.include_router(get_visualize_router(), prefix="/api/v1/visualize", tags=["vi app.include_router(get_delete_router(), prefix="/api/v1/delete", tags=["delete"]) +app.include_router(get_responses_router(), prefix="/api/v1/responses", tags=["responses"]) + codegraph_routes = get_code_pipeline_router() if codegraph_routes: app.include_router(codegraph_routes, prefix="/api/v1/code-pipeline", tags=["code-pipeline"]) diff --git a/cognee/api/v1/config/config.py b/cognee/api/v1/config/config.py index a24a7bb92..27b3e4f04 100644 --- a/cognee/api/v1/config/config.py +++ b/cognee/api/v1/config/config.py @@ -155,7 +155,7 @@ class config: if hasattr(graph_db_config, key): object.__setattr__(graph_db_config, key, value) else: - raise AttributeError(message=f"'{key}' is not a valid attribute of the config.") + raise AttributeError(f"'{key}' is not a valid attribute of the config.") @staticmethod def set_vector_db_config(config_dict: dict): diff --git a/cognee/api/v1/delete/routers/get_delete_router.py b/cognee/api/v1/delete/routers/get_delete_router.py index 0b9733a06..cef687a1e 100644 --- a/cognee/api/v1/delete/routers/get_delete_router.py +++ b/cognee/api/v1/delete/routers/get_delete_router.py @@ -58,8 +58,8 @@ def get_delete_router() -> APIRouter: ) results.append(result) else: - # Handle uploaded file - result = await cognee_delete(file, dataset_name=dataset_name, mode=mode) + # Handle uploaded file by accessing its file attribute + result = await cognee_delete(file.file, dataset_name=dataset_name, mode=mode) results.append(result) if len(results) == 1: diff --git a/cognee/api/v1/responses/__init__.py b/cognee/api/v1/responses/__init__.py new file mode 100644 index 000000000..73d14a3bc --- /dev/null +++ b/cognee/api/v1/responses/__init__.py @@ -0,0 +1,3 @@ +from cognee.api.v1.responses.routers import get_responses_router + +__all__ = ["get_responses_router"] diff --git a/cognee/api/v1/responses/default_tools.py b/cognee/api/v1/responses/default_tools.py new file mode 100644 index 000000000..df3dff797 --- /dev/null +++ b/cognee/api/v1/responses/default_tools.py @@ -0,0 +1,63 @@ +DEFAULT_TOOLS = [ + { + "type": "function", + "name": "search", + "description": "Search for information within the knowledge graph", + "parameters": { + "type": "object", + "properties": { + "search_query": { + "type": "string", + "description": "The query to search for in the knowledge graph", + }, + "search_type": { + "type": "string", + "description": "Type of search to perform", + "enum": [ + "INSIGHTS", + "CODE", + "GRAPH_COMPLETION", + "SEMANTIC", + "NATURAL_LANGUAGE", + ], + }, + "top_k": { + "type": "integer", + "description": "Maximum number of results to return", + "default": 10, + }, + "datasets": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of dataset names to search within", + }, + }, + "required": ["search_query"], + }, + }, + { + "type": "function", + "name": "cognify", + "description": "Convert text into a knowledge graph or process all added content", + "parameters": { + "type": "object", + "properties": { + "text": { + "type": "string", + "description": "Text content to be converted into a knowledge graph", + }, + "ontology_file_path": { + "type": "string", + "description": "Path to a custom ontology file", + }, + }, + "required": ["text"], + }, + }, + # Commented as dangerous + # { + # "type": "function", + # "name": "prune", + # "description": "Prune memory", + # }, +] diff --git a/cognee/api/v1/responses/dispatch_function.py b/cognee/api/v1/responses/dispatch_function.py new file mode 100644 index 000000000..37114b32a --- /dev/null +++ b/cognee/api/v1/responses/dispatch_function.py @@ -0,0 +1,107 @@ +import json +import logging +from typing import Any, Dict, Union + +from cognee.api.v1.responses.models import ToolCall +from cognee.modules.search.types import SearchType +from cognee.api.v1.add import add +from cognee.api.v1.search import search +from cognee.api.v1.cognify import cognify +from cognee.api.v1.prune import prune + + +from cognee.modules.users.methods import get_default_user +from cognee.api.v1.responses.default_tools import DEFAULT_TOOLS + +logger = logging.getLogger(__name__) + + +async def dispatch_function(tool_call: Union[ToolCall, Dict[str, Any]]) -> str: + """ + Dispatches a function call to the appropriate Cognee function. + """ + if isinstance(tool_call, dict): + function_data = tool_call.get("function", {}) + function_name = function_data.get("name", "") + arguments_str = function_data.get("arguments", "{}") + else: + function_name = tool_call.function.name + arguments_str = tool_call.function.arguments + + arguments = json.loads(arguments_str) + + logger.info(f"Dispatching function: {function_name} with args: {arguments}") + + user = await get_default_user() + + if function_name == "search": + return await handle_search(arguments, user) + elif function_name == "cognify": + return await handle_cognify(arguments, user) + elif function_name == "prune": + return await handle_prune(arguments, user) + else: + return f"Error: Unknown function {function_name}" + + +async def handle_search(arguments: Dict[str, Any], user) -> list: + """Handle search function call""" + search_tool = next((tool for tool in DEFAULT_TOOLS if tool["name"] == "search"), None) + required_params = ( + search_tool["parameters"].get("required", []) if search_tool else ["search_query"] + ) + + query = arguments.get("search_query") + if not query and "search_query" in required_params: + return "Error: Missing required 'search_query' parameter" + + search_type_str = arguments.get("search_type", "GRAPH_COMPLETION") + valid_search_types = ( + search_tool["parameters"]["properties"]["search_type"]["enum"] + if search_tool + else ["INSIGHTS", "CODE", "GRAPH_COMPLETION", "SEMANTIC", "NATURAL_LANGUAGE"] + ) + + if search_type_str not in valid_search_types: + logger.warning(f"Invalid search_type: {search_type_str}, defaulting to GRAPH_COMPLETION") + search_type_str = "GRAPH_COMPLETION" + + query_type = SearchType[search_type_str] + + top_k = arguments.get("top_k") + datasets = arguments.get("datasets") + system_prompt_path = arguments.get("system_prompt_path", "answer_simple_question.txt") + + results = await search( + query_text=query, + query_type=query_type, + datasets=datasets, + user=user, + system_prompt_path=system_prompt_path, + top_k=top_k if isinstance(top_k, int) else 10, + ) + + return results + + +async def handle_cognify(arguments: Dict[str, Any], user) -> str: + """Handle cognify function call""" + text = arguments.get("text") + ontology_file_path = arguments.get("ontology_file_path") + + if text: + await add(data=text, user=user) + + await cognify(user=user, ontology_file_path=ontology_file_path if ontology_file_path else None) + + return ( + "Text successfully converted into knowledge graph." + if text + else "Knowledge graph successfully updated with new information." + ) + + +async def handle_prune(arguments: Dict[str, Any], user) -> str: + """Handle prune function call""" + await prune() + return "Memory has been pruned successfully." diff --git a/cognee/api/v1/responses/models.py b/cognee/api/v1/responses/models.py new file mode 100644 index 000000000..f23b105ee --- /dev/null +++ b/cognee/api/v1/responses/models.py @@ -0,0 +1,102 @@ +import time +import uuid +from typing import Any, Dict, List, Optional, Union + +from pydantic import BaseModel, Field + +from enum import Enum + +from cognee.api.DTO import InDTO, OutDTO + + +class CogneeModel(str, Enum): + """Enum for supported model types""" + + COGNEEV1 = "cognee-v1" + + +class FunctionParameters(BaseModel): + """JSON Schema for function parameters""" + + type: str = "object" + properties: Dict[str, Dict[str, Any]] + required: Optional[List[str]] = None + + +class Function(BaseModel): + """Function definition compatible with OpenAI's format""" + + name: str + description: str + parameters: FunctionParameters + + +class ToolFunction(BaseModel): + """Tool function wrapper (for OpenAI compatibility)""" + + type: str = "function" + function: Function + + +class FunctionCall(BaseModel): + """Function call made by the assistant""" + + name: str + arguments: str + + +class ToolCall(BaseModel): + """Tool call made by the assistant""" + + id: str = Field(default_factory=lambda: f"call_{uuid.uuid4().hex}") + type: str = "function" + function: FunctionCall + + +class ChatUsage(BaseModel): + """Token usage information""" + + prompt_tokens: int = 0 + completion_tokens: int = 0 + total_tokens: int = 0 + + +class ResponseRequest(InDTO): + """Request body for the new responses endpoint (OpenAI Responses API format)""" + + model: CogneeModel = CogneeModel.COGNEEV1 + input: str + tools: Optional[List[ToolFunction]] = None + tool_choice: Optional[Union[str, Dict[str, Any]]] = "auto" + user: Optional[str] = None + temperature: Optional[float] = 1.0 + max_tokens: Optional[int] = None + + +class ToolCallOutput(BaseModel): + """Output of a tool call in the responses API""" + + status: str = "success" # success/error + data: Optional[Dict[str, Any]] = None + + +class ResponseToolCall(BaseModel): + """Tool call in a response""" + + id: str = Field(default_factory=lambda: f"call_{uuid.uuid4().hex}") + type: str = "function" + function: FunctionCall + output: Optional[ToolCallOutput] = None + + +class ResponseBody(OutDTO): + """Response body for the new responses endpoint""" + + id: str = Field(default_factory=lambda: f"resp_{uuid.uuid4().hex}") + created: int = Field(default_factory=lambda: int(time.time())) + model: str + object: str = "response" + status: str = "completed" + tool_calls: List[ResponseToolCall] + usage: Optional[ChatUsage] = None + metadata: Dict[str, Any] = None diff --git a/cognee/api/v1/responses/routers/__init__.py b/cognee/api/v1/responses/routers/__init__.py new file mode 100644 index 000000000..6d484e8f4 --- /dev/null +++ b/cognee/api/v1/responses/routers/__init__.py @@ -0,0 +1,3 @@ +from cognee.api.v1.responses.routers.get_responses_router import get_responses_router + +__all__ = ["get_responses_router"] diff --git a/cognee/api/v1/responses/routers/default_tools.py b/cognee/api/v1/responses/routers/default_tools.py new file mode 100644 index 000000000..b5fcaebab --- /dev/null +++ b/cognee/api/v1/responses/routers/default_tools.py @@ -0,0 +1,86 @@ +DEFAULT_TOOLS = [ + { + "type": "function", + "name": "search", + "description": "Search for information within the knowledge graph", + "parameters": { + "type": "object", + "properties": { + "search_query": { + "type": "string", + "description": "The query to search for in the knowledge graph", + }, + "search_type": { + "type": "string", + "description": "Type of search to perform", + "enum": [ + "INSIGHTS", + "CODE", + "GRAPH_COMPLETION", + "SEMANTIC", + "NATURAL_LANGUAGE", + ], + }, + "top_k": { + "type": "integer", + "description": "Maximum number of results to return", + "default": 10, + }, + "datasets": { + "type": "array", + "items": {"type": "string"}, + "description": "Optional list of dataset names to search within", + }, + }, + "required": ["search_query"], + }, + }, + { + "type": "function", + "name": "cognify", + "description": "Convert text into a knowledge graph or process all added content", + "parameters": { + "type": "object", + "properties": { + "text": { + "type": "string", + "description": "Text content to be converted into a knowledge graph", + }, + "graph_model_name": { + "type": "string", + "description": "Name of the graph model to use", + }, + "graph_model_file": { + "type": "string", + "description": "Path to a custom graph model file", + }, + }, + }, + }, + { + "type": "function", + "name": "prune", + "description": "Remove unnecessary or outdated information from the knowledge graph", + "parameters": { + "type": "object", + "properties": { + "prune_strategy": { + "type": "string", + "enum": ["light", "moderate", "aggressive"], + "description": "Strategy for pruning the knowledge graph", + "default": "moderate", + }, + "min_confidence": { + "type": "number", + "description": "Minimum confidence score to retain (0-1)", + "minimum": 0, + "maximum": 1, + }, + "older_than": { + "type": "string", + "description": "ISO date string - prune nodes older than this date", + }, + }, + }, + }, +] diff --git a/cognee/api/v1/responses/routers/get_responses_router.py b/cognee/api/v1/responses/routers/get_responses_router.py new file mode 100644 index 000000000..16e025403 --- /dev/null +++ b/cognee/api/v1/responses/routers/get_responses_router.py @@ -0,0 +1,149 @@ +""" +Get router for the OpenAI-compatible responses API. +""" + +import logging +import uuid +from typing import Dict, List, Optional, Any +import openai +from fastapi import APIRouter, Depends +from cognee.api.v1.responses.models import ( + ResponseRequest, + ResponseBody, + ResponseToolCall, + ChatUsage, + FunctionCall, + ToolCallOutput, +) +from cognee.api.v1.responses.dispatch_function import dispatch_function +from cognee.api.v1.responses.default_tools import DEFAULT_TOOLS +from cognee.infrastructure.llm.config import get_llm_config +from cognee.modules.users.models import User +from cognee.modules.users.methods import get_authenticated_user + + +def get_responses_router() -> APIRouter: + """ + Returns the FastAPI router for OpenAI-compatible responses. + + This implementation follows the new OpenAI Responses API format as described in: + https://platform.openai.com/docs/api-reference/responses/create + """ + + router = APIRouter() + logger = logging.getLogger(__name__) + + def _get_model_client(): + """ + Get appropriate client based on model name + """ + llm_config = get_llm_config() + return openai.AsyncOpenAI(api_key=llm_config.llm_api_key) + + async def call_openai_api_for_model( + input_text: str, + model: str, + tools: Optional[List[Dict[str, Any]]] = DEFAULT_TOOLS, + tool_choice: Any = "auto", + temperature: float = 1.0, + ) -> Dict[str, Any]: + """ + Call appropriate model API based on model name + """ + + # TODO: Support other models (e.g. cognee-v1-openai-gpt-3.5-turbo, etc.) + model = "gpt-4o" + + client = _get_model_client() + + logger.debug(f"Using model: {model}") + + response = await client.responses.create( + model=model, + input=input_text, + temperature=temperature, + tools=tools, + tool_choice=tool_choice, + ) + logger.info(f"Response: {response}") + return response.model_dump() + + @router.post("/", response_model=ResponseBody) + async def create_response( + request: ResponseRequest, + user: User = Depends(get_authenticated_user), + ) -> ResponseBody: + """ + OpenAI-compatible responses endpoint with function calling support + """ + # Use default tools if none provided + tools = request.tools or DEFAULT_TOOLS + + # Call the API + response = await call_openai_api_for_model( + input_text=request.input, + model=request.model, + tools=tools, + tool_choice=request.tool_choice, + temperature=request.temperature, + ) + + # Use the response ID from the API or generate a new one + response_id = response.get("id", f"resp_{uuid.uuid4().hex}") + + # Check if there are function tool calls in the output + output = response.get("output", []) + + processed_tool_calls = [] + + # Process any function tool calls from the output + for item in output: + if isinstance(item, dict) and item.get("type") == "function_call": + # This is a tool call from the new format + function_name = item.get("name", "") + arguments_str = item.get("arguments", "{}") + call_id = item.get("call_id", f"call_{uuid.uuid4().hex}") + + # Create a format the dispatcher can handle + tool_call = { + "id": call_id, + "function": {"name": function_name, "arguments": arguments_str}, + "type": "function", + } + + # Dispatch the function + try: + function_result = await dispatch_function(tool_call) + output_status = "success" + except Exception as e: + logger.exception(f"Error executing function {function_name}: {e}") + function_result = f"Error executing {function_name}: {str(e)}" + output_status = "error" + + processed_call = ResponseToolCall( + id=call_id, + type="function", + function=FunctionCall(name=function_name, arguments=arguments_str), + output=ToolCallOutput(status=output_status, data={"result": function_result}), + ) + + processed_tool_calls.append(processed_call) + + # Get usage data from the response if available + usage = response.get("usage", {}) + + # Create the response object with all processed tool calls + response_obj = ResponseBody( + id=response_id, + model=request.model, + tool_calls=processed_tool_calls, + usage=ChatUsage( + prompt_tokens=usage.get("input_tokens", 0), + completion_tokens=usage.get("output_tokens", 0), + total_tokens=usage.get("total_tokens", 0), + ), + ) + + return response_obj + + return router diff --git a/cognee/api/v1/search/search.py b/cognee/api/v1/search/search.py index 723f41bdb..0e817c14e 100644 --- a/cognee/api/v1/search/search.py +++ b/cognee/api/v1/search/search.py @@ -1,4 +1,4 @@ -from typing import Union +from typing import Union, Optional, List, Type from cognee.modules.users.models import User from cognee.modules.search.types import SearchType @@ -13,6 +13,8 @@ async def search( datasets: Union[list[str], str, None] = None, system_prompt_path: str = "answer_simple_question.txt", top_k: int = 10, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> list: # We use lists from now on for datasets if isinstance(datasets, str): @@ -22,12 +24,14 @@ async def search( user = await get_default_user() filtered_search_results = await search_function( - query_text, - query_type, - datasets, - user, + query_text=query_text, + query_type=query_type, + datasets=datasets, + user=user, system_prompt_path=system_prompt_path, top_k=top_k, + node_type=node_type, + node_name=node_name, ) return filtered_search_results diff --git a/cognee/eval_framework/answer_generation/answer_generation_executor.py b/cognee/eval_framework/answer_generation/answer_generation_executor.py index 67eb02578..6f166657e 100644 --- a/cognee/eval_framework/answer_generation/answer_generation_executor.py +++ b/cognee/eval_framework/answer_generation/answer_generation_executor.py @@ -1,5 +1,9 @@ -from typing import List, Dict +from typing import List, Dict, Any from cognee.modules.retrieval.completion_retriever import CompletionRetriever +from cognee.modules.retrieval.graph_completion_context_extension_retriever import ( + GraphCompletionContextExtensionRetriever, +) +from cognee.modules.retrieval.graph_completion_cot_retriever import GraphCompletionCotRetriever from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever from cognee.modules.retrieval.graph_summary_completion_retriever import ( GraphSummaryCompletionRetriever, @@ -8,8 +12,10 @@ from cognee.modules.retrieval.graph_summary_completion_retriever import ( from cognee.modules.retrieval.base_retriever import BaseRetriever -retriever_options: Dict[str, BaseRetriever] = { +retriever_options: Dict[str, Any] = { "cognee_graph_completion": GraphCompletionRetriever, + "cognee_graph_completion_cot": GraphCompletionCotRetriever, + "cognee_graph_completion_context_extension": GraphCompletionContextExtensionRetriever, "cognee_completion": CompletionRetriever, "graph_summary_completion": GraphSummaryCompletionRetriever, } diff --git a/cognee/eval_framework/eval_config.py b/cognee/eval_framework/eval_config.py index dbc32150f..7baf58cf9 100644 --- a/cognee/eval_framework/eval_config.py +++ b/cognee/eval_framework/eval_config.py @@ -1,6 +1,6 @@ from functools import lru_cache from pydantic_settings import BaseSettings, SettingsConfigDict -from typing import List +from typing import List, Optional class EvalConfig(BaseSettings): @@ -14,9 +14,7 @@ class EvalConfig(BaseSettings): # Question answering params answering_questions: bool = True - qa_engine: str = ( - "cognee_completion" # Options: 'cognee_completion' or 'cognee_graph_completion' - ) + qa_engine: str = "cognee_completion" # Options: 'cognee_completion' or 'cognee_graph_completion' or 'cognee_graph_completion_cot' or 'cognee_graph_completion_context_extension' # Evaluation params evaluating_answers: bool = True @@ -43,6 +41,7 @@ class EvalConfig(BaseSettings): dashboard_path: str = "dashboard.html" direct_llm_system_prompt: str = "direct_llm_eval_system.txt" direct_llm_eval_prompt: str = "direct_llm_eval_prompt.txt" + instance_filter: Optional[List[str]] = None model_config = SettingsConfigDict(env_file=".env", extra="allow") @@ -68,6 +67,7 @@ class EvalConfig(BaseSettings): "task_getter_type": self.task_getter_type, "direct_llm_system_prompt": self.direct_llm_system_prompt, "direct_llm_eval_prompt": self.direct_llm_eval_prompt, + "instance_filter": self.instance_filter, } diff --git a/cognee/eval_framework/metrics_dashboard.py b/cognee/eval_framework/metrics_dashboard.py index eb4d2ed8e..e7ae7c2aa 100644 --- a/cognee/eval_framework/metrics_dashboard.py +++ b/cognee/eval_framework/metrics_dashboard.py @@ -167,4 +167,4 @@ def create_dashboard( with open(output_file, "w", encoding="utf-8") as f: f.write(dashboard_html) - return output_file + return dashboard_html diff --git a/cognee/eval_framework/modal_eval_dashboard.py b/cognee/eval_framework/modal_eval_dashboard.py new file mode 100644 index 000000000..acc0c3aa9 --- /dev/null +++ b/cognee/eval_framework/modal_eval_dashboard.py @@ -0,0 +1,96 @@ +import os +import json +import pandas as pd +import subprocess +import modal +import streamlit as st + +# ---------------------------------------------------------------------------- +# Volume and Image Setup +# ---------------------------------------------------------------------------- +metrics_volume = modal.Volume.from_name("evaluation_dashboard_results", create_if_missing=True) + +image = ( + modal.Image.debian_slim(python_version="3.11") + .pip_install("streamlit", "pandas", "plotly") + .add_local_file(__file__, "/root/serve_dashboard.py") +) + +# ---------------------------------------------------------------------------- +# Define and Deploy the App +# ---------------------------------------------------------------------------- +app = modal.App( + name="metrics-dashboard", + image=image, + volumes={"/data": metrics_volume}, +) + + +@app.function() +@modal.web_server(port=8000) +def run(): + """ + Launch Streamlit server on port 8000 inside the container. + """ + cmd = ( + "streamlit run /root/serve_dashboard.py " + "--server.port 8000 " + "--server.enableCORS=false " + "--server.enableXsrfProtection=false" + ) + subprocess.Popen(cmd, shell=True) + + +# ---------------------------------------------------------------------------- +# Streamlit Dashboard Application Logic +# ---------------------------------------------------------------------------- +def main(): + metrics_volume.reload() + + st.set_page_config(page_title="Metrics Dashboard", layout="wide") + st.title("📊 Cognee Evaluations Dashboard") + + data_path = "/data" + records = [] + + for filename in sorted(os.listdir(data_path)): + if not filename.endswith(".json"): + continue + base = filename.rsplit(".", 1)[0] + parts = base.split("_") + benchmark = parts[1] if len(parts) >= 3 else "" + + full_path = os.path.join(data_path, filename) + with open(full_path, "r") as f: + items = json.load(f) + num_q = len(items) + total_em = sum(q["metrics"]["EM"]["score"] for q in items) + total_f1 = sum(q["metrics"]["f1"]["score"] for q in items) + total_corr = sum(q["metrics"]["correctness"]["score"] for q in items) + records.append( + { + "file": parts[0].upper() + "_____" + parts[2], + "benchmark": benchmark, + "questions": num_q, + "avg_EM": round(total_em / num_q, 4), + "avg_F1": round(total_f1 / num_q, 4), + "avg_correctness": round(total_corr / num_q, 4), + } + ) + + df = pd.DataFrame(records) + if df.empty: + st.warning("No JSON files found in the volume.") + return + + st.subheader("Results by benchmark") + for bm, group in df.groupby("benchmark"): + st.markdown(f"### {bm}") + st.dataframe( + group[["file", "questions", "avg_EM", "avg_F1", "avg_correctness"]], + use_container_width=True, + ) + + +if __name__ == "__main__": + main() diff --git a/cognee/eval_framework/modal_run_eval.py b/cognee/eval_framework/modal_run_eval.py index b2cfd4065..aca2686a5 100644 --- a/cognee/eval_framework/modal_run_eval.py +++ b/cognee/eval_framework/modal_run_eval.py @@ -1,8 +1,9 @@ import modal import os -import json import asyncio import datetime +import hashlib +import json from cognee.shared.logging_utils import get_logger from cognee.eval_framework.eval_config import EvalConfig from cognee.eval_framework.corpus_builder.run_corpus_builder import run_corpus_builder @@ -10,8 +11,10 @@ from cognee.eval_framework.answer_generation.run_question_answering_module impor run_question_answering, ) from cognee.eval_framework.evaluation.run_evaluation_module import run_evaluation +from cognee.eval_framework.metrics_dashboard import create_dashboard logger = get_logger() +vol = modal.Volume.from_name("evaluation_dashboard_results", create_if_missing=True) def read_and_combine_metrics(eval_params: dict) -> dict: @@ -46,32 +49,54 @@ image = ( "OPENAI_API_KEY": os.getenv("OPENAI_API_KEY"), } ) - .poetry_install_from_file(poetry_pyproject_toml="pyproject.toml") .pip_install("protobuf", "h2", "deepeval", "gdown", "plotly") ) -@app.function(image=image, concurrency_limit=2, timeout=1800, retries=1) +@app.function(image=image, concurrency_limit=10, timeout=86400, volumes={"/data": vol}) async def modal_run_eval(eval_params=None): """Runs evaluation pipeline and returns combined metrics results.""" if eval_params is None: eval_params = EvalConfig().to_dict() + version_name = "baseline" + benchmark_name = os.environ.get("BENCHMARK", eval_params.get("benchmark", "benchmark")) + timestamp = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ") + + answers_filename = ( + f"{version_name}_{benchmark_name}_{timestamp}_{eval_params.get('answers_path')}" + ) + html_filename = ( + f"{version_name}_{benchmark_name}_{timestamp}_{eval_params.get('dashboard_path')}" + ) + logger.info(f"Running evaluation with params: {eval_params}") # Run the evaluation pipeline - await run_corpus_builder(eval_params) + await run_corpus_builder(eval_params, instance_filter=eval_params.get("instance_filter")) await run_question_answering(eval_params) - await run_evaluation(eval_params) + answers = await run_evaluation(eval_params) - # Early return if metrics calculation wasn't requested - if not eval_params.get("evaluating_answers") or not eval_params.get("calculate_metrics"): - logger.info( - "Skipping metrics collection as either evaluating_answers or calculate_metrics is False" + with open("/data/" + answers_filename, "w") as f: + json.dump(answers, f, ensure_ascii=False, indent=4) + vol.commit() + + if eval_params.get("dashboard"): + logger.info("Generating dashboard...") + html_output = create_dashboard( + metrics_path=eval_params["metrics_path"], + aggregate_metrics_path=eval_params["aggregate_metrics_path"], + output_file=eval_params["dashboard_path"], + benchmark=eval_params["benchmark"], ) - return None - return read_and_combine_metrics(eval_params) + with open("/data/" + html_filename, "w") as f: + f.write(html_output) + vol.commit() + + logger.info("Evaluation set finished...") + + return True @app.local_entrypoint() @@ -80,37 +105,39 @@ async def main(): configs = [ EvalConfig( task_getter_type="Default", - number_of_samples_in_corpus=2, + number_of_samples_in_corpus=10, + benchmark="HotPotQA", + qa_engine="cognee_graph_completion", building_corpus_from_scratch=True, answering_questions=True, evaluating_answers=True, calculate_metrics=True, - dashboard=False, + dashboard=True, ), EvalConfig( task_getter_type="Default", number_of_samples_in_corpus=10, + benchmark="TwoWikiMultiHop", + qa_engine="cognee_graph_completion", building_corpus_from_scratch=True, answering_questions=True, evaluating_answers=True, calculate_metrics=True, - dashboard=False, + dashboard=True, + ), + EvalConfig( + task_getter_type="Default", + number_of_samples_in_corpus=10, + benchmark="Musique", + qa_engine="cognee_graph_completion", + building_corpus_from_scratch=True, + answering_questions=True, + evaluating_answers=True, + calculate_metrics=True, + dashboard=True, ), ] # Run evaluations in parallel with different configurations modal_tasks = [modal_run_eval.remote.aio(config.to_dict()) for config in configs] - results = await asyncio.gather(*modal_tasks) - - # Filter out None results and save combined results - results = [r for r in results if r is not None] - if results: - timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") - output_file = f"combined_results_{timestamp}.json" - - with open(output_file, "w") as f: - json.dump(results, f, indent=2) - - logger.info(f"Completed parallel evaluation runs. Results saved to {output_file}") - else: - logger.info("No metrics were collected from any of the evaluation runs") + await asyncio.gather(*modal_tasks) diff --git a/cognee/infrastructure/context/BaseContextProvider.py b/cognee/infrastructure/context/BaseContextProvider.py index 0d0aaf1e6..2fb329538 100644 --- a/cognee/infrastructure/context/BaseContextProvider.py +++ b/cognee/infrastructure/context/BaseContextProvider.py @@ -5,9 +5,29 @@ from cognee.infrastructure.engine import DataPoint class BaseContextProvider(ABC): - """Base class for context retrieval strategies.""" + """ + Base class for context retrieval strategies. + + This class serves as a blueprint for creating different strategies that retrieve context + based on specified entities and a query. Any subclass must implement the `get_context` + method to define how context is retrieved. + """ @abstractmethod async def get_context(self, entities: List[DataPoint], query: str) -> str: - """Get relevant context based on extracted entities and original query.""" + """ + Get relevant context based on extracted entities and original query. + + This method must be implemented by subclasses to define the logic for retrieving context + relevant to the provided `entities` and `query`. It is an asynchronous method that will + return a string representing the context. + + Parameters: + ----------- + + - entities (List[DataPoint]): A list of data points representing extracted entities + relevant to the query. + - query (str): The original query string used to extract the entities and retrieve + context. + """ pass diff --git a/cognee/infrastructure/data/chunking/DefaultChunkEngine.py b/cognee/infrastructure/data/chunking/DefaultChunkEngine.py index 0d67d5e38..3ab8e81aa 100644 --- a/cognee/infrastructure/data/chunking/DefaultChunkEngine.py +++ b/cognee/infrastructure/data/chunking/DefaultChunkEngine.py @@ -9,6 +9,10 @@ from cognee.shared.data_models import ChunkStrategy class DefaultChunkEngine: + """ + Manage the process of chunking data based on specified strategies. + """ + def __init__(self, chunk_strategy=None, chunk_size=None, chunk_overlap=None): self.chunk_strategy = chunk_strategy self.chunk_size = chunk_size @@ -42,13 +46,17 @@ class DefaultChunkEngine: Chunk data based on the specified strategy. Parameters: - - chunk_strategy: The strategy to use for chunking. - - source_data: The data to be chunked. - - chunk_size: The size of each chunk. - - chunk_overlap: The overlap between chunks. + ----------- + + - chunk_strategy: The strategy to use for chunking the data. (default None) + - source_data: The data to be chunked. (default None) + - chunk_size: The size of each chunk. (default None) + - chunk_overlap: The overlap between chunks. (default None) Returns: - - The chunked data. + -------- + + Returns the chunked data and the respective chunk numbers. """ if self.chunk_strategy == ChunkStrategy.PARAGRAPH: @@ -69,6 +77,21 @@ class DefaultChunkEngine: return chunked_data, chunk_number def chunk_data_exact(self, data_chunks, chunk_size, chunk_overlap): + """ + Chunk data exactly by specified sizes and overlaps. + + Parameters: + ----------- + + - data_chunks: The chunks of data to be processed into exact sizes. + - chunk_size: The defined size for each chunk to be created. + - chunk_overlap: The number of overlapping characters between chunks. + + Returns: + -------- + + Returns the created chunks and their numbered indices. + """ data = "".join(data_chunks) chunks = [] for i in range(0, len(data), chunk_size - chunk_overlap): @@ -80,6 +103,21 @@ class DefaultChunkEngine: return chunks, numbered_chunks def chunk_by_sentence(self, data_chunks, chunk_size, chunk_overlap): + """ + Chunk data into sentences based on specified sizes and overlaps. + + Parameters: + ----------- + + - data_chunks: The chunks of data to be processed into sentences. + - chunk_size: The defined size for each chunk to be created. + - chunk_overlap: The number of overlapping characters between chunks. + + Returns: + -------- + + Returns the resulting sentence chunks and their numbered indices. + """ # Split by periods, question marks, exclamation marks, and ellipses data = "".join(data_chunks) @@ -104,6 +142,23 @@ class DefaultChunkEngine: return sentence_chunks, numbered_chunks def chunk_data_by_paragraph(self, data_chunks, chunk_size, chunk_overlap, bound=0.75): + """ + Chunk data based on paragraphs while considering overlaps and boundaries. + + Parameters: + ----------- + + - data_chunks: The chunks of data to be processed into paragraphs. + - chunk_size: The defined size for each chunk to be created. + - chunk_overlap: The number of overlapping characters between chunks. + - bound: A weighting factor to determine splitting within a chunk (default is 0.75). + (default 0.75) + + Returns: + -------- + + Returns the paragraph chunks and their numbered indices. + """ data = "".join(data_chunks) total_length = len(data) chunks = [] diff --git a/cognee/infrastructure/data/chunking/HaystackChunkEngine.py b/cognee/infrastructure/data/chunking/HaystackChunkEngine.py index 51eb1cea9..88994a64b 100644 --- a/cognee/infrastructure/data/chunking/HaystackChunkEngine.py +++ b/cognee/infrastructure/data/chunking/HaystackChunkEngine.py @@ -1,4 +1,13 @@ class HaystackChunkEngine: + """ + Manage chunking of source data using specified strategies and parameters. + + The class provides functionality to process source data into manageable chunks according + to defined strategies, chunk sizes, and overlaps. The primary instance variables include + chunk_strategy, source_data, chunk_size, and chunk_overlap, which dictate how the data + is chunked. + """ + def __init__(self, chunk_strategy=None, source_data=None, chunk_size=None, chunk_overlap=None): self.chunk_strategy = chunk_strategy self.source_data = source_data diff --git a/cognee/infrastructure/data/chunking/LangchainChunkingEngine.py b/cognee/infrastructure/data/chunking/LangchainChunkingEngine.py index 5de642ca8..4d163fbb4 100644 --- a/cognee/infrastructure/data/chunking/LangchainChunkingEngine.py +++ b/cognee/infrastructure/data/chunking/LangchainChunkingEngine.py @@ -6,6 +6,10 @@ from cognee.shared.data_models import ChunkStrategy class LangchainChunkEngine: + """ + Handles chunking of data using specified strategies. + """ + def __init__(self, chunk_strategy=None, source_data=None, chunk_size=None, chunk_overlap=None): self.chunk_strategy = chunk_strategy self.source_data = source_data @@ -22,14 +26,26 @@ class LangchainChunkEngine: """ Chunk data based on the specified strategy. + Select and apply a chunking strategy to the provided source data, returning the + resulting chunks and their corresponding indices. If an invalid strategy is provided, an + error message is returned instead. + Parameters: - - chunk_strategy: The strategy to use for chunking. - - source_data: The data to be chunked. - - chunk_size: The size of each chunk. - - chunk_overlap: The overlap between chunks. + ----------- + + - chunk_strategy: The strategy to use for chunking; should be one of the predefined + strategies. (default None) + - source_data: The data to be chunked, passed into the chunking strategy. (default + None) + - chunk_size: The size of each chunk; determines how large each piece of data will + be. (default None) + - chunk_overlap: The amount of overlap between consecutive chunks; affects the + continuity of data. (default None) Returns: - - The chunked data. + -------- + + A tuple containing the chunked data and its corresponding indices. """ if chunk_strategy == ChunkStrategy.CODE: @@ -46,6 +62,27 @@ class LangchainChunkEngine: return chunked_data, chunk_number def chunk_data_by_code(self, data_chunks, chunk_size, chunk_overlap=10, language=None): + """ + Chunk data specifically for code snippets. + + Utilize a text splitter to break down code into manageable chunks based on the provided + size and overlap, returning the content and numbered indices of each chunk. + + Parameters: + ----------- + + - data_chunks: The code data that needs to be chunked. + - chunk_size: The desired size of each code chunk. + - chunk_overlap: The number of lines or characters that overlap between consecutive + chunks. (default 10) + - language: The programming language of the code, defaulting to Python if not + specified. (default None) + + Returns: + -------- + + A tuple with the contents of the code chunks and their respective numbered lists. + """ from langchain_text_splitters import ( Language, RecursiveCharacterTextSplitter, @@ -68,6 +105,25 @@ class LangchainChunkEngine: return only_content, numbered_chunks def chunk_data_by_character(self, data_chunks, chunk_size=1500, chunk_overlap=10): + """ + Chunk data based on character count. + + Apply a character-based text splitter to divide the input data into chunks of specified + size and overlap, returning the content and the chunk indices. + + Parameters: + ----------- + + - data_chunks: The data to be chunked based on character count. + - chunk_size: The maximum number of characters allowed in each chunk. (default 1500) + - chunk_overlap: The number of characters that overlap between chunks. (default 10) + + Returns: + -------- + + A tuple comprising the content of the character chunks and their indexed + representations. + """ from langchain_text_splitters import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter( diff --git a/cognee/infrastructure/data/chunking/config.py b/cognee/infrastructure/data/chunking/config.py index e367e4de8..3c81ee7dd 100644 --- a/cognee/infrastructure/data/chunking/config.py +++ b/cognee/infrastructure/data/chunking/config.py @@ -6,6 +6,10 @@ from cognee.shared.data_models import ChunkStrategy, ChunkEngine class ChunkConfig(BaseSettings): + """ + Manage configuration settings for chunk processing. + """ + chunk_size: int = 1500 chunk_overlap: int = 10 chunk_strategy: object = ChunkStrategy.PARAGRAPH @@ -14,6 +18,14 @@ class ChunkConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def to_dict(self) -> dict: + """ + Convert the chunk settings to a dictionary format. + + Returns: + -------- + + - dict: A dictionary representation of the chunk configuration settings. + """ return { "chunk_size": self.chunk_size, "chunk_overlap": self.chunk_overlap, @@ -24,4 +36,18 @@ class ChunkConfig(BaseSettings): @lru_cache def get_chunk_config(): + """ + Retrieve the configuration for chunking data, caching the result for efficiency. + + This function creates an instance of the ChunkConfig class, which contains settings such + as chunk size, overlap, strategy, and engine. The use of lru_cache ensures that + subsequent calls to this function will return the cached instance, improving performance + by avoiding re-creation of the object. + + Returns: + -------- + + - ChunkConfig: An instance of the ChunkConfig class containing the chunking + configuration settings. + """ return ChunkConfig() diff --git a/cognee/infrastructure/data/chunking/create_chunking_engine.py b/cognee/infrastructure/data/chunking/create_chunking_engine.py index 7966bbf6c..bd6246800 100644 --- a/cognee/infrastructure/data/chunking/create_chunking_engine.py +++ b/cognee/infrastructure/data/chunking/create_chunking_engine.py @@ -5,12 +5,41 @@ from cognee.shared.data_models import ChunkEngine class ChunkingConfig(Dict): + """ + Represent configuration settings for chunking operations, inheriting from the built-in + Dict class. The class contains the following public attributes: + + - vector_db_url: A string representing the URL of the vector database. + - vector_db_key: A string representing the key for accessing the vector database. + - vector_db_provider: A string representing the provider of the vector database. + """ + vector_db_url: str vector_db_key: str vector_db_provider: str def create_chunking_engine(config: ChunkingConfig): + """ + Create a chunking engine based on the provided configuration. + + The function selects and returns an instance of a chunking engine class based on the + `chunk_engine` specified in the `config`. Supported engines are Langchain, Default, and + Haystack, with their respective configurations for chunk size, overlap, and strategy. + + Parameters: + ----------- + + - config (ChunkingConfig): Configuration object containing the settings for the + chunking engine, including the engine type, chunk size, chunk overlap, and chunk + strategy. + + Returns: + -------- + + An instance of the selected chunking engine class (LangchainChunkEngine, + DefaultChunkEngine, or HaystackChunkEngine). + """ if config["chunk_engine"] == ChunkEngine.LANGCHAIN_ENGINE: from cognee.infrastructure.data.chunking.LangchainChunkingEngine import LangchainChunkEngine diff --git a/cognee/infrastructure/data/chunking/get_chunking_engine.py b/cognee/infrastructure/data/chunking/get_chunking_engine.py index 8d40a35a9..9d54ed49b 100644 --- a/cognee/infrastructure/data/chunking/get_chunking_engine.py +++ b/cognee/infrastructure/data/chunking/get_chunking_engine.py @@ -4,4 +4,13 @@ from .create_chunking_engine import create_chunking_engine def get_chunk_engine(): + """ + Create a chunking engine instance. + + Returns: + -------- + + Returns an instance of the chunking engine created based on the configuration + settings. + """ return create_chunking_engine(get_chunk_config().to_dict()) diff --git a/cognee/infrastructure/data/utils/extract_keywords.py b/cognee/infrastructure/data/utils/extract_keywords.py index 42b06884a..bd4fedd56 100644 --- a/cognee/infrastructure/data/utils/extract_keywords.py +++ b/cognee/infrastructure/data/utils/extract_keywords.py @@ -5,6 +5,25 @@ from cognee.shared.utils import extract_pos_tags def extract_keywords(text: str) -> list[str]: + """ + Extract keywords from the provided text string. + + This function raises an InvalidValueError if the input text is empty. It processes the + text to extract parts of speech, focusing on nouns, and uses TF-IDF to identify the most + relevant keywords based on their frequency. The function returns a list of up to 15 + keywords, each having more than 3 characters. + + Parameters: + ----------- + + - text (str): The input text from which to extract keywords. + + Returns: + -------- + + - list[str]: A list of keywords extracted from the text, containing up to 15 nouns + with more than 3 characters. + """ if len(text) == 0: raise InvalidValueError(message="extract_keywords cannot extract keywords from empty text.") diff --git a/cognee/infrastructure/databases/exceptions/EmbeddingException.py b/cognee/infrastructure/databases/exceptions/EmbeddingException.py index df3ba93c0..62616899c 100644 --- a/cognee/infrastructure/databases/exceptions/EmbeddingException.py +++ b/cognee/infrastructure/databases/exceptions/EmbeddingException.py @@ -3,7 +3,13 @@ from fastapi import status class EmbeddingException(CogneeApiError): - """Custom exception for handling embedding-related errors.""" + """ + Custom exception for handling embedding-related errors. + + This exception class is designed to indicate issues specifically related to embeddings + within the application. It extends the base exception class CogneeApiError and allows + for customization of the error message, name, and status code. + """ def __init__( self, diff --git a/cognee/infrastructure/databases/exceptions/exceptions.py b/cognee/infrastructure/databases/exceptions/exceptions.py index eacfc4095..7a4220358 100644 --- a/cognee/infrastructure/databases/exceptions/exceptions.py +++ b/cognee/infrastructure/databases/exceptions/exceptions.py @@ -3,6 +3,14 @@ from cognee.exceptions import CogneeApiError, CriticalError class DatabaseNotCreatedError(CriticalError): + """ + Represents an error indicating that the database has not been created. This error should + be raised when an attempt is made to access the database before it has been initialized. + + Inherits from CriticalError. Overrides the constructor to include a default message and + status code. + """ + def __init__( self, message: str = "The database has not been created yet. Please call `await setup()` first.", @@ -13,7 +21,21 @@ class DatabaseNotCreatedError(CriticalError): class EntityNotFoundError(CogneeApiError): - """Database returns nothing""" + """ + Represents an error when a requested entity is not found in the database. This class + inherits from CogneeApiError. + + Public methods: + + - __init__ : Initializes the EntityNotFoundError with a specific message, name, and + status code. + + Instance variables: + + - message: A string containing the error message. + - name: A string representing the name of the error type. + - status_code: An integer indicating the HTTP status code associated with the error. + """ def __init__( self, @@ -28,7 +50,12 @@ class EntityNotFoundError(CogneeApiError): class EntityAlreadyExistsError(CogneeApiError): - """Conflict detected, like trying to create a resource that already exists""" + """ + Represents an error when an entity creation is attempted but the entity already exists. + + This class is derived from CogneeApiError and is used to signal a conflict in operations + involving resource creation. + """ def __init__( self, @@ -37,3 +64,23 @@ class EntityAlreadyExistsError(CogneeApiError): status_code=status.HTTP_409_CONFLICT, ): super().__init__(message, name, status_code) + + +class NodesetFilterNotSupportedError(CogneeApiError): + """ + Raise an exception when a nodeset filter is not supported by the current database. + + This exception inherits from `CogneeApiError` and is designed to provide information + about the specific issue of unsupported nodeset filters in the context of graph + databases. + """ + + def __init__( + self, + message: str = "The nodeset filter is not supported in the current graph database.", + name: str = "NodeSetFilterNotSupportedError", + status_code=status.HTTP_404_NOT_FOUND, + ): + self.message = message + self.name = name + self.status_code = status_code diff --git a/cognee/infrastructure/databases/graph/__init__.py b/cognee/infrastructure/databases/graph/__init__.py index 68ab3b409..4bee8e02c 100644 --- a/cognee/infrastructure/databases/graph/__init__.py +++ b/cognee/infrastructure/databases/graph/__init__.py @@ -1,2 +1,3 @@ from .config import get_graph_config from .get_graph_engine import get_graph_engine +from .use_graph_adapter import use_graph_adapter diff --git a/cognee/infrastructure/databases/graph/config.py b/cognee/infrastructure/databases/graph/config.py index a17e551b1..4ae4767d8 100644 --- a/cognee/infrastructure/databases/graph/config.py +++ b/cognee/infrastructure/databases/graph/config.py @@ -8,6 +8,27 @@ from cognee.root_dir import get_absolute_path class GraphConfig(BaseSettings): + """ + Represents the configuration for a graph system, including parameters for graph file + storage and database connections. + + Public methods: + - to_dict + - to_hashable_dict + + Instance variables: + - graph_filename + - graph_database_provider + - graph_database_url + - graph_database_username + - graph_database_password + - graph_database_port + - graph_file_path + - graph_model + - graph_topology + - model_config + """ + graph_filename: str = "cognee_graph.pkl" graph_database_provider: str = "NETWORKX" graph_database_url: str = "" @@ -22,6 +43,17 @@ class GraphConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def to_dict(self) -> dict: + """ + Return the configuration as a dictionary. + + This dictionary contains all the configurations related to the graph, which includes + details for file storage and database connectivity. + + Returns: + -------- + + - dict: A dictionary representation of the configuration settings. + """ return { "graph_filename": self.graph_filename, "graph_database_provider": self.graph_database_provider, @@ -36,6 +68,18 @@ class GraphConfig(BaseSettings): } def to_hashable_dict(self) -> dict: + """ + Return a hashable dictionary with essential database configuration parameters. + + This dictionary excludes certain non-hashable objects and focuses on unique identifiers + for database configurations. + + Returns: + -------- + + - dict: A dictionary representation of the essential database configuration + settings. + """ return { "graph_database_provider": self.graph_database_provider, "graph_database_url": self.graph_database_url, @@ -48,4 +92,16 @@ class GraphConfig(BaseSettings): @lru_cache def get_graph_config(): + """ + Retrieve the graph configuration. This function utilizes caching to return a singleton + instance of the GraphConfig class for efficiency. + + It creates and returns a GraphConfig object, which contains various settings related to + graph configuration. + + Returns: + -------- + + - GraphConfig: A GraphConfig instance containing the graph configuration settings. + """ return GraphConfig() diff --git a/cognee/infrastructure/databases/graph/get_graph_engine.py b/cognee/infrastructure/databases/graph/get_graph_engine.py index dcd228443..f7e452795 100644 --- a/cognee/infrastructure/databases/graph/get_graph_engine.py +++ b/cognee/infrastructure/databases/graph/get_graph_engine.py @@ -2,12 +2,28 @@ from functools import lru_cache + from .config import get_graph_config from .graph_db_interface import GraphDBInterface +from .supported_databases import supported_databases async def get_graph_engine() -> GraphDBInterface: - """Factory function to get the appropriate graph client based on the graph type.""" + """ + Factory function to get the appropriate graph client based on the graph type. + + This function retrieves the graph configuration and creates a graph engine by calling + the `create_graph_engine` function. If the configured graph database provider is + 'networkx', it ensures that the graph is loaded from a file asynchronously if it hasn't + been loaded yet. It raises an `EnvironmentError` if the necessary configurations for the + selected graph provider are missing. + + Returns: + -------- + + - GraphDBInterface: Returns an instance of GraphDBInterface which represents the + selected graph client. + """ config = get_graph_config() graph_client = create_graph_engine(**get_graph_config().to_hashable_dict()) @@ -30,7 +46,44 @@ def create_graph_engine( graph_database_port, graph_file_path, ): - """Factory function to create the appropriate graph client based on the graph type.""" + """ + Create a graph engine based on the specified provider type. + + This factory function initializes and returns the appropriate graph client depending on + the database provider specified. It validates required parameters and raises an + EnvironmentError if any are missing for the respective provider implementations. + + Parameters: + ----------- + + - graph_database_provider: The type of graph database provider to use (e.g., neo4j, + falkordb, kuzu, memgraph). + - graph_database_url: The URL for the graph database instance. Required for neo4j, + falkordb, and memgraph providers. + - graph_database_username: The username for authentication with the graph database. + Required for neo4j and memgraph providers. + - graph_database_password: The password for authentication with the graph database. + Required for neo4j and memgraph providers. + - graph_database_port: The port number for the graph database connection. Required + for the falkordb provider. + - graph_file_path: The filesystem path to the graph file. Required for the kuzu + provider. + + Returns: + -------- + + Returns an instance of the appropriate graph adapter depending on the provider type + specified. + """ + + if graph_database_provider in supported_databases: + adapter = supported_databases[graph_database_provider] + + return adapter( + graph_database_url=graph_database_url, + graph_database_username=graph_database_username, + graph_database_password=graph_database_password, + ) if graph_database_provider == "neo4j": if not (graph_database_url and graph_database_username and graph_database_password): diff --git a/cognee/infrastructure/databases/graph/graph_db_interface.py b/cognee/infrastructure/databases/graph/graph_db_interface.py index 8c582107f..b7684b6a2 100644 --- a/cognee/infrastructure/databases/graph/graph_db_interface.py +++ b/cognee/infrastructure/databases/graph/graph_db_interface.py @@ -2,7 +2,7 @@ import inspect from functools import wraps from abc import abstractmethod, ABC from datetime import datetime, timezone -from typing import Optional, Dict, Any, List, Tuple +from typing import Optional, Dict, Any, List, Tuple, Type from uuid import NAMESPACE_OID, UUID, uuid5 from cognee.shared.logging_utils import get_logger from cognee.infrastructure.engine import DataPoint @@ -20,10 +20,42 @@ Node = Tuple[str, NodeData] # (node_id, properties) def record_graph_changes(func): - """Decorator to record graph changes in the relationship database.""" + """ + Decorator to record graph changes in the relationship database. + + Parameters: + ----------- + + - func: The asynchronous function to wrap, which likely modifies graph data. + + Returns: + -------- + + Returns the wrapped function that manages database relationships. + """ @wraps(func) async def wrapper(self, *args, **kwargs): + """ + Wraps the given asynchronous function to handle database relationships. + + Tracks the caller's function and class name for context. When the wrapped function is + called, it manages database relationships for nodes or edges by adding entries to a + ledger and committing the changes to the database session. Errors during relationship + addition or session commit are logged and will not disrupt the execution of the wrapped + function. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + Returns the result of the wrapped function call. + """ db_engine = get_relational_engine() frame = inspect.currentframe() while frame: @@ -93,42 +125,115 @@ def record_graph_changes(func): class GraphDBInterface(ABC): - """Interface for graph database operations.""" + """ + Define an interface for graph database operations to be implemented by concrete classes. + + Public methods include: + - query + - add_node + - add_nodes + - delete_node + - delete_nodes + - get_node + - get_nodes + - add_edge + - add_edges + - delete_graph + - get_graph_data + - get_graph_metrics + - has_edge + - has_edges + - get_edges + - get_neighbors + - get_nodeset_subgraph + - get_connections + """ @abstractmethod async def query(self, query: str, params: dict) -> List[Any]: - """Execute a raw query against the database.""" + """ + Execute a raw database query and return the results. + + Parameters: + ----------- + + - query (str): The query string to execute against the database. + - params (dict): A dictionary of parameters to be used in the query. + """ raise NotImplementedError @abstractmethod async def add_node(self, node_id: str, properties: Dict[str, Any]) -> None: - """Add a single node to the graph.""" + """ + 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. + """ raise NotImplementedError @abstractmethod @record_graph_changes async def add_nodes(self, nodes: List[Node]) -> None: - """Add multiple nodes to the graph.""" + """ + Add multiple nodes to the graph in a single operation. + + Parameters: + ----------- + + - nodes (List[Node]): A list of Node objects to be added to the graph. + """ raise NotImplementedError @abstractmethod async def delete_node(self, node_id: str) -> None: - """Delete a node from the graph.""" + """ + Delete a specified node from the graph by its ID. + + Parameters: + ----------- + + - node_id (str): Unique identifier for the node to delete. + """ raise NotImplementedError @abstractmethod async def delete_nodes(self, node_ids: List[str]) -> None: - """Delete multiple nodes from the graph.""" + """ + Delete multiple nodes from the graph by their identifiers. + + Parameters: + ----------- + + - node_ids (List[str]): A list of unique identifiers for the nodes to delete. + """ raise NotImplementedError @abstractmethod async def get_node(self, node_id: str) -> Optional[NodeData]: - """Get a single node by ID.""" + """ + Retrieve a single node from the graph using its ID. + + Parameters: + ----------- + + - node_id (str): Unique identifier of the node to retrieve. + """ raise NotImplementedError @abstractmethod async def get_nodes(self, node_ids: List[str]) -> List[NodeData]: - """Get multiple nodes by their IDs.""" + """ + Retrieve multiple nodes from the graph using their IDs. + + Parameters: + ----------- + + - node_ids (List[str]): A list of unique identifiers for the nodes to retrieve. + """ raise NotImplementedError @abstractmethod @@ -139,53 +244,137 @@ class GraphDBInterface(ABC): relationship_name: str, properties: Optional[Dict[str, Any]] = None, ) -> None: - """Add a single edge to the graph.""" + """ + 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) + """ raise NotImplementedError @abstractmethod @record_graph_changes async def add_edges(self, edges: List[EdgeData]) -> None: - """Add multiple edges to the graph.""" + """ + Add multiple edges to the graph in a single operation. + + Parameters: + ----------- + + - edges (List[EdgeData]): A list of EdgeData objects representing edges to be added. + """ raise NotImplementedError @abstractmethod async def delete_graph(self) -> None: - """Delete the entire graph.""" + """ + Remove the entire graph, including all nodes and edges. + """ raise NotImplementedError @abstractmethod async def get_graph_data(self) -> Tuple[List[Node], List[EdgeData]]: - """Get all nodes and edges in the graph.""" + """ + Retrieve all nodes and edges within the graph. + """ raise NotImplementedError @abstractmethod async def get_graph_metrics(self, include_optional: bool = False) -> Dict[str, Any]: - """Get graph metrics and statistics.""" + """ + 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) + """ raise NotImplementedError @abstractmethod async def has_edge(self, source_id: str, target_id: str, relationship_name: str) -> bool: - """Check if an edge exists.""" + """ + 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. + """ raise NotImplementedError @abstractmethod async def has_edges(self, edges: List[EdgeData]) -> List[EdgeData]: - """Check if multiple edges exist.""" + """ + Determine the existence of multiple edges in the graph. + + Parameters: + ----------- + + - edges (List[EdgeData]): A list of EdgeData objects to check for existence in the + graph. + """ raise NotImplementedError @abstractmethod async def get_edges(self, node_id: str) -> List[EdgeData]: - """Get all edges connected to a node.""" + """ + 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. + """ raise NotImplementedError @abstractmethod async def get_neighbors(self, node_id: str) -> List[NodeData]: - """Get all neighboring nodes.""" + """ + Get all neighboring nodes connected to the specified node. + + Parameters: + ----------- + + - node_id (str): Unique identifier of the node for which to retrieve neighbors. + """ + raise NotImplementedError + + @abstractmethod + 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. + """ raise NotImplementedError @abstractmethod async def get_connections( self, node_id: str ) -> List[Tuple[NodeData, Dict[str, Any], NodeData]]: - """Get all nodes connected to a given node with their relationships.""" + """ + Get all nodes connected to a specified node and their relationship details. + + Parameters: + ----------- + + - node_id (str): Unique identifier of the node for which to retrieve connections. + """ raise NotImplementedError diff --git a/cognee/infrastructure/databases/graph/kuzu/adapter.py b/cognee/infrastructure/databases/graph/kuzu/adapter.py index 380f3f713..a012033ee 100644 --- a/cognee/infrastructure/databases/graph/kuzu/adapter.py +++ b/cognee/infrastructure/databases/graph/kuzu/adapter.py @@ -1,11 +1,12 @@ """Adapter for Kuzu graph database.""" +from cognee.infrastructure.databases.exceptions.exceptions import NodesetFilterNotSupportedError from cognee.shared.logging_utils import get_logger import json import os import shutil import asyncio -from typing import Dict, Any, List, Union, Optional, Tuple +from typing import Dict, Any, List, Union, Optional, Tuple, Type from datetime import datetime, timezone from uuid import UUID from contextlib import asynccontextmanager @@ -25,7 +26,14 @@ logger = get_logger() class KuzuAdapter(GraphDBInterface): - """Adapter for Kuzu graph database operations with improved consistency and async support.""" + """ + Adapter for Kuzu graph database operations with improved consistency and async support. + + This class facilitates operations for working with the Kuzu graph database, supporting + both direct database queries and a structured asynchronous interface for node and edge + management. It contains methods for querying, adding, and deleting nodes and edges as + well as for graph metrics and data extraction. + """ def __init__(self, db_path: str): """Initialize Kuzu database connection and schema.""" @@ -69,7 +77,25 @@ class KuzuAdapter(GraphDBInterface): raise async def query(self, query: str, params: Optional[dict] = None) -> List[Tuple]: - """Execute a Kuzu query asynchronously with automatic reconnection.""" + """ + Execute a Kuzu query asynchronously with automatic reconnection. + + This method runs a database query while managing potential reconnections. It handles + parameters in a dictionary and processes results to return structured data. The method + raises any exceptions encountered during query execution. + + Parameters: + ----------- + + - query (str): The Kuzu query string to be executed. + - params (Optional[dict]): A dictionary of parameters for the query, if applicable. + (default None) + + Returns: + -------- + + - List[Tuple]: A list of tuples representing the query results. + """ loop = asyncio.get_running_loop() params = params or {} @@ -99,9 +125,12 @@ class KuzuAdapter(GraphDBInterface): @asynccontextmanager async def get_session(self): - """Get a database session. + """ + Get a database session. - Kuzu doesn't have session management like Neo4j, so this provides API compatibility. + This provides an API-compatible session management for Kuzu, even though it does not + have built-in session management like other databases. It yields the current connection + and on exit performs cleanup if necessary. """ try: yield self.connection @@ -165,13 +194,39 @@ class KuzuAdapter(GraphDBInterface): # Node Operations async def has_node(self, node_id: str) -> bool: - """Check if a node exists.""" + """ + Check if a node exists. + + This method checks for the existence of a node in the database by its identifier. It + returns a boolean indicating whether the node is present or not. + + Parameters: + ----------- + + - node_id (str): The identifier of the node to check. + + Returns: + -------- + + - bool: True if the node exists, False otherwise. + """ query_str = "MATCH (n:Node) WHERE n.id = $id RETURN COUNT(n) > 0" result = await self.query(query_str, {"id": node_id}) return result[0][0] if result else False async def add_node(self, node: DataPoint) -> None: - """Add a single node to the graph if it doesn't exist.""" + """ + Add a single node to the graph if it doesn't exist. + + This method constructs and executes a query to add a node to the graph, ensuring that it + is not duplicated by checking its existence first. An error is raised if the operation + fails. + + Parameters: + ----------- + + - node (DataPoint): The node to be added, represented as a DataPoint. + """ try: properties = node.model_dump() if hasattr(node, "model_dump") else vars(node) @@ -216,7 +271,19 @@ class KuzuAdapter(GraphDBInterface): @record_graph_changes async def add_nodes(self, nodes: List[DataPoint]) -> None: - """Add multiple nodes to the graph in a batch operation.""" + """ + Add multiple nodes to the graph in a batch operation. + + This method allows for the addition of multiple nodes in a single operation to enhance + performance. It processes a list of nodes and constructs the necessary query for + insertion. Errors encountered during the addition will be logged and raised. + + Parameters: + ----------- + + - nodes (List[DataPoint]): A list of nodes to be added to the graph, each + represented as a DataPoint. + """ if not nodes: return @@ -272,17 +339,54 @@ class KuzuAdapter(GraphDBInterface): raise async def delete_node(self, node_id: str) -> None: - """Delete a node and its relationships.""" + """ + Delete a node and its relationships. + + This method removes a node identified by its ID along with all associated relationships. + It encapsulates the delete operation for simplicity in usage. + + Parameters: + ----------- + + - node_id (str): The identifier of the node to be deleted. + """ query_str = "MATCH (n:Node) WHERE n.id = $id DETACH DELETE n" await self.query(query_str, {"id": node_id}) async def delete_nodes(self, node_ids: List[str]) -> None: - """Delete multiple nodes at once.""" + """ + Delete multiple nodes at once. + + This method facilitates the deletion of a list of nodes, identified by their IDs, + concurrently. It ensures efficiency by using a single query to detach deletes for all + nodes in the list. + + Parameters: + ----------- + + - node_ids (List[str]): A list of identifiers for the nodes to be deleted. + """ query_str = "MATCH (n:Node) WHERE n.id IN $ids DETACH DELETE n" await self.query(query_str, {"ids": node_ids}) async def extract_node(self, node_id: str) -> Optional[Dict[str, Any]]: - """Extract a node by its ID.""" + """ + Extract a node by its ID. + + This method retrieves a node's data by its identifier and returns it as a dictionary. If + the node is not found or an error occurs, it returns None. + + Parameters: + ----------- + + - node_id (str): The identifier of the node to be extracted. + + Returns: + -------- + + - Optional[Dict[str, Any]]: A dictionary of the node's properties if found, + otherwise None. + """ query_str = """ MATCH (n:Node) WHERE n.id = $id @@ -304,7 +408,24 @@ class KuzuAdapter(GraphDBInterface): return None async def extract_nodes(self, node_ids: List[str]) -> List[Dict[str, Any]]: - """Extract multiple nodes by their IDs.""" + """ + Extract multiple nodes by their IDs. + + This method retrieves a list of nodes identified by their IDs and returns their data as + a list of dictionaries. It handles possible retrieval errors internally and will return + an empty list if no nodes are found. + + Parameters: + ----------- + + - node_ids (List[str]): A list of identifiers for the nodes to be extracted. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries containing the properties of the + extracted nodes. + """ query_str = """ MATCH (n:Node) WHERE n.id IN $node_ids @@ -327,7 +448,25 @@ class KuzuAdapter(GraphDBInterface): # Edge Operations async def has_edge(self, from_node: str, to_node: str, edge_label: str) -> bool: - """Check if an edge exists between nodes with the given relationship name.""" + """ + Check if an edge exists between nodes with the given relationship name. + + This method verifies the existence of a directed edge defined by the relationship name + between two specified nodes. It returns a boolean value indicating presence or absence + of the edge. + + Parameters: + ----------- + + - from_node (str): The identifier of the source node. + - to_node (str): The identifier of the target node. + - edge_label (str): The label of the edge representing the relationship name. + + Returns: + -------- + + - bool: True if the edge exists, False otherwise. + """ query_str = """ MATCH (from:Node)-[r:EDGE]->(to:Node) WHERE from.id = $from_id AND to.id = $to_id AND r.relationship_name = $edge_label @@ -339,7 +478,25 @@ class KuzuAdapter(GraphDBInterface): return result[0][0] if result else False async def has_edges(self, edges: List[Tuple[str, str, str]]) -> List[Tuple[str, str, str]]: - """Check if multiple edges exist in a batch operation.""" + """ + Check if multiple edges exist in a batch operation. + + This method checks for the presence of specified edges in the database and returns a + list of edges that exist. It is beneficial for efficiency in checking multiple edges + simultaneously. + + Parameters: + ----------- + + - edges (List[Tuple[str, str, str]]): A list of edges where each edge is represented + as a tuple of (from_node, to_node, edge_label). + + Returns: + -------- + + - List[Tuple[str, str, str]]: A list of tuples representing the existing edges from + the provided list. + """ if not edges: return [] @@ -383,7 +540,23 @@ class KuzuAdapter(GraphDBInterface): relationship_name: str, edge_properties: Dict[str, Any] = {}, ) -> None: - """Add an edge between two nodes.""" + """ + Add an edge between two nodes. + + This method constructs and executes a query to create a directed edge between two + specified nodes with certain properties. It will raise an error if the addition fails + during execution. + + Parameters: + ----------- + + - from_node (str): The identifier of the source node from which the edge originates. + - to_node (str): The identifier of the target node to which the edge points. + - relationship_name (str): The label of the edge to be created, representing the + relationship name. + - edge_properties (Dict[str, Any]): A dictionary containing properties for the edge. + (default {}) + """ try: query, params = self._edge_query_and_params( from_node, to_node, relationship_name, edge_properties @@ -395,7 +568,19 @@ class KuzuAdapter(GraphDBInterface): @record_graph_changes async def add_edges(self, edges: List[Tuple[str, str, str, Dict[str, Any]]]) -> None: - """Add multiple edges in a batch operation.""" + """ + Add multiple edges in a batch operation. + + This method enables efficient insertion of multiple edges at once by processing a list + of edge details. It improves performance for batch operations compared to adding edges + individually. Errors during execution are logged and raised as necessary. + + Parameters: + ----------- + + - edges (List[Tuple[str, str, str, Dict[str, Any]]]): A list of edges represented as + tuples of (from_node, to_node, relationship_name, edge_properties). + """ if not edges: return @@ -437,12 +622,23 @@ class KuzuAdapter(GraphDBInterface): raise async def get_edges(self, node_id: str) -> List[Tuple[Dict[str, Any], str, Dict[str, Any]]]: - """Get all edges connected to a node. + """ + Get all edges connected to a node. + + This method retrieves all edges that are linked to a specified node and returns them in + a structured format. If an error occurs or no edges exist, an empty list is returned. + + Parameters: + ----------- + + - node_id (str): The identifier of the node for which to retrieve edges. Returns: - List of tuples containing (source_node, relationship_name, target_node) - where source_node and target_node are dictionaries with node properties, - and relationship_name is a string. + -------- + + - List[Tuple[Dict[str, Any], str, Dict[str, Any]]]: A list of tuples where each + tuple contains (source_node, relationship_name, target_node), with source_node and + target_node as dictionaries of node properties. """ query_str = """ MATCH (n:Node)-[r]-(m:Node) @@ -477,11 +673,44 @@ class KuzuAdapter(GraphDBInterface): # Neighbor Operations async def get_neighbors(self, node_id: str) -> List[Dict[str, Any]]: - """Get all neighboring nodes.""" + """ + Get all neighboring nodes. + + This method simply calls the get_neighbours method for API compatibility and retrieves + connected nodes neighboring the specified node. It returns a list of neighbor nodes' + properties as dictionaries. + + Parameters: + ----------- + + - node_id (str): The identifier of the node for which to find neighbors. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing neighboring nodes' + properties. + """ return await self.get_neighbours(node_id) async def get_node(self, node_id: str) -> Optional[Dict[str, Any]]: - """Get a single node by ID.""" + """ + Get a single node by ID. + + This method retrieves the properties of a node identified by its ID and returns them as + a dictionary. If the node does not exist, None is returned. + + Parameters: + ----------- + + - node_id (str): The identifier of the node to retrieve. + + Returns: + -------- + + - Optional[Dict[str, Any]]: A dictionary containing the properties of the node if + found, otherwise None. + """ query_str = """ MATCH (n:Node) WHERE n.id = $id @@ -502,7 +731,24 @@ class KuzuAdapter(GraphDBInterface): return None async def get_nodes(self, node_ids: List[str]) -> List[Dict[str, Any]]: - """Get multiple nodes by their IDs.""" + """ + Get multiple nodes by their IDs. + + This method retrieves properties for multiple nodes identified by their IDs and returns + them as a list of dictionaries. An empty list is returned if no nodes are found or an + error occurs. + + Parameters: + ----------- + + - node_ids (List[str]): A list of identifiers for the nodes to be retrieved. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries containing properties of each + retrieved node. + """ query_str = """ MATCH (n:Node) WHERE n.id IN $node_ids @@ -521,7 +767,24 @@ class KuzuAdapter(GraphDBInterface): return [] async def get_neighbours(self, node_id: str) -> List[Dict[str, Any]]: - """Get all neighbouring nodes.""" + """ + Get all neighbouring nodes. + + This method retrieves all neighboring nodes connected to a specified node and returns + them as a list of dictionaries. It may return an empty list if no neighbors exist or an + error occurs. + + Parameters: + ----------- + + - node_id (str): The identifier of the node for which to find neighbors. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing neighboring nodes' + properties. + """ query_str = """ MATCH (n)-[r]-(m) WHERE n.id = $id @@ -537,7 +800,26 @@ class KuzuAdapter(GraphDBInterface): async def get_predecessors( self, node_id: Union[str, UUID], edge_label: Optional[str] = None ) -> List[Dict[str, Any]]: - """Get all predecessor nodes.""" + """ + Get all predecessor nodes. + + This method retrieves all nodes that are predecessors of the specified node. If an edge + label is provided, it filters the results accordingly. It returns a list of dictionaries + containing properties of these predecessor nodes. + + Parameters: + ----------- + + - node_id (Union[str, UUID]): The identifier of the specified node. + - edge_label (Optional[str]): An optional label to filter the edges by relationship + name. (default None) + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing all predecessor nodes' + properties. + """ try: if edge_label: query_str = """ @@ -562,7 +844,26 @@ class KuzuAdapter(GraphDBInterface): async def get_successors( self, node_id: Union[str, UUID], edge_label: Optional[str] = None ) -> List[Dict[str, Any]]: - """Get all successor nodes.""" + """ + Get all successor nodes. + + This method retrieves all nodes that are successors of the specified node. An edge label + can be provided to filter the results. It returns a list of dictionaries detailing these + successor nodes' properties. + + Parameters: + ----------- + + - node_id (Union[str, UUID]): The identifier of the specified node. + - edge_label (Optional[str]): An optional label to filter the edges by relationship + name. (default None) + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing all successor nodes' + properties. + """ try: if edge_label: query_str = """ @@ -587,7 +888,25 @@ class KuzuAdapter(GraphDBInterface): async def get_connections( self, node_id: str ) -> List[Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]]: - """Get all nodes connected to a given node.""" + """ + Get all nodes connected to a given node. + + This method retrieves all nodes directly connected to a specified node along with the + relationships between them, returning structured data in a list of tuples. Each tuple + contains source and target node properties along with the relationship information. + + Parameters: + ----------- + + - node_id (str): The identifier of the node for which to retrieve connections. + + Returns: + -------- + + - List[Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]]}: A list of tuples + containing (source_node, relationship_name, target_node) with dictionaries for + source_node and target_node properties. + """ query_str = """ MATCH (n:Node)-[r:EDGE]-(m:Node) WHERE n.id = $node_id @@ -635,7 +954,19 @@ class KuzuAdapter(GraphDBInterface): async def remove_connection_to_predecessors_of( self, node_ids: List[str], edge_label: str ) -> None: - """Remove all incoming edges of specified type for given nodes.""" + """ + Remove all incoming edges of specified type for given nodes. + + This method disconnects predecessor relationships of a specific type for the specified + nodes, managing edges in a single operation effectively. + + Parameters: + ----------- + + - node_ids (List[str]): A list of identifiers for the nodes whose relationships to + be removed. + - edge_label (str): The label of the edge to be removed. + """ query_str = """ MATCH (n)<-[r:EDGE]-(m) WHERE n.id IN $node_ids AND r.relationship_name = $edge_label @@ -646,7 +977,19 @@ class KuzuAdapter(GraphDBInterface): async def remove_connection_to_successors_of( self, node_ids: List[str], edge_label: str ) -> None: - """Remove all outgoing edges of specified type for given nodes.""" + """ + Remove all outgoing edges of specified type for given nodes. + + This method disconnects successor relationships of a specified type for the specified + nodes in a single efficient operation. + + Parameters: + ----------- + + - node_ids (List[str]): A list of identifiers for the nodes whose relationships to + be removed. + - edge_label (str): The label of the edge to be removed. + """ query_str = """ MATCH (n)-[r:EDGE]->(m) WHERE n.id IN $node_ids AND r.relationship_name = $edge_label @@ -659,7 +1002,20 @@ class KuzuAdapter(GraphDBInterface): async def get_graph_data( self, ) -> Tuple[List[Tuple[str, Dict[str, Any]]], List[Tuple[str, str, str, Dict[str, Any]]]]: - """Get all nodes and edges in the graph.""" + """ + Get all nodes and edges in the graph. + + This method fetches the entire graph's structure, including all nodes and their + properties as well as relationships and their details, returning them in a structured + format. Errors during query execution will result in raised exceptions. + + Returns: + -------- + + - Tuple[List[Tuple[str, Dict[str, Any]]], List[Tuple[str, str, str, Dict[str, Any]]]]: + A tuple with two elements: a list of tuples of (node_id, properties) and a list of + tuples of (source_id, target_id, relationship_name, properties). + """ try: nodes_query = """ MATCH (n:Node) @@ -728,10 +1084,107 @@ class KuzuAdapter(GraphDBInterface): logger.error(f"Failed to get graph data: {e}") raise + async def get_nodeset_subgraph( + self, node_type: Type[Any], node_name: List[str] + ) -> Tuple[List[Tuple[str, dict]], List[Tuple[str, str, str, dict]]]: + """ + Get subgraph for a set of nodes based on type and names. + + This method queries for nodes of a specific type and their corresponding neighbors, + returning both nodes and edges connecting them. It's useful for analyzing a targeted + subset of the graph. + + Parameters: + ----------- + + - node_type (Type[Any]): Type of nodes to retrieve as specified by the user. + - node_name (List[str]): List of names corresponding to the nodes to be retrieved. + + Returns: + -------- + + - Tuple[List[Tuple[str, dict]], List[Tuple[str, str, str, dict]]]}: A tuple + containing a list of nodes and a list of edges related to those nodes. + """ + label = node_type.__name__ + primary_query = """ + UNWIND $names AS wantedName + MATCH (n:Node) + WHERE n.type = $label AND n.name = wantedName + RETURN DISTINCT n.id + """ + primary_rows = await self.query(primary_query, {"names": node_name, "label": label}) + primary_ids = [row[0] for row in primary_rows] + if not primary_ids: + return [], [] + + neighbor_query = """ + MATCH (n:Node)-[:EDGE]-(nbr:Node) + WHERE n.id IN $ids + RETURN DISTINCT nbr.id + """ + nbr_rows = await self.query(neighbor_query, {"ids": primary_ids}) + neighbor_ids = [row[0] for row in nbr_rows] + + all_ids = list({*primary_ids, *neighbor_ids}) + + nodes_query = """ + MATCH (n:Node) + WHERE n.id IN $ids + RETURN n.id, n.name, n.type, n.properties + """ + node_rows = await self.query(nodes_query, {"ids": all_ids}) + nodes: List[Tuple[str, dict]] = [] + for node_id, name, typ, props in node_rows: + data = {"id": node_id, "name": name, "type": typ} + if props: + try: + data.update(json.loads(props)) + except json.JSONDecodeError: + logger.warning(f"Failed to parse JSON props for node {node_id}") + nodes.append((node_id, data)) + + edges_query = """ + MATCH (a:Node)-[r:EDGE]-(b:Node) + WHERE a.id IN $ids AND b.id IN $ids + RETURN a.id, b.id, r.relationship_name, r.properties + """ + edge_rows = await self.query(edges_query, {"ids": all_ids}) + edges: List[Tuple[str, str, str, dict]] = [] + for from_id, to_id, rel_type, props in edge_rows: + data = {} + if props: + try: + data = json.loads(props) + except json.JSONDecodeError: + logger.warning(f"Failed to parse JSON props for edge {from_id}->{to_id}") + edges.append((from_id, to_id, rel_type, data)) + + return nodes, edges + async def get_filtered_graph_data( self, attribute_filters: List[Dict[str, List[Union[str, int]]]] ): - """Get filtered nodes and relationships based on attributes.""" + """ + Get filtered nodes and relationships based on attributes. + + This method accepts attribute filters and retrieves nodes and relationships that match + the specified conditions. It allows complex filtering across node properties and edge + attributes. + + Parameters: + ----------- + + - attribute_filters (List[Dict[str, List[Union[str, int]]]]): A list of dictionaries + specifying attributes and their corresponding values for filtering nodes and + edges. + + Returns: + -------- + + A tuple containing a list of filtered node properties and a list of filtered edge + properties. + """ where_clauses = [] params = {} @@ -755,8 +1208,24 @@ class KuzuAdapter(GraphDBInterface): return ([n[0] for n in nodes], [e[0] for e in edges]) async def get_graph_metrics(self, include_optional=False) -> Dict[str, Any]: - """For the definition of these metrics, please refer to - https://docs.cognee.ai/core_concepts/graph_generation/descriptive_metrics""" + """ + Get metrics on graph structure and connectivity. + + This method computes various metrics around the graph, such as node and edge counts, + mean degree, and connected component sizes. Optionally, it can include additional + metrics based on user request. + + Parameters: + ----------- + + - include_optional: A boolean flag indicating whether to include optional metrics in + the output. (default False) + + Returns: + -------- + + - Dict[str, Any]: A dictionary containing various metrics related to the graph. + """ try: # Get basic graph data @@ -871,7 +1340,18 @@ class KuzuAdapter(GraphDBInterface): return result[0][0] if result and result[0][0] is not None else -1 async def get_disconnected_nodes(self) -> List[str]: - """Get nodes that are not connected to any other node.""" + """ + Get nodes that are not connected to any other node. + + This method retrieves identifiers of nodes that lack any relationships in the graph, + indicating they are standalone. It will return an empty list if no disconnected nodes + exist. + + Returns: + -------- + + - List[str]: A list of identifiers for disconnected nodes. + """ query_str = """ MATCH (n:Node) WHERE NOT EXISTS((n)-[]-()) @@ -883,7 +1363,19 @@ class KuzuAdapter(GraphDBInterface): # Graph Meta-Data Operations async def get_model_independent_graph_data(self) -> Dict[str, List[str]]: - """Get graph data independent of any specific data model.""" + """ + Get graph data independent of any specific data model. + + This method returns a representation of the graph that includes distinct node labels and + relationship types, making it easier to analyze the graph's structure without tying it + to a specific implementation. + + Returns: + -------- + + - Dict[str, List[str]]: A dictionary summarizing the node labels and relationship + types present in the graph. + """ node_labels = await self.query("MATCH (n:Node) RETURN DISTINCT labels(n)") rel_types = await self.query("MATCH ()-[r:EDGE]->() RETURN DISTINCT r.relationship_name") return { @@ -892,17 +1384,43 @@ class KuzuAdapter(GraphDBInterface): } async def get_node_labels_string(self) -> str: - """Get all node labels as a string.""" + """ + Get all node labels as a string. + + This method aggregates all unique node labels from the graph into a single string + representation, which can be helpful for overview and debugging purposes. + + Returns: + -------- + + - str: A string of all distinct node labels, separated by '|'. + """ labels = await self.query("MATCH (n:Node) RETURN DISTINCT labels(n)") return "|".join(sorted(set([label[0] for label in labels]))) async def get_relationship_labels_string(self) -> str: - """Get all relationship types as a string.""" + """ + Get all relationship types as a string. + + This method aggregates all unique relationship types from the graph into a single string + representation, providing an overview of the relationships defined in the graph. + + Returns: + -------- + + - str: A string of all distinct relationship types, separated by '|'. + """ types = await self.query("MATCH ()-[r:EDGE]->() RETURN DISTINCT r.relationship_name") return "|".join(sorted(set([t[0] for t in types]))) async def delete_graph(self) -> None: - """Delete all data from the graph while preserving the database structure.""" + """ + Delete all data from the graph while preserving the database structure. + + This method removes all nodes and relationships from the graph but maintains the + underlying database for future use. It raises exceptions for failures occurring during + deletion processes. + """ try: # Use DETACH DELETE to remove both nodes and their relationships in one operation await self.query("MATCH (n:Node) DETACH DELETE n") @@ -912,7 +1430,13 @@ class KuzuAdapter(GraphDBInterface): raise async def clear_database(self) -> None: - """Clear all data from the database by deleting the database files and reinitializing.""" + """ + Clear all data from the database by deleting the database files and reinitializing. + + This method removes all files associated with the database and reinitializes the Kuzu + database structure, ensuring a completely empty state. It handles exceptions that might + occur during file deletions or initializations carefully. + """ try: if self.connection: self.connection = None @@ -938,10 +1462,17 @@ class KuzuAdapter(GraphDBInterface): raise async def save_graph_to_file(self, file_path: str) -> None: - """Export the Kuzu database to a file. + """ + Export the Kuzu database to a file. - Args: - file_path: Path where to export the database + This method exports the entire Kuzu graph database to a specified file path, utilizing + Kuzu's native export command. Ensure the directory exists prior to attempting the + export, and manage related exceptions as they arise. + + Parameters: + ----------- + + - file_path (str): Path where to export the database. """ try: # Ensure directory exists @@ -958,10 +1489,17 @@ class KuzuAdapter(GraphDBInterface): raise async def load_graph_from_file(self, file_path: str) -> None: - """Import a Kuzu database from a file. + """ + Import a Kuzu database from a file. - Args: - file_path: Path to the exported database file + This method imports a database from a specified file path, ensuring that the file exists + before attempting to import. Errors during the import process are managed accordingly, + allowing for smooth operation. + + Parameters: + ----------- + + - file_path (str): Path to the exported database file. """ try: if not os.path.exists(file_path): @@ -979,7 +1517,24 @@ class KuzuAdapter(GraphDBInterface): raise async def get_document_subgraph(self, content_hash: str): - """Get all nodes that should be deleted when removing a document.""" + """ + Get all nodes that should be deleted when removing a document. + + This method constructs a complex query that identifies all nodes related to a specified + document and returns a dictionary of these nodes. Ensures thorough checks for orphaned + entities and inaccurate relationships that should be removed alongside the document. + + Parameters: + ----------- + + - content_hash (str): The identifier for the document to query against. + + Returns: + -------- + + A dictionary containing details of the document and associated nodes that need to be + deleted, or None if no related nodes are found. + """ query = """ MATCH (doc:Node) WHERE (doc.type = 'TextDocument' OR doc.type = 'PdfDocument') AND doc.name = $content_hash @@ -1034,7 +1589,23 @@ class KuzuAdapter(GraphDBInterface): } async def get_degree_one_nodes(self, node_type: str): - """Get all nodes that have only one connection.""" + """ + Get all nodes that have only one connection. + + This method retrieves nodes which are connected to exactly one other node, identified by + their specific type. It raises a ValueError if the input type is invalid and processes + queries efficiently to return targeted results. + + Parameters: + ----------- + + - node_type (str): The type of nodes to filter by, must be 'Entity' or 'EntityType'. + + Returns: + -------- + + A list of nodes that have only one connection, as identified by the specified type. + """ if not node_type or node_type not in ["Entity", "EntityType"]: raise ValueError("node_type must be either 'Entity' or 'EntityType'") diff --git a/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py b/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py index f74143971..75c0722a8 100644 --- a/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py +++ b/cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py @@ -4,7 +4,7 @@ import json from cognee.shared.logging_utils import get_logger, ERROR import asyncio from textwrap import dedent -from typing import Optional, Any, List, Dict +from typing import Optional, Any, List, Dict, Type, Tuple from contextlib import asynccontextmanager from uuid import UUID from neo4j import AsyncSession @@ -13,11 +13,48 @@ from neo4j.exceptions import Neo4jError from cognee.infrastructure.engine import DataPoint from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInterface from cognee.modules.storage.utils import JSONEncoder +from cognee.infrastructure.databases.exceptions.exceptions import NodesetFilterNotSupportedError logger = get_logger("MemgraphAdapter", level=ERROR) class MemgraphAdapter(GraphDBInterface): + """ + Handles interaction with a Memgraph database through various graph operations. + + Public methods include: + - get_session + - query + - has_node + - add_node + - add_nodes + - extract_node + - extract_nodes + - delete_node + - delete_nodes + - has_edge + - has_edges + - add_edge + - add_edges + - get_edges + - get_disconnected_nodes + - get_predecessors + - get_successors + - get_neighbours + - get_connections + - remove_connection_to_predecessors_of + - remove_connection_to_successors_of + - delete_graph + - serialize_properties + - get_model_independent_graph_data + - get_graph_data + - get_nodeset_subgraph + - get_filtered_graph_data + - get_node_labels_string + - get_relationship_labels_string + - get_graph_metrics + """ + def __init__( self, graph_database_url: str, @@ -33,6 +70,9 @@ class MemgraphAdapter(GraphDBInterface): @asynccontextmanager async def get_session(self) -> AsyncSession: + """ + Manage a session with the database, yielding the session for use in operations. + """ async with self.driver.session() as session: yield session @@ -41,6 +81,22 @@ class MemgraphAdapter(GraphDBInterface): query: str, params: Optional[Dict[str, Any]] = None, ) -> List[Dict[str, Any]]: + """ + Execute a provided query on the Memgraph database and return the results. + + Parameters: + ----------- + + - query (str): The Cypher query to be executed against the database. + - params (Optional[Dict[str, Any]]): Optional parameters to be used in the query. + (default None) + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing the result set of the + query. + """ try: async with self.get_session() as session: result = await session.run(query, params) @@ -51,6 +107,19 @@ class MemgraphAdapter(GraphDBInterface): raise error async def has_node(self, node_id: str) -> bool: + """ + Determine if a node with the given ID exists in the database. + + Parameters: + ----------- + + - node_id (str): The ID of the node to check for existence. + + Returns: + -------- + + - bool: True if the node exists; otherwise, False. + """ results = await self.query( """ MATCH (n) @@ -62,6 +131,19 @@ class MemgraphAdapter(GraphDBInterface): return results[0]["node_exists"] if len(results) > 0 else False async def add_node(self, node: DataPoint): + """ + Add a new node to the database with specified properties. + + Parameters: + ----------- + + - node (DataPoint): The DataPoint object representing the node to add. + + Returns: + -------- + + The result of the node addition, including its internal ID and node ID. + """ serialized_properties = self.serialize_properties(node.model_dump()) query = """ @@ -79,6 +161,20 @@ class MemgraphAdapter(GraphDBInterface): return await self.query(query, params) async def add_nodes(self, nodes: list[DataPoint]) -> None: + """ + Add multiple nodes to the database in a single operation. + + Parameters: + ----------- + + - nodes (list[DataPoint]): A list of DataPoint objects representing the nodes to + add. + + Returns: + -------- + + - None: None. + """ query = """ UNWIND $nodes AS node MERGE (n {id: node.node_id}) @@ -100,11 +196,37 @@ class MemgraphAdapter(GraphDBInterface): return results async def extract_node(self, node_id: str): + """ + Retrieve a single node based on its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node to retrieve. + + Returns: + -------- + + The node corresponding to the provided ID, or None if not found. + """ results = await self.extract_nodes([node_id]) return results[0] if len(results) > 0 else None async def extract_nodes(self, node_ids: List[str]): + """ + Retrieve multiple nodes based on their IDs. + + Parameters: + ----------- + + - node_ids (List[str]): A list of IDs for the nodes to retrieve. + + Returns: + -------- + + A list of nodes corresponding to the provided IDs. + """ query = """ UNWIND $node_ids AS id MATCH (node {id: id}) @@ -117,6 +239,19 @@ class MemgraphAdapter(GraphDBInterface): return [result["node"] for result in results] async def delete_node(self, node_id: str): + """ + Delete a node from the database based on its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node to delete. + + Returns: + -------- + + None. + """ sanitized_id = node_id.replace(":", "_") query = "MATCH (node: {{id: $node_id}}) DETACH DELETE node" @@ -125,6 +260,19 @@ class MemgraphAdapter(GraphDBInterface): return await self.query(query, params) async def delete_nodes(self, node_ids: list[str]) -> None: + """ + Delete multiple nodes from the database based on their IDs. + + Parameters: + ----------- + + - node_ids (list[str]): A list of IDs for the nodes to delete. + + Returns: + -------- + + - None: None. + """ query = """ UNWIND $node_ids AS id MATCH (node {id: id}) @@ -135,6 +283,21 @@ class MemgraphAdapter(GraphDBInterface): return await self.query(query, params) async def has_edge(self, from_node: UUID, to_node: UUID, edge_label: str) -> bool: + """ + Check if a directed edge exists between two nodes identified by their IDs. + + Parameters: + ----------- + + - from_node (UUID): The ID of the source node. + - to_node (UUID): The ID of the target node. + - edge_label (str): The label of the edge to check. + + Returns: + -------- + + - bool: True if the edge exists; otherwise, False. + """ query = """ MATCH (from_node)-[relationship]->(to_node) WHERE from_node.id = $from_node_id AND to_node.id = $to_node_id AND type(relationship) = $edge_label @@ -151,6 +314,19 @@ class MemgraphAdapter(GraphDBInterface): return records[0]["edge_exists"] if records else False async def has_edges(self, edges): + """ + Check for the existence of multiple edges based on provided criteria. + + Parameters: + ----------- + + - edges: A list of edges to verify existence for. + + Returns: + -------- + + A list of boolean values indicating the existence of each edge. + """ query = """ UNWIND $edges AS edge MATCH (a)-[r]->(b) @@ -183,6 +359,23 @@ class MemgraphAdapter(GraphDBInterface): relationship_name: str, edge_properties: Optional[Dict[str, Any]] = None, ): + """ + Add a directed edge between two nodes with optional properties. + + Parameters: + ----------- + + - from_node (UUID): The ID of the source node. + - to_node (UUID): The ID of the target node. + - relationship_name (str): The type/label of the relationship to create. + - edge_properties (Optional[Dict[str, Any]]): Optional properties associated with + the edge. (default None) + + Returns: + -------- + + The result of the edge addition operation, including relationship details. + """ serialized_properties = self.serialize_properties(edge_properties or {}) query = dedent( @@ -206,6 +399,20 @@ class MemgraphAdapter(GraphDBInterface): return await self.query(query, params) async def add_edges(self, edges: list[tuple[str, str, str, dict[str, Any]]]) -> None: + """ + Batch add multiple edges between nodes, enforcing specified relationships. + + Parameters: + ----------- + + - edges (list[tuple[str, str, str, dict[str, Any]]): A list of tuples containing + specifications for each edge to add. + + Returns: + -------- + + - None: None. + """ query = """ UNWIND $edges AS edge MATCH (from_node {id: edge.from_node}) @@ -245,6 +452,19 @@ class MemgraphAdapter(GraphDBInterface): raise error async def get_edges(self, node_id: str): + """ + Retrieve all edges connected to a specific node identified by its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node for which to retrieve connected edges. + + Returns: + -------- + + A list of tuples representing the edges connected to the node. + """ query = """ MATCH (n {id: $node_id})-[r]-(m) RETURN n, r, m @@ -258,6 +478,14 @@ class MemgraphAdapter(GraphDBInterface): ] async def get_disconnected_nodes(self) -> list[str]: + """ + Identify nodes in the graph that do not belong to the largest connected component. + + Returns: + -------- + + - list[str]: A list of IDs representing the disconnected nodes. + """ query = """ // Step 1: Collect all nodes MATCH (n) @@ -293,6 +521,20 @@ class MemgraphAdapter(GraphDBInterface): return results[0]["ids"] if len(results) > 0 else [] async def get_predecessors(self, node_id: str, edge_label: str = None) -> list[str]: + """ + Retrieve all predecessors of a node based on its ID and optional edge label. + + Parameters: + ----------- + + - node_id (str): The ID of the node to find predecessors for. + - edge_label (str): Optional edge label to filter predecessors. (default None) + + Returns: + -------- + + - list[str]: A list of predecessor node IDs. + """ if edge_label is not None: query = """ MATCH (node)<-[r]-(predecessor) @@ -326,6 +568,20 @@ class MemgraphAdapter(GraphDBInterface): return [result["predecessor"] for result in results] async def get_successors(self, node_id: str, edge_label: str = None) -> list[str]: + """ + Retrieve all successors of a node based on its ID and optional edge label. + + Parameters: + ----------- + + - node_id (str): The ID of the node to find successors for. + - edge_label (str): Optional edge label to filter successors. (default None) + + Returns: + -------- + + - list[str]: A list of successor node IDs. + """ if edge_label is not None: query = """ MATCH (node)-[r]->(successor) @@ -358,33 +614,40 @@ class MemgraphAdapter(GraphDBInterface): return [result["successor"] for result in results] - async def get_neighbors(self, node_id: str) -> List[Dict[str, Any]]: + async def get_neighbours(self, node_id: str) -> List[Dict[str, Any]]: + """ + Get both predecessors and successors of a node. + + Parameters: + ----------- + + - node_id (str): The ID of the node to find neighbors for. + + Returns: + -------- + + - List[Dict[str, Any]]: A combined list of neighbor node IDs. + """ predecessors, successors = await asyncio.gather( self.get_predecessors(node_id), self.get_successors(node_id) ) return predecessors + successors - async def get_node(self, node_id: str) -> Optional[Dict[str, Any]]: - """Get a single node by ID.""" - query = """ - MATCH (node {id: $node_id}) - RETURN node - """ - results = await self.query(query, {"node_id": node_id}) - return results[0]["node"] if results else None - - async def get_nodes(self, node_ids: List[str]) -> List[Dict[str, Any]]: - """Get multiple nodes by their IDs.""" - query = """ - UNWIND $node_ids AS id - MATCH (node {id: id}) - RETURN node - """ - results = await self.query(query, {"node_ids": node_ids}) - return [result["node"] for result in results] - async def get_connections(self, node_id: UUID) -> list: + """ + Retrieve connections for a given node, including both predecessors and successors. + + Parameters: + ----------- + + - node_id (UUID): The ID of the node for which to retrieve connections. + + Returns: + -------- + + - list: A list of connections associated with the node. + """ predecessors_query = """ MATCH (node)<-[relation]-(neighbour) WHERE node.id = $node_id @@ -416,6 +679,21 @@ class MemgraphAdapter(GraphDBInterface): async def remove_connection_to_predecessors_of( self, node_ids: list[str], edge_label: str ) -> None: + """ + Remove specified connections to the predecessors of the given node IDs. + + Parameters: + ----------- + + - node_ids (list[str]): A list of node IDs from which to remove predecessor + connections. + - edge_label (str): The label of the edges to remove. + + Returns: + -------- + + - None: None. + """ query = f""" UNWIND $node_ids AS nid MATCH (node {id: nid})-[r]->(predecessor) @@ -430,6 +708,21 @@ class MemgraphAdapter(GraphDBInterface): async def remove_connection_to_successors_of( self, node_ids: list[str], edge_label: str ) -> None: + """ + Remove specified connections to the successors of the given node IDs. + + Parameters: + ----------- + + - node_ids (list[str]): A list of node IDs from which to remove successor + connections. + - edge_label (str): The label of the edges to remove. + + Returns: + -------- + + - None: None. + """ query = f""" UNWIND $node_ids AS id MATCH (node:`{id}`)<-[r:{edge_label}]-(successor) @@ -441,12 +734,33 @@ class MemgraphAdapter(GraphDBInterface): return await self.query(query, params) async def delete_graph(self): + """ + Completely delete the graph from the database, removing all nodes and edges. + + Returns: + -------- + + None. + """ query = """MATCH (node) DETACH DELETE node;""" return await self.query(query) def serialize_properties(self, properties=dict()): + """ + Convert property values to a suitable representation for storage. + + Parameters: + ----------- + + - properties: A dictionary of properties to serialize. (default dict()) + + Returns: + -------- + + A dictionary of serialized properties. + """ serialized_properties = {} for property_key, property_value in properties.items(): @@ -463,6 +777,14 @@ class MemgraphAdapter(GraphDBInterface): return serialized_properties async def get_model_independent_graph_data(self): + """ + Fetch nodes and relationships without any specific model filtering. + + Returns: + -------- + + A tuple containing nodes and edges as collections. + """ query_nodes = "MATCH (n) RETURN collect(n) AS nodes" nodes = await self.query(query_nodes) @@ -472,6 +794,14 @@ class MemgraphAdapter(GraphDBInterface): return (nodes, edges) async def get_graph_data(self): + """ + Retrieve all nodes and edges from the graph, including their properties. + + Returns: + -------- + + A tuple containing lists of nodes and edges. + """ query = "MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties" result = await self.query(query) @@ -501,16 +831,33 @@ class MemgraphAdapter(GraphDBInterface): return (nodes, edges) + 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]]]: + """ + Throw an error indicating that node set filtering is not supported. + + Parameters: + ----------- + + - node_type (Type[Any]): The type of nodes to filter. + - node_name (List[str]): A list of node names to filter. + """ + raise NodesetFilterNotSupportedError + async def get_filtered_graph_data(self, attribute_filters): """ - Fetches nodes and relationships filtered by specified attribute values. + Fetch nodes and relationships based on specified attribute filters. - Args: - attribute_filters (list of dict): A list of dictionaries where keys are attributes and values are lists of values to filter on. - Example: [{"community": ["1", "2"]}] + Parameters: + ----------- + + - attribute_filters: A list of criteria to filter nodes and relationships. Returns: - tuple: A tuple containing two lists: nodes and edges. + -------- + + A tuple containing filtered nodes and edges. """ where_clauses = [] for attribute, values in attribute_filters[0].items(): @@ -556,6 +903,14 @@ class MemgraphAdapter(GraphDBInterface): return (nodes, edges) async def get_node_labels_string(self): + """ + Retrieve a string representation of all unique node labels in the graph. + + Returns: + -------- + + A string containing unique node labels. + """ node_labels_query = """ MATCH (n) WITH DISTINCT labels(n) AS labelList @@ -572,6 +927,14 @@ class MemgraphAdapter(GraphDBInterface): return node_labels_str async def get_relationship_labels_string(self): + """ + Retrieve a string representation of all unique relationship types in the graph. + + Returns: + -------- + + A string containing unique relationship types. + """ relationship_types_query = ( "MATCH ()-[r]->() RETURN collect(DISTINCT type(r)) AS relationships;" ) @@ -591,8 +954,21 @@ class MemgraphAdapter(GraphDBInterface): return relationship_types_undirected_str async def get_graph_metrics(self, include_optional=False): - """For the definition of these metrics, please refer to - https://docs.cognee.ai/core_concepts/graph_generation/descriptive_metrics""" + """ + Calculate and return various metrics of the graph, including mandatory and optional + metrics. + + Parameters: + ----------- + + - include_optional: Specify whether to include optional metrics in the results. + (default False) + + Returns: + -------- + + A dictionary containing calculated graph metrics. + """ try: # Basic metrics diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py index ccc76cbcf..4b54489a8 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/adapter.py @@ -6,7 +6,7 @@ import json from cognee.shared.logging_utils import get_logger, ERROR import asyncio from textwrap import dedent -from typing import Optional, Any, List, Dict +from typing import Optional, Any, List, Dict, Type, Tuple from contextlib import asynccontextmanager from uuid import UUID from neo4j import AsyncSession @@ -31,6 +31,12 @@ logger = get_logger("Neo4jAdapter", level=ERROR) class Neo4jAdapter(GraphDBInterface): + """ + Adapter for interacting with a Neo4j graph database, implementing the GraphDBInterface. + This class provides methods for querying, adding, deleting nodes and edges, as well as + managing sessions and projecting graphs. + """ + def __init__( self, graph_database_url: str, @@ -46,6 +52,9 @@ class Neo4jAdapter(GraphDBInterface): @asynccontextmanager async def get_session(self) -> AsyncSession: + """ + Get a session for database operations. + """ async with self.driver.session() as session: yield session @@ -54,6 +63,22 @@ class Neo4jAdapter(GraphDBInterface): query: str, params: Optional[Dict[str, Any]] = None, ) -> List[Dict[str, Any]]: + """ + Execute a Cypher query against the Neo4j database and return the result. + + Parameters: + ----------- + + - query (str): A string containing the Cypher query to execute. + - params (Optional[Dict[str, Any]]): A dictionary of parameters to be passed to the + query. (default None) + + Returns: + -------- + + - List[Dict[str, Any]]: A list of dictionaries representing the result of the query + execution. + """ try: async with self.get_session() as session: result = await session.run(query, parameters=params) @@ -64,6 +89,19 @@ class Neo4jAdapter(GraphDBInterface): raise error async def has_node(self, node_id: str) -> bool: + """ + Check if a node with the specified ID exists in the database. + + Parameters: + ----------- + + - node_id (str): The ID of the node to check for existence. + + Returns: + -------- + + - bool: True if the node exists, otherwise False. + """ results = self.query( """ MATCH (n) @@ -75,6 +113,19 @@ class Neo4jAdapter(GraphDBInterface): return results[0]["node_exists"] if len(results) > 0 else False async def add_node(self, node: DataPoint): + """ + Add a new node to the database based on the provided DataPoint object. + + Parameters: + ----------- + + - node (DataPoint): An instance of DataPoint representing the node to add. + + Returns: + -------- + + The result of the query execution, typically the ID of the added node. + """ serialized_properties = self.serialize_properties(node.model_dump()) query = dedent( @@ -96,6 +147,20 @@ class Neo4jAdapter(GraphDBInterface): @record_graph_changes async def add_nodes(self, nodes: list[DataPoint]) -> None: + """ + Add multiple nodes to the database in a single query. + + Parameters: + ----------- + + - nodes (list[DataPoint]): A list of DataPoint instances representing the nodes to + add. + + Returns: + -------- + + - None: None + """ query = """ UNWIND $nodes AS node MERGE (n {id: node.node_id}) @@ -119,11 +184,37 @@ class Neo4jAdapter(GraphDBInterface): return results async def extract_node(self, node_id: str): + """ + Retrieve a single node from the database by its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node to retrieve. + + Returns: + -------- + + The node represented as a dictionary, or None if it does not exist. + """ results = await self.extract_nodes([node_id]) return results[0] if len(results) > 0 else None async def extract_nodes(self, node_ids: List[str]): + """ + Retrieve multiple nodes from the database by their IDs. + + Parameters: + ----------- + + - node_ids (List[str]): A list of IDs for the nodes to retrieve. + + Returns: + -------- + + A list of nodes represented as dictionaries. + """ query = """ UNWIND $node_ids AS id MATCH (node {id: id}) @@ -136,12 +227,38 @@ class Neo4jAdapter(GraphDBInterface): return [result["node"] for result in results] async def delete_node(self, node_id: str): + """ + Remove a node from the database identified by its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node to delete. + + Returns: + -------- + + The result of the query execution, typically indicating success or failure. + """ query = "MATCH (node {id: $node_id}) DETACH DELETE node" params = {"node_id": node_id} return await self.query(query, params) async def delete_nodes(self, node_ids: list[str]) -> None: + """ + Delete multiple nodes from the database using their IDs. + + Parameters: + ----------- + + - node_ids (list[str]): A list of IDs of the nodes to delete. + + Returns: + -------- + + - None: None + """ query = """ UNWIND $node_ids AS id MATCH (node {id: id}) @@ -152,6 +269,21 @@ class Neo4jAdapter(GraphDBInterface): return await self.query(query, params) async def has_edge(self, from_node: UUID, to_node: UUID, edge_label: str) -> bool: + """ + Check if an edge exists between two nodes with the specified IDs and edge label. + + Parameters: + ----------- + + - from_node (UUID): The ID of the node from which the edge originates. + - to_node (UUID): The ID of the node to which the edge points. + - edge_label (str): The label of the edge to check for existence. + + Returns: + -------- + + - bool: True if the edge exists, otherwise False. + """ query = """ MATCH (from_node)-[relationship]->(to_node) WHERE from_node.id = $from_node_id AND to_node.id = $to_node_id AND type(relationship) = $edge_label @@ -168,6 +300,19 @@ class Neo4jAdapter(GraphDBInterface): return edge_exists async def has_edges(self, edges): + """ + Check if multiple edges exist based on provided edge criteria. + + Parameters: + ----------- + + - edges: A list of edge specifications to check for existence. + + Returns: + -------- + + A list of boolean values indicating the existence of each edge. + """ query = """ UNWIND $edges AS edge MATCH (a)-[r]->(b) @@ -200,6 +345,23 @@ class Neo4jAdapter(GraphDBInterface): relationship_name: str, edge_properties: Optional[Dict[str, Any]] = {}, ): + """ + Create a new edge between two nodes with specified properties. + + Parameters: + ----------- + + - from_node (UUID): The ID of the source node of the edge. + - to_node (UUID): The ID of the target node of the edge. + - relationship_name (str): The type/label of the edge to create. + - edge_properties (Optional[Dict[str, Any]]): A dictionary of properties to assign + to the edge. (default {}) + + Returns: + -------- + + The result of the query execution, typically indicating the created edge. + """ serialized_properties = self.serialize_properties(edge_properties) query = dedent( @@ -224,6 +386,20 @@ class Neo4jAdapter(GraphDBInterface): @record_graph_changes async def add_edges(self, edges: list[tuple[str, str, str, dict[str, Any]]]) -> None: + """ + Add multiple edges between nodes in a single query. + + Parameters: + ----------- + + - edges (list[tuple[str, str, str, dict[str, Any]]]): A list of tuples where each + tuple contains edge details to add. + + Returns: + -------- + + - None: None + """ query = """ UNWIND $edges AS edge MATCH (from_node {id: edge.from_node}) @@ -262,6 +438,19 @@ class Neo4jAdapter(GraphDBInterface): raise error async def get_edges(self, node_id: str): + """ + Retrieve all edges connected to a specified node. + + Parameters: + ----------- + + - node_id (str): The ID of the node for which edges are retrieved. + + Returns: + -------- + + A list of edges connecting to the specified node, represented as tuples of details. + """ query = """ MATCH (n {id: $node_id})-[r]-(m) RETURN n, r, m @@ -275,6 +464,14 @@ class Neo4jAdapter(GraphDBInterface): ] async def get_disconnected_nodes(self) -> list[str]: + """ + Find and return nodes that are not connected to any other nodes in the graph. + + Returns: + -------- + + - list[str]: A list of IDs of disconnected nodes. + """ # return await self.query( # "MATCH (node) WHERE NOT (node)<-[:*]-() RETURN node.id as id", # ) @@ -313,6 +510,20 @@ class Neo4jAdapter(GraphDBInterface): return results[0]["ids"] if len(results) > 0 else [] async def get_predecessors(self, node_id: str, edge_label: str = None) -> list[str]: + """ + Retrieve the predecessor nodes of a specified node based on an optional edge label. + + Parameters: + ----------- + + - node_id (str): The ID of the node whose predecessors are to be retrieved. + - edge_label (str): Optional edge label to filter predecessors. (default None) + + Returns: + -------- + + - list[str]: A list of predecessor node IDs. + """ if edge_label is not None: query = """ MATCH (node)<-[r]-(predecessor) @@ -346,6 +557,20 @@ class Neo4jAdapter(GraphDBInterface): return [result["predecessor"] for result in results] async def get_successors(self, node_id: str, edge_label: str = None) -> list[str]: + """ + Retrieve the successor nodes of a specified node based on an optional edge label. + + Parameters: + ----------- + + - node_id (str): The ID of the node whose successors are to be retrieved. + - edge_label (str): Optional edge label to filter successors. (default None) + + Returns: + -------- + + - list[str]: A list of successor node IDs. + """ if edge_label is not None: query = """ MATCH (node)-[r]->(successor) @@ -379,11 +604,36 @@ class Neo4jAdapter(GraphDBInterface): return [result["successor"] for result in results] async def get_neighbors(self, node_id: str) -> List[Dict[str, Any]]: - """Get all neighboring nodes.""" + """ + Get all neighbors of a specified node, including all directly connected nodes. + + Parameters: + ----------- + + - node_id (str): The ID of the node for which neighbors are retrieved. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of neighboring nodes represented as dictionaries. + """ return await self.get_neighbours(node_id) async def get_node(self, node_id: str) -> Optional[Dict[str, Any]]: - """Get a single node by ID.""" + """ + Retrieve a single node based on its ID. + + Parameters: + ----------- + + - node_id (str): The ID of the node to retrieve. + + Returns: + -------- + + - Optional[Dict[str, Any]]: The requested node as a dictionary, or None if it does + not exist. + """ query = """ MATCH (node {id: $node_id}) RETURN node @@ -392,7 +642,19 @@ class Neo4jAdapter(GraphDBInterface): return results[0]["node"] if results else None async def get_nodes(self, node_ids: List[str]) -> List[Dict[str, Any]]: - """Get multiple nodes by their IDs.""" + """ + Retrieve multiple nodes based on their IDs. + + Parameters: + ----------- + + - node_ids (List[str]): A list of node IDs to retrieve. + + Returns: + -------- + + - List[Dict[str, Any]]: A list of nodes represented as dictionaries. + """ query = """ UNWIND $node_ids AS id MATCH (node {id: id}) @@ -402,6 +664,19 @@ class Neo4jAdapter(GraphDBInterface): return [result["node"] for result in results] async def get_connections(self, node_id: UUID) -> list: + """ + Retrieve all connections (predecessors and successors) for a specified node. + + Parameters: + ----------- + + - node_id (UUID): The ID of the node for which connections are retrieved. + + Returns: + -------- + + - list: A list of connections represented as tuples of details. + """ predecessors_query = """ MATCH (node)<-[relation]-(neighbour) WHERE node.id = $node_id @@ -433,6 +708,21 @@ class Neo4jAdapter(GraphDBInterface): async def remove_connection_to_predecessors_of( self, node_ids: list[str], edge_label: str ) -> None: + """ + Remove connections (edges) to all predecessors of specified nodes based on edge label. + + Parameters: + ----------- + + - node_ids (list[str]): A list of IDs of nodes from which connections are to be + removed. + - edge_label (str): The label of the edges to remove. + + Returns: + -------- + + - None: None + """ query = f""" UNWIND $node_ids AS id MATCH (node:`{id}`)-[r:{edge_label}]->(predecessor) @@ -446,6 +736,21 @@ class Neo4jAdapter(GraphDBInterface): async def remove_connection_to_successors_of( self, node_ids: list[str], edge_label: str ) -> None: + """ + Remove connections (edges) to all successors of specified nodes based on edge label. + + Parameters: + ----------- + + - node_ids (list[str]): A list of IDs of nodes from which connections are to be + removed. + - edge_label (str): The label of the edges to remove. + + Returns: + -------- + + - None: None + """ query = f""" UNWIND $node_ids AS id MATCH (node:`{id}`)<-[r:{edge_label}]-(successor) @@ -457,12 +762,34 @@ class Neo4jAdapter(GraphDBInterface): return await self.query(query, params) async def delete_graph(self): + """ + Delete all nodes and edges from the graph database. + + Returns: + -------- + + The result of the query execution, typically indicating success or failure. + """ query = """MATCH (node) DETACH DELETE node;""" return await self.query(query) def serialize_properties(self, properties=dict()): + """ + Convert properties of a node or edge into a serializable format suitable for storage. + + Parameters: + ----------- + + - properties: A dictionary of properties to serialize, defaults to an empty + dictionary. (default dict()) + + Returns: + -------- + + A dictionary with serialized property values. + """ serialized_properties = {} for property_key, property_value in properties.items(): @@ -479,6 +806,15 @@ class Neo4jAdapter(GraphDBInterface): return serialized_properties async def get_model_independent_graph_data(self): + """ + Retrieve the basic graph data without considering the model specifics, returning nodes + and edges. + + Returns: + -------- + + A tuple of nodes and edges data. + """ query_nodes = "MATCH (n) RETURN collect(n) AS nodes" nodes = await self.query(query_nodes) @@ -488,6 +824,14 @@ class Neo4jAdapter(GraphDBInterface): return (nodes, edges) async def get_graph_data(self): + """ + Retrieve comprehensive data about nodes and relationships within the graph. + + Returns: + -------- + + A tuple containing two lists: nodes and edges with their properties. + """ query = "MATCH (n) RETURN ID(n) AS id, labels(n) AS labels, properties(n) AS properties" result = await self.query(query) @@ -517,16 +861,84 @@ class Neo4jAdapter(GraphDBInterface): return (nodes, edges) - async def get_filtered_graph_data(self, attribute_filters): + 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]]]: """ - Fetches nodes and relationships filtered by specified attribute values. + Retrieve a subgraph based on specified node names and type, including their + relationships. - Args: - attribute_filters (list of dict): A list of dictionaries where keys are attributes and values are lists of values to filter on. - Example: [{"community": ["1", "2"]}] + Parameters: + ----------- + + - node_type (Type[Any]): The type of nodes to include in the subgraph. + - node_name (List[str]): A list of names for nodes to filter the subgraph. Returns: - tuple: A tuple containing two lists: nodes and edges. + -------- + + - Tuple[List[Tuple[int, dict]], List[Tuple[int, int, str, dict]]}: A tuple + containing nodes and edges in the requested subgraph. + """ + label = node_type.__name__ + + query = f""" + UNWIND $names AS wantedName + MATCH (n:`{label}`) + WHERE n.name = wantedName + WITH collect(DISTINCT n) AS primary + UNWIND primary AS p + OPTIONAL MATCH (p)--(nbr) + WITH primary, collect(DISTINCT nbr) AS nbrs + WITH primary + nbrs AS nodelist + UNWIND nodelist AS node + WITH collect(DISTINCT node) AS nodes + MATCH (a)-[r]-(b) + WHERE a IN nodes AND b IN nodes + WITH nodes, collect(DISTINCT r) AS rels + RETURN + [n IN nodes | + {{ id: n.id, + properties: properties(n) }}] AS rawNodes, + [r IN rels | + {{ type: type(r), + properties: properties(r) }}] AS rawRels + """ + + result = await self.query(query, {"names": node_name}) + if not result: + return [], [] + + raw_nodes = result[0]["rawNodes"] + raw_rels = result[0]["rawRels"] + + nodes = [(n["properties"]["id"], n["properties"]) for n in raw_nodes] + edges = [ + ( + r["properties"]["source_node_id"], + r["properties"]["target_node_id"], + r["type"], + r["properties"], + ) + for r in raw_rels + ] + + return nodes, edges + + async def get_filtered_graph_data(self, attribute_filters): + """ + Fetch nodes and edges filtered by specific attribute criteria. + + Parameters: + ----------- + + - attribute_filters: A list of dictionaries representing attributes and associated + values for filtering. + + Returns: + -------- + + A tuple containing filtered nodes and edges based on the specified criteria. """ where_clauses = [] for attribute, values in attribute_filters[0].items(): @@ -572,12 +984,34 @@ class Neo4jAdapter(GraphDBInterface): return (nodes, edges) async def graph_exists(self, graph_name="myGraph"): + """ + Check if a graph with a given name exists in the database. + + Parameters: + ----------- + + - graph_name: The name of the graph to check for existence, defaults to 'myGraph'. + (default 'myGraph') + + Returns: + -------- + + True if the graph exists, otherwise False. + """ query = "CALL gds.graph.list() YIELD graphName RETURN collect(graphName) AS graphNames;" result = await self.query(query) graph_names = result[0]["graphNames"] if result else [] return graph_name in graph_names async def get_node_labels_string(self): + """ + Fetch all node labels from the database and return them as a formatted string. + + Returns: + -------- + + A formatted string of node labels. + """ node_labels_query = "CALL db.labels() YIELD label RETURN collect(label) AS labels;" node_labels_result = await self.query(node_labels_query) node_labels = node_labels_result[0]["labels"] if node_labels_result else [] @@ -589,6 +1023,14 @@ class Neo4jAdapter(GraphDBInterface): return node_labels_str async def get_relationship_labels_string(self): + """ + Fetch all relationship types from the database and return them as a formatted string. + + Returns: + -------- + + A formatted string of relationship types. + """ relationship_types_query = "CALL db.relationshipTypes() YIELD relationshipType RETURN collect(relationshipType) AS relationships;" relationship_types_result = await self.query(relationship_types_query) relationship_types = ( @@ -607,7 +1049,13 @@ class Neo4jAdapter(GraphDBInterface): async def project_entire_graph(self, graph_name="myGraph"): """ - Projects all node labels and all relationship types into an undirected in-memory GDS graph. + Project all node labels and relationship types into an in-memory graph using GDS. + + Parameters: + ----------- + + - graph_name: The name of the graph to project, defaults to 'myGraph'. (default + 'myGraph') """ if await self.graph_exists(graph_name): return @@ -626,13 +1074,36 @@ class Neo4jAdapter(GraphDBInterface): await self.query(query) async def drop_graph(self, graph_name="myGraph"): + """ + Drop an existing graph from the database based on its name. + + Parameters: + ----------- + + - graph_name: The name of the graph to drop, defaults to 'myGraph'. (default + 'myGraph') + """ if await self.graph_exists(graph_name): drop_query = f"CALL gds.graph.drop('{graph_name}');" await self.query(drop_query) async def get_graph_metrics(self, include_optional=False): - """For the definition of these metrics, please refer to - https://docs.cognee.ai/core_concepts/graph_generation/descriptive_metrics""" + """ + Retrieve metrics related to the graph such as number of nodes, edges, and connected + components. + + Parameters: + ----------- + + - include_optional: Specify whether to include optional metrics; defaults to False. + (default False) + + Returns: + -------- + + A dictionary containing graph metrics, both mandatory and optional based on the + input flag. + """ nodes, edges = await self.get_model_independent_graph_data() graph_name = "myGraph" @@ -674,6 +1145,21 @@ class Neo4jAdapter(GraphDBInterface): return mandatory_metrics | optional_metrics async def get_document_subgraph(self, content_hash: str): + """ + Retrieve a subgraph related to a document identified by its content hash, including + related entities and chunks. + + Parameters: + ----------- + + - content_hash (str): The hash identifying the document whose subgraph should be + retrieved. + + Returns: + -------- + + The subgraph data as a dictionary, or None if not found. + """ query = """ MATCH (doc) WHERE (doc:TextDocument OR doc:PdfDocument) @@ -705,6 +1191,19 @@ class Neo4jAdapter(GraphDBInterface): return result[0] if result else None async def get_degree_one_nodes(self, node_type: str): + """ + Fetch nodes of a specified type that have exactly one connection. + + Parameters: + ----------- + + - node_type (str): The type of nodes to retrieve, must be 'Entity' or 'EntityType'. + + Returns: + -------- + + A list of nodes with exactly one connection of the specified type. + """ if not node_type or node_type not in ["Entity", "EntityType"]: raise ValueError("node_type must be either 'Entity' or 'EntityType'") diff --git a/cognee/infrastructure/databases/graph/neo4j_driver/neo4j_metrics_utils.py b/cognee/infrastructure/databases/graph/neo4j_driver/neo4j_metrics_utils.py index bebb0f547..eeb0f26e8 100644 --- a/cognee/infrastructure/databases/graph/neo4j_driver/neo4j_metrics_utils.py +++ b/cognee/infrastructure/databases/graph/neo4j_driver/neo4j_metrics_utils.py @@ -6,6 +6,24 @@ if TYPE_CHECKING: async def get_edge_density(adapter: Neo4jAdapter): + """ + Calculate the edge density of a graph in a Neo4j database. + + This function executes a Cypher query to determine the ratio of edges to the maximum + possible edges in a graph, based on the number of nodes. If there are fewer than two + nodes, it returns an edge density of zero. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): An instance of Neo4jAdapter used to interface with the + Neo4j database. + + Returns: + -------- + + Returns the calculated edge density as a float, or 0 if no results are found. + """ query = """ MATCH (n) WITH count(n) AS num_nodes @@ -21,6 +39,23 @@ async def get_edge_density(adapter: Neo4jAdapter): async def get_num_connected_components(adapter: Neo4jAdapter, graph_name: str): + """ + Retrieve the number of connected components in a specified graph using the Neo4j + adapter. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): An instance of Neo4jAdapter for executing database + queries. + - graph_name (str): The name of the graph to analyze for connected components. + + Returns: + -------- + + Returns the number of connected components in the graph. Returns 0 if no results are + found. + """ query = f""" CALL gds.wcc.stream('{graph_name}') YIELD componentId @@ -32,6 +67,27 @@ async def get_num_connected_components(adapter: Neo4jAdapter, graph_name: str): async def get_size_of_connected_components(adapter: Neo4jAdapter, graph_name: str): + """ + Retrieve sizes of connected components in a graph. + + This function executes a query to calculate the sizes of connected components in the + specified graph using the Graph Data Science library, and returns a list of these sizes + in descending order. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): An instance of Neo4jAdapter used to execute the database + query. + - graph_name (str): The name of the graph for which to retrieve connected component + sizes. + + Returns: + -------- + + - list: A list of sizes of the connected components, ordered from largest to + smallest. Returns an empty list if no results are found. + """ query = f""" CALL gds.wcc.stream('{graph_name}') YIELD componentId @@ -44,6 +100,24 @@ async def get_size_of_connected_components(adapter: Neo4jAdapter, graph_name: st async def count_self_loops(adapter: Neo4jAdapter): + """ + Count the number of self-loop relationships in the Neo4j database. + + This function executes a Cypher query to find and count all edge relationships that + begin and end at the same node (self-loops). It returns the count of such relationships + or 0 if no results are found. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): An instance of Neo4jAdapter used to interact with the + Neo4j database. + + Returns: + -------- + + The count of self-loop relationships found in the database, or 0 if none were found. + """ query = """ MATCH (n)-[r]->(n) RETURN count(r) AS adapter_loop_count; @@ -53,6 +127,27 @@ async def count_self_loops(adapter: Neo4jAdapter): async def get_shortest_path_lengths(adapter: Neo4jAdapter, graph_name: str): + """ + Fetches the shortest path lengths for a specified graph. + + Executes a Cypher query to retrieve the shortest path distances from a Neo4j graph + represented by the given graph name. If no results are returned, an empty list is + provided as output. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): The Neo4jAdapter instance used to communicate with the + Neo4j database. + - graph_name (str): The name of the graph for which the shortest path lengths are to + be retrieved. + + Returns: + -------- + + A list containing the shortest path distances or an empty list if no results are + found. + """ query = f""" CALL gds.allShortestPaths.stream('{graph_name}') YIELD distance @@ -64,6 +159,27 @@ async def get_shortest_path_lengths(adapter: Neo4jAdapter, graph_name: str): async def get_avg_clustering(adapter: Neo4jAdapter, graph_name: str): + """ + Calculate the average clustering coefficient for the specified graph. + + This function constructs a Cypher query to calculate the average of local clustering + coefficients for all nodes in the provided graph. It utilizes the Neo4j Graph Data + Science (GDS) library to execute the query asynchronously and return the computed + average value. + + Parameters: + ----------- + + - adapter (Neo4jAdapter): An instance of Neo4jAdapter used to execute the query + against the Neo4j database. + - graph_name (str): The name of the graph for which the average clustering + coefficient is to be calculated. + + Returns: + -------- + + The average clustering coefficient as a float, or 0 if no results are available. + """ query = f""" CALL gds.localClusteringCoefficient.stream('{graph_name}') YIELD localClusteringCoefficient diff --git a/cognee/infrastructure/databases/graph/networkx/adapter.py b/cognee/infrastructure/databases/graph/networkx/adapter.py index 2c86dba0a..c83d82d8d 100644 --- a/cognee/infrastructure/databases/graph/networkx/adapter.py +++ b/cognee/infrastructure/databases/graph/networkx/adapter.py @@ -4,8 +4,10 @@ from datetime import datetime, timezone import os import json import asyncio + +from cognee.infrastructure.databases.exceptions.exceptions import NodesetFilterNotSupportedError from cognee.shared.logging_utils import get_logger -from typing import Dict, Any, List, Union +from typing import Dict, Any, List, Union, Type, Tuple from uuid import UUID import aiofiles import aiofiles.os as aiofiles_os @@ -23,6 +25,12 @@ logger = get_logger() class NetworkXAdapter(GraphDBInterface): + """ + Manage a singleton instance of a graph database interface, utilizing the NetworkX + library. Handles graph data access and manipulation, including nodes and edges + management, persistence, and auxiliary functionalities. + """ + _instance = None graph = None # Class variable to store the singleton instance @@ -36,33 +44,118 @@ class NetworkXAdapter(GraphDBInterface): self.filename = filename async def get_graph_data(self): + """ + Retrieve graph data including nodes and edges. + + Returns: + -------- + + A tuple containing a list of node data and a list of edge data. + """ await self.load_graph_from_file() return (list(self.graph.nodes(data=True)), list(self.graph.edges(data=True, keys=True))) async def query(self, query: str, params: dict): + """ + Execute a query against the graph data. The specifics of the query execution need to be + implemented. + + Parameters: + ----------- + + - query (str): The query string to run against the graph. + - params (dict): Parameters for the query, if necessary. + """ pass async def has_node(self, node_id: UUID) -> bool: + """ + Determine if a specific node exists in the graph. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node to check. + + Returns: + -------- + + - bool: True if the node exists, otherwise False. + """ return self.graph.has_node(node_id) async def add_node(self, node: DataPoint) -> None: + """ + Add a node to the graph and persist the graph state to the file. + + Parameters: + ----------- + + - node (DataPoint): The node to be added, represented as a DataPoint object. + """ self.graph.add_node(node.id, **node.model_dump()) await self.save_graph_to_file(self.filename) @record_graph_changes async def add_nodes(self, nodes: list[DataPoint]) -> None: + """ + Bulk add multiple nodes to the graph and persist the graph state to the file. + + Parameters: + ----------- + + - nodes (list[DataPoint]): A list of DataPoint objects defining the nodes to be + added. + """ nodes = [(node.id, node.model_dump()) for node in nodes] self.graph.add_nodes_from(nodes) await self.save_graph_to_file(self.filename) async def get_graph(self): + """ + Retrieve the current state of the graph. + + Returns: + -------- + + The current graph instance. + """ return self.graph async def has_edge(self, from_node: str, to_node: str, edge_label: str) -> bool: + """ + Check for the existence of a specific edge in the graph. + + Parameters: + ----------- + + - from_node (str): The identifier of the source node. + - to_node (str): The identifier of the target node. + - edge_label (str): The label of the edge to check. + + Returns: + -------- + + - bool: True if the edge exists, otherwise False. + """ return self.graph.has_edge(from_node, to_node, key=edge_label) async def has_edges(self, edges): + """ + Check for the existence of multiple edges in the graph. + + Parameters: + ----------- + + - edges: A list of edges to check, defined as tuples of (from_node, to_node, + edge_label). + + Returns: + -------- + + A list of edges that exist in the graph. + """ result = [] for from_node, to_node, edge_label in edges: @@ -79,6 +172,18 @@ class NetworkXAdapter(GraphDBInterface): relationship_name: str, edge_properties: Dict[str, Any] = {}, ) -> None: + """ + Add a single edge to the graph and persist the graph state to the file. + + Parameters: + ----------- + + - from_node (str): The identifier of the source node for the edge. + - to_node (str): The identifier of the target node for the edge. + - relationship_name (str): The label for the relationship as the edge is created. + - edge_properties (Dict[str, Any]): Additional properties for the edge, if any. + (default {}) + """ edge_properties["updated_at"] = datetime.now(timezone.utc) self.graph.add_edge( from_node, @@ -91,6 +196,15 @@ class NetworkXAdapter(GraphDBInterface): @record_graph_changes async def add_edges(self, edges: list[tuple[str, str, str, dict]]) -> None: + """ + Bulk add multiple edges to the graph and persist the graph state to the file. + + Parameters: + ----------- + + - edges (list[tuple[str, str, str, dict]]): A list of edges defined as tuples + containing (from_node, to_node, relationship_name, edge_properties). + """ if not edges: logger.debug("No edges to add") return @@ -137,12 +251,32 @@ class NetworkXAdapter(GraphDBInterface): raise async def get_edges(self, node_id: UUID): + """ + Retrieve edges connected to a specific node. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node whose edges are to be retrieved. + + Returns: + -------- + + A list of edges connected to the specified node. + """ return list(self.graph.in_edges(node_id, data=True)) + list( self.graph.out_edges(node_id, data=True) ) async def delete_node(self, node_id: UUID) -> None: - """Asynchronously delete a node and all its relationships from the graph if it exists.""" + """ + Remove a node and its associated edges from the graph, then persist the changes. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node to delete. + """ if self.graph.has_node(node_id): # First remove all edges connected to the node @@ -159,10 +293,26 @@ class NetworkXAdapter(GraphDBInterface): logger.error(f"Node {node_id} not found in graph") async def delete_nodes(self, node_ids: List[UUID]) -> None: + """ + Bulk delete nodes from the graph and persist the changes. + + Parameters: + ----------- + + - node_ids (List[UUID]): A list of node identifiers to delete. + """ self.graph.remove_nodes_from(node_ids) await self.save_graph_to_file(self.filename) async def get_disconnected_nodes(self) -> List[str]: + """ + Identify nodes that are not connected to any other nodes in the graph. + + Returns: + -------- + + - List[str]: A list of identifiers for disconnected nodes. + """ connected_components = list(nx.weakly_connected_components(self.graph)) disconnected_nodes = [] @@ -175,15 +325,56 @@ class NetworkXAdapter(GraphDBInterface): return disconnected_nodes async def extract_node(self, node_id: UUID) -> dict: + """ + Retrieve data for a specific node based on its identifier. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node to retrieve. + + Returns: + -------- + + - dict: The data of the specified node, or None if not found. + """ if self.graph.has_node(node_id): return self.graph.nodes[node_id] return None async def extract_nodes(self, node_ids: List[UUID]) -> List[dict]: + """ + Retrieve data for multiple nodes based on their identifiers. + + Parameters: + ----------- + + - node_ids (List[UUID]): A list of node identifiers to retrieve data. + + Returns: + -------- + + - List[dict]: A list of data for each node identified that exists in the graph. + """ return [self.graph.nodes[node_id] for node_id in node_ids if self.graph.has_node(node_id)] async def get_predecessors(self, node_id: UUID, edge_label: str = None) -> list: + """ + Retrieve the predecessor nodes of a specified node according to a specific edge label. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node for which to find predecessors. + - edge_label (str): The label for the edges connecting to predecessors; if None, all + predecessors are retrieved. (default None) + + Returns: + -------- + + - list: A list of predecessor nodes. + """ if self.graph.has_node(node_id): if edge_label is None: return [ @@ -200,6 +391,21 @@ class NetworkXAdapter(GraphDBInterface): return nodes async def get_successors(self, node_id: UUID, edge_label: str = None) -> list: + """ + Retrieve the successor nodes of a specified node according to a specific edge label. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node for which to find successors. + - edge_label (str): The label for the edges connecting to successors; if None, all + successors are retrieved. (default None) + + Returns: + -------- + + - list: A list of successor nodes. + """ if self.graph.has_node(node_id): if edge_label is None: return [ @@ -216,6 +422,20 @@ class NetworkXAdapter(GraphDBInterface): return nodes async def get_neighbors(self, node_id: UUID) -> list: + """ + Get the neighboring nodes of a specified node, including both predecessors and + successors. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node whose neighbors are to be retrieved. + + Returns: + -------- + + - list: A list of neighboring nodes. + """ if not self.graph.has_node(node_id): return [] @@ -229,6 +449,19 @@ class NetworkXAdapter(GraphDBInterface): return neighbors async def get_connections(self, node_id: UUID) -> list: + """ + Get the connections of a specified node to its neighbors. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node for which to get connections. + + Returns: + -------- + + - list: A list of connections involving the specified node and its neighbors. + """ if not self.graph.has_node(node_id): return [] @@ -266,6 +499,17 @@ class NetworkXAdapter(GraphDBInterface): async def remove_connection_to_predecessors_of( self, node_ids: list[UUID], edge_label: str ) -> None: + """ + Remove connections to predecessors of specified nodes based on an edge label and persist + changes. + + Parameters: + ----------- + + - node_ids (list[UUID]): A list of node identifiers whose predecessor connections + need to be removed. + - edge_label (str): The label of the edges to remove. + """ for node_id in node_ids: if self.graph.has_node(node_id): for predecessor_id in list(self.graph.predecessors(node_id)): @@ -277,6 +521,17 @@ class NetworkXAdapter(GraphDBInterface): async def remove_connection_to_successors_of( self, node_ids: list[UUID], edge_label: str ) -> None: + """ + Remove connections to successors of specified nodes based on an edge label and persist + changes. + + Parameters: + ----------- + + - node_ids (list[UUID]): A list of node identifiers whose successor connections need + to be removed. + - edge_label (str): The label of the edges to remove. + """ for node_id in node_ids: if self.graph.has_node(node_id): for successor_id in list(self.graph.successors(node_id)): @@ -286,6 +541,14 @@ class NetworkXAdapter(GraphDBInterface): await self.save_graph_to_file(self.filename) async def create_empty_graph(self, file_path: str) -> None: + """ + Initialize an empty graph and save it to a specified file path. + + Parameters: + ----------- + + - file_path (str): The file path where the empty graph should be saved. + """ self.graph = nx.MultiDiGraph() # Only create directory if file_path contains a directory @@ -296,7 +559,15 @@ class NetworkXAdapter(GraphDBInterface): await self.save_graph_to_file(file_path) async def save_graph_to_file(self, file_path: str = None) -> None: - """Asynchronously save the graph to a file in JSON format.""" + """ + Save the graph data asynchronously to a specified file in JSON format. + + Parameters: + ----------- + + - file_path (str): The file path to save the graph data; if None, saves to the + default filename. (default None) + """ if not file_path: file_path = self.filename @@ -307,7 +578,15 @@ class NetworkXAdapter(GraphDBInterface): await file.write(json_data) async def load_graph_from_file(self, file_path: str = None): - """Asynchronously load the graph from a file in JSON format.""" + """ + Load graph data asynchronously from a specified file in JSON format. + + Parameters: + ----------- + + - file_path (str): The file path from which to load the graph data; if None, loads + from the default filename. (default None) + """ if not file_path: file_path = self.filename try: @@ -381,7 +660,15 @@ class NetworkXAdapter(GraphDBInterface): await self.create_empty_graph(file_path) async def delete_graph(self, file_path: str = None): - """Asynchronously delete the graph file from the filesystem.""" + """ + Delete the graph file from the filesystem asynchronously. + + Parameters: + ----------- + + - file_path (str): The file path of the graph to delete; if None, deletes the + default graph file. (default None) + """ if file_path is None: file_path = ( self.filename @@ -396,20 +683,37 @@ class NetworkXAdapter(GraphDBInterface): logger.error("Failed to delete graph: %s", error) raise error + 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]]]: + """ + Obtain a subgraph based on specific node types and names. Not supported in this + implementation. + + Parameters: + ----------- + + - node_type (Type[Any]): The type of nodes to include in the subgraph. + - node_name (List[str]): A list of node names to filter by. + """ + raise NodesetFilterNotSupportedError + async def get_filtered_graph_data( self, attribute_filters: List[Dict[str, List[Union[str, int]]]] ): """ - Fetches nodes and relationships filtered by specified attribute values. + Fetch nodes and relationships filtered by specified attributes. - Args: - attribute_filters (list of dict): A list of dictionaries where keys are attributes and values are lists of values to filter on. - Example: [{"community": ["1", "2"]}] + Parameters: + ----------- + + - attribute_filters (List[Dict[str, List[Union[str, int]]]]): A list of dictionaries + defining attributes to filter on. Returns: - tuple: A tuple containing two lists: - - Nodes: List of tuples (node_id, node_properties). - - Edges: List of tuples (source_id, target_id, relationship_type, edge_properties). + -------- + + A tuple containing filtered nodes and edges based on the specified attributes. """ # Create filters for nodes based on the attribute filters where_clauses = [] @@ -438,6 +742,20 @@ class NetworkXAdapter(GraphDBInterface): return filtered_nodes, filtered_edges async def get_graph_metrics(self, include_optional=False): + """ + Calculate various metrics related to the graph, optionally including optional metrics. + + Parameters: + ----------- + + - include_optional: Indicates whether optional metrics should be included in the + calculation. (default False) + + Returns: + -------- + + A dictionary containing the calculated graph metrics. + """ graph = self.graph def _get_mean_degree(graph): @@ -501,7 +819,22 @@ class NetworkXAdapter(GraphDBInterface): return mandatory_metrics | optional_metrics async def get_document_subgraph(self, content_hash: str): - """Get all nodes that should be deleted when removing a document.""" + """ + Retrieve all relevant nodes when a document is being deleted, including chunks and + orphaned entities. + + Parameters: + ----------- + + - content_hash (str): The hash identifying the content of the document to fetch + related nodes for. + + Returns: + -------- + + A dictionary containing the document, its chunks, orphan entities, made from nodes, + and orphan types. + """ # Ensure graph is loaded if self.graph is None: await self.load_graph_from_file() @@ -608,7 +941,19 @@ class NetworkXAdapter(GraphDBInterface): } async def get_degree_one_nodes(self, node_type: str): - """Get all nodes that have only one connection.""" + """ + Retrieve nodes that have only a single connection, filtered by node type. + + Parameters: + ----------- + + - node_type (str): Type of nodes to filter by ('Entity' or 'EntityType'). + + Returns: + -------- + + A list of nodes that have a single connection of the specified type. + """ if not node_type or node_type not in ["Entity", "EntityType"]: raise ValueError("node_type must be either 'Entity' or 'EntityType'") @@ -622,11 +967,39 @@ class NetworkXAdapter(GraphDBInterface): return nodes async def get_node(self, node_id: UUID) -> dict: + """ + Retrieve the details of a specific node identified by its identifier. + + Parameters: + ----------- + + - node_id (UUID): The identifier of the node to retrieval. + + Returns: + -------- + + - dict: The data of the specified node if found, otherwise None. + """ if self.graph.has_node(node_id): return self.graph.nodes[node_id] return None async def get_nodes(self, node_ids: List[UUID] = None) -> List[dict]: + """ + Retrieve data for multiple nodes by their identifiers, or all nodes if no identifiers + are provided. + + Parameters: + ----------- + + - node_ids (List[UUID]): List of node identifiers to fetch data for; if None, + retrieves all nodes in the graph. (default None) + + Returns: + -------- + + - List[dict]: A list of node data for each found node. + """ if node_ids is None: return [{"id": node_id, **data} for node_id, data in self.graph.nodes(data=True)] return [ diff --git a/cognee/infrastructure/databases/graph/supported_databases.py b/cognee/infrastructure/databases/graph/supported_databases.py new file mode 100644 index 000000000..c067e43f1 --- /dev/null +++ b/cognee/infrastructure/databases/graph/supported_databases.py @@ -0,0 +1 @@ +supported_databases = {} diff --git a/cognee/infrastructure/databases/graph/use_graph_adapter.py b/cognee/infrastructure/databases/graph/use_graph_adapter.py new file mode 100644 index 000000000..17d7a8e97 --- /dev/null +++ b/cognee/infrastructure/databases/graph/use_graph_adapter.py @@ -0,0 +1,5 @@ +from .supported_databases import supported_databases + + +def use_graph_adapter(vector_db_name, vector_db_adapter): + supported_databases[vector_db_name] = vector_db_adapter diff --git a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py index 3d9a53548..671b7cd99 100644 --- a/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py +++ b/cognee/infrastructure/databases/hybrid/falkordb/FalkorDBAdapter.py @@ -16,12 +16,53 @@ 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 + """ + def __init__( self, database_url: str, @@ -36,6 +77,25 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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: @@ -46,10 +106,52 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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: @@ -69,6 +171,22 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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 string containing the query to be executed for the data point. + """ node_label = type(data_point).__name__ property_names = DataPoint.get_embeddable_property_names(data_point) @@ -97,6 +215,20 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): ).strip() 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. + """ properties = await self.stringify_properties(edge[3]) properties = f"{{{properties}}}" @@ -111,14 +243,46 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): ).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 = {} @@ -155,6 +319,17 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): self.query(query) 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): @@ -163,6 +338,21 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): ) 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() @@ -179,26 +369,85 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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: 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_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) async def add_edge(self, edge: tuple[str, str, str, dict]): + """ + Add an edge between two existing nodes in the graph based on the provided details. + + Parameters: + ----------- + + - edge (tuple[str, str, str, dict]): A tuple containing details of the edge to be + added. + """ query = await self.create_edge_query(edge) self.query(query) async def add_edges(self, edges: list[tuple[str, str, str, dict]]): + """ + Add multiple edges to the graph in a batch operation. + + Parameters: + ----------- + + - edges (list[tuple[str, str, str, dict]]): A list of tuples, each containing + details of the edges to be added. + """ queries = [await self.create_edge_query(edge) for edge in edges] for query in queries: self.query(query) 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 boolean values indicating the existence of each edge. + """ query = dedent( """ UNWIND $edges AS edge @@ -224,6 +473,20 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): return [result["edge_exists"] for result in results] 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", { @@ -233,14 +496,54 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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 @@ -277,6 +580,27 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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 InvalidValueError(message="One of query_text or query_vector must be provided!") @@ -307,6 +631,25 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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( @@ -322,6 +665,14 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): ) 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) @@ -352,6 +703,22 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): 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", { @@ -360,12 +727,41 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): ) async def delete_node(self, collection_name: str, data_point_id: str): + """ + Delete a single node specified by its data point ID from the database. + + Parameters: + ----------- + + - collection_name (str): The name of the collection containing the node to be + deleted. + - data_point_id (str): The ID of the data point to delete. + + Returns: + -------- + + Returns the result of the deletion operation from the database. + """ return await self.delete_data_points([data_point_id]) async def delete_nodes(self, collection_name: str, data_point_ids: list[str]): + """ + Delete multiple nodes specified by their IDs from the database. + + Parameters: + ----------- + + - collection_name (str): The name of the collection containing the nodes to be + deleted. + - data_point_ids (list[str]): A list of IDs of the data points to delete from the + collection. + """ self.delete_data_points(data_point_ids) 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) @@ -379,4 +775,7 @@ class FalkorDBAdapter(VectorDBInterface, GraphDBInterface): print(f"Error deleting graph: {e}") async def prune(self): + """ + Prune the graph by deleting the entire graph structure. + """ await self.delete_graph() diff --git a/cognee/infrastructure/databases/relational/ModelBase.py b/cognee/infrastructure/databases/relational/ModelBase.py index fa2b68a5d..a4d3a1a19 100644 --- a/cognee/infrastructure/databases/relational/ModelBase.py +++ b/cognee/infrastructure/databases/relational/ModelBase.py @@ -2,4 +2,11 @@ from sqlalchemy.orm import DeclarativeBase class Base(DeclarativeBase): + """ + Represents a base class for declarative models using SQLAlchemy. + + The Base class provides the foundation for creating ORM-mapped classes and manages the + mapping of classes to database tables. + """ + pass diff --git a/cognee/infrastructure/databases/relational/config.py b/cognee/infrastructure/databases/relational/config.py index bfa07b174..acf1e7836 100644 --- a/cognee/infrastructure/databases/relational/config.py +++ b/cognee/infrastructure/databases/relational/config.py @@ -6,6 +6,10 @@ from cognee.root_dir import get_absolute_path class RelationalConfig(BaseSettings): + """ + Configure database connection settings. + """ + db_path: str = os.path.join(get_absolute_path(".cognee_system"), "databases") db_name: str = "cognee_db" db_host: Union[str, None] = None # "localhost" @@ -17,6 +21,15 @@ class RelationalConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def to_dict(self) -> dict: + """ + Return the database configuration as a dictionary. + + Returns: + -------- + + - dict: A dictionary containing database configuration settings including db_path, + db_name, db_host, db_port, db_username, db_password, and db_provider. + """ return { "db_path": self.db_path, "db_name": self.db_name, @@ -30,10 +43,39 @@ class RelationalConfig(BaseSettings): @lru_cache def get_relational_config(): + """ + Cache and return the relational database configuration. + + This function retrieves an instance of the RelationalConfig class, caching it to avoid + recreation on subsequent calls. It is designed to provide a consistent configuration + globally for relational database connections. + + Returns: + -------- + + - RelationalConfig: An instance of the RelationalConfig containing the database + configuration settings. + """ return RelationalConfig() class MigrationConfig(BaseSettings): + """ + Manage and configure migration settings for a database, inheriting from BaseSettings. + + Public methods: + - to_dict: Convert the migration configuration to a dictionary format. + + Instance variables: + - migration_db_path: Path to the migration database. + - migration_db_name: Name of the migration database. + - migration_db_host: Host of the migration database. + - migration_db_port: Port of the migration database. + - migration_db_username: Username for connecting to the migration database. + - migration_db_password: Password for connecting to the migration database. + - migration_db_provider: Provider type for the migration database. + """ + migration_db_path: Union[str, None] = None migration_db_name: Union[str, None] = None migration_db_host: Union[str, None] = None @@ -45,6 +87,14 @@ class MigrationConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def to_dict(self) -> dict: + """ + Convert migration configuration to dictionary format. + + Returns: + -------- + + - dict: A dictionary containing the migration configuration details. + """ return { "migration_db_path": self.migration_db_path, "migration_db_name": self.migration_db_name, @@ -58,4 +108,19 @@ class MigrationConfig(BaseSettings): @lru_cache def get_migration_config(): + """ + Retrieve the migration configuration instance. + + This function uses memoization to cache the MigrationConfig instance, ensuring that + subsequent calls return the same instance. It is crucial to call this function in a + context where the environment variables are properly set, as they will configure the + MigrationConfig instance accordingly, potentially affecting the application's database + connectivity. + + Returns: + -------- + + - MigrationConfig: An instance of MigrationConfig containing the migration database + configuration. + """ return MigrationConfig() diff --git a/cognee/infrastructure/databases/relational/create_db_and_tables.py b/cognee/infrastructure/databases/relational/create_db_and_tables.py index 5b017538e..3f1704ff0 100644 --- a/cognee/infrastructure/databases/relational/create_db_and_tables.py +++ b/cognee/infrastructure/databases/relational/create_db_and_tables.py @@ -2,6 +2,12 @@ from .get_relational_engine import get_relational_engine async def create_db_and_tables(): + """ + Create a database and its tables. + + This asynchronous function retrieves the relational engine and calls its method to + create a database. + """ relational_engine = get_relational_engine() await relational_engine.create_database() diff --git a/cognee/infrastructure/databases/relational/create_relational_engine.py b/cognee/infrastructure/databases/relational/create_relational_engine.py index 054428896..a889e1758 100644 --- a/cognee/infrastructure/databases/relational/create_relational_engine.py +++ b/cognee/infrastructure/databases/relational/create_relational_engine.py @@ -12,6 +12,29 @@ def create_relational_engine( db_password: str, db_provider: str, ): + """ + Create a relational database engine based on the specified parameters. + + Parameters: + ----------- + + - db_path (str): The file path to the database directory, applicable for SQLite. + - db_name (str): The name of the database to be accessed or created. + - db_host (str): The hostname or IP address of the database server, required for + PostgreSQL. + - db_port (str): The port number on which the database server is listening, required + for PostgreSQL. + - db_username (str): The username for database authentication, required for + PostgreSQL. + - db_password (str): The password for database authentication, required for + PostgreSQL. + - db_provider (str): The type of database provider (e.g., 'sqlite' or 'postgres'). + + Returns: + -------- + + Returns a SQLAlchemyAdapter instance for the specified database connection. + """ if db_provider == "sqlite": connection_string = f"sqlite+aiosqlite:///{db_path}/{db_name}" diff --git a/cognee/infrastructure/databases/relational/get_migration_relational_engine.py b/cognee/infrastructure/databases/relational/get_migration_relational_engine.py index 5b9f88626..993240226 100644 --- a/cognee/infrastructure/databases/relational/get_migration_relational_engine.py +++ b/cognee/infrastructure/databases/relational/get_migration_relational_engine.py @@ -3,6 +3,18 @@ from .create_relational_engine import create_relational_engine def get_migration_relational_engine(): + """ + Create and return a relational database engine using the migration configuration. + + This function fetches the migration configuration details and uses them to create a + relational database engine. It does not explicitly handle exceptions but may propagate + any errors raised in the underlying functions. + + Returns: + -------- + + A relational database engine instance configured with the migration settings. + """ migration_config = get_migration_config() return create_relational_engine( diff --git a/cognee/infrastructure/databases/relational/get_relational_engine.py b/cognee/infrastructure/databases/relational/get_relational_engine.py index 6024c7bd0..a346f4954 100644 --- a/cognee/infrastructure/databases/relational/get_relational_engine.py +++ b/cognee/infrastructure/databases/relational/get_relational_engine.py @@ -3,6 +3,19 @@ from .create_relational_engine import create_relational_engine def get_relational_engine(): + """ + Create and return a relational engine instance. + + This function retrieves the relational configuration and then uses it to create a + relational engine using provided settings. The behavior of this function depends on the + successful retrieval and conversion of the relational configuration into the appropriate + format for the engine creation. + + Returns: + -------- + + A relational engine instance configured according to the retrieved settings. + """ relational_config = get_relational_config() return create_relational_engine(**relational_config.to_dict()) diff --git a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py index ade2a0821..0419eea72 100644 --- a/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py +++ b/cognee/infrastructure/databases/relational/sqlalchemy/SqlAlchemyAdapter.py @@ -20,6 +20,11 @@ logger = get_logger() class SQLAlchemyAdapter: + """ + Adapt a SQLAlchemy connection for asynchronous operations with database management + functions. + """ + def __init__(self, connection_string: str): self.db_path: str = None self.db_uri: str = connection_string @@ -32,6 +37,9 @@ class SQLAlchemyAdapter: @asynccontextmanager async def get_async_session(self) -> AsyncGenerator[AsyncSession, None]: + """ + Provide an asynchronous context manager for obtaining a database session. + """ async_session_maker = self.sessionmaker async with async_session_maker() as session: try: @@ -40,6 +48,9 @@ class SQLAlchemyAdapter: await session.close() # Ensure the session is closed def get_session(self): + """ + Provide a context manager for obtaining a database session. + """ session_maker = self.sessionmaker with session_maker() as session: try: @@ -48,6 +59,14 @@ class SQLAlchemyAdapter: session.close() # Ensure the session is closed async def get_datasets(self): + """ + Retrieve a list of datasets from the database. + + Returns: + -------- + + A list of datasets retrieved from the database. + """ from cognee.modules.data.models import Dataset async with self.get_async_session() as session: @@ -56,6 +75,17 @@ class SQLAlchemyAdapter: return datasets async def create_table(self, schema_name: str, table_name: str, table_config: list[dict]): + """ + Create a table with the specified schema and configuration if it does not exist. + + Parameters: + ----------- + + - schema_name (str): The name of the schema to create the table within. + - table_name (str): The name of the table to be created. + - table_config (list[dict]): A list of dictionaries representing the table's fields + and their types. + """ fields_query_parts = [f"{item['name']} {item['type']}" for item in table_config] async with self.engine.begin() as connection: await connection.execute(text(f"CREATE SCHEMA IF NOT EXISTS {schema_name};")) @@ -67,6 +97,16 @@ class SQLAlchemyAdapter: await connection.close() async def delete_table(self, table_name: str, schema_name: Optional[str] = "public"): + """ + Delete a table from the specified schema, if it exists. + + Parameters: + ----------- + + - table_name (str): The name of the table to delete. + - schema_name (Optional[str]): The name of the schema containing the table to + delete, defaults to 'public'. (default 'public') + """ async with self.engine.begin() as connection: if self.engine.dialect.name == "sqlite": # SQLite doesn't support schema namespaces and the CASCADE keyword. @@ -84,19 +124,21 @@ class SQLAlchemyAdapter: schema_name: Optional[str] = "public", ) -> int: """ - Insert data into specified table using SQLAlchemy Core with batch optimization - Returns number of inserted rows + Insert data into a specified table, returning the number of rows inserted. - Usage Example: - from cognee.infrastructure.databases.relational.get_relational_engine import get_relational_engine - from uuid import UUID - db = get_relational_engine() - table_name = "groups" - data = { - "id": UUID("c70a3cec-3309-44df-8ee6-eced820cf438"), - "name": "test" - } - await db.insert_data(table_name, data) + Parameters: + ----------- + + - table_name (str): The name of the table to insert data into. + - data (list[dict]): A list of dictionaries representing the rows to add to the + table. + - schema_name (Optional[str]): The name of the schema containing the table, defaults + to 'public'. (default 'public') + + Returns: + -------- + + - int: The number of rows inserted into the table. """ if not data: logger.info("No data provided for insertion") @@ -125,7 +167,12 @@ class SQLAlchemyAdapter: async def get_schema_list(self) -> List[str]: """ - Return a list of all schema names in database + Return a list of all schema names in the database, excluding system schemas. + + Returns: + -------- + + - List[str]: A list of schema names in the database. """ if self.engine.dialect.name == "postgresql": async with self.engine.begin() as connection: @@ -144,7 +191,15 @@ class SQLAlchemyAdapter: self, table_name: str, data_id: UUID, schema_name: Optional[str] = "public" ): """ - Delete entity in given table based on id. Table must have an id Column. + Delete an entity from the specified table based on its unique ID. + + Parameters: + ----------- + + - table_name (str): The name of the table from which to delete the entity. + - data_id (UUID): The unique identifier of the entity to be deleted. + - schema_name (Optional[str]): The name of the schema where the table resides, + defaults to 'public'. (default 'public') """ if self.engine.dialect.name == "sqlite": async with self.get_async_session() as session: @@ -164,7 +219,12 @@ class SQLAlchemyAdapter: async def delete_data_entity(self, data_id: UUID): """ - Delete data and local files related to data if there are no references to it anymore. + Delete a data entity along with its local files if no references remain in the database. + + Parameters: + ----------- + + - data_id (UUID): The unique identifier of the data entity to be deleted. """ async with self.get_async_session() as session: if self.engine.dialect.name == "sqlite": @@ -205,7 +265,19 @@ class SQLAlchemyAdapter: async def get_table(self, table_name: str, schema_name: Optional[str] = "public") -> Table: """ - Dynamically loads a table using the given table name and schema name. + Load a table dynamically using the specified name and schema information. + + Parameters: + ----------- + + - table_name (str): The name of the table to load. + - schema_name (Optional[str]): The name of the schema containing the table, defaults + to 'public'. (default 'public') + + Returns: + -------- + + - Table: The SQLAlchemy Table object corresponding to the specified table. """ async with self.engine.begin() as connection: if self.engine.dialect.name == "sqlite": @@ -229,7 +301,12 @@ class SQLAlchemyAdapter: async def get_table_names(self) -> List[str]: """ - Return a list of all table names in the database, even if they don't have defined SQLAlchemy models. + Return a list of all table names in the database, regardless of their model definitions. + + Returns: + -------- + + - List[str]: A list of all table names in the database. """ table_names = [] async with self.engine.begin() as connection: @@ -249,6 +326,21 @@ class SQLAlchemyAdapter: return table_names async def get_data(self, table_name: str, filters: dict = None): + """ + Retrieve data from a specified table using optional filtering conditions. + + Parameters: + ----------- + + - table_name (str): The name of the table from which to retrieve data. + - filters (dict): A dictionary of filtering conditions to apply to the data + retrieval, defaults to None for no filters. (default None) + + Returns: + -------- + + A dictionary of data results keyed by their unique identifiers. + """ async with self.engine.begin() as connection: query = f'SELECT * FROM "{table_name}"' if filters: @@ -270,6 +362,20 @@ class SQLAlchemyAdapter: return {result["data_id"]: result["status"] for result in results} async def get_all_data_from_table(self, table_name: str, schema: str = "public"): + """ + Fetch all records from a specified table, validating inputs against SQL injection. + + Parameters: + ----------- + + - table_name (str): The name of the table to query data from. + - schema (str): The schema of the table, defaults to 'public'. (default 'public') + + Returns: + -------- + + A list of dictionaries representing all rows in the specified table. + """ async with self.get_async_session() as session: # Validate inputs to prevent SQL injection if not table_name.isidentifier(): @@ -291,11 +397,28 @@ class SQLAlchemyAdapter: return rows async def execute_query(self, query): + """ + Execute a raw SQL query against the database asynchronously. + + Parameters: + ----------- + + - query: The SQL query string to execute. + + Returns: + -------- + + The result set as a list of dictionaries, with each dictionary representing a row. + """ async with self.engine.begin() as connection: result = await connection.execute(text(query)) return [dict(row) for row in result] async def drop_tables(self): + """ + Drop specified tables from the database, if they exist, and handle errors that may occur + during the process. + """ async with self.engine.begin() as connection: try: await connection.execute(text("DROP TABLE IF EXISTS group_permission CASCADE")) @@ -307,6 +430,10 @@ class SQLAlchemyAdapter: raise e async def create_database(self): + """ + Create the database if it does not exist, ensuring necessary directories are in place + for SQLite. + """ if self.engine.dialect.name == "sqlite" and not os.path.exists(self.db_path): from cognee.infrastructure.files.storage import LocalStorage @@ -318,6 +445,9 @@ class SQLAlchemyAdapter: await connection.run_sync(Base.metadata.create_all) async def delete_database(self): + """ + Delete the entire database, including all tables and files if applicable. + """ try: if self.engine.dialect.name == "sqlite": from cognee.infrastructure.files.storage import LocalStorage @@ -347,6 +477,15 @@ class SQLAlchemyAdapter: logger.info("Database deleted successfully.") async def extract_schema(self): + """ + Extract the schema information of the database, including tables and their respective + columns and constraints. + + Returns: + -------- + + A dictionary containing the schema details of the database. + """ async with self.engine.begin() as connection: tables = await self.get_table_names() @@ -400,6 +539,22 @@ class SQLAlchemyAdapter: # Helper function to get table details using the inspector. def get_details(sync_conn, table, schema_name): + """ + Retrieve detailed information about a specific table including columns, primary keys, + and foreign keys. + + Parameters: + ----------- + + - sync_conn: The synchronous connection used to interact with the database. + - table: The name of the table to retrieve details for. + - schema_name: The schema name in which the table resides. + + Returns: + -------- + + Details about the table's columns, primary keys, and foreign keys. + """ insp = inspect(sync_conn) cols = insp.get_columns(table, schema=schema_name) pk = insp.get_pk_constraint(table, schema=schema_name) diff --git a/cognee/infrastructure/databases/vector/__init__.py b/cognee/infrastructure/databases/vector/__init__.py index 0a6e3c1fa..7f19efcb1 100644 --- a/cognee/infrastructure/databases/vector/__init__.py +++ b/cognee/infrastructure/databases/vector/__init__.py @@ -3,3 +3,4 @@ from .models.CollectionConfig import CollectionConfig from .vector_db_interface import VectorDBInterface from .config import get_vectordb_config from .get_vector_engine import get_vector_engine +from .use_vector_adapter import use_vector_adapter diff --git a/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py b/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py index 7f8391c1a..d4b858348 100644 --- a/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py +++ b/cognee/infrastructure/databases/vector/chromadb/ChromaDBAdapter.py @@ -19,17 +19,59 @@ logger = get_logger("ChromaDBAdapter") class IndexSchema(DataPoint): + """ + Define a schema for indexing textual data along with metadata. + + Public methods: + + - model_dump: Serialize the instance data into a format suitable for ChromaDB storage. + + Instance variables: + + - text: The text content to be indexed. + - metadata: A dictionary containing index-related fields. + """ + text: str metadata: dict = {"index_fields": ["text"]} def model_dump(self): + """ + Serialize the instance data for storage. + + Invoke the superclass method and process the resulting data into a format compatible + with ChromaDB. + + Returns: + -------- + + A dictionary containing serialized data processed for ChromaDB storage. + """ data = super().model_dump() return process_data_for_chroma(data) def process_data_for_chroma(data): - """Convert complex data types to a format suitable for ChromaDB storage.""" + """ + Convert complex data types to a format suitable for ChromaDB storage. + + This function processes various data types from the input dictionary, converting UUIDs + to strings, and serializing dictionaries and lists into JSON strings prefixed with the + key type. Other supported types (strings, integers, floats, booleans, and None) are + stored as-is. Unsupported types are converted to their string representation. + + Parameters: + ----------- + + - data: A dictionary containing data with various types including potentially + complex structures. + + Returns: + -------- + + A dictionary containing the processed key-value pairs suitable for ChromaDB storage. + """ processed_data = {} for key, value in data.items(): if isinstance(value, UUID): @@ -48,7 +90,26 @@ def process_data_for_chroma(data): def restore_data_from_chroma(data): - """Restore original data structure from ChromaDB storage format.""" + """ + Restore original data structure from ChromaDB storage format. + + This function processes a data dictionary, identifies special keys that end with + '__dict' and '__list', and attempts to restore the original data structure by converting + JSON strings back into Python objects. If restoration fails, it logs an error and + retains the original value. + + Parameters: + ----------- + + - data: A dictionary containing data stored in ChromaDB format, which includes + special keys indicating dictionary and list entries. + + Returns: + -------- + + A dictionary representing the restored original data structure, with JSON strings + converted to their appropriate Python types. + """ restored_data = {} dict_keys = [] list_keys = [] @@ -84,6 +145,11 @@ def restore_data_from_chroma(data): class ChromaDBAdapter(VectorDBInterface): + """ + Manage a connection to the ChromaDB and facilitate operations for embedding, searching, + and managing collections of data points. + """ + name = "ChromaDB" url: str api_key: str @@ -97,6 +163,15 @@ class ChromaDBAdapter(VectorDBInterface): self.api_key = api_key async def get_connection(self) -> AsyncHttpClient: + """ + Establish and return a connection to the ChromaDB if one doesn't already exist. + + Returns: + -------- + + - AsyncHttpClient: Returns an instance of AsyncHttpClient for interacting with + ChromaDB. + """ if self.connection is None: settings = Settings( chroma_client_auth_provider="token", chroma_client_auth_credentials=self.api_key @@ -106,19 +181,68 @@ class ChromaDBAdapter(VectorDBInterface): return self.connection async def embed_data(self, data: list[str]) -> list[list[float]]: + """ + Embed a list of text data into vector representations. + + Parameters: + ----------- + + - data (list[str]): A list of strings to be embedded. + + Returns: + -------- + + - list[list[float]]: Returns a list of lists containing the embedded vector + representations. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Check if a collection with the specified name exists in the ChromaDB. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check for. + + Returns: + -------- + + - bool: Returns True if the collection exists, otherwise False. + """ collections = await self.get_collection_names() return collection_name in collections async def create_collection(self, collection_name: str, payload_schema=None): + """ + Create a new collection in ChromaDB if it does not already exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to create. + - payload_schema: The schema for the payload; can be None. (default None) + """ client = await self.get_connection() if not await self.has_collection(collection_name): await client.create_collection(name=collection_name, metadata={"hnsw:space": "cosine"}) async def get_collection(self, collection_name: str) -> AsyncHttpClient: + """ + Retrieve a collection by its name from ChromaDB. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to retrieve. + + Returns: + -------- + + - AsyncHttpClient: Returns an AsyncHttpClient representing the requested collection. + """ if not await self.has_collection(collection_name): raise CollectionNotFoundError(f"Collection '{collection_name}' not found!") @@ -126,6 +250,16 @@ class ChromaDBAdapter(VectorDBInterface): return await client.get_collection(collection_name) async def create_data_points(self, collection_name: str, data_points: list[DataPoint]): + """ + Create and upsert data points into the specified collection in ChromaDB. + + Parameters: + ----------- + + - collection_name (str): The name of the collection where data points will be added. + - data_points (list[DataPoint]): A list of DataPoint instances to be added to the + collection. + """ await self.create_collection(collection_name) collection = await self.get_collection(collection_name) @@ -144,13 +278,30 @@ class ChromaDBAdapter(VectorDBInterface): ) async def create_vector_index(self, index_name: str, index_property_name: str): - """Create a vector index as a ChromaDB collection.""" + """ + Create a vector index as a ChromaDB collection based on provided names. + + Parameters: + ----------- + + - index_name (str): The base name for the vector index. + - index_property_name (str): The property name associated with the index. + """ await self.create_collection(f"{index_name}_{index_property_name}") async def index_data_points( self, index_name: str, index_property_name: str, data_points: list[DataPoint] ): - """Index data points using the specified index property.""" + """ + Index the provided data points based on the specified index property in ChromaDB. + + Parameters: + ----------- + + - index_name (str): The index name used for the data points. + - index_property_name (str): The property name to index data points by. + - data_points (list[DataPoint]): A list of DataPoint instances to be indexed. + """ await self.create_data_points( f"{index_name}_{index_property_name}", [ @@ -163,7 +314,22 @@ class ChromaDBAdapter(VectorDBInterface): ) async def retrieve(self, collection_name: str, data_point_ids: list[str]): - """Retrieve data points by their IDs from a collection.""" + """ + Retrieve data points by their IDs from a ChromaDB collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to retrieve data + points. + - data_point_ids (list[str]): A list of data point IDs to retrieve. + + Returns: + -------- + + Returns a list of ScoredResult instances containing the retrieved data points and + their metadata. + """ collection = await self.get_collection(collection_name) results = await collection.get(ids=data_point_ids, include=["metadatas"]) @@ -185,7 +351,27 @@ class ChromaDBAdapter(VectorDBInterface): with_vector: bool = False, normalized: bool = True, ): - """Search for similar items in a collection using text or vector query.""" + """ + Search for items in a collection using either a text or a vector query. + + Parameters: + ----------- + + - collection_name (str): The name of the collection in which to perform the search. + - query_text (str): Text query used for search; can be None if query_vector is + provided. (default None) + - query_vector (List[float]): Vector query used for search; can be None if + query_text is provided. (default None) + - limit (int): The maximum number of results to return; defaults to 15. (default 15) + - with_vector (bool): Whether to include vectors in the results. (default False) + - normalized (bool): Whether to normalize the distance scores before returning them. + (default True) + + Returns: + -------- + + Returns a list of ScoredResult instances representing the search results. + """ if query_text is None and query_vector is None: raise InvalidValueError(message="One of query_text or query_vector must be provided!") @@ -247,7 +433,26 @@ class ChromaDBAdapter(VectorDBInterface): limit: int = 5, with_vectors: bool = False, ): - """Perform multiple searches in a single request for efficiency.""" + """ + Perform multiple searches in a single request for efficiency, returning results for each + query. + + 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 be searched. + - limit (int): The maximum number of results to return for each query; defaults to + 5. (default 5) + - with_vectors (bool): Whether to include vectors in the results for each query. + (default False) + + Returns: + -------- + + Returns a list of lists of ScoredResult instances for each query's results. + """ query_vectors = await self.embed_data(query_texts) collection = await self.get_collection(collection_name) @@ -298,13 +503,35 @@ class ChromaDBAdapter(VectorDBInterface): return all_results async def delete_data_points(self, collection_name: str, data_point_ids: list[str]): - """Remove data points from a collection by their IDs.""" + """ + Remove data points from a collection based on their IDs. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to delete data + points. + - data_point_ids (list[str]): A list of data point IDs to remove from the + collection. + + Returns: + -------- + + Returns True upon successful deletion of the data points. + """ collection = await self.get_collection(collection_name) await collection.delete(ids=data_point_ids) return True async def prune(self): - """Delete all collections in the ChromaDB database.""" + """ + Delete all collections in the ChromaDB database. + + Returns: + -------- + + Returns True upon successful deletion of all collections. + """ client = await self.get_connection() collections = await self.list_collections() for collection_name in collections: @@ -312,7 +539,14 @@ class ChromaDBAdapter(VectorDBInterface): return True async def get_collection_names(self): - """Get a list of all collection names in the database.""" + """ + Retrieve the names of all collections in the ChromaDB database. + + Returns: + -------- + + Returns a list of collection names. + """ client = await self.get_connection() collections = await client.list_collections() return [ diff --git a/cognee/infrastructure/databases/vector/config.py b/cognee/infrastructure/databases/vector/config.py index f2c180446..a172e633c 100644 --- a/cognee/infrastructure/databases/vector/config.py +++ b/cognee/infrastructure/databases/vector/config.py @@ -5,6 +5,21 @@ from cognee.root_dir import get_absolute_path class VectorConfig(BaseSettings): + """ + Manage the configuration settings for the vector database. + + Public methods: + + - to_dict: Convert the configuration to a dictionary. + + Instance variables: + + - vector_db_url: The URL of the vector database. + - vector_db_port: The port for the vector database. + - vector_db_key: The key for accessing the vector database. + - vector_db_provider: The provider for the vector database. + """ + vector_db_url: str = os.path.join( os.path.join(get_absolute_path(".cognee_system"), "databases"), "cognee.lancedb" ) @@ -15,6 +30,14 @@ class VectorConfig(BaseSettings): model_config = SettingsConfigDict(env_file=".env", extra="allow") def to_dict(self) -> dict: + """ + Convert the configuration settings to a dictionary. + + Returns: + -------- + + - dict: A dictionary containing the vector database configuration settings. + """ return { "vector_db_url": self.vector_db_url, "vector_db_port": self.vector_db_port, @@ -25,4 +48,17 @@ class VectorConfig(BaseSettings): @lru_cache def get_vectordb_config(): + """ + Retrieve the cached vector database configuration. + + This function uses the LRU cache to store the instance of `VectorConfig`, allowing for + efficient reuse without needing to recreate the object multiple times. If a + configuration is already cached, it returns that instead of creating a new one. + + Returns: + -------- + + - VectorConfig: An instance of `VectorConfig` containing the vector database + configuration. + """ return VectorConfig() diff --git a/cognee/infrastructure/databases/vector/create_vector_engine.py b/cognee/infrastructure/databases/vector/create_vector_engine.py index e49a2c4a8..34416343b 100644 --- a/cognee/infrastructure/databases/vector/create_vector_engine.py +++ b/cognee/infrastructure/databases/vector/create_vector_engine.py @@ -1,3 +1,4 @@ +from .supported_databases import supported_databases from .embeddings import get_embedding_engine from functools import lru_cache @@ -10,8 +11,43 @@ def create_vector_engine( vector_db_key: str, vector_db_provider: str, ): + """ + Create a vector database engine based on the specified provider. + + This function initializes and returns a database adapter for vector storage, depending + on the provided vector database provider. The function checks for required credentials + for each provider, raising an EnvironmentError if any are missing, or ImportError if the + ChromaDB package is not installed. + + Supported providers include: Weaviate, Qdrant, Milvus, pgvector, FalkorDB, ChromaDB, and + LanceDB. + + Parameters: + ----------- + + - vector_db_url (str): The URL for the vector database instance. + - vector_db_port (str): The port for the vector database instance. Required for some + providers. + - vector_db_key (str): The API key or access token for the vector database instance. + - vector_db_provider (str): The name of the vector database provider to use (e.g., + 'weaviate', 'qdrant'). + + Returns: + -------- + + An instance of the corresponding database adapter class for the specified provider. + """ embedding_engine = get_embedding_engine() + if vector_db_provider in supported_databases: + adapter = supported_databases[vector_db_provider] + + return adapter( + utl=vector_db_url, + api_key=vector_db_key, + embedding_engine=embedding_engine, + ) + if vector_db_provider == "weaviate": from .weaviate_db import WeaviateAdapter diff --git a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py index ea07ff4e2..afa180c67 100644 --- a/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/EmbeddingEngine.py @@ -2,8 +2,35 @@ from typing import Protocol class EmbeddingEngine(Protocol): + """ + Defines an interface for embedding text. Provides methods to embed text and get the + vector size. + """ + async def embed_text(self, text: list[str]) -> list[list[float]]: + """ + Embed the provided text and return a list of embedded vectors. + + Parameters: + ----------- + + - text (list[str]): A list of strings representing the text to be embedded. + + Returns: + -------- + + - list[list[float]]: A list of lists, where each sublist contains the encoded + representation of the corresponding text input. + """ raise NotImplementedError() def get_vector_size(self) -> int: + """ + Retrieve the size of the embedding vector. + + Returns: + -------- + + - int: An integer representing the number of dimensions in the embedding vector. + """ raise NotImplementedError() diff --git a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py index ef1f4ca37..99bb4ec8a 100644 --- a/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/FastembedEmbeddingEngine.py @@ -12,6 +12,23 @@ logger = get_logger("FastembedEmbeddingEngine") class FastembedEmbeddingEngine(EmbeddingEngine): + """ + Manages the embedding process using a specified model to generate text embeddings. + + Public methods: + + - embed_text + - get_vector_size + - get_tokenizer + + Instance variables: + + - model: The name of the embedding model. + - dimensions: The dimensionality of the embeddings. + - mock: A flag indicating whether to use mocking instead of the actual embedding model. + - MAX_RETRIES: The maximum number of retries for embedding operations. + """ + model: str dimensions: int mock: bool @@ -37,6 +54,24 @@ class FastembedEmbeddingEngine(EmbeddingEngine): self.mock = enable_mocking in ("true", "1", "yes") async def embed_text(self, text: List[str]) -> List[List[float]]: + """ + Embed the given text into numerical vectors. + + This method generates embeddings for a list of text strings. If mocking is enabled, it + returns zero vectors instead. It handles exceptions by logging the error and raising an + `EmbeddingException` on failure. + + Parameters: + ----------- + + - text (List[str]): A list of strings to be embedded. + + Returns: + -------- + + - List[List[float]]: A list of embeddings, where each embedding is a list of floats + representing the vector form of the input text. + """ try: if self.mock: return [[0.0] * self.dimensions for _ in text] @@ -44,7 +79,7 @@ class FastembedEmbeddingEngine(EmbeddingEngine): embeddings = self.embedding_model.embed( text, batch_size=len(text), - parallel=0, + parallel=None, ) return list(embeddings) @@ -54,9 +89,25 @@ class FastembedEmbeddingEngine(EmbeddingEngine): raise EmbeddingException(f"Failed to index data points using model {self.model}") def get_vector_size(self) -> int: + """ + Return the size of the embedding vector produced by this engine. + + Returns: + -------- + + - int: The dimensionality of the embedding vectors. + """ return self.dimensions def get_tokenizer(self): + """ + Instantiate and return the tokenizer used for preparing text for embedding. + + Returns: + -------- + + A tokenizer object configured for the specified model and maximum token size. + """ logger.debug("Loading tokenizer for FastembedEmbeddingEngine...") tokenizer = TikTokenTokenizer(model="gpt-4o", max_tokens=self.max_tokens) diff --git a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py index 07ad1d987..b51d397ed 100644 --- a/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py @@ -21,6 +21,16 @@ logger = get_logger("LiteLLMEmbeddingEngine") class LiteLLMEmbeddingEngine(EmbeddingEngine): + """ + Engine for embedding text using a specific LLM model, supporting mock and actual + embedding calls. + + Public methods: + - embed_text: Embed a list of strings into vector representations. + - get_vector_size: Retrieve the size of the embedding vectors. + - get_tokenizer: Load the appropriate tokenizer for the specified model. + """ + api_key: str endpoint: str api_version: str @@ -59,6 +69,24 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): @embedding_sleep_and_retry_async() @embedding_rate_limit_async async def embed_text(self, text: List[str]) -> List[List[float]]: + """ + Embed a list of text strings into vector representations. + + If the input exceeds the model's context window, the method will recursively split the + input and combine the results. It handles both mock and live embedding scenarios, + logging errors for any encountered exceptions, and raising specific exceptions for + context window issues and embedding failures. + + Parameters: + ----------- + + - text (List[str]): A list of strings to be embedded. + + Returns: + -------- + + - List[List[float]]: A list of vectors representing the embedded texts. + """ try: if self.mock: response = {"data": [{"embedding": [0.0] * self.dimensions} for _ in text]} @@ -119,9 +147,25 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine): raise error def get_vector_size(self) -> int: + """ + Retrieve the dimensionality of the embedding vectors. + + Returns: + -------- + + - int: The size (dimensionality) of the embedding vectors. + """ return self.dimensions def get_tokenizer(self): + """ + Load and return the appropriate tokenizer for the specified model based on the provider. + + Returns: + -------- + + The tokenizer instance compatible with the model. + """ logger.debug(f"Loading tokenizer for model {self.model}...") # If model also contains provider information, extract only model information model = self.model.split("/")[-1] diff --git a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py index 26b98df08..31f615477 100644 --- a/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py +++ b/cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py @@ -18,6 +18,24 @@ logger = get_logger("OllamaEmbeddingEngine") class OllamaEmbeddingEngine(EmbeddingEngine): + """ + Implements an embedding engine using the Ollama embedding model. + + Public methods: + - embed_text + - get_vector_size + - get_tokenizer + + Instance variables: + - model + - dimensions + - max_tokens + - endpoint + - mock + - huggingface_tokenizer_name + - tokenizer + """ + model: str dimensions: int max_tokens: int @@ -50,7 +68,19 @@ class OllamaEmbeddingEngine(EmbeddingEngine): @embedding_rate_limit_async async def embed_text(self, text: List[str]) -> List[List[float]]: """ - Given a list of text prompts, returns a list of embedding vectors. + Generate embedding vectors for a list of text prompts. + + If mocking is enabled, returns a list of zero vectors instead of actual embeddings. + + Parameters: + ----------- + + - text (List[str]): A list of text prompts for which to generate embeddings. + + Returns: + -------- + + - List[List[float]]: A list of embedding vectors corresponding to the text prompts. """ if self.mock: return [[0.0] * self.dimensions for _ in text] @@ -80,9 +110,25 @@ class OllamaEmbeddingEngine(EmbeddingEngine): return data["embedding"] def get_vector_size(self) -> int: + """ + Retrieve the size of the embedding vectors. + + Returns: + -------- + + - int: The dimension of the embedding vectors. + """ return self.dimensions def get_tokenizer(self): + """ + Load and return a HuggingFace tokenizer for the embedding engine. + + Returns: + -------- + + The instantiated HuggingFace tokenizer used by the embedding engine. + """ logger.debug("Loading HuggingfaceTokenizer for OllamaEmbeddingEngine...") tokenizer = HuggingFaceTokenizer( model=self.huggingface_tokenizer_name, max_tokens=self.max_tokens diff --git a/cognee/infrastructure/databases/vector/embeddings/config.py b/cognee/infrastructure/databases/vector/embeddings/config.py index 13f4092bc..2ae60d64a 100644 --- a/cognee/infrastructure/databases/vector/embeddings/config.py +++ b/cognee/infrastructure/databases/vector/embeddings/config.py @@ -4,6 +4,14 @@ from pydantic_settings import BaseSettings, SettingsConfigDict class EmbeddingConfig(BaseSettings): + """ + Manage configuration settings for embedding operations, including provider, model + details, API configuration, and tokenizer settings. + + Public methods: + - to_dict: Serialize the configuration settings to a dictionary. + """ + embedding_provider: Optional[str] = "openai" embedding_model: Optional[str] = "openai/text-embedding-3-large" embedding_dimensions: Optional[int] = 3072 @@ -17,6 +25,11 @@ class EmbeddingConfig(BaseSettings): def to_dict(self) -> dict: """ Serialize all embedding configuration settings to a dictionary. + + Returns: + -------- + + - dict: A dictionary containing the embedding configuration settings. """ return { "embedding_provider": self.embedding_provider, @@ -32,4 +45,17 @@ class EmbeddingConfig(BaseSettings): @lru_cache def get_embedding_config(): + """ + Retrieve a cached instance of the EmbeddingConfig class. + + This function returns an instance of EmbeddingConfig with default settings. It uses + memoization to cache the result, ensuring that subsequent calls return the same instance + without re-initialization, improving performance and resource utilization. + + Returns: + -------- + + - EmbeddingConfig: An instance of EmbeddingConfig containing the embedding + configuration settings. + """ return EmbeddingConfig() diff --git a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py index 4adcaf13b..ef06eb7b2 100644 --- a/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py +++ b/cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py @@ -5,6 +5,19 @@ from functools import lru_cache def get_embedding_engine() -> EmbeddingEngine: + """ + Retrieve the embedding engine singleton based on configuration. + + This function calls the configuration retrieval functions to get the necessary settings + for the embedding engine and creates a singleton instance. This ensures that too many + requests won't be sent to HuggingFace by reusing the same instance for subsequent calls. + + Returns: + -------- + + - EmbeddingEngine: An instance of the embedding engine configured based on the + retrieved settings. + """ config = get_embedding_config() llm_config = get_llm_config() # Embedding engine has to be a singleton based on configuration to ensure too many requests won't be sent to HuggingFace @@ -33,6 +46,33 @@ def create_embedding_engine( huggingface_tokenizer, llm_api_key, ): + """ + Create and return an embedding engine based on the specified provider. + + Parameters: + ----------- + + - embedding_provider: The name of the embedding provider, e.g., 'fastembed', + 'ollama', or another supported provider. + - embedding_model: The model to be used for the embedding engine. + - embedding_dimensions: The number of dimensions for the embeddings. + - embedding_max_tokens: The maximum number of tokens for the embeddings. + - embedding_endpoint: The endpoint for the embedding service, relevant for certain + providers. + - embedding_api_key: API key to authenticate with the embedding service, if + required. + - embedding_api_version: Version of the API to be used for the embedding service, if + applicable. + - huggingface_tokenizer: Tokenizer from Hugging Face for tokenizing input text, used + for specific providers. + - llm_api_key: API key for the LLM service, to be used if embedding_api_key is not + provided. + + Returns: + -------- + + Returns an instance of an embedding engine based on the specified provider. + """ if embedding_provider == "fastembed": from .FastembedEmbeddingEngine import FastembedEmbeddingEngine diff --git a/cognee/infrastructure/databases/vector/exceptions/exceptions.py b/cognee/infrastructure/databases/vector/exceptions/exceptions.py index f43d7773c..b6a8237d8 100644 --- a/cognee/infrastructure/databases/vector/exceptions/exceptions.py +++ b/cognee/infrastructure/databases/vector/exceptions/exceptions.py @@ -3,6 +3,14 @@ from cognee.exceptions import CriticalError class CollectionNotFoundError(CriticalError): + """ + Represents an error that occurs when a requested collection cannot be found. + + This class extends the CriticalError to handle specific cases where a requested + collection is unavailable. It can be initialized with a custom message and allows for + logging options including log level and whether to log the error. + """ + def __init__( self, message, diff --git a/cognee/infrastructure/databases/vector/get_vector_engine.py b/cognee/infrastructure/databases/vector/get_vector_engine.py index 1523f334c..c92492649 100644 --- a/cognee/infrastructure/databases/vector/get_vector_engine.py +++ b/cognee/infrastructure/databases/vector/get_vector_engine.py @@ -3,4 +3,12 @@ from .create_vector_engine import create_vector_engine def get_vector_engine(): + """ + Create and return a vector engine instance. + + Returns: + -------- + + A vector engine instance created from the vector database configuration. + """ return create_vector_engine(**get_vectordb_config().to_dict()) diff --git a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py index e1e85560f..a3a81a105 100644 --- a/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py +++ b/cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py @@ -18,6 +18,17 @@ from ..vector_db_interface import VectorDBInterface class IndexSchema(DataPoint): + """ + Represents a schema for an index data point containing an ID and text. + + Attributes: + + - id: A string representing the unique identifier for the data point. + - text: A string representing the content of the data point. + - metadata: A dictionary with default index fields for the schema, currently configured + to include 'text'. + """ + id: str text: str @@ -41,15 +52,55 @@ class LanceDBAdapter(VectorDBInterface): self.embedding_engine = embedding_engine async def get_connection(self): + """ + Establishes and returns a connection to the LanceDB. + + If a connection already exists, it will return the existing connection. + + Returns: + -------- + + - lancedb.AsyncConnection: An active connection to the LanceDB. + """ if self.connection is None: self.connection = await lancedb.connect_async(self.url, api_key=self.api_key) return self.connection async def embed_data(self, data: list[str]) -> list[list[float]]: + """ + Embeds the provided textual data into vector representation. + + Uses the embedding engine to convert the list of strings into a list of float vectors. + + Parameters: + ----------- + + - data (list[str]): A list of strings representing the data to be embedded. + + Returns: + -------- + + - list[list[float]]: A list of embedded vectors corresponding to the input data. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Checks if the specified collection exists in the LanceDB. + + Returns True if the collection is present, otherwise False. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check. + + Returns: + -------- + + - bool: True if the collection exists, otherwise False. + """ connection = await self.get_connection() collection_names = await connection.table_names() return collection_name in collection_names @@ -61,6 +112,15 @@ class LanceDBAdapter(VectorDBInterface): data_point_types = get_type_hints(payload_schema) class LanceDataPoint(LanceModel): + """ + Represents a data point in the Lance model with an ID, vector, and associated payload. + + The class inherits from LanceModel and defines the following public attributes: + - id: A unique identifier for the data point. + - vector: A vector representing the data point in a specified dimensional space. + - payload: Additional data or metadata associated with the data point. + """ + id: data_point_types["id"] vector: Vector(vector_size) payload: payload_schema @@ -100,6 +160,14 @@ class LanceDBAdapter(VectorDBInterface): vector_size = self.embedding_engine.get_vector_size() class LanceDataPoint(LanceModel, Generic[IdType, PayloadSchema]): + """ + Represents a data point in the Lance model with an ID, vector, and payload. + + This class encapsulates a data point consisting of an identifier, a vector representing + the data, and an associated payload, allowing for operations and manipulations specific + to the Lance data structure. + """ + id: IdType vector: Vector(vector_size) payload: PayloadSchema diff --git a/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py b/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py index 9daebb760..7af852327 100644 --- a/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py +++ b/cognee/infrastructure/databases/vector/milvus/MilvusAdapter.py @@ -16,12 +16,40 @@ logger = get_logger("MilvusAdapter") class IndexSchema(DataPoint): + """ + Represent a schema for an index that includes text data and associated metadata. + + This class inherits from DataPoint and includes attributes for text and metadata. It + defines the structure of the data points used in the index, holding the text as a string + and metadata as a dictionary with predefined index fields. + """ + text: str metadata: dict = {"index_fields": ["text"]} class MilvusAdapter(VectorDBInterface): + """ + Interface for interacting with a Milvus vector database. + + Public methods: + + - __init__ + - get_milvus_client + - embed_data + - has_collection + - create_collection + - create_data_points + - create_vector_index + - index_data_points + - retrieve + - search + - batch_search + - delete_data_points + - prune + """ + name = "Milvus" url: str api_key: Optional[str] @@ -34,6 +62,16 @@ class MilvusAdapter(VectorDBInterface): self.embedding_engine = embedding_engine def get_milvus_client(self): + """ + Retrieve a Milvus client instance. + + Returns a MilvusClient object configured with the provided URL and optional API key. + + Returns: + -------- + + A MilvusClient instance. + """ from pymilvus import MilvusClient if self.api_key: @@ -43,9 +81,40 @@ class MilvusAdapter(VectorDBInterface): return client async def embed_data(self, data: List[str]) -> list[list[float]]: + """ + Embed a list of text data into vectors asynchronously. + + Accepts a list of strings and utilizes the embedding engine to convert them into + vectors. + + Parameters: + ----------- + + - data (List[str]): A list of textual data to be embedded into vectors. + + Returns: + -------- + + - list[list[float]]: A list of lists containing embedded vectors. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Check if a collection exists in the database asynchronously. + + Returns a boolean indicating whether the specified collection is present. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check for its existence. + + Returns: + -------- + + - bool: True if the collection exists, False otherwise. + """ future = asyncio.Future() client = self.get_milvus_client() future.set_result(client.has_collection(collection_name=collection_name)) @@ -57,6 +126,24 @@ class MilvusAdapter(VectorDBInterface): collection_name: str, payload_schema=None, ): + """ + Create a new collection in the vector database asynchronously. + + Raises a MilvusException if there are issues creating the collection, such as already + existing collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to be created. + - payload_schema: Optional schema for the collection, defaults to None if not + provided. (default None) + + Returns: + -------- + + True if the collection is created successfully, otherwise returns None. + """ from pymilvus import DataType, MilvusException client = self.get_milvus_client() @@ -97,6 +184,24 @@ class MilvusAdapter(VectorDBInterface): raise e async def create_data_points(self, collection_name: str, data_points: List[DataPoint]): + """ + Insert multiple data points into a specified collection asynchronously. + + Raises CollectionNotFoundError if the specified collection does not exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection where data points will be + inserted. + - data_points (List[DataPoint]): A list of DataPoint objects to be inserted into the + collection. + + Returns: + -------- + + The result of the insert operation, includes count of inserted data points. + """ from pymilvus import MilvusException, exceptions client = self.get_milvus_client() @@ -130,11 +235,30 @@ class MilvusAdapter(VectorDBInterface): raise e async def create_vector_index(self, index_name: str, index_property_name: str): + """ + Create a vector index for a given collection asynchronously. + + Parameters: + ----------- + + - index_name (str): The name of the vector index being created. + - index_property_name (str): The property name associated with the index. + """ await self.create_collection(f"{index_name}_{index_property_name}") async def index_data_points( self, index_name: str, index_property_name: str, data_points: List[DataPoint] ): + """ + Index the provided data points into the collection based on index names asynchronously. + + Parameters: + ----------- + + - index_name (str): The name of the index where data points will be indexed. + - index_property_name (str): The property name associated with the index. + - data_points (List[DataPoint]): A list of DataPoint objects to be indexed. + """ formatted_data_points = [ IndexSchema( id=data_point.id, @@ -146,6 +270,24 @@ class MilvusAdapter(VectorDBInterface): await self.create_data_points(collection_name, formatted_data_points) async def retrieve(self, collection_name: str, data_point_ids: list[UUID]): + """ + Retrieve data points from a collection based on their IDs asynchronously. + + Raises CollectionNotFoundError if the specified collection does not exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which data points will be + retrieved. + - data_point_ids (list[UUID]): A list of UUIDs representing the IDs of the data + points to be retrieved. + + Returns: + -------- + + The results of the query, including the requested data points. + """ from pymilvus import MilvusException, exceptions client = self.get_milvus_client() @@ -176,12 +318,43 @@ class MilvusAdapter(VectorDBInterface): limit: int = 15, with_vector: bool = False, ): + """ + Search for data points in a collection based on a text query or vector asynchronously. + + Raises ValueError if neither query_text nor query_vector is provided. Raises + MilvusException for errors during the search process. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to search within. + - query_text (Optional[str]): Optional text query used for searching, defaults to + None. (default None) + - query_vector (Optional[List[float]]): Optional vector query used for searching, + defaults to None. (default None) + - limit (int): Maximum number of results to return, defaults to 15. (default 15) + - with_vector (bool): Flag to indicate if the vector should be included in the + results, defaults to False. (default False) + + Returns: + -------- + + A list of scored results that match the query; may include vector data if requested. + """ from pymilvus import MilvusException, exceptions + if limit <= 0: + return [] client = self.get_milvus_client() if query_text is None and query_vector is None: raise ValueError("One of query_text or query_vector must be provided!") + if not client.has_collection(collection_name=collection_name): + logger.warning( + f"Collection '{collection_name}' not found in MilvusAdapter.search; returning []." + ) + return [] + try: query_vector = query_vector or (await self.embed_data([query_text]))[0] @@ -208,17 +381,45 @@ class MilvusAdapter(VectorDBInterface): ) for result in results[0] ] - except exceptions.CollectionNotExistException as error: - raise CollectionNotFoundError( - f"Collection '{collection_name}' does not exist!" - ) from error + except exceptions.CollectionNotExistException: + logger.warning( + f"Collection '{collection_name}' not found (exception) in MilvusAdapter.search; returning []." + ) + return [] except MilvusException as e: - logger.error(f"Error during search in collection '{collection_name}': {str(e)}") + # Catch other Milvus errors that are "collection not found" (paranoid safety) + if "collection not found" in str(e).lower() or "schema" in str(e).lower(): + logger.warning( + f"Collection '{collection_name}' not found (MilvusException) in MilvusAdapter.search; returning []." + ) + return [] + logger.error(f"Error searching Milvus collection '{collection_name}': {e}") raise e async def batch_search( self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False ): + """ + Perform a batch search in a collection for multiple textual queries asynchronously. + + Utilizes embed_data to convert texts into vectors and returns the search results for + each query. + + Parameters: + ----------- + + - collection_name (str): The name of the collection where the search will be + performed. + - query_texts (List[str]): A list of texts to search for in the collection. + - limit (int): Maximum number of results to return per query. + - with_vectors (bool): Specifies if the vectors should be included in the search + results, defaults to False. (default False) + + Returns: + -------- + + A list of search result sets, one for each query input. + """ query_vectors = await self.embed_data(query_texts) return await asyncio.gather( @@ -234,6 +435,24 @@ class MilvusAdapter(VectorDBInterface): ) async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]): + """ + Delete specific data points from a collection based on their IDs asynchronously. + + Raises MilvusException for errors during the deletion process. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which data points will be + deleted. + - data_point_ids (list[UUID]): A list of UUIDs representing the IDs of the data + points to be deleted. + + Returns: + -------- + + The result of the delete operation, indicating success or failure. + """ from pymilvus import MilvusException client = self.get_milvus_client() @@ -253,6 +472,9 @@ class MilvusAdapter(VectorDBInterface): raise e async def prune(self): + """ + Remove all collections from the connected Milvus client asynchronously. + """ client = self.get_milvus_client() if client: collections = client.list_collections() diff --git a/cognee/infrastructure/databases/vector/models/CollectionConfig.py b/cognee/infrastructure/databases/vector/models/CollectionConfig.py index b279a6be2..a1d3a32d2 100644 --- a/cognee/infrastructure/databases/vector/models/CollectionConfig.py +++ b/cognee/infrastructure/databases/vector/models/CollectionConfig.py @@ -3,4 +3,19 @@ from .VectorConfig import VectorConfig class CollectionConfig(BaseModel): + """ + Represent a configuration for a collection of vector embeddings. + + This class is a subclass of BaseModel and encapsulates the configuration details for a + vector collection. + Public methods include: + + - __init__() : Initialize a new CollectionConfig instance with a vector configuration. + + Instance variables: + + - vector_config : An instance of VectorConfig representing the configuration for the + vector embeddings. + """ + vector_config: VectorConfig diff --git a/cognee/infrastructure/databases/vector/models/ScoredResult.py b/cognee/infrastructure/databases/vector/models/ScoredResult.py index 8b6f00952..0a8cc9888 100644 --- a/cognee/infrastructure/databases/vector/models/ScoredResult.py +++ b/cognee/infrastructure/databases/vector/models/ScoredResult.py @@ -4,6 +4,18 @@ from pydantic import BaseModel class ScoredResult(BaseModel): + """ + Represents the result of a scoring operation with an identification and associated data. + + Attributes: + + - id (UUID): Unique identifier for the scored result. + - score (float): The score associated with the result, where a lower score indicates a + better outcome. + - payload (Dict[str, Any]): Additional information related to the score, stored as + key-value pairs in a dictionary. + """ + id: UUID score: float # Lower score is better payload: Dict[str, Any] diff --git a/cognee/infrastructure/databases/vector/models/VectorConfig.py b/cognee/infrastructure/databases/vector/models/VectorConfig.py index fb89d90b5..9a5dacbec 100644 --- a/cognee/infrastructure/databases/vector/models/VectorConfig.py +++ b/cognee/infrastructure/databases/vector/models/VectorConfig.py @@ -3,5 +3,12 @@ from pydantic import BaseModel class VectorConfig(BaseModel): + """ + Represent a configuration for vector operations, including distance metric and size. + + Public methods include: None + Instance variables: distance, size + """ + distance: Literal["Cosine", "Dot"] size: int diff --git a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py index 5796a4619..64b031159 100644 --- a/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py +++ b/cognee/infrastructure/databases/vector/pgvector/PGVectorAdapter.py @@ -22,6 +22,14 @@ from .serialize_data import serialize_data class IndexSchema(DataPoint): + """ + Define a schema for indexing data points with a text field. + + This class inherits from the DataPoint class and specifies the structure of a single + data point that includes a text attribute. It also includes a metadata field that + indicates which fields should be indexed. + """ + text: str metadata: dict = {"index_fields": ["text"]} @@ -56,9 +64,35 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): self.Vector = Vector async def embed_data(self, data: list[str]) -> list[list[float]]: + """ + Embed a list of texts into vectors using the specified embedding engine. + + Parameters: + ----------- + + - data (list[str]): A list of strings to be embedded into vectors. + + Returns: + -------- + + - list[list[float]]: A list of lists of floats representing embedded vectors. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Check if a specified collection exists in the database. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check for existence. + + Returns: + -------- + + - bool: Returns True if the collection exists, False otherwise. + """ async with self.engine.begin() as connection: # Create a MetaData instance to load table information metadata = MetaData() @@ -77,6 +111,21 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): 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. + + 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. + + Instance variables: + - primary_key: Unique identifier for the data point, generated by uuid4. + - 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 @@ -111,6 +160,17 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface): vector_size = self.embedding_engine.get_vector_size() class PGVectorDataPoint(Base): + """ + Represents a data point in a PGVector database. This class maps to a table defined by + the SQLAlchemy ORM. + + It contains the following public instance variables: + - primary_key: The primary key of the data point, generated automatically. + - id: An identifier for the data point. + - payload: A JSON object containing additional data related to the data point. + - vector: A vector representation of the data point, configured to the specified size. + """ + __tablename__ = collection_name __table_args__ = {"extend_existing": True} # PGVector requires one column to be the primary key diff --git a/cognee/infrastructure/databases/vector/pgvector/create_db_and_tables.py b/cognee/infrastructure/databases/vector/pgvector/create_db_and_tables.py index 881c1e72f..68e339d2f 100644 --- a/cognee/infrastructure/databases/vector/pgvector/create_db_and_tables.py +++ b/cognee/infrastructure/databases/vector/pgvector/create_db_and_tables.py @@ -3,6 +3,13 @@ from sqlalchemy import text async def create_db_and_tables(): + """ + Create the database and its associated tables if necessary. + + This function checks the vector database provider configuration and, if it is set to + "pgvector", creates the necessary vector extension in the PostgreSQL database using an + asynchronous context manager. + """ vector_config = get_vectordb_config() vector_engine = get_vector_engine() diff --git a/cognee/infrastructure/databases/vector/pgvector/serialize_data.py b/cognee/infrastructure/databases/vector/pgvector/serialize_data.py index a61a2682f..3a2e2a0e2 100644 --- a/cognee/infrastructure/databases/vector/pgvector/serialize_data.py +++ b/cognee/infrastructure/databases/vector/pgvector/serialize_data.py @@ -3,7 +3,25 @@ from uuid import UUID def serialize_data(data): - """Recursively convert datetime objects in dictionaries/lists to ISO format.""" + """ + Recursively convert various data types to serializable formats. + + This function processes dictionaries and lists, converting any datetime objects to ISO + 8601 strings, and UUID objects to their string representation. Other data types are + returned unchanged. It handles recursive structures if present. + + Parameters: + ----------- + + - data: The input data to serialize, which can be a dict, list, datetime, UUID, or + other types. + + Returns: + -------- + + The serialized representation of the input data, with datetime objects converted to + ISO format and UUIDs to strings. + """ if isinstance(data, dict): return {key: serialize_data(value) for key, value in data.items()} elif isinstance(data, list): diff --git a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py index 72d66e2de..4e3e677bf 100644 --- a/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py +++ b/cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py @@ -15,6 +15,13 @@ logger = get_logger("QDrantAdapter") class IndexSchema(DataPoint): + """ + Represents a schema for indexing where each data point contains a text field. + + This class inherits from DataPoint and defines a text attribute as well as metadata + containing index fields used for indexing operations. + """ + text: str metadata: dict = {"index_fields": ["text"]} @@ -28,24 +35,95 @@ class IndexSchema(DataPoint): def create_hnsw_config(hnsw_config: Dict): + """ + Create HNSW configuration. + + This function returns an HNSW configuration object if the provided configuration is not + None, otherwise it returns None. + + Parameters: + ----------- + + - hnsw_config (Dict): A dictionary containing HNSW configuration parameters. + + Returns: + -------- + + An instance of models.HnswConfig if hnsw_config is not None, otherwise None. + """ if hnsw_config is not None: return models.HnswConfig() return None def create_optimizers_config(optimizers_config: Dict): + """ + Create and return an OptimizersConfig instance if the input configuration is provided. + + This function checks if the given optimizers configuration is not None. If valid, it + initializes and returns a new instance of the OptimizersConfig class from the models + module. If the configuration is None, it returns None instead. + + Parameters: + ----------- + + - optimizers_config (Dict): A dictionary containing optimizer configuration + settings. + + Returns: + -------- + + Returns an instance of OptimizersConfig if optimizers_config is provided; otherwise, + returns None. + """ if optimizers_config is not None: return models.OptimizersConfig() return None def create_quantization_config(quantization_config: Dict): + """ + Create a quantization configuration based on the provided settings. + + This function generates an instance of `QuantizationConfig` if the provided + `quantization_config` is not None. If it is None, the function returns None. + + Parameters: + ----------- + + - quantization_config (Dict): A dictionary containing the quantization configuration + settings. + + Returns: + -------- + + An instance of `QuantizationConfig` if `quantization_config` is provided; otherwise, + returns None. + """ if quantization_config is not None: return models.QuantizationConfig() return None class QDrantAdapter(VectorDBInterface): + """ + Adapt to the Qdrant vector database interface. + + Public methods: + - get_qdrant_client + - embed_data + - has_collection + - create_collection + - create_data_points + - create_vector_index + - index_data_points + - retrieve + - search + - batch_search + - delete_data_points + - prune + """ + name = "Qdrant" url: str = None api_key: str = None @@ -61,6 +139,18 @@ class QDrantAdapter(VectorDBInterface): self.api_key = api_key def get_qdrant_client(self) -> AsyncQdrantClient: + """ + Retrieve an instance of AsyncQdrantClient configured with the appropriate + settings based on the instance's attributes. + + Returns an instance of AsyncQdrantClient configured to connect to the database. + + Returns: + -------- + + - AsyncQdrantClient: An instance of AsyncQdrantClient configured for database + operations. + """ if self.qdrant_path is not None: return AsyncQdrantClient(path=self.qdrant_path, port=6333) elif self.url is not None: @@ -69,9 +159,35 @@ class QDrantAdapter(VectorDBInterface): return AsyncQdrantClient(location=":memory:") async def embed_data(self, data: List[str]) -> List[float]: + """ + Embed a list of text data into vector representations asynchronously. + + Parameters: + ----------- + + - data (List[str]): A list of strings containing the text data to be embedded. + + Returns: + -------- + + - List[float]: A list of floating-point vectors representing the embedded text data. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Check if a specified collection exists in the Qdrant database asynchronously. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check for existence. + + Returns: + -------- + + - bool: True if the specified collection exists, False otherwise. + """ client = self.get_qdrant_client() result = await client.collection_exists(collection_name) await client.close() @@ -82,6 +198,17 @@ class QDrantAdapter(VectorDBInterface): collection_name: str, payload_schema=None, ): + """ + Create a new collection in the Qdrant database if it does not already exist. + + If the collection already exists, this operation has no effect. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to create. + - payload_schema: Optional schema for the payload. Defaults to None. (default None) + """ client = self.get_qdrant_client() if not await client.collection_exists(collection_name): @@ -97,6 +224,23 @@ class QDrantAdapter(VectorDBInterface): await client.close() async def create_data_points(self, collection_name: str, data_points: List[DataPoint]): + """ + Create and upload data points to a specified collection in the database. + + Raises CollectionNotFoundError if the collection does not exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to which data points will be + uploaded. + - data_points (List[DataPoint]): A list of DataPoint objects to be uploaded. + + Returns: + -------- + + None if the operation is successful; raises exceptions on error. + """ from qdrant_client.http.exceptions import UnexpectedResponse client = self.get_qdrant_client() @@ -106,6 +250,19 @@ class QDrantAdapter(VectorDBInterface): ) def convert_to_qdrant_point(data_point: DataPoint): + """ + Convert a DataPoint object into the format expected by Qdrant for upload. + + Parameters: + ----------- + + - data_point (DataPoint): The DataPoint object to convert. + + Returns: + -------- + + None; performs an operation without returning a value. + """ return models.PointStruct( id=str(data_point.id), payload=data_point.model_dump(), @@ -130,11 +287,36 @@ class QDrantAdapter(VectorDBInterface): await client.close() async def create_vector_index(self, index_name: str, index_property_name: str): + """ + Create a vector index for a specified property name. + + This is essentially a wrapper around create_collection, which allows for more + flexibility + in index naming. + + Parameters: + ----------- + + - index_name (str): The base name for the index to be created. + - index_property_name (str): The property name that will be part of the index name. + """ await self.create_collection(f"{index_name}_{index_property_name}") async def index_data_points( self, index_name: str, index_property_name: str, data_points: list[DataPoint] ): + """ + Index data points into a specific collection based on provided metadata. + + Transforms DataPoint objects into an appropriate format and uploads them. + + Parameters: + ----------- + + - index_name (str): The base name for the index used for naming the collection. + - index_property_name (str): The property name used for naming the collection. + - data_points (list[DataPoint]): A list of DataPoint objects to index. + """ await self.create_data_points( f"{index_name}_{index_property_name}", [ @@ -147,6 +329,22 @@ class QDrantAdapter(VectorDBInterface): ) async def retrieve(self, collection_name: str, data_point_ids: list[str]): + """ + Retrieve data points from a specified collection based on their IDs. + + Returns the data corresponding to the provided IDs from the collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to retrieve from. + - data_point_ids (list[str]): A list of IDs of the data points to retrieve. + + Returns: + -------- + + The retrieved data points, including payloads for each ID. + """ client = self.get_qdrant_client() results = await client.retrieve(collection_name, data_point_ids, with_payload=True) await client.close() @@ -159,12 +357,50 @@ class QDrantAdapter(VectorDBInterface): query_vector: Optional[List[float]] = None, limit: int = 15, with_vector: bool = False, - ): + ) -> List[ScoredResult]: + """ + Search for data points in a collection based on either a textual query or a vector + query. + + Raises InvalidValueError if both query_text and query_vector are None. + + Returns a list of scored results that match the search criteria. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to search within. + - query_text (Optional[str]): The text to be used in the search query; optional if + query_vector is provided. (default None) + - query_vector (Optional[List[float]]): The vector to be used in the search query; + optional if query_text is provided. (default None) + - limit (int): The maximum number of results to return; defaults to 15. (default 15) + - with_vector (bool): Indicates whether to return vector data along with results; + defaults to False. (default False) + + Returns: + -------- + + - List[ScoredResult]: A list of ScoredResult objects representing the results of the + search. + """ from qdrant_client.http.exceptions import UnexpectedResponse if query_text is None and query_vector is None: raise InvalidValueError(message="One of query_text or query_vector must be provided!") + if limit <= 0: + return [] + + if not await self.has_collection(collection_name): + logger.warning( + f"Collection '{collection_name}' not found in QdrantAdapter.search; returning []." + ) + return [] + + if query_vector is None: + query_vector = (await self.embed_data([query_text]))[0] + try: client = self.get_qdrant_client() @@ -211,16 +447,25 @@ class QDrantAdapter(VectorDBInterface): with_vectors: bool = False, ): """ - Perform batch search in a Qdrant collection with dynamic search requests. + Perform a batch search in a specified collection using multiple query texts. - Args: - - collection_name (str): Name of the collection to search in. - - query_texts (List[str]): List of query texts to search for. - - limit (int): List of result limits for search requests. - - with_vectors (bool, optional): Bool indicating whether to return vectors for search requests. + Returns the results of the search for each query, filtering for results with a score + higher than 0.9. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to search in. + - query_texts (List[str]): A list of query texts to search for in the collection. + - limit (int): The maximum number of results to return for each search request; can + be None. (default None) + - with_vectors (bool): Indicates whether to include vector data in the results; + defaults to False. (default False) Returns: - - results: The search results from Qdrant. + -------- + + A list containing the filtered search results for each query text. """ vectors = await self.embed_data(query_texts) @@ -245,11 +490,29 @@ class QDrantAdapter(VectorDBInterface): return [filter(lambda result: result.score > 0.9, result_group) for result_group in results] async def delete_data_points(self, collection_name: str, data_point_ids: list[str]): + """ + Delete specific data points from a specified collection based on their IDs. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to delete the data + points. + - data_point_ids (list[str]): The list of IDs of data points to be deleted. + + Returns: + -------- + + The result of the delete operation from the database. + """ client = self.get_qdrant_client() results = await client.delete(collection_name, data_point_ids) return results async def prune(self): + """ + Remove all collections from the Qdrant database asynchronously. + """ client = self.get_qdrant_client() response = await client.get_collections() diff --git a/cognee/infrastructure/databases/vector/supported_databases.py b/cognee/infrastructure/databases/vector/supported_databases.py new file mode 100644 index 000000000..c067e43f1 --- /dev/null +++ b/cognee/infrastructure/databases/vector/supported_databases.py @@ -0,0 +1 @@ +supported_databases = {} diff --git a/cognee/infrastructure/databases/vector/use_vector_adapter.py b/cognee/infrastructure/databases/vector/use_vector_adapter.py new file mode 100644 index 000000000..b381ebdd5 --- /dev/null +++ b/cognee/infrastructure/databases/vector/use_vector_adapter.py @@ -0,0 +1,5 @@ +from .supported_databases import supported_databases + + +def use_vector_adapter(vector_db_name, vector_db_adapter): + supported_databases[vector_db_name] = vector_db_adapter diff --git a/cognee/infrastructure/databases/vector/utils.py b/cognee/infrastructure/databases/vector/utils.py index 9507207c0..09741d9e7 100644 --- a/cognee/infrastructure/databases/vector/utils.py +++ b/cognee/infrastructure/databases/vector/utils.py @@ -2,6 +2,25 @@ from typing import List def normalize_distances(result_values: List[dict]) -> List[float]: + """ + Normalize distances in the provided result values. + + This function takes a list of dictionaries containing distance values and normalizes + these distances to a range of 0 to 1. If all distances are the same, it assigns all + normalized values to 0 to avoid division by zero. + + Parameters: + ----------- + + - result_values (List[dict]): A list of dictionaries, each containing a '_distance' + key with a numeric value to be normalized. + + Returns: + -------- + + - List[float]: A list of normalized float values corresponding to the distances in + the input list. + """ min_value = min(result["_distance"] for result in result_values) max_value = max(result["_distance"] for result in result_values) diff --git a/cognee/infrastructure/databases/vector/vector_db_interface.py b/cognee/infrastructure/databases/vector/vector_db_interface.py index 939997fec..93c19adec 100644 --- a/cognee/infrastructure/databases/vector/vector_db_interface.py +++ b/cognee/infrastructure/databases/vector/vector_db_interface.py @@ -5,10 +5,26 @@ from .models.PayloadSchema import PayloadSchema class VectorDBInterface(Protocol): - """Collections""" + """ + Defines an interface for interacting with a vector database, including operations for + managing collections and data points. + """ @abstractmethod async def has_collection(self, collection_name: str) -> bool: + """ + Check if a specified collection exists. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check for existence. + + Returns: + -------- + + - bool: True if the collection exists, otherwise False. + """ raise NotImplementedError @abstractmethod @@ -17,16 +33,46 @@ class VectorDBInterface(Protocol): collection_name: str, payload_schema: Optional[PayloadSchema] = None, ): + """ + Create a new collection with an optional payload schema. + + Parameters: + ----------- + + - collection_name (str): The name of the new collection to create. + - payload_schema (Optional[PayloadSchema]): An optional schema for the payloads + within this collection. (default None) + """ raise NotImplementedError """ Data points """ @abstractmethod async def create_data_points(self, collection_name: str, data_points: List[DataPoint]): + """ + Insert new data points into the specified collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection where data points will be added. + - data_points (List[DataPoint]): A list of data points to be added to the + collection. + """ raise NotImplementedError @abstractmethod async def retrieve(self, collection_name: str, data_point_ids: list[str]): + """ + Retrieve data points from a collection using their IDs. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to retrieve data + points. + - data_point_ids (list[str]): A list of IDs of the data points to retrieve. + """ raise NotImplementedError """ Search """ @@ -40,18 +86,59 @@ class VectorDBInterface(Protocol): limit: int, with_vector: bool = False, ): + """ + Perform a search in the specified collection using either a text query or a vector + query. + + Parameters: + ----------- + + - collection_name (str): The name of the collection in which to perform the search. + - query_text (Optional[str]): An optional text query to search for in the + collection. + - query_vector (Optional[List[float]]): An optional vector representation for + searching the collection. + - limit (int): The maximum number of results to return from the search. + - with_vector (bool): Whether to return the vector representations with search + results. (default False) + """ raise NotImplementedError @abstractmethod async def batch_search( self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False ): + """ + Perform a batch search using multiple text queries against a collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to conduct the batch search in. + - query_texts (List[str]): A list of text queries to use for the search. + - limit (int): The maximum number of results to return for each query. + - with_vectors (bool): Whether to include vector representations with search + results. (default False) + """ raise NotImplementedError @abstractmethod async def delete_data_points(self, collection_name: str, data_point_ids: list[str]): + """ + Delete specified data points from a collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to delete data + points. + - data_point_ids (list[str]): A list of IDs of the data points to delete. + """ raise NotImplementedError @abstractmethod async def prune(self): + """ + Remove obsolete or unnecessary data from the database. + """ raise NotImplementedError diff --git a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py index dac6bc83f..8b009bcea 100644 --- a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py +++ b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py @@ -14,12 +14,43 @@ logger = get_logger("WeaviateAdapter") class IndexSchema(DataPoint): + """ + Define a schema for indexing data points with textual content. + + The IndexSchema class inherits from DataPoint and includes the following public + attributes: + + - text: A string representing the main content of the data point. + - metadata: A dictionary containing indexing information, specifically the fields to be + indexed (in this case, the 'text' field). + """ + text: str metadata: dict = {"index_fields": ["text"]} class WeaviateAdapter(VectorDBInterface): + """ + Adapt the Weaviate vector database to an interface for managing collections and data + points. + + Public methods: + - get_client + - embed_data + - has_collection + - create_collection + - get_collection + - create_data_points + - create_vector_index + - index_data_points + - retrieve + - search + - batch_search + - delete_data_points + - prune + """ + name = "Weaviate" url: str api_key: str @@ -41,14 +72,55 @@ class WeaviateAdapter(VectorDBInterface): ) async def get_client(self): + """ + Establish a connection to the Weaviate client. + + Return the Weaviate client instance after connecting asynchronously. + + Returns: + -------- + + The Weaviate client instance. + """ await self.client.connect() return self.client async def embed_data(self, data: List[str]) -> List[float]: + """ + Embed the given text data into vector representations. + + Given a list of strings, return their vector embeddings using the configured embedding + engine. + + Parameters: + ----------- + + - data (List[str]): A list of strings to be embedded. + + Returns: + -------- + + - List[float]: A list of float vectors corresponding to the embedded text data. + """ return await self.embedding_engine.embed_text(data) async def has_collection(self, collection_name: str) -> bool: + """ + Check if a collection exists in the Weaviate database. + + Return a boolean indicating the presence of the specified collection. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to check. + + Returns: + -------- + + - bool: True if the collection exists, otherwise False. + """ client = await self.get_client() return await client.collections.exists(collection_name) @@ -57,6 +129,24 @@ class WeaviateAdapter(VectorDBInterface): collection_name: str, payload_schema=None, ): + """ + Create a new collection in the Weaviate database if it does not already exist. + + The collection will be initialized with a default schema. + + Parameters: + ----------- + + - collection_name (str): The name of the new collection to be created. + - payload_schema: Optional schema definition for the collection payload. (default + None) + + Returns: + -------- + + The created collection's configuration, if a new collection was made, otherwise + information about the existing collection. + """ import weaviate.classes.config as wvcc if not await self.has_collection(collection_name): @@ -73,6 +163,21 @@ class WeaviateAdapter(VectorDBInterface): return await self.get_collection(collection_name) async def get_collection(self, collection_name: str): + """ + Retrieve a collection from the Weaviate database by its name. + + Raise a CollectionNotFoundError if the specified collection does not exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to be retrieved. + + Returns: + -------- + + The requested collection object from the database. + """ if not await self.has_collection(collection_name): raise CollectionNotFoundError(f"Collection '{collection_name}' not found.") @@ -80,6 +185,24 @@ class WeaviateAdapter(VectorDBInterface): return client.collections.get(collection_name) async def create_data_points(self, collection_name: str, data_points: List[DataPoint]): + """ + Create or update data points in the specified collection in the Weaviate database. + + Process the list of data points, embedding them and either inserting them or updating if + they already exist. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to add data points to. + - data_points (List[DataPoint]): A list of DataPoint objects to be created or + updated in the collection. + + Returns: + -------- + + Information about the inserted or updated data points in the collection. + """ from weaviate.classes.data import DataObject data_vectors = await self.embed_data( @@ -87,6 +210,22 @@ class WeaviateAdapter(VectorDBInterface): ) def convert_to_weaviate_data_points(data_point: DataPoint): + """ + Transform a DataPoint object into a Weaviate DataObject format for insertion. + + Return a DataObject ready for use in Weaviate with the properties and vector included. + + Parameters: + ----------- + + - data_point (DataPoint): The DataPoint to convert into the Weaviate DataObject + format. + + Returns: + -------- + + The corresponding Weaviate DataObject representing the data point. + """ vector = data_vectors[data_points.index(data_point)] properties = data_point.model_dump() @@ -113,7 +252,7 @@ class WeaviateAdapter(VectorDBInterface): # ) else: data_point: DataObject = data_points[0] - if collection.data.exists(data_point.uuid): + if await collection.data.exists(data_point.uuid): return await collection.data.update( uuid=data_point.uuid, vector=data_point.vector, @@ -132,11 +271,43 @@ class WeaviateAdapter(VectorDBInterface): raise error async def create_vector_index(self, index_name: str, index_property_name: str): + """ + Create a vector index based on an index name and property name by creating a + corresponding collection. + + Parameters: + ----------- + + - index_name (str): The name for the vector index. + - index_property_name (str): The property name associated with the vector index. + + Returns: + -------- + + The created collection representing the vector index. + """ return await self.create_collection(f"{index_name}_{index_property_name}") async def index_data_points( self, index_name: str, index_property_name: str, data_points: list[DataPoint] ): + """ + Index a list of data points by creating an associated vector index collection. + + Data points are transformed into embeddable data before being processed for indexing. + + Parameters: + ----------- + + - index_name (str): The index name under which to store the data points. + - index_property_name (str): The associated property name for the index. + - data_points (list[DataPoint]): A list of DataPoint objects to be indexed. + + Returns: + -------- + + Information about the operation of indexing the data points. + """ return await self.create_data_points( f"{index_name}_{index_property_name}", [ @@ -149,6 +320,23 @@ class WeaviateAdapter(VectorDBInterface): ) async def retrieve(self, collection_name: str, data_point_ids: list[str]): + """ + Fetch data points from a specified collection based on their IDs. + + Return data points wrapped in an object containing their properties after + transformation. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to retrieve data points from. + - data_point_ids (list[str]): A list of IDs for the data points to retrieve. + + Returns: + -------- + + A list of objects representing the retrieved data points. + """ from weaviate.classes.query import Filter collection = await self.get_collection(collection_name) @@ -171,6 +359,28 @@ class WeaviateAdapter(VectorDBInterface): limit: int = 15, with_vector: bool = False, ): + """ + Perform a search on a collection using either a text query or a vector query. + + Return scored results based on the search criteria provided. Raise InvalidValueError if + no query is provided. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to search within. + - query_text (Optional[str]): Optional plain text query for searching. (default + None) + - query_vector (Optional[List[float]]): Optional vector representation for + searching. (default None) + - limit (int): The maximum number of results to return. (default 15) + - with_vector (bool): Include vector information in the results. (default False) + + Returns: + -------- + + A list of scored results matching the search criteria. + """ import weaviate.classes as wvc import weaviate.exceptions @@ -206,7 +416,43 @@ class WeaviateAdapter(VectorDBInterface): async def batch_search( self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False ): + """ + Execute a batch search for multiple query texts in the specified collection. + + Return a list of results for each query performed in parallel. + + Parameters: + ----------- + + - collection_name (str): The name of the collection to search within. + - query_texts (List[str]): A list of text queries to be processed in a batch. + - limit (int): The maximum number of results to return for each query. + - with_vectors (bool): Indicate whether to include vector information in the + results. (default False) + + Returns: + -------- + + A list containing results for each search query executed. + """ + def query_search(query_vector): + """ + Wrap the search operation based on a query vector for fetching results. + + This function coordinates the search call, ensuring the collection name and search + parameters are applied. + + Parameters: + ----------- + + - query_vector: The vector representation of the query for searching. + + Returns: + -------- + + The results of the search operation on the specified collection. + """ return self.search( collection_name, query_vector=query_vector, limit=limit, with_vector=with_vectors ) @@ -216,6 +462,24 @@ class WeaviateAdapter(VectorDBInterface): ] async def delete_data_points(self, collection_name: str, data_point_ids: list[str]): + """ + Remove specified data points from a collection based on their IDs. + + Return information about the deletion result, ideally confirming the operation's + success. + + Parameters: + ----------- + + - collection_name (str): The name of the collection from which to delete data + points. + - data_point_ids (list[str]): A list of IDs for the data points to be deleted. + + Returns: + -------- + + Confirmation of deletion operation result. + """ from weaviate.classes.query import Filter collection = await self.get_collection(collection_name) @@ -226,5 +490,10 @@ class WeaviateAdapter(VectorDBInterface): return result async def prune(self): + """ + Delete all collections from the Weaviate database. + + This operation will remove all data and cannot be undone. + """ client = await self.get_client() await client.collections.delete_all() diff --git a/cognee/infrastructure/engine/models/DataPoint.py b/cognee/infrastructure/engine/models/DataPoint.py index 986b13a0e..2ab3e1752 100644 --- a/cognee/infrastructure/engine/models/DataPoint.py +++ b/cognee/infrastructure/engine/models/DataPoint.py @@ -8,12 +8,32 @@ from typing import Optional, Any, Dict, List # Define metadata type class MetaData(TypedDict): + """ + Represent a metadata structure with type and index fields. + """ + type: str index_fields: list[str] # Updated DataPoint model with versioning and new fields class DataPoint(BaseModel): + """ + Model representing a data point with versioning and metadata support. + + Public methods include: + - get_embeddable_data + - get_embeddable_properties + - get_embeddable_property_names + - update_version + - to_json + - from_json + - to_pickle + - from_pickle + - to_dict + - from_dict + """ + id: UUID = Field(default_factory=uuid4) created_at: int = Field( default_factory=lambda: int(datetime.now(timezone.utc).timestamp() * 1000) @@ -34,6 +54,24 @@ class DataPoint(BaseModel): @classmethod def get_embeddable_data(self, data_point: "DataPoint"): + """ + Retrieve embeddable data from the data point object based on index fields. + + This method checks if there are defined index fields in the metadata and retrieves the + value of the first indexed attribute. If the attribute is a string, it strips whitespace + from it before returning. + + Parameters: + ----------- + + - data_point ('DataPoint'): The DataPoint instance from which to retrieve embeddable + data. + + Returns: + -------- + + The value of the embeddable data, or None if not found. + """ if ( data_point.metadata and len(data_point.metadata["index_fields"]) > 0 @@ -47,7 +85,23 @@ class DataPoint(BaseModel): @classmethod def get_embeddable_properties(self, data_point: "DataPoint"): - """Retrieve all embeddable properties.""" + """ + Retrieve a list of embeddable properties from the data point. + + This method returns a list of attribute values based on the index fields defined in the + data point's metadata. If there are no index fields, it returns an empty list. + + Parameters: + ----------- + + - data_point ('DataPoint'): The DataPoint instance from which to retrieve embeddable + properties. + + Returns: + -------- + + A list of embeddable property values, or an empty list if none exist. + """ if data_point.metadata and len(data_point.metadata["index_fields"]) > 0: return [ getattr(data_point, field, None) for field in data_point.metadata["index_fields"] @@ -57,40 +111,145 @@ class DataPoint(BaseModel): @classmethod def get_embeddable_property_names(self, data_point: "DataPoint"): - """Retrieve names of embeddable properties.""" + """ + Retrieve the names of embeddable properties defined in the metadata. + + If no index fields are defined in the metadata, this method will return an empty list. + + Parameters: + ----------- + + - data_point ('DataPoint'): The DataPoint instance from which to retrieve embeddable + property names. + + Returns: + -------- + + A list of property names corresponding to the index fields, or an empty list if none + exist. + """ return data_point.metadata["index_fields"] or [] def update_version(self): - """Update the version and updated_at timestamp.""" + """ + Increment the version number of the data point and update the timestamp. + + This method will automatically modify the version attribute and refresh the updated_at + timestamp to the current time. + """ self.version += 1 self.updated_at = int(datetime.now(timezone.utc).timestamp() * 1000) # JSON Serialization def to_json(self) -> str: - """Serialize the instance to a JSON string.""" + """ + Serialize the DataPoint instance to a JSON string format. + + This method uses the model's built-in serialization functionality to convert the + instance into a JSON-compatible string. + + Returns: + -------- + + - str: The JSON string representation of the DataPoint instance. + """ return self.json() @classmethod def from_json(self, json_str: str): - """Deserialize the instance from a JSON string.""" + """ + Deserialize a DataPoint instance from a JSON string. + + The method transforms the input JSON string back into a DataPoint instance using model + validation. + + Parameters: + ----------- + + - json_str (str): The JSON string representation of a DataPoint instance to be + deserialized. + + Returns: + -------- + + A new DataPoint instance created from the JSON data. + """ return self.model_validate_json(json_str) # Pickle Serialization def to_pickle(self) -> bytes: - """Serialize the instance to pickle-compatible bytes.""" + """ + Serialize the DataPoint instance to a byte format for pickling. + + This method uses the built-in Python pickle module to convert the instance into a byte + stream for persistence or transmission. + + Returns: + -------- + + - bytes: The pickled byte representation of the DataPoint instance. + """ return pickle.dumps(self.dict()) @classmethod def from_pickle(self, pickled_data: bytes): - """Deserialize the instance from pickled bytes.""" + """ + Deserialize a DataPoint instance from a pickled byte stream. + + The method converts the byte stream back into a DataPoint instance by loading the data + and validating it through the model's constructor. + + Parameters: + ----------- + + - pickled_data (bytes): The bytes representation of a pickled DataPoint instance to + be deserialized. + + Returns: + -------- + + A new DataPoint instance created from the pickled data. + """ data = pickle.loads(pickled_data) return self(**data) def to_dict(self, **kwargs) -> Dict[str, Any]: - """Serialize model to a dictionary.""" + """ + Convert the DataPoint instance to a dictionary representation. + + This method uses the model's built-in functionality to serialize the instance attributes + to a dictionary, which can optionally include additional arguments. + + Parameters: + ----------- + + - **kwargs: Additional keyword arguments for serialization options. + + Returns: + -------- + + - Dict[str, Any]: A dictionary representation of the DataPoint instance. + """ return self.model_dump(**kwargs) @classmethod def from_dict(cls, data: Dict[str, Any]) -> "DataPoint": - """Deserialize model from a dictionary.""" + """ + Instantiate a DataPoint from a dictionary of attribute values. + + The method validates the incoming dictionary data against the model's schema and + constructs a new DataPoint instance accordingly. + + Parameters: + ----------- + + - data (Dict[str, Any]): A dictionary containing the attributes of a DataPoint + instance. + + Returns: + -------- + + - 'DataPoint': A new DataPoint instance constructed from the provided dictionary + data. + """ return cls.model_validate(data) diff --git a/cognee/infrastructure/engine/models/ExtendableDataPoint.py b/cognee/infrastructure/engine/models/ExtendableDataPoint.py index fb4483fa5..329e85c87 100644 --- a/cognee/infrastructure/engine/models/ExtendableDataPoint.py +++ b/cognee/infrastructure/engine/models/ExtendableDataPoint.py @@ -2,4 +2,8 @@ from .DataPoint import DataPoint class ExtendableDataPoint(DataPoint): + """ + Represent an extendable data point subclassing from DataPoint. + """ + pass diff --git a/cognee/infrastructure/engine/utils/parse_id.py b/cognee/infrastructure/engine/utils/parse_id.py index de284d287..0e006a136 100644 --- a/cognee/infrastructure/engine/utils/parse_id.py +++ b/cognee/infrastructure/engine/utils/parse_id.py @@ -2,6 +2,26 @@ from uuid import UUID def parse_id(id: any): + """ + Parse the input ID and convert it to a UUID object if it is a valid string + representation. + + If the input is not a string or if the string cannot be converted to a UUID, return the + input unchanged. This function catches exceptions that may arise from invalid string + formats during conversion, but does not raise those exceptions further. + + Parameters: + ----------- + + - id (any): The input ID, which can be of any type and may be a string + representation of a UUID. + + Returns: + -------- + + The original input ID if it is not a string or cannot be converted to a UUID; + otherwise, a UUID object if the conversion is successful. + """ if isinstance(id, str): try: return UUID(id) diff --git a/cognee/infrastructure/entities/BaseEntityExtractor.py b/cognee/infrastructure/entities/BaseEntityExtractor.py index a5657b17f..3644ec233 100644 --- a/cognee/infrastructure/entities/BaseEntityExtractor.py +++ b/cognee/infrastructure/entities/BaseEntityExtractor.py @@ -5,9 +5,25 @@ from cognee.modules.engine.models import Entity class BaseEntityExtractor(ABC): - """Base class for entity extraction strategies.""" + """ + Base class for entity extraction strategies. + + This class defines the interface for entity extraction methods that derived classes must + implement. It serves as a blueprint for entity extraction strategies. + """ @abstractmethod async def extract_entities(self, text: str) -> List[Entity]: - """Extract entities from the given text.""" + """ + Extract entities from the given text. + + This is an abstract method that must be implemented by any subclass of + BaseEntityExtractor. The implementation should take a string as input and return a list + of extracted entities, defined by the Entity type. + + Parameters: + ----------- + + - text (str): A string containing the text from which to extract entities. + """ pass diff --git a/cognee/infrastructure/files/add_file_to_storage.py b/cognee/infrastructure/files/add_file_to_storage.py index 78713f447..a61fbf843 100644 --- a/cognee/infrastructure/files/add_file_to_storage.py +++ b/cognee/infrastructure/files/add_file_to_storage.py @@ -5,6 +5,18 @@ from .storage.LocalStorage import LocalStorage async def add_file_to_storage(file_path: str, file: BinaryIO): + """ + Store a file in local storage. + + This function initializes a storage manager and uses it to store the provided file at + the specified file path. + + Parameters: + ----------- + + - file_path (str): The path where the file will be stored. + - file (BinaryIO): The file object to be stored, which must be a binary file. + """ storage_manager = StorageManager(LocalStorage(get_absolute_path("data/files"))) storage_manager.store(file_path, file) diff --git a/cognee/infrastructure/files/remove_file_from_storage.py b/cognee/infrastructure/files/remove_file_from_storage.py index b657ead43..c35797bac 100644 --- a/cognee/infrastructure/files/remove_file_from_storage.py +++ b/cognee/infrastructure/files/remove_file_from_storage.py @@ -4,6 +4,20 @@ from .storage.LocalStorage import LocalStorage async def remove_file_from_storage(file_path: str): + """ + Remove a specified file from storage. + + This function initializes a storage manager with a local storage instance and calls the + remove method of the storage manager to delete the file identified by the given path. + Ensure that the file exists in the specified storage before calling this function to + avoid + potential errors. + + Parameters: + ----------- + + - file_path (str): The path of the file to remove from storage. + """ storage_manager = StorageManager(LocalStorage(get_absolute_path("data/files"))) storage_manager.remove(file_path) diff --git a/cognee/infrastructure/files/storage/LocalStorage.py b/cognee/infrastructure/files/storage/LocalStorage.py index 7ef9131d9..007a61082 100644 --- a/cognee/infrastructure/files/storage/LocalStorage.py +++ b/cognee/infrastructure/files/storage/LocalStorage.py @@ -5,12 +5,32 @@ from .StorageManager import Storage class LocalStorage(Storage): + """ + Manage local file storage operations such as storing, retrieving, and managing files on + the filesystem. + """ + storage_path: str = None def __init__(self, storage_path: str): self.storage_path = storage_path def store(self, file_path: str, data: Union[BinaryIO, str]): + """ + Store data into a specified file path. The data can be either a string or a binary + stream. + + This method ensures that the storage directory exists before attempting to write the + data. If the provided data is a stream, it reads from the stream and writes to the file; + otherwise, it directly writes the provided data. + + Parameters: + ----------- + + - file_path (str): The relative path of the file where the data will be stored. + - data (Union[BinaryIO, str]): The data to be stored, which can be a string or a + binary stream. + """ full_file_path = self.storage_path + "/" + file_path LocalStorage.ensure_directory_exists(self.storage_path) @@ -27,6 +47,24 @@ class LocalStorage(Storage): f.write(data) def retrieve(self, file_path: str, mode: str = "rb"): + """ + Retrieve data from a specified file path, returning the content as bytes. + + This method opens the file in read mode and reads its content. The function expects the + file to exist; if it does not, a FileNotFoundError will be raised. + + Parameters: + ----------- + + - file_path (str): The relative path of the file to retrieve data from. + - mode (str): The mode to open the file, with 'rb' as the default for reading binary + files. (default 'rb') + + Returns: + -------- + + The content of the retrieved file as bytes. + """ full_file_path = self.storage_path + "/" + file_path with open(full_file_path, mode=mode) as f: @@ -35,24 +73,80 @@ class LocalStorage(Storage): @staticmethod def file_exists(file_path: str): + """ + Check if a specified file exists in the filesystem. + + Parameters: + ----------- + + - file_path (str): The path of the file to check for existence. + + Returns: + -------- + + - bool: True if the file exists, otherwise False. + """ return os.path.exists(file_path) @staticmethod def ensure_directory_exists(file_path: str): + """ + Ensure that the specified directory exists, creating it if necessary. + + If the directory already exists, no action is taken. + + Parameters: + ----------- + + - file_path (str): The path of the directory to check or create. + """ if not os.path.exists(file_path): os.makedirs(file_path, exist_ok=True) @staticmethod def remove(file_path: str): + """ + Remove the specified file from the filesystem if it exists. + + Parameters: + ----------- + + - file_path (str): The path of the file to be removed. + """ if os.path.exists(file_path): os.remove(file_path) @staticmethod def copy_file(source_file_path: str, destination_file_path: str): + """ + Copy a file from a source path to a destination path. + + Parameters: + ----------- + + - source_file_path (str): The path of the file to be copied. + - destination_file_path (str): The path where the file will be copied to. + + Returns: + -------- + + - str: The path to the copied file. + """ return shutil.copy2(source_file_path, destination_file_path) @staticmethod def remove_all(tree_path: str): + """ + Remove an entire directory tree at the specified path, including all files and + subdirectories. + + If the directory does not exist, no action is taken and no exception is raised. + + Parameters: + ----------- + + - tree_path (str): The root path of the directory tree to be removed. + """ try: shutil.rmtree(tree_path) except FileNotFoundError: diff --git a/cognee/infrastructure/files/storage/StorageManager.py b/cognee/infrastructure/files/storage/StorageManager.py index 389800e00..007ab3e98 100644 --- a/cognee/infrastructure/files/storage/StorageManager.py +++ b/cognee/infrastructure/files/storage/StorageManager.py @@ -2,28 +2,108 @@ from typing import Protocol, BinaryIO class Storage(Protocol): + """ + Abstract interface for storage operations. + """ + def store(self, file_path: str, data: bytes): + """ + Store data at the specified file path. + + Parameters: + ----------- + + - file_path (str): The path where the data will be stored. + - data (bytes): The binary data to be stored. + """ pass def retrieve(self, file_path: str): + """ + Retrieve data from the specified file path. + + Parameters: + ----------- + + - file_path (str): The path from where the data will be retrieved. + """ pass @staticmethod def remove(file_path: str): + """ + Remove the storage at the specified file path. + + Parameters: + ----------- + + - file_path (str): The path of the file to be removed. + """ pass class StorageManager: + """ + Manages storage operations by delegating tasks to a storage backend. + + Public methods include: + - store: Store data in the specified path. + - retrieve: Retrieve data from the specified path. + - remove: Remove the file at the specified path. + """ + storage: Storage = None def __init__(self, storage: Storage): self.storage = storage def store(self, file_path: str, data: BinaryIO): + """ + Store data at the specified file path. + + Parameters: + ----------- + + - file_path (str): The path where the data should be stored. + - data (BinaryIO): The data in a binary format that needs to be stored. + + Returns: + -------- + + Returns the outcome of the store operation, as defined by the storage + implementation. + """ return self.storage.store(file_path, data) def retrieve(self, file_path: str): + """ + Retrieve data from the specified file path. + + Parameters: + ----------- + + - file_path (str): The path from which to retrieve the data. + + Returns: + -------- + + Returns the retrieved data, as defined by the storage implementation. + """ return self.storage.retrieve(file_path) def remove(self, file_path: str): + """ + Remove the file at the specified path. + + Parameters: + ----------- + + - file_path (str): The path of the file to be removed. + + Returns: + -------- + + Returns the outcome of the remove operation, as defined by the storage + implementation. + """ return self.storage.remove(file_path) diff --git a/cognee/infrastructure/files/utils/extract_text_from_file.py b/cognee/infrastructure/files/utils/extract_text_from_file.py index 7a5e16829..bd5fc530c 100644 --- a/cognee/infrastructure/files/utils/extract_text_from_file.py +++ b/cognee/infrastructure/files/utils/extract_text_from_file.py @@ -4,7 +4,26 @@ import filetype def extract_text_from_file(file: BinaryIO, file_type: filetype.Type) -> str: - """Extract text from a file""" + """ + Extract text from a file based on its type. + + Supports extraction from PDF and plain text file formats. For PDF files, it reads the + first three pages and returns the extracted text. For plain text files, it decodes the + file content and returns it as a string. It will raise an error if the file format is + unsupported or if there is an issue during reading. + + Parameters: + ----------- + + - file (BinaryIO): The file stream from which to extract text. + - file_type (filetype.Type): An object that provides the type of the file, including + its extension. + + Returns: + -------- + + - str: The extracted text as a string. + """ if file_type.extension == "pdf": reader = PdfReader(stream=file) pages = list(reader.pages[:3]) diff --git a/cognee/infrastructure/files/utils/get_file_metadata.py b/cognee/infrastructure/files/utils/get_file_metadata.py index aea34c04c..13f9743ea 100644 --- a/cognee/infrastructure/files/utils/get_file_metadata.py +++ b/cognee/infrastructure/files/utils/get_file_metadata.py @@ -5,6 +5,14 @@ from cognee.shared.utils import get_file_content_hash class FileMetadata(TypedDict): + """ + Represents metadata for a file. + + This class defines a structure to store various attributes related to a file, including + its name, file path, MIME type, file extension, and a content hash for integrity + checking. + """ + name: str file_path: str mime_type: str @@ -13,7 +21,24 @@ class FileMetadata(TypedDict): def get_file_metadata(file: BinaryIO) -> FileMetadata: - """Get metadata from a file""" + """ + Retrieve metadata from a file object. + + Reset the file pointer to the beginning of the file and compute the content hash. Guess + the file type and extract the file path and name. Construct and return a dictionary + containing the file's metadata attributes. + + Parameters: + ----------- + + - file (BinaryIO): A file-like object from which to extract metadata. + + Returns: + -------- + + - FileMetadata: A dictionary containing the file's name, path, MIME type, file + extension, and content hash. + """ file.seek(0) content_hash = get_file_content_hash(file) file.seek(0) diff --git a/cognee/infrastructure/files/utils/guess_file_type.py b/cognee/infrastructure/files/utils/guess_file_type.py index 3e26827d8..7daa954ea 100644 --- a/cognee/infrastructure/files/utils/guess_file_type.py +++ b/cognee/infrastructure/files/utils/guess_file_type.py @@ -4,6 +4,18 @@ from .is_text_content import is_text_content class FileTypeException(Exception): + """ + Represents an exception for file type errors. + + This exception is raised when an invalid file type is encountered. It includes a custom + message to describe the error. + + Parameters: + ----------- + + - message (str): The message describing the exception error. + """ + message: str def __init__(self, message: str): @@ -11,7 +23,12 @@ class FileTypeException(Exception): class TxtFileType(filetype.Type): - """Text file type""" + """ + Represents a text file type with specific MIME and extension properties. + + Public methods: + - match: Determines whether a given buffer matches the text file type. + """ MIME = "text/plain" EXTENSION = "txt" @@ -20,6 +37,19 @@ class TxtFileType(filetype.Type): super(TxtFileType, self).__init__(mime=TxtFileType.MIME, extension=TxtFileType.EXTENSION) def match(self, buf): + """ + Determine if the given buffer contains text content. + + Parameters: + ----------- + + - buf: The buffer to check for text content. + + Returns: + -------- + + Returns True if the buffer is identified as text content, otherwise False. + """ return is_text_content(buf) @@ -29,6 +59,17 @@ filetype.add_type(txt_file_type) class CustomPdfMatcher(filetype.Type): + """ + Match PDF file types based on MIME type and extension. + + Public methods: + - match + + Instance variables: + - MIME: The MIME type of the PDF. + - EXTENSION: The file extension of the PDF. + """ + MIME = "application/pdf" EXTENSION = "pdf" @@ -38,6 +79,24 @@ class CustomPdfMatcher(filetype.Type): ) def match(self, buf): + """ + Determine if the provided buffer is a PDF file. + + This method checks for the presence of the PDF signature in the buffer. + + Raises: + - TypeError: If the buffer is not of bytes type. + + Parameters: + ----------- + + - buf: The buffer containing the data to be checked. + + Returns: + -------- + + Returns True if the buffer contains a PDF signature, otherwise returns False. + """ return b"PDF-" in buf @@ -47,6 +106,22 @@ filetype.add_type(custom_pdf_matcher) def guess_file_type(file: BinaryIO) -> filetype.Type: + """ + Guess the file type from the given binary file stream. + + If the file type cannot be determined, raise a FileTypeException with an appropriate + message. + + Parameters: + ----------- + + - file (BinaryIO): A binary file stream to analyze for determining the file type. + + Returns: + -------- + + - filetype.Type: The guessed file type, represented as filetype.Type. + """ file_type = filetype.guess(file) if file_type is None: diff --git a/cognee/infrastructure/files/utils/is_text_content.py b/cognee/infrastructure/files/utils/is_text_content.py index 83a961e89..a369263b3 100644 --- a/cognee/infrastructure/files/utils/is_text_content.py +++ b/cognee/infrastructure/files/utils/is_text_content.py @@ -1,5 +1,21 @@ def is_text_content(content): - """Check if the content is text.""" + """ + Determine if the content is text-based. + + This function checks for various indicators of text content, including null bytes, byte + order marks (BOMs), ASCII character ranges, and common line breaks. If none of these + indicators are present, the function defaults to assuming the content is text-based. + + Parameters: + ----------- + + - content: The byte content to be checked for text characteristics. + + Returns: + -------- + + - bool: Returns True if the content is determined to be text, False otherwise. + """ # Check for null bytes if b"\0" in content: return False diff --git a/cognee/infrastructure/llm/anthropic/adapter.py b/cognee/infrastructure/llm/anthropic/adapter.py index 773a2fc28..ec2f35f7b 100644 --- a/cognee/infrastructure/llm/anthropic/adapter.py +++ b/cognee/infrastructure/llm/anthropic/adapter.py @@ -9,7 +9,10 @@ from cognee.infrastructure.llm.rate_limiter import rate_limit_async, sleep_and_r class AnthropicAdapter(LLMInterface): - """Adapter for Anthropic API""" + """ + Adapter for interfacing with the Anthropic API, enabling structured output generation + and prompt display. + """ name = "Anthropic" model: str @@ -18,7 +21,7 @@ class AnthropicAdapter(LLMInterface): import anthropic self.aclient = instructor.patch( - create=anthropic.Anthropic().messages.create, mode=instructor.Mode.ANTHROPIC_TOOLS + create=anthropic.AsyncAnthropic().messages.create, mode=instructor.Mode.ANTHROPIC_TOOLS ) self.model = model @@ -29,7 +32,22 @@ class AnthropicAdapter(LLMInterface): async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a response from a user query.""" + """ + Generate a response from a 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. + """ return await self.aclient( model=self.model, @@ -46,7 +64,21 @@ class AnthropicAdapter(LLMInterface): ) def show_prompt(self, text_input: str, system_prompt: str) -> str: - """Format and display the prompt for a user query.""" + """ + Format and display the prompt for a user query. + + Parameters: + ----------- + + - text_input (str): The input text from the user, defaults to a placeholder if + empty. + - system_prompt (str): The path to the system prompt to be read and formatted. + + Returns: + -------- + + - str: A formatted string displaying the system prompt and user input. + """ if not text_input: text_input = "No user input provided." diff --git a/cognee/infrastructure/llm/config.py b/cognee/infrastructure/llm/config.py index f31e18308..a94e847d2 100644 --- a/cognee/infrastructure/llm/config.py +++ b/cognee/infrastructure/llm/config.py @@ -6,6 +6,32 @@ from pydantic import model_validator class LLMConfig(BaseSettings): + """ + Configuration settings for the LLM (Large Language Model) provider and related options. + + Public instance variables include: + - llm_provider + - llm_model + - llm_endpoint + - llm_api_key + - llm_api_version + - llm_temperature + - llm_streaming + - llm_max_tokens + - transcription_model + - graph_prompt_path + - llm_rate_limit_enabled + - llm_rate_limit_requests + - llm_rate_limit_interval + - embedding_rate_limit_enabled + - embedding_rate_limit_requests + - embedding_rate_limit_interval + + Public methods include: + - ensure_env_vars_for_ollama + - to_dict + """ + llm_provider: str = "openai" llm_model: str = "gpt-4o-mini" llm_endpoint: str = "" @@ -28,11 +54,15 @@ class LLMConfig(BaseSettings): @model_validator(mode="after") def ensure_env_vars_for_ollama(self) -> "LLMConfig": """ - Only if llm_provider is 'ollama': - - If any of (LLM_MODEL, LLM_ENDPOINT, LLM_API_KEY) is set, all must be set. - - If any of (EMBEDDING_PROVIDER, EMBEDDING_MODEL, EMBEDDING_DIMENSIONS, - HUGGINGFACE_TOKENIZER) is set, all must be set. - Otherwise, skip these checks. + Validate required environment variables for the 'ollama' LLM provider. + + Raises ValueError if some required environment variables are set without the others. + Only checks are performed when 'llm_provider' is set to 'ollama'. + + Returns: + -------- + + - 'LLMConfig': The instance of LLMConfig after validation. """ if self.llm_provider != "ollama": @@ -40,7 +70,19 @@ class LLMConfig(BaseSettings): return self def is_env_set(var_name: str) -> bool: - """Return True if environment variable is present and non-empty.""" + """ + Check if a given environment variable is set and non-empty. + + Parameters: + ----------- + + - var_name (str): The name of the environment variable to check. + + Returns: + -------- + + - bool: True if the environment variable exists and is not empty, otherwise False. + """ val = os.environ.get(var_name) return val is not None and val.strip() != "" @@ -80,6 +122,15 @@ class LLMConfig(BaseSettings): return self def to_dict(self) -> dict: + """ + Convert the LLMConfig instance into a dictionary representation. + + Returns: + -------- + + - dict: A dictionary containing the configuration settings of the LLMConfig + instance. + """ return { "provider": self.llm_provider, "model": self.llm_model, @@ -102,4 +153,17 @@ class LLMConfig(BaseSettings): @lru_cache def get_llm_config(): + """ + Retrieve and cache the LLM configuration. + + This function returns an instance of the LLMConfig class. It leverages + caching to ensure that repeated calls do not create new instances, + but instead return the already created configuration object. + + Returns: + -------- + + - LLMConfig: An instance of the LLMConfig class containing the configuration for the + LLM. + """ return LLMConfig() diff --git a/cognee/infrastructure/llm/embedding_rate_limiter.py b/cognee/infrastructure/llm/embedding_rate_limiter.py index 8383a8a0d..24312dab1 100644 --- a/cognee/infrastructure/llm/embedding_rate_limiter.py +++ b/cognee/infrastructure/llm/embedding_rate_limiter.py @@ -46,6 +46,20 @@ class EmbeddingRateLimiter: The rate limiter uses the same configuration as the LLM API rate limiter but uses a separate key to track embedding API calls independently. + + Public Methods: + - get_instance + - reset_instance + - hit_limit + - wait_if_needed + - async_wait_if_needed + + Instance Variables: + - enabled + - requests_limit + - interval_seconds + - request_times + - lock """ _instance = None @@ -53,6 +67,18 @@ class EmbeddingRateLimiter: @classmethod def get_instance(cls): + """ + Retrieve the singleton instance of the EmbeddingRateLimiter. + + This method ensures that only one instance of the class exists and + is thread-safe. It lazily initializes the instance if it doesn't + already exist. + + Returns: + -------- + + The singleton instance of the EmbeddingRateLimiter class. + """ if cls._instance is None: with cls.lock: if cls._instance is None: @@ -61,6 +87,12 @@ class EmbeddingRateLimiter: @classmethod def reset_instance(cls): + """ + Reset the singleton instance of the EmbeddingRateLimiter. + + This method is thread-safe and sets the instance to None, allowing + for a new instance to be created when requested again. + """ with cls.lock: cls._instance = None @@ -81,8 +113,16 @@ class EmbeddingRateLimiter: """ Check if the current request would exceed the rate limit. + This method checks if the rate limiter is enabled and evaluates + the number of requests made in the elapsed interval. + Returns: - bool: True if the rate limit would be exceeded, False otherwise. + - bool: True if the rate limit would be exceeded, False otherwise. + + Returns: + -------- + + - bool: True if the rate limit would be exceeded, otherwise False. """ if not self.enabled: return False @@ -108,8 +148,16 @@ class EmbeddingRateLimiter: """ Block until a request can be made without exceeding the rate limit. + This method will wait if the current request would exceed the + rate limit and returns the time waited in seconds. + Returns: - float: Time waited in seconds. + - float: Time waited in seconds before a request is allowed. + + Returns: + -------- + + - float: Time waited in seconds before proceeding. """ if not self.enabled: return 0 @@ -131,8 +179,16 @@ class EmbeddingRateLimiter: """ Asynchronously wait until a request can be made without exceeding the rate limit. + This method will wait if the current request would exceed the + rate limit and returns the time waited in seconds. + Returns: - float: Time waited in seconds. + - float: Time waited in seconds before a request is allowed. + + Returns: + -------- + + - float: Time waited in seconds before proceeding. """ if not self.enabled: return 0 @@ -153,20 +209,39 @@ class EmbeddingRateLimiter: def embedding_rate_limit_sync(func): """ - Decorator that applies rate limiting to a synchronous embedding function. + Apply rate limiting to a synchronous embedding function. - This decorator checks if the request would exceed the rate limit, - and blocks if necessary. + Parameters: + ----------- - Args: - func: Function to decorate. + - func: Function to decorate with rate limiting logic. Returns: - Decorated function that applies rate limiting. + -------- + + Returns the decorated function that applies rate limiting. """ @functools.wraps(func) def wrapper(*args, **kwargs): + """ + Wrap the given function with rate limiting logic to control the embedding API usage. + + Checks if the rate limit has been exceeded before allowing the function to execute. If + the limit is hit, it logs a warning and raises an EmbeddingException. Otherwise, it + updates the request count and proceeds to call the original function. + + Parameters: + ----------- + + - *args: Variable length argument list for the wrapped function. + - **kwargs: Keyword arguments for the wrapped function. + + Returns: + -------- + + Returns the result of the wrapped function if rate limiting conditions are met. + """ limiter = EmbeddingRateLimiter.get_instance() # Check if rate limiting is enabled and if we're at the limit @@ -192,18 +267,38 @@ def embedding_rate_limit_async(func): """ Decorator that applies rate limiting to an asynchronous embedding function. - This decorator checks if the request would exceed the rate limit, - and waits asynchronously if necessary. + Parameters: + ----------- - Args: - func: Async function to decorate. + - func: Async function to decorate. Returns: - Decorated async function that applies rate limiting. + -------- + + Returns the decorated async function that applies rate limiting. """ @functools.wraps(func) async def wrapper(*args, **kwargs): + """ + Handle function calls with embedding rate limiting. + + This asynchronous wrapper checks if the embedding API rate limit is exceeded before + allowing the function to execute. If the limit is exceeded, it logs a warning and raises + an EmbeddingException. If not, it waits as necessary and proceeds with the function + call. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + Returns the result of the wrapped function after handling rate limiting. + """ limiter = EmbeddingRateLimiter.get_instance() # Check if rate limiting is enabled and if we're at the limit @@ -227,23 +322,62 @@ def embedding_rate_limit_async(func): def embedding_sleep_and_retry_sync(max_retries=5, base_backoff=1.0, jitter=0.5): """ - Decorator that adds retry with exponential backoff for synchronous embedding functions. + Add retry with exponential backoff for synchronous embedding functions. - The decorator will retry the function with exponential backoff if it - fails due to a rate limit error. + Parameters: + ----------- - Args: - max_retries: Maximum number of retries. - base_backoff: Base backoff time in seconds. - jitter: Jitter factor to randomize backoff time. + - max_retries: Maximum number of retries before giving up. (default 5) + - base_backoff: Base backoff time in seconds for retry intervals. (default 1.0) + - jitter: Jitter factor to randomize the backoff time to avoid collision. (default + 0.5) Returns: - Decorated function that retries on rate limit errors. + -------- + + A decorator that retries the wrapped function on rate limit errors, applying + exponential backoff with jitter. """ def decorator(func): + """ + Wraps a function to apply retry logic on rate limit errors. + + Parameters: + ----------- + + - func: The function to be wrapped with retry logic. + + Returns: + -------- + + Returns the wrapped function with retry logic applied. + """ + @functools.wraps(func) def wrapper(*args, **kwargs): + """ + Retry the execution of a function with backoff on failure due to rate limit errors. + + This wrapper function will call the specified function and if it raises an exception, it + will handle retries according to defined conditions. It will check the environment for a + DISABLE_RETRIES flag to determine whether to retry or propagate errors immediately + during tests. If the error is identified as a rate limit error, it will apply an + exponential backoff strategy with jitter before retrying, up to a maximum number of + retries. If the retries are exhausted, it raises the last encountered error. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + Returns the result of the wrapped function if successful; otherwise, raises the last + error encountered after maximum retries are exhausted. + """ # If DISABLE_RETRIES is set, don't retry for testing purposes disable_retries = os.environ.get("DISABLE_RETRIES", "false").lower() in ( "true", @@ -299,23 +433,68 @@ def embedding_sleep_and_retry_sync(max_retries=5, base_backoff=1.0, jitter=0.5): def embedding_sleep_and_retry_async(max_retries=5, base_backoff=1.0, jitter=0.5): """ - Decorator that adds retry with exponential backoff for asynchronous embedding functions. + Add retry logic with exponential backoff for asynchronous embedding functions. - The decorator will retry the function with exponential backoff if it - fails due to a rate limit error. + This decorator retries the wrapped asynchronous function upon encountering rate limit + errors, utilizing exponential backoff with optional jitter to space out retry attempts. + It allows for a maximum number of retries before giving up and raising the last error + encountered. - Args: - max_retries: Maximum number of retries. - base_backoff: Base backoff time in seconds. - jitter: Jitter factor to randomize backoff time. + Parameters: + ----------- + + - max_retries: Maximum number of retries allowed before giving up. (default 5) + - base_backoff: Base amount of time in seconds to wait before retrying after a rate + limit error. (default 1.0) + - jitter: Amount of randomness to add to the backoff duration to help mitigate burst + issues on retries. (default 0.5) Returns: - Decorated async function that retries on rate limit errors. + -------- + + Returns a decorated asynchronous function that implements the retry logic on rate + limit errors. """ def decorator(func): + """ + Handle retries for an async function with exponential backoff and jitter. + + Parameters: + ----------- + + - func: An asynchronous function to be wrapped with retry logic. + + Returns: + -------- + + Returns the wrapper function that manages the retry behavior for the wrapped async + function. + """ + @functools.wraps(func) async def wrapper(*args, **kwargs): + """ + Handle retries for an async function with exponential backoff and jitter. + + If the environment variable DISABLE_RETRIES is set to true, 1, or yes, the function will + not retry on errors. + It attempts to call the wrapped function until it succeeds or the maximum number of + retries is reached. If an exception occurs, it checks if it's a rate limit error to + determine if a retry is needed. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + Returns the result of the wrapped async function if successful; raises the last + encountered error if all retries fail. + """ # If DISABLE_RETRIES is set, don't retry for testing purposes disable_retries = os.environ.get("DISABLE_RETRIES", "false").lower() in ( "true", diff --git a/cognee/infrastructure/llm/gemini/adapter.py b/cognee/infrastructure/llm/gemini/adapter.py index e62755f6a..d141d1c84 100644 --- a/cognee/infrastructure/llm/gemini/adapter.py +++ b/cognee/infrastructure/llm/gemini/adapter.py @@ -18,6 +18,14 @@ observe = get_observe() class GeminiAdapter(LLMInterface): + """ + Handles interactions with a language model API. + + Public methods include: + - acreate_structured_output + - show_prompt + """ + MAX_RETRIES = 5 def __init__( @@ -42,6 +50,28 @@ class GeminiAdapter(LLMInterface): 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. + + 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. + + 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. + + Returns: + -------- + + - BaseModel: Returns the generated response as an instance of the specified response + model. + """ try: if response_model is str: response_schema = {"type": "string"} @@ -83,7 +113,23 @@ class GeminiAdapter(LLMInterface): 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.""" + """ + Format and display the prompt for a user query. + + Raises an InvalidValueError if no system prompt is provided. + + Parameters: + ----------- + + - text_input (str): The user input text to display along with the system prompt. + - system_prompt (str): The path or content of the system prompt to be read and + displayed. + + Returns: + -------- + + - str: Returns a formatted string containing the system prompt and user input. + """ if not text_input: text_input = "No user input provided." if not system_prompt: diff --git a/cognee/infrastructure/llm/generic_llm_api/adapter.py b/cognee/infrastructure/llm/generic_llm_api/adapter.py index 07085c076..9c00054f6 100644 --- a/cognee/infrastructure/llm/generic_llm_api/adapter.py +++ b/cognee/infrastructure/llm/generic_llm_api/adapter.py @@ -11,7 +11,17 @@ import litellm class GenericAPIAdapter(LLMInterface): - """Adapter for Generic API LLM provider API""" + """ + Adapter for Generic API LLM provider API. + + This class initializes the API adapter with necessary credentials and configurations for + interacting with a language model. 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 + """ name: str model: str @@ -33,15 +43,35 @@ class GenericAPIAdapter(LLMInterface): async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a response from a user query.""" + """ + Generate a response from a user query. + + 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 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: An instance of the specified response model containing the structured + output from the language model. + """ return await self.aclient.chat.completions.create( model=self.model, messages=[ { "role": "user", - "content": f"""Use the given format to - extract information from the following input: {text_input}. """, + "content": f"""{text_input}""", }, { "role": "system", diff --git a/cognee/infrastructure/llm/get_llm_client.py b/cognee/infrastructure/llm/get_llm_client.py index 4a095d179..a5a0b1f1e 100644 --- a/cognee/infrastructure/llm/get_llm_client.py +++ b/cognee/infrastructure/llm/get_llm_client.py @@ -9,6 +9,17 @@ from cognee.infrastructure.llm.ollama.adapter import OllamaAPIAdapter # Define an Enum for LLM Providers class LLMProvider(Enum): + """ + Define an Enum for identifying different LLM Providers. + + This Enum includes the following members: + - OPENAI: Represents the OpenAI provider. + - OLLAMA: Represents the Ollama provider. + - ANTHROPIC: Represents the Anthropic provider. + - CUSTOM: Represents a custom provider option. + - GEMINI: Represents the Gemini provider. + """ + OPENAI = "openai" OLLAMA = "ollama" ANTHROPIC = "anthropic" @@ -17,7 +28,20 @@ class LLMProvider(Enum): def get_llm_client(): - """Get the LLM client based on the configuration using Enums.""" + """ + Get the LLM client based on the configuration using Enums. + + This function retrieves the configuration for the LLM provider and model, and + initializes the appropriate LLM client adapter accordingly. It raises an + InvalidValueError if the LLM API key is not set for certain providers or if the provider + is unsupported. + + Returns: + -------- + + An instance of the appropriate LLM client adapter based on the provider + configuration. + """ llm_config = get_llm_config() provider = LLMProvider(llm_config.llm_provider) diff --git a/cognee/infrastructure/llm/llm_interface.py b/cognee/infrastructure/llm/llm_interface.py index dfcbecd29..609bb0a9f 100644 --- a/cognee/infrastructure/llm/llm_interface.py +++ b/cognee/infrastructure/llm/llm_interface.py @@ -7,17 +7,52 @@ from cognee.infrastructure.llm.prompts import read_query_prompt class LLMInterface(Protocol): - """LLM Interface""" + """ + Define an interface for LLM models with methods for structured output and prompt + display. + + Methods: + - acreate_structured_output(text_input: str, system_prompt: str, response_model: + Type[BaseModel]) + - show_prompt(text_input: str, system_prompt: str) + """ @abstractmethod async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """To get structured output, import/call this function""" + """ + Obtain structured output from text input using a specified response model. + + This function must be implemented by subclasses to provide the actual functionality for + generating structured output. Raises NotImplementedError if not implemented. + + Parameters: + ----------- + + - text_input (str): Input text from the user to be processed. + - system_prompt (str): The system prompt that guides the model's response. + - response_model (Type[BaseModel]): The model type that will structure the response + output. + """ raise NotImplementedError def show_prompt(self, text_input: str, system_prompt: str) -> str: - """Format and display the prompt for a user query.""" + """ + 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: diff --git a/cognee/infrastructure/llm/ollama/adapter.py b/cognee/infrastructure/llm/ollama/adapter.py index 275e59313..82f50ede5 100644 --- a/cognee/infrastructure/llm/ollama/adapter.py +++ b/cognee/infrastructure/llm/ollama/adapter.py @@ -14,7 +14,24 @@ import os class OllamaAPIAdapter(LLMInterface): - """Adapter for a Generic API LLM provider using instructor with an OpenAI backend.""" + """ + Adapter for a Generic API LLM provider using instructor with an OpenAI backend. + + Public methods: + + - acreate_structured_output + - create_transcript + - transcribe_image + + Instance variables: + + - name + - model + - api_key + - endpoint + - max_tokens + - aclient + """ def __init__(self, endpoint: str, api_key: str, model: str, name: str, max_tokens: int): self.name = name @@ -32,14 +49,32 @@ class OllamaAPIAdapter(LLMInterface): async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a structured output from the LLM using the provided text and system prompt.""" + """ + Generate a structured output from the LLM using the provided text and system prompt. + + This asynchronous method sends a request to the API with the user's input and the system + prompt, and returns a structured response based on the specified model. + + Parameters: + ----------- + + - text_input (str): The input text provided by the user. + - system_prompt (str): The system prompt that guides the response generation. + - response_model (Type[BaseModel]): The model type that the response should conform + to. + + Returns: + -------- + + - BaseModel: A structured output that conforms to the specified response model. + """ response = self.aclient.chat.completions.create( model=self.model, messages=[ { "role": "user", - "content": f"Use the given format to extract information from the following input: {text_input}", + "content": f"{text_input}", }, { "role": "system", @@ -54,7 +89,23 @@ class OllamaAPIAdapter(LLMInterface): @rate_limit_sync def create_transcript(self, input_file: str) -> str: - """Generate an audio transcript from a user query.""" + """ + Generate an audio transcript from a user query. + + This synchronous method takes an input audio file and returns its transcription. Raises + a FileNotFoundError if the input file does not exist, and raises a ValueError if + transcription fails or returns no text. + + Parameters: + ----------- + + - input_file (str): The path to the audio file to be transcribed. + + Returns: + -------- + + - str: The transcription of the audio as a string. + """ if not os.path.isfile(input_file): raise FileNotFoundError(f"The file {input_file} does not exist.") @@ -74,7 +125,24 @@ class OllamaAPIAdapter(LLMInterface): @rate_limit_sync def transcribe_image(self, input_file: str) -> str: - """Transcribe content from an image using base64 encoding.""" + """ + Transcribe content from an image using base64 encoding. + + This synchronous method takes an input image file, encodes it as base64, and returns the + transcription of its content. Raises a FileNotFoundError if the input file does not + exist, and raises a ValueError if the transcription fails or no valid response is + received. + + Parameters: + ----------- + + - input_file (str): The path to the image file to be transcribed. + + Returns: + -------- + + - str: The transcription of the image's content as a string. + """ if not os.path.isfile(input_file): raise FileNotFoundError(f"The file {input_file} does not exist.") diff --git a/cognee/infrastructure/llm/openai/adapter.py b/cognee/infrastructure/llm/openai/adapter.py index 4a135c1cc..a816e3e3f 100644 --- a/cognee/infrastructure/llm/openai/adapter.py +++ b/cognee/infrastructure/llm/openai/adapter.py @@ -21,6 +21,26 @@ observe = get_observe() class OpenAIAdapter(LLMInterface): + """ + Adapter for OpenAI's GPT-3, GPT-4 API. + + Public methods: + + - acreate_structured_output + - create_structured_output + - create_transcript + - transcribe_image + - show_prompt + + Instance variables: + + - name + - model + - api_key + - api_version + - MAX_RETRIES + """ + name = "OpenAI" model: str api_key: str @@ -56,15 +76,33 @@ class OpenAIAdapter(LLMInterface): async def acreate_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a response from a user query.""" + """ + Generate a response from a user query. + + This method asynchronously creates structured output by sending a request to the OpenAI + API using the provided parameters to generate a completion based on the user input and + system prompt. + + Parameters: + ----------- + + - text_input (str): The input text provided by the user for generating a response. + - system_prompt (str): The system's prompt to guide the model's response. + - response_model (Type[BaseModel]): The expected model type for the response. + + Returns: + -------- + + - BaseModel: A structured output generated by the model, returned as an instance of + BaseModel. + """ return await self.aclient.chat.completions.create( model=self.model, messages=[ { "role": "user", - "content": f"""Use the given format to - extract information from the following input: {text_input}. """, + "content": f"""{text_input}""", }, { "role": "system", @@ -84,15 +122,33 @@ class OpenAIAdapter(LLMInterface): def create_structured_output( self, text_input: str, system_prompt: str, response_model: Type[BaseModel] ) -> BaseModel: - """Generate a response from a user query.""" + """ + Generate a response from a user query. + + This method creates structured output by sending a synchronous request to the OpenAI API + using the provided parameters to generate a completion based on the user input and + system prompt. + + Parameters: + ----------- + + - text_input (str): The input text provided by the user for generating a response. + - system_prompt (str): The system's prompt to guide the model's response. + - response_model (Type[BaseModel]): The expected model type for the response. + + Returns: + -------- + + - BaseModel: A structured output generated by the model, returned as an instance of + BaseModel. + """ return self.client.chat.completions.create( model=self.model, messages=[ { "role": "user", - "content": f"""Use the given format to - extract information from the following input: {text_input}. """, + "content": f"""{text_input}""", }, { "role": "system", @@ -108,7 +164,23 @@ class OpenAIAdapter(LLMInterface): @rate_limit_sync def create_transcript(self, input): - """Generate a audio transcript from a user query.""" + """ + Generate an audio transcript from a user query. + + This method creates a transcript from the specified audio file, raising a + FileNotFoundError if the file does not exist. The audio file is processed and the + transcription is retrieved from the API. + + Parameters: + ----------- + + - input: The path to the audio file that needs to be transcribed. + + Returns: + -------- + + The generated transcription of the audio file. + """ if not input.startswith("s3://") and not os.path.isfile(input): raise FileNotFoundError(f"The file {input} does not exist.") @@ -127,6 +199,23 @@ class OpenAIAdapter(LLMInterface): @rate_limit_sync def transcribe_image(self, input) -> BaseModel: + """ + Generate a transcription of an image from a user query. + + This method encodes the image and sends a request to the OpenAI API to obtain a + description of the contents of the image. + + Parameters: + ----------- + + - input: The path to the image file that needs to be transcribed. + + Returns: + -------- + + - BaseModel: A structured output generated by the model, returned as an instance of + BaseModel. + """ with open_data_file(input, mode="rb") as image_file: encoded_image = base64.b64encode(image_file.read()).decode("utf-8") @@ -157,7 +246,24 @@ class OpenAIAdapter(LLMInterface): ) def show_prompt(self, text_input: str, system_prompt: str) -> str: - """Format and display the prompt for a user query.""" + """ + Format and display the prompt for a user query. + + This method formats the prompt using the provided user input and system prompt, + returning a string representation. Raises InvalidValueError if the system prompt is not + provided. + + Parameters: + ----------- + + - text_input (str): The input text provided by the user. + - system_prompt (str): The system's prompt to guide the model's response. + + Returns: + -------- + + - str: A formatted string representing the user input and system prompt. + """ if not text_input: text_input = "No user input provided." if not system_prompt: diff --git a/cognee/infrastructure/llm/prompts/cot_followup_system_prompt.txt b/cognee/infrastructure/llm/prompts/cot_followup_system_prompt.txt new file mode 100644 index 000000000..5c7ac1035 --- /dev/null +++ b/cognee/infrastructure/llm/prompts/cot_followup_system_prompt.txt @@ -0,0 +1,3 @@ +You are a helpful assistant whose job is to ask exactly one clarifying follow-up question, +to collect the missing piece of information needed to fully answer the user’s original query. +Respond with the question only (no extra text, no punctuation beyond what’s needed). diff --git a/cognee/infrastructure/llm/prompts/cot_followup_user_prompt.txt b/cognee/infrastructure/llm/prompts/cot_followup_user_prompt.txt new file mode 100644 index 000000000..5547ca58d --- /dev/null +++ b/cognee/infrastructure/llm/prompts/cot_followup_user_prompt.txt @@ -0,0 +1,14 @@ +Based on the following, ask exactly one question that would directly resolve the gap identified in the validation reasoning and allow a valid answer. +Think in a way that with the followup question you are exploring a knowledge graph which contains entities, entity types and document chunks + + +`{{ query}}` + + + +`{{ answer }}` + + + +`{{ reasoning }}` + diff --git a/cognee/infrastructure/llm/prompts/cot_validation_system_prompt.txt b/cognee/infrastructure/llm/prompts/cot_validation_system_prompt.txt new file mode 100644 index 000000000..b8066d081 --- /dev/null +++ b/cognee/infrastructure/llm/prompts/cot_validation_system_prompt.txt @@ -0,0 +1,2 @@ +You are a helpful agent who are allowed to use only the provided question answer and context. +I want to you find reasoning what is missing from the context or why the answer is not answering the question or not correct strictly based on the context. diff --git a/cognee/infrastructure/llm/prompts/cot_validation_user_prompt.txt b/cognee/infrastructure/llm/prompts/cot_validation_user_prompt.txt new file mode 100644 index 000000000..b989595de --- /dev/null +++ b/cognee/infrastructure/llm/prompts/cot_validation_user_prompt.txt @@ -0,0 +1,11 @@ + +`{{ query}}` + + + +`{{ answer }}` + + + +`{{ context }}` + diff --git a/cognee/infrastructure/llm/prompts/read_query_prompt.py b/cognee/infrastructure/llm/prompts/read_query_prompt.py index 95f12a887..592114d4e 100644 --- a/cognee/infrastructure/llm/prompts/read_query_prompt.py +++ b/cognee/infrastructure/llm/prompts/read_query_prompt.py @@ -4,7 +4,27 @@ from cognee.root_dir import get_absolute_path def read_query_prompt(prompt_file_name: str, base_directory: str = None): - """Read a query prompt from a file.""" + """ + Read a query prompt from a file. + + Retrieve the contents of a specified prompt file, optionally using a provided base + directory for the file path. If the base directory is not specified, a default path is + used. Log errors if the file is not found or if another error occurs during file + reading. + + Parameters: + ----------- + + - prompt_file_name (str): The name of the prompt file to be read. + - base_directory (str): The base directory from which to read the prompt file. If + None, a default path is used. (default None) + + Returns: + -------- + + Returns the contents of the prompt file as a string, or None if the file cannot be + read due to an error. + """ logger = get_logger(level=ERROR) try: if base_directory is None: diff --git a/cognee/infrastructure/llm/prompts/render_prompt.py b/cognee/infrastructure/llm/prompts/render_prompt.py index 6c8f1251b..4f0de3549 100644 --- a/cognee/infrastructure/llm/prompts/render_prompt.py +++ b/cognee/infrastructure/llm/prompts/render_prompt.py @@ -3,10 +3,25 @@ from cognee.root_dir import get_absolute_path def render_prompt(filename: str, context: dict, base_directory: str = None) -> str: - """Render a Jinja2 template asynchronously. - :param filename: The name of the template file to render. - :param context: The context to render the template with. - :return: The rendered template as a string.""" + """ + Render a Jinja2 template asynchronously. + + Set the base directory if not provided, initialize the Jinja2 environment, + load the specified template, and render it using the provided context. + + Parameters: + ----------- + + - filename (str): The name of the template file to render. + - context (dict): The context to render the template with. + - base_directory (str): The base directory to load the template from, defaults to a + defined path if None. (default None) + + Returns: + -------- + + - str: The rendered template as a string. + """ # Set the base directory relative to the cognee root directory if base_directory is None: diff --git a/cognee/infrastructure/llm/rate_limiter.py b/cognee/infrastructure/llm/rate_limiter.py index 382d5edbe..bfc9bcc09 100644 --- a/cognee/infrastructure/llm/rate_limiter.py +++ b/cognee/infrastructure/llm/rate_limiter.py @@ -93,6 +93,20 @@ class llm_rate_limiter: The rate limiter converts the configured requests/interval to a per-minute rate for compatibility with the limits library's built-in rate limit items. + + Public methods: + - hit_limit + - wait_if_needed + - async_wait_if_needed + + Instance variables: + - _enabled + - _requests + - _interval + - _storage + - _limiter + - _rate_per_minute + - _initialized """ _instance = None @@ -139,7 +153,12 @@ class llm_rate_limiter: returns True. Returns: - bool: True if the request is allowed, False otherwise. + bool: True if the request is allowed, False otherwise. + + Returns: + -------- + + - bool: True if the request is allowed, False otherwise. """ if not self._enabled: return True @@ -158,7 +177,12 @@ class llm_rate_limiter: the rate limit. It polls every 0.5 seconds. Returns: - float: Time waited in seconds. + float: Time waited in seconds. + + Returns: + -------- + + - float: Time waited in seconds. """ if not self._enabled: return 0 @@ -178,7 +202,12 @@ class llm_rate_limiter: exceeding the rate limit. It polls every 0.5 seconds. Returns: - float: Time waited in seconds. + float: Time waited in seconds. + + Returns: + -------- + + - float: Time waited in seconds. """ if not self._enabled: return 0 @@ -195,19 +224,38 @@ def rate_limit_sync(func): """ Decorator for rate limiting synchronous functions. - This decorator ensures that the decorated function respects the - configured rate limits. If the rate limit would be exceeded, - the decorator blocks until the request can be made. + Parameters: + ----------- - Args: - func: The synchronous function to decorate. + - func: The synchronous function to decorate. Returns: - The decorated function. + -------- + + The decorated function that applies rate limiting to the original function. """ @wraps(func) def wrapper(*args, **kwargs): + """ + Manage rate limiting for the wrapped function's execution. + + This decorator checks if the rate limit for LLM API calls is exceeded before executing + the function. It waits if necessary and logs the time waited. The rate limiter instance + is obtained from the llm_rate_limiter class, ensuring consistent access to rate limiting + behavior across function calls. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + The return value of the wrapped function after applying the rate limiting logic. + """ limiter = llm_rate_limiter() waited = limiter.wait_if_needed() if waited > 0: @@ -219,21 +267,43 @@ def rate_limit_sync(func): def rate_limit_async(func): """ - Decorator for rate limiting asynchronous functions. + Decorate an asynchronous function for rate limiting. - This decorator ensures that the decorated async function respects the - configured rate limits. If the rate limit would be exceeded, - the decorator asynchronously waits until the request can be made. + This decorator ensures that the decorated async function respects the configured rate + limits. If the rate limit would be exceeded, the decorator asynchronously waits until + the request can be made. - Args: - func: The asynchronous function to decorate. + Parameters: + ----------- + + - func: The asynchronous function to decorate. Returns: + -------- + The decorated async function. """ @wraps(func) async def wrapper(*args, **kwargs): + """ + Handle rate limiting for LLM API calls in an asynchronous context. + + This wrapper function first ensures that the LLM rate limiter is utilized before + executing the provided function. It waits if necessary to adhere to any configured rate + limits and logs the waiting duration if applicable. + + Parameters: + ----------- + + - *args: Positional arguments passed to the wrapped function. + - **kwargs: Keyword arguments passed to the wrapped function. + + Returns: + -------- + + The return value of the wrapped function, after ensuring rate limiting compliance. + """ limiter = llm_rate_limiter() waited = await limiter.async_wait_if_needed() if waited > 0: @@ -247,11 +317,19 @@ def is_rate_limit_error(error): """ Check if an error is related to rate limiting. - Args: - error: The exception to check. + Evaluate the provided error to determine if it signifies a rate limiting issue by + checking against known patterns. The check is case-insensitive and looks for matches in + the string representation of the error. + + Parameters: + ----------- + + - error: The exception to check for rate limiting indications. Returns: - bool: True if the error is rate-limit related, False otherwise. + -------- + + - bool: True if the error is rate-limit related, False otherwise. """ error_str = str(error).lower() return any(pattern.lower() in error_str for pattern in RATE_LIMIT_ERROR_PATTERNS) @@ -266,14 +344,21 @@ def calculate_backoff( """ Calculate the backoff time for a retry attempt with jitter. - Args: - attempt: The current retry attempt (0-based). - initial_backoff: The initial backoff time in seconds. - backoff_factor: The multiplier for exponential backoff. - jitter: The jitter factor to avoid thundering herd. + Parameters: + ----------- + + - attempt: The current retry attempt (0-based). + - initial_backoff: The initial backoff time in seconds. (default + DEFAULT_INITIAL_BACKOFF) + - backoff_factor: The multiplier for exponential backoff. (default + DEFAULT_BACKOFF_FACTOR) + - jitter: The jitter factor to avoid thundering herd. (default DEFAULT_JITTER) Returns: - float: The backoff time in seconds. + -------- + + The backoff time in seconds, calculated using the exponential backoff formula + adjusted by a jitter component. """ backoff = initial_backoff * (backoff_factor**attempt) jitter_amount = backoff * jitter @@ -287,24 +372,68 @@ def sleep_and_retry_sync( jitter=DEFAULT_JITTER, ): """ - Decorator that automatically retries a synchronous function when rate limit errors occur. + Decorate a synchronous function to automatically retry on rate limit errors. - This decorator implements an exponential backoff strategy with jitter - to handle rate limit errors efficiently. + This decorator implements an exponential backoff strategy with jitter to handle rate + limit errors efficiently. It will retry the decorated function until success or until + the maximum number of retries is reached. - Args: - max_retries: Maximum number of retry attempts. - initial_backoff: Initial backoff time in seconds. - backoff_factor: Multiplier for exponential backoff. - jitter: Jitter factor to avoid the thundering herd problem. + Parameters: + ----------- + + - max_retries: Maximum number of retry attempts. (default DEFAULT_MAX_RETRIES) + - initial_backoff: Initial backoff time in seconds. (default + DEFAULT_INITIAL_BACKOFF) + - backoff_factor: Multiplier for exponential backoff. (default + DEFAULT_BACKOFF_FACTOR) + - jitter: Jitter factor to avoid the thundering herd problem. (default + DEFAULT_JITTER) Returns: - The decorated function. + -------- + + The decorated function that retries on rate limit errors. """ def decorator(func): + """ + Apply a retry mechanism to a function, specifically for handling rate limit errors. + + Parameters: + ----------- + + - func: The function to be wrapped with retry logic. + + Returns: + -------- + + A wrapped function that retries on rate limit errors until successful or max retries + are reached. + """ + @wraps(func) def wrapper(*args, **kwargs): + """ + Handle retries for a function call in case of errors, specifically for rate limit + errors. + + This decorator will continually attempt to call the provided function until it either + succeeds or the maximum number of retries is reached. If the caught exception is + determined to be a rate limit error, it will calculate the appropriate backoff time and + wait before retrying. + + Parameters: + ----------- + + - *args: Positional arguments to be passed to the wrapped function. + - **kwargs: Keyword arguments to be passed to the wrapped function. + + Returns: + -------- + + The return value of the wrapped function if the call succeeds before exceeding + max_retries. + """ attempt = 0 while True: try: @@ -335,24 +464,61 @@ def sleep_and_retry_async( jitter=DEFAULT_JITTER, ): """ - Decorator that automatically retries an asynchronous function when rate limit errors occur. + Retry an asynchronous function with an exponential backoff strategy upon encountering + rate limit errors. - This decorator implements an exponential backoff strategy with jitter - to handle rate limit errors efficiently. + This decorator will automatically retry the specified asynchronous function if it raises + an error related to rate limiting. It uses a backoff strategy to control the timing of + retries, which includes a random jitter to prevent overwhelming the server. Args: - max_retries: Maximum number of retry attempts. - initial_backoff: Initial backoff time in seconds. - backoff_factor: Multiplier for exponential backoff. - jitter: Jitter factor to avoid the thundering herd problem. + max_retries: Maximum number of retry attempts. + initial_backoff: Initial backoff time in seconds. + backoff_factor: Multiplier for exponential backoff. + jitter: Jitter factor to avoid the thundering herd problem. Returns: - The decorated async function. + The decorated async function that handles retries on rate limit errors, returning the + function's original output after successful completion. """ def decorator(func): + """ + Wrap an asynchronous function to handle retries with a backoff strategy. + + Parameters: + ----------- + + - func: The asynchronous function to be wrapped and retried. + + Returns: + -------- + + The wrapped asynchronous function that handles retries. + """ + @wraps(func) async def wrapper(*args, **kwargs): + """ + Handle retries for a given async function in case of errors, with rate limit check and + backoff strategy. + + Wrap the provided asynchronous function and execute it with retries based on error + handling. If a rate limit error occurs, retry the function after calculating an + appropriate backoff time, respecting the maximum number of retries allowed. In case of + non-rate limit errors or exceeding max retries, re-raise the exception. + + Parameters: + ----------- + + - *args: Positional arguments to be passed to the wrapped async function. + - **kwargs: Keyword arguments to be passed to the wrapped async function. + + Returns: + -------- + + The result of the wrapped async function upon successful completion after retries. + """ attempt = 0 while True: try: diff --git a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py index 61acdd7ab..1b7e5a6e6 100644 --- a/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/Gemini/adapter.py @@ -4,6 +4,16 @@ from ..tokenizer_interface import TokenizerInterface class GeminiTokenizer(TokenizerInterface): + """ + Implements a tokenizer interface for the Gemini model, managing token extraction and + counting. + + Public methods: + - extract_tokens + - decode_single_token + - count_tokens + """ + def __init__( self, model: str, @@ -24,21 +34,46 @@ class GeminiTokenizer(TokenizerInterface): genai.configure(api_key=config.embedding_api_key or llm_config.llm_api_key) def extract_tokens(self, text: str) -> List[Any]: + """ + Raise NotImplementedError when called, as this method should be implemented in a + subclass. + + Parameters: + ----------- + + - text (str): Input text from which to extract tokens. + """ raise NotImplementedError def decode_single_token(self, encoding: int): + """ + Raise NotImplementedError when called, as Gemini tokenizer does not support decoding of + tokens. + + Parameters: + ----------- + + - encoding (int): The token encoding to decode. + """ # Gemini tokenizer doesn't have the option to decode tokens raise NotImplementedError def count_tokens(self, text: str) -> int: """ Returns the number of tokens in the given text. - Args: - text: str + + This method utilizes the Google Generative AI API to embed the content and count the + tokens. + + Parameters: + ----------- + + - text (str): Input text for which to count tokens. Returns: - number of tokens in the given text + -------- + - int: The number of tokens in the given text. """ import google.generativeai as genai diff --git a/cognee/infrastructure/llm/tokenizer/HuggingFace/adapter.py b/cognee/infrastructure/llm/tokenizer/HuggingFace/adapter.py index 29318458b..4ac68ff20 100644 --- a/cognee/infrastructure/llm/tokenizer/HuggingFace/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/HuggingFace/adapter.py @@ -4,6 +4,20 @@ from ..tokenizer_interface import TokenizerInterface class HuggingFaceTokenizer(TokenizerInterface): + """ + Implements a tokenizer using the Hugging Face Transformers library. + + Public methods include: + - extract_tokens + - count_tokens + - decode_single_token + + Instance variables include: + - model: str + - max_tokens: int + - tokenizer: AutoTokenizer + """ + def __init__( self, model: str, @@ -18,21 +32,47 @@ class HuggingFaceTokenizer(TokenizerInterface): self.tokenizer = AutoTokenizer.from_pretrained(model) def extract_tokens(self, text: str) -> List[Any]: + """ + Extract tokens from the given text using the tokenizer. + + Parameters: + ----------- + + - text (str): The input text to be tokenized. + + Returns: + -------- + + - List[Any]: A list of tokens extracted from the input text. + """ tokens = self.tokenizer.tokenize(text) return tokens def count_tokens(self, text: str) -> int: """ - Returns the number of tokens in the given text. - Args: - text: str + Count the number of tokens in the given text. + + Parameters: + ----------- + + - text (str): The input text for which to count tokens. Returns: - number of tokens in the given text + -------- + - int: The total number of tokens in the input text. """ return len(self.tokenizer.tokenize(text)) def decode_single_token(self, encoding: int): + """ + Attempt to decode a single token from its encoding, which is not implemented in this + tokenizer. + + Parameters: + ----------- + + - encoding (int): The integer encoding of the token to decode. + """ # HuggingFace tokenizer doesn't have the option to decode tokens raise NotImplementedError diff --git a/cognee/infrastructure/llm/tokenizer/Mistral/adapter.py b/cognee/infrastructure/llm/tokenizer/Mistral/adapter.py index b88190c05..5f23046ca 100644 --- a/cognee/infrastructure/llm/tokenizer/Mistral/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/Mistral/adapter.py @@ -4,6 +4,22 @@ from ..tokenizer_interface import TokenizerInterface class MistralTokenizer(TokenizerInterface): + """ + Tokenizes input text based on a specified model while adhering to a maximum token limit. + The class implements the TokenizerInterface and provides methods to extract tokens, + count them, and decode single tokens (although decoding is not supported). Public + methods include: + + - extract_tokens(text: str) + - count_tokens(text: str) + - decode_single_token(encoding: int) + + Instance variables include: + - model: str + - max_tokens: int + - tokenizer: MistralTokenizer + """ + def __init__( self, model: str, @@ -18,6 +34,19 @@ class MistralTokenizer(TokenizerInterface): self.tokenizer = MistralTokenizer.from_model(model) def extract_tokens(self, text: str) -> List[Any]: + """ + Extracts tokens from the given text using the tokenizer model. + + Parameters: + ----------- + + - text (str): The input text from which to extract tokens. + + Returns: + -------- + + - List[Any]: A list of extracted tokens. + """ from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.protocol.instruct.messages import UserMessage from mistral_common.tokens.tokenizers.base import Tokenized @@ -32,16 +61,29 @@ class MistralTokenizer(TokenizerInterface): def count_tokens(self, text: str) -> int: """ - Returns the number of tokens in the given text. - Args: - text: str + Counts the number of tokens in the given text. + + Parameters: + ----------- + + - text (str): The input text for which to count tokens. Returns: - number of tokens in the given text + -------- + - int: The number of tokens in the given text. """ return len(self.extract_tokens(text)) def decode_single_token(self, encoding: int): + """ + Attempt to decode a single token, although this functionality is not implemented and + raises NotImplementedError. + + Parameters: + ----------- + + - encoding (int): The integer representation of the token to decode. + """ # Mistral tokenizer doesn't have the option to decode tokens raise NotImplementedError diff --git a/cognee/infrastructure/llm/tokenizer/TikToken/adapter.py b/cognee/infrastructure/llm/tokenizer/TikToken/adapter.py index 7a01fe511..881ffaba7 100644 --- a/cognee/infrastructure/llm/tokenizer/TikToken/adapter.py +++ b/cognee/infrastructure/llm/tokenizer/TikToken/adapter.py @@ -6,8 +6,8 @@ from ..tokenizer_interface import TokenizerInterface class TikTokenTokenizer(TokenizerInterface): """ - Tokenizer adapter for OpenAI. - Inteded to be used as part of LLM Embedding and LLM Adapters classes + Tokenizer adapter for OpenAI. Intended to be used as part of LLM Embedding and LLM + Adapters classes. """ def __init__( @@ -21,40 +21,87 @@ class TikTokenTokenizer(TokenizerInterface): self.tokenizer = tiktoken.encoding_for_model(self.model) def extract_tokens(self, text: str) -> List[Any]: + """ + Extract tokens from the given text. + + Parameters: + ----------- + + - text (str): The text to be tokenized. + + Returns: + -------- + + - List[Any]: A list of token IDs representing the encoded text. + """ # Using TikToken's method to tokenize text token_ids = self.tokenizer.encode(text) return token_ids def decode_token_list(self, tokens: List[Any]) -> List[Any]: + """ + Decode a list of token IDs back into their corresponding text representations. + + Parameters: + ----------- + + - tokens (List[Any]): A list of token IDs to be decoded. + + Returns: + -------- + + - List[Any]: A list of decoded text representations of the tokens. + """ if not isinstance(tokens, list): tokens = [tokens] return [self.tokenizer.decode(i) for i in tokens] def decode_single_token(self, token: int): + """ + Decode a single token ID into its corresponding text representation. + + Parameters: + ----------- + + - token (int): A single token ID to be decoded. + + Returns: + -------- + + The decoded text representation of the token. + """ return self.tokenizer.decode_single_token_bytes(token).decode("utf-8", errors="replace") def count_tokens(self, text: str) -> int: """ - Returns the number of tokens in the given text. - Args: - text: str + Count the number of tokens in the given text. + + Parameters: + ----------- + + - text (str): The text for which to count the tokens. Returns: - number of tokens in the given text + -------- + - int: The number of tokens in the given text. """ num_tokens = len(self.tokenizer.encode(text)) return num_tokens def trim_text_to_max_tokens(self, text: str) -> str: """ - Trims the text so that the number of tokens does not exceed max_tokens. + Trim the text so that the number of tokens does not exceed max_tokens. - Args: - text (str): Original text string to be trimmed. + Parameters: + ----------- + + - text (str): Original text string to be trimmed. Returns: - str: Trimmed version of text or original text if under the limit. + -------- + + - str: Trimmed version of text or original text if under the limit. """ # First check the number of tokens num_tokens = self.count_tokens(text) diff --git a/cognee/infrastructure/llm/tokenizer/tokenizer_interface.py b/cognee/infrastructure/llm/tokenizer/tokenizer_interface.py index 456c69f64..18bcde318 100644 --- a/cognee/infrastructure/llm/tokenizer/tokenizer_interface.py +++ b/cognee/infrastructure/llm/tokenizer/tokenizer_interface.py @@ -3,16 +3,53 @@ from abc import abstractmethod class TokenizerInterface(Protocol): - """Tokenizer interface""" + """ + Defines an interface for tokenizers that provides methods for token extraction, + counting, and decoding. + """ @abstractmethod def extract_tokens(self, text: str) -> List[Any]: + """ + Extract tokens from the given text. + + Parameters: + ----------- + + - text (str): The input text from which to extract tokens. + """ raise NotImplementedError @abstractmethod def count_tokens(self, text: str) -> int: + """ + Count the number of tokens in the given text. + + Parameters: + ----------- + + - text (str): The input text for which to count tokens. + + Returns: + -------- + + - int: The total count of tokens in the input text. + """ raise NotImplementedError @abstractmethod def decode_single_token(self, token: int) -> str: + """ + Decode a single token represented by an integer into its string representation. + + Parameters: + ----------- + + - token (int): The integer representation of the token to decode. + + Returns: + -------- + + - str: The string representation of the decoded token. + """ raise NotImplementedError diff --git a/cognee/infrastructure/llm/utils.py b/cognee/infrastructure/llm/utils.py index 769053ee2..d25e41326 100644 --- a/cognee/infrastructure/llm/utils.py +++ b/cognee/infrastructure/llm/utils.py @@ -8,6 +8,20 @@ logger = get_logger() def get_max_chunk_tokens(): + """ + Calculate the maximum number of tokens allowed in a chunk. + + The function determines the maximum chunk size based on the maximum token limit of the + embedding engine and half of the LLM maximum context token size. It ensures that the + chunk size does not exceed these constraints. + + Returns: + -------- + + - int: The maximum number of tokens that can be included in a chunk, determined by + the smaller value of the embedding engine's max tokens and half of the LLM's + maximum tokens. + """ # Calculate max chunk size based on the following formula embedding_engine = get_vector_engine().embedding_engine llm_client = get_llm_client() @@ -22,10 +36,21 @@ def get_max_chunk_tokens(): def get_model_max_tokens(model_name: str): """ - Args: - model_name: name of LLM or embedding model + Retrieve the maximum token limit for a specified model name if it exists. - Returns: Number of max tokens of model, or None if model is unknown + Checks if the provided model name is present in the predefined model cost dictionary. If + found, it logs the maximum token count for that model and returns it. If the model name + is not recognized, it logs an informational message and returns None. + + Parameters: + ----------- + + - model_name (str): Name of LLM or embedding model + + Returns: + -------- + + Number of max tokens of model, or None if model is unknown """ max_tokens = None @@ -39,6 +64,13 @@ def get_model_max_tokens(model_name: str): async def test_llm_connection(): + """ + Establish a connection to the LLM and create a structured output. + + Attempt to connect to the LLM client and uses the adapter to create a structured output + with a predefined text input and system prompt. Log any exceptions encountered during + the connection attempt and re-raise the exception for further handling. + """ try: llm_adapter = get_llm_client() await llm_adapter.acreate_structured_output( @@ -54,6 +86,12 @@ async def test_llm_connection(): async def test_embedding_connection(): + """ + Test the connection to the embedding engine by embedding a sample text. + + Handles exceptions that may occur during the operation, logs the error, and re-raises + the exception if the connection to the embedding handler cannot be established. + """ try: await get_vector_engine().embedding_engine.embed_text("test") except Exception as e: diff --git a/cognee/infrastructure/pipeline/models/Operation.py b/cognee/infrastructure/pipeline/models/Operation.py index b0e5624cb..29a2ec043 100644 --- a/cognee/infrastructure/pipeline/models/Operation.py +++ b/cognee/infrastructure/pipeline/models/Operation.py @@ -5,11 +5,26 @@ from cognee.infrastructure.databases.relational import Base, UUID class OperationType(Enum): + """ + Define various types of operations for data handling. + + Public methods: + - __str__(): Returns a string representation of the operation type. + + Instance variables: + - MERGE_DATA: Represents the merge data operation type. + - APPEND_DATA: Represents the append data operation type. + """ + MERGE_DATA = "MERGE_DATA" APPEND_DATA = "APPEND_DATA" class OperationStatus(Enum): + """ + Represent the status of an operation with predefined states. + """ + STARTED = "OPERATION_STARTED" IN_PROGRESS = "OPERATION_IN_PROGRESS" COMPLETE = "OPERATION_COMPLETE" @@ -18,6 +33,21 @@ class OperationStatus(Enum): class Operation(Base): + """ + Represents an operation in the system, extending the Base class. + + This class defines the structure of the 'operation' table, including fields for the + operation's ID, status, type, associated data, metadata, and creation timestamp. The + public methods available in this class are inherited from the Base class. Instance + variables include: + - id: Unique identifier for the operation. + - status: The current status of the operation. + - operation_type: The type of operation being represented. + - data_id: Foreign key referencing the associated data's ID. + - meta_data: Additional metadata related to the operation. + - created_at: Timestamp for when the operation was created. + """ + __tablename__ = "operation" id = Column(UUID, primary_key=True) diff --git a/cognee/modules/chunking/models/DocumentChunk.py b/cognee/modules/chunking/models/DocumentChunk.py index 143a9d585..475703265 100644 --- a/cognee/modules/chunking/models/DocumentChunk.py +++ b/cognee/modules/chunking/models/DocumentChunk.py @@ -6,6 +6,25 @@ from cognee.modules.engine.models import Entity class DocumentChunk(DataPoint): + """ + Represents a chunk of text from a document with associated metadata. + + Public methods include: + + - No public methods defined in the provided code. + + Instance variables include: + + - text: The textual content of the chunk. + - chunk_size: The size of the chunk. + - chunk_index: The index of the chunk in the original document. + - cut_type: The type of cut that defined this chunk. + - is_part_of: The document to which this chunk belongs. + - contains: A list of entities contained within the chunk (default is None). + - metadata: A dictionary to hold meta information related to the chunk, including index + fields. + """ + text: str chunk_size: int chunk_index: int diff --git a/cognee/modules/engine/models/ColumnValue.py b/cognee/modules/engine/models/ColumnValue.py new file mode 100644 index 000000000..6ad8d992c --- /dev/null +++ b/cognee/modules/engine/models/ColumnValue.py @@ -0,0 +1,9 @@ +from cognee.infrastructure.engine import DataPoint + + +class ColumnValue(DataPoint): + name: str + description: str + properties: str + + metadata: dict = {"index_fields": ["properties"]} diff --git a/cognee/modules/engine/models/EntityType.py b/cognee/modules/engine/models/EntityType.py index df577acbd..5f1f859f5 100644 --- a/cognee/modules/engine/models/EntityType.py +++ b/cognee/modules/engine/models/EntityType.py @@ -2,6 +2,14 @@ from cognee.infrastructure.engine import DataPoint class EntityType(DataPoint): + """ + Represents a type of entity with a name and description. + + This class inherits from DataPoint and includes two primary attributes: `name` and + `description`. Additionally, it contains a metadata dictionary that specifies + `index_fields` for indexing purposes. + """ + name: str description: str diff --git a/cognee/modules/engine/models/__init__.py b/cognee/modules/engine/models/__init__.py index 4ab2de0de..2535f00f3 100644 --- a/cognee/modules/engine/models/__init__.py +++ b/cognee/modules/engine/models/__init__.py @@ -3,3 +3,4 @@ from .EntityType import EntityType from .TableRow import TableRow from .TableType import TableType from .node_set import NodeSet +from .ColumnValue import ColumnValue diff --git a/cognee/modules/engine/models/node_set.py b/cognee/modules/engine/models/node_set.py index 33fe3f557..480752bca 100644 --- a/cognee/modules/engine/models/node_set.py +++ b/cognee/modules/engine/models/node_set.py @@ -5,4 +5,3 @@ class NodeSet(DataPoint): """NodeSet data point.""" name: str - metadata: dict = {"index_fields": ["name"]} diff --git a/cognee/modules/engine/operations/setup.py b/cognee/modules/engine/operations/setup.py index e958642f7..a54d4b949 100644 --- a/cognee/modules/engine/operations/setup.py +++ b/cognee/modules/engine/operations/setup.py @@ -7,5 +7,11 @@ from cognee.infrastructure.databases.vector.pgvector import ( async def setup(): + """ + Set up the necessary databases and tables. + + This function asynchronously creates a relational database and its corresponding tables, + followed by creating a PGVector database and its tables. + """ await create_relational_db_and_tables() await create_pgvector_db_and_tables() diff --git a/cognee/modules/graph/cognee_graph/CogneeGraph.py b/cognee/modules/graph/cognee_graph/CogneeGraph.py index f79bbd010..ada8821eb 100644 --- a/cognee/modules/graph/cognee_graph/CogneeGraph.py +++ b/cognee/modules/graph/cognee_graph/CogneeGraph.py @@ -1,5 +1,5 @@ from cognee.shared.logging_utils import get_logger -from typing import List, Dict, Union +from typing import List, Dict, Union, Optional, Type from cognee.exceptions import InvalidValueError from cognee.modules.graph.exceptions import EntityNotFoundError, EntityAlreadyExistsError @@ -61,22 +61,33 @@ class CogneeGraph(CogneeAbstractGraph): node_dimension=1, edge_dimension=1, memory_fragment_filter=[], + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> None: if node_dimension < 1 or edge_dimension < 1: raise InvalidValueError(message="Dimensions must be positive integers") - try: - if len(memory_fragment_filter) == 0: + if node_type is not None and node_name is not None: + nodes_data, edges_data = await adapter.get_nodeset_subgraph( + node_type=node_type, node_name=node_name + ) + if not nodes_data or not edges_data: + raise EntityNotFoundError( + message="Nodeset does not exist, or empty nodetes projected from the database." + ) + elif len(memory_fragment_filter) == 0: nodes_data, edges_data = await adapter.get_graph_data() + if not nodes_data or not edges_data: + raise EntityNotFoundError(message="Empty graph projected from the database.") else: nodes_data, edges_data = await adapter.get_filtered_graph_data( attribute_filters=memory_fragment_filter ) - if not nodes_data: - raise EntityNotFoundError(message="No node data retrieved from the database.") - if not edges_data: - raise EntityNotFoundError(message="No edge data retrieved from the database.") + if not nodes_data or not edges_data: + raise EntityNotFoundError( + message="Empty filtered graph projected from the database." + ) for node_id, properties in nodes_data: node_attributes = {key: properties.get(key) for key in node_properties_to_project} diff --git a/cognee/modules/graph/utils/expand_with_nodes_and_edges.py b/cognee/modules/graph/utils/expand_with_nodes_and_edges.py index 775930638..00998f4e7 100644 --- a/cognee/modules/graph/utils/expand_with_nodes_and_edges.py +++ b/cognee/modules/graph/utils/expand_with_nodes_and_edges.py @@ -15,12 +15,15 @@ from cognee.modules.ontology.rdf_xml.OntologyResolver import OntologyResolver def expand_with_nodes_and_edges( data_chunks: list[DocumentChunk], chunk_graphs: list[KnowledgeGraph], - ontology_resolver: OntologyResolver = OntologyResolver(), + ontology_resolver: OntologyResolver = None, existing_edges_map: Optional[dict[str, bool]] = None, ): if existing_edges_map is None: existing_edges_map = {} + if ontology_resolver is None: + ontology_resolver = OntologyResolver() + added_nodes_map = {} added_ontology_nodes_map = {} relationships = [] @@ -95,6 +98,7 @@ def expand_with_nodes_and_edges( name=ont_node_name, description=ont_node_name, ontology_valid=True, + belongs_to_set=data_chunk.belongs_to_set, ) for source, relation, target in ontology_entity_type_edges: @@ -144,6 +148,7 @@ def expand_with_nodes_and_edges( is_a=type_node, description=node.description, ontology_valid=ontology_validated_source_ent, + belongs_to_set=data_chunk.belongs_to_set, ) added_nodes_map[entity_node_key] = entity_node @@ -174,6 +179,7 @@ def expand_with_nodes_and_edges( name=ont_node_name, description=ont_node_name, ontology_valid=True, + belongs_to_set=data_chunk.belongs_to_set, ) for source, relation, target in ontology_entity_edges: diff --git a/cognee/modules/retrieval/EntityCompletionRetriever.py b/cognee/modules/retrieval/EntityCompletionRetriever.py index c11ccfb82..bdb8d9a8c 100644 --- a/cognee/modules/retrieval/EntityCompletionRetriever.py +++ b/cognee/modules/retrieval/EntityCompletionRetriever.py @@ -11,7 +11,21 @@ logger = get_logger("entity_completion_retriever") class EntityCompletionRetriever(BaseRetriever): - """Retriever that uses entity-based completion for generating responses.""" + """ + Retriever that uses entity-based completion for generating responses. + + Public methods: + + - get_context + - get_completion + + Instance variables: + + - extractor + - context_provider + - user_prompt_path + - system_prompt_path + """ def __init__( self, @@ -26,7 +40,24 @@ class EntityCompletionRetriever(BaseRetriever): self.system_prompt_path = system_prompt_path async def get_context(self, query: str) -> Any: - """Get context using entity extraction and context provider.""" + """ + Get context using entity extraction and context provider. + + Logs the processing of the query and retrieves entities. If entities are extracted, it + attempts to retrieve the corresponding context using the context provider. Returns None + if no entities or context are found, or logs the error if an exception occurs. + + Parameters: + ----------- + + - query (str): The query string for which context is being retrieved. + + Returns: + -------- + + - Any: The context retrieved from the context provider or None if not found or an + error occurred. + """ try: logger.info(f"Processing query: {query[:100]}") @@ -47,7 +78,26 @@ class EntityCompletionRetriever(BaseRetriever): return None async def get_completion(self, query: str, context: Optional[Any] = None) -> List[str]: - """Generate completion using provided context or fetch new context.""" + """ + Generate completion using provided context or fetch new context. + + If context is not provided, it fetches context using the query. If no context is + available, it returns an error message. Logs an error if completion generation fails due + to an exception. + + Parameters: + ----------- + + - query (str): The query string for which completion is being generated. + - context (Optional[Any]): Optional context to be used for generating completion; + fetched if not provided. (default None) + + Returns: + -------- + + - List[str]: A list containing the generated completion or an error message if no + relevant entities were found. + """ try: if context is None: context = await self.get_context(query) diff --git a/cognee/modules/retrieval/chunks_retriever.py b/cognee/modules/retrieval/chunks_retriever.py index db17f2e57..1519612b5 100644 --- a/cognee/modules/retrieval/chunks_retriever.py +++ b/cognee/modules/retrieval/chunks_retriever.py @@ -7,7 +7,16 @@ from cognee.infrastructure.databases.vector.exceptions.exceptions import Collect class ChunksRetriever(BaseRetriever): - """Retriever for handling document chunk-based searches.""" + """ + Handles document chunk-based searches by retrieving relevant chunks and generating + completions from them. + + Public methods: + + - get_context: Retrieves document chunks based on a query. + - get_completion: Generates a completion using provided context or retrieves context if + not given. + """ def __init__( self, @@ -16,7 +25,22 @@ class ChunksRetriever(BaseRetriever): self.top_k = top_k async def get_context(self, query: str) -> Any: - """Retrieves document chunks context based on the query.""" + """ + Retrieves document chunks context based on the query. + + Searches for document chunks relevant to the specified query using a vector engine. + Raises a NoDataError if no data is found in the system. + + Parameters: + ----------- + + - query (str): The query string to search for relevant document chunks. + + Returns: + -------- + + - Any: A list of document chunk payloads retrieved from the search. + """ vector_engine = get_vector_engine() try: @@ -27,7 +51,25 @@ class ChunksRetriever(BaseRetriever): return [result.payload for result in found_chunks] async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Generates a completion using document chunks context.""" + """ + Generates a completion using document chunks context. + + If the context is not provided, it retrieves the context based on the query. Returns the + context, which can be used for further processing or generation of outputs. + + Parameters: + ----------- + + - query (str): The query string to be used for generating a completion. + - context (Optional[Any]): Optional pre-fetched context to use for generating the + completion; if None, it retrieves the context for the query. (default None) + + Returns: + -------- + + - Any: The context used for the completion or the retrieved context if none was + provided. + """ if context is None: context = await self.get_context(query) return context diff --git a/cognee/modules/retrieval/code_retriever.py b/cognee/modules/retrieval/code_retriever.py index 7d689ca87..185ba3e8b 100644 --- a/cognee/modules/retrieval/code_retriever.py +++ b/cognee/modules/retrieval/code_retriever.py @@ -14,7 +14,12 @@ class CodeRetriever(BaseRetriever): """Retriever for handling code-based searches.""" class CodeQueryInfo(BaseModel): - """Response model for information extraction from the query""" + """ + Model for representing the result of a query related to code files. + + This class holds a list of filenames and the corresponding source code extracted from a + query. It is used to encapsulate response data in a structured format. + """ filenames: List[str] = [] sourcecode: str diff --git a/cognee/modules/retrieval/completion_retriever.py b/cognee/modules/retrieval/completion_retriever.py index 1ec3a0eb3..b36a58569 100644 --- a/cognee/modules/retrieval/completion_retriever.py +++ b/cognee/modules/retrieval/completion_retriever.py @@ -8,7 +8,13 @@ from cognee.infrastructure.databases.vector.exceptions import CollectionNotFound class CompletionRetriever(BaseRetriever): - """Retriever for handling LLM-based completion searches.""" + """ + Retriever for handling LLM-based completion searches. + + Public methods: + - get_context(query: str) -> str + - get_completion(query: str, context: Optional[Any] = None) -> Any + """ def __init__( self, @@ -22,7 +28,24 @@ class CompletionRetriever(BaseRetriever): self.top_k = top_k if top_k is not None else 1 async def get_context(self, query: str) -> str: - """Retrieves relevant document chunks as context.""" + """ + Retrieves relevant document chunks as context. + + Fetches document chunks based on a query from a vector engine and combines their text. + Returns empty string if no chunks are found. Raises NoDataError if the collection is not + found. + + Parameters: + ----------- + + - query (str): The query string used to search for relevant document chunks. + + Returns: + -------- + + - str: A string containing the combined text of the retrieved document chunks, or an + empty string if none are found. + """ vector_engine = get_vector_engine() try: @@ -38,7 +61,24 @@ class CompletionRetriever(BaseRetriever): raise NoDataError("No data found in the system, please add data first.") from error async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Generates an LLM completion using the context.""" + """ + Generates an LLM completion using the context. + + Retrieves context if not provided and generates a completion based on the query and + context using an external completion generator. + + Parameters: + ----------- + + - query (str): The input query for which the completion is generated. + - context (Optional[Any]): Optional context to use for generating the completion; if + not provided, it will be retrieved using get_context. (default None) + + Returns: + -------- + + - Any: A list containing the generated completion from the LLM. + """ if context is None: context = await self.get_context(query) diff --git a/cognee/modules/retrieval/cypher_search_retriever.py b/cognee/modules/retrieval/cypher_search_retriever.py index f69a001e7..2329bae51 100644 --- a/cognee/modules/retrieval/cypher_search_retriever.py +++ b/cognee/modules/retrieval/cypher_search_retriever.py @@ -10,7 +10,13 @@ logger = get_logger("CypherSearchRetriever") class CypherSearchRetriever(BaseRetriever): - """Retriever for handling cypher-based search""" + """ + Retriever for handling cypher-based search. + + Public methods include: + - get_context: Retrieves relevant context using a cypher query. + - get_completion: Returns the graph connections context. + """ def __init__( self, @@ -22,7 +28,22 @@ class CypherSearchRetriever(BaseRetriever): self.system_prompt_path = system_prompt_path async def get_context(self, query: str) -> Any: - """Retrieves relevant context using a cypher query.""" + """ + Retrieves relevant context using a cypher query. + + If the graph engine is an instance of NetworkXAdapter, raises SearchTypeNotSupported. If + any error occurs during execution, logs the error and raises CypherSearchError. + + Parameters: + ----------- + + - query (str): The cypher query used to retrieve context. + + Returns: + -------- + + - Any: The result of the cypher query execution. + """ try: graph_engine = await get_graph_engine() @@ -38,7 +59,23 @@ class CypherSearchRetriever(BaseRetriever): return result async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Returns the graph connections context.""" + """ + Returns the graph connections context. + + If no context is provided, it retrieves the context using the specified query. + + Parameters: + ----------- + + - query (str): The query to retrieve context. + - context (Optional[Any]): Optional context to use, otherwise fetched using the + query. (default None) + + Returns: + -------- + + - Any: The context, either provided or retrieved. + """ if context is None: context = await self.get_context(query) return context diff --git a/cognee/modules/retrieval/graph_completion_context_extension_retriever.py b/cognee/modules/retrieval/graph_completion_context_extension_retriever.py new file mode 100644 index 000000000..2479a454d --- /dev/null +++ b/cognee/modules/retrieval/graph_completion_context_extension_retriever.py @@ -0,0 +1,117 @@ +from typing import Any, Optional, List, Type +from cognee.shared.logging_utils import get_logger +from cognee.infrastructure.llm.get_llm_client import get_llm_client +from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever +from cognee.modules.retrieval.utils.completion import generate_completion +from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt + +logger = get_logger() + + +class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever): + """ + Handles graph context completion for question answering tasks, extending context based + on retrieved triplets. + + Public methods: + - get_completion + + Instance variables: + - user_prompt_path + - system_prompt_path + - top_k + - node_type + - node_name + """ + + def __init__( + self, + user_prompt_path: str = "graph_context_for_question.txt", + system_prompt_path: str = "answer_simple_question.txt", + top_k: Optional[int] = 5, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, + ): + super().__init__( + user_prompt_path=user_prompt_path, + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, + ) + + async def get_completion( + self, query: str, context: Optional[Any] = None, context_extension_rounds=4 + ) -> List[str]: + """ + Extends the context for a given query by retrieving related triplets and generating new + completions based on them. + + The method runs for a specified number of rounds to enhance context until no new + triplets are found or the maximum rounds are reached. It retrieves triplet suggestions + based on a generated completion from previous iterations, logging the process of context + extension. + + Parameters: + ----------- + + - query (str): The input query for which the completion is generated. + - context (Optional[Any]): The existing context to use for enhancing the query; if + None, it will be initialized from triplets generated for the query. (default None) + - context_extension_rounds: The maximum number of rounds to extend the context with + new triplets before halting. (default 4) + + Returns: + -------- + + - List[str]: A list containing the generated answer based on the query and the + extended context. + """ + triplets = [] + + if context is None: + triplets += await self.get_triplets(query) + context = await self.resolve_edges_to_text(triplets) + + round_idx = 1 + + while round_idx <= context_extension_rounds: + prev_size = len(triplets) + + logger.info( + f"Context extension: round {round_idx} - generating next graph locational query." + ) + completion = await generate_completion( + query=query, + context=context, + user_prompt_path=self.user_prompt_path, + system_prompt_path=self.system_prompt_path, + ) + + triplets += await self.get_triplets(completion) + triplets = list(set(triplets)) + context = await self.resolve_edges_to_text(triplets) + + num_triplets = len(triplets) + + if num_triplets == prev_size: + logger.info( + f"Context extension: round {round_idx} – no new triplets found; stopping early." + ) + break + + logger.info( + f"Context extension: round {round_idx} - " + f"number of unique retrieved triplets: {num_triplets}" + ) + + round_idx += 1 + + answer = await generate_completion( + query=query, + context=context, + user_prompt_path=self.user_prompt_path, + system_prompt_path=self.system_prompt_path, + ) + + return [answer] diff --git a/cognee/modules/retrieval/graph_completion_cot_retriever.py b/cognee/modules/retrieval/graph_completion_cot_retriever.py new file mode 100644 index 000000000..95eb1a9b6 --- /dev/null +++ b/cognee/modules/retrieval/graph_completion_cot_retriever.py @@ -0,0 +1,125 @@ +from typing import Any, Optional, List, Type +from cognee.shared.logging_utils import get_logger +from cognee.infrastructure.llm.get_llm_client import get_llm_client +from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever +from cognee.modules.retrieval.utils.completion import generate_completion +from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt + +logger = get_logger() + + +class GraphCompletionCotRetriever(GraphCompletionRetriever): + """ + Handles graph completion by generating responses based on a series of interactions with + a language model. This class extends from GraphCompletionRetriever and is designed to + manage the retrieval and validation process for user queries, integrating follow-up + questions based on reasoning. The public methods are: + + - get_completion + + Instance variables include: + - validation_system_prompt_path + - validation_user_prompt_path + - followup_system_prompt_path + - followup_user_prompt_path + """ + + def __init__( + self, + user_prompt_path: str = "graph_context_for_question.txt", + system_prompt_path: str = "answer_simple_question.txt", + validation_user_prompt_path: str = "cot_validation_user_prompt.txt", + validation_system_prompt_path: str = "cot_validation_system_prompt.txt", + followup_system_prompt_path: str = "cot_followup_system_prompt.txt", + followup_user_prompt_path: str = "cot_followup_user_prompt.txt", + top_k: Optional[int] = 5, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, + ): + super().__init__( + user_prompt_path=user_prompt_path, + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, + ) + self.validation_system_prompt_path = validation_system_prompt_path + self.validation_user_prompt_path = validation_user_prompt_path + self.followup_system_prompt_path = followup_system_prompt_path + self.followup_user_prompt_path = followup_user_prompt_path + + async def get_completion( + self, query: str, context: Optional[Any] = None, max_iter=4 + ) -> List[str]: + """ + Generate completion responses based on a user query and contextual information. + + This method interacts with a language model client to retrieve a structured response, + using a series of iterations to refine the answers and generate follow-up questions + based on reasoning derived from previous outputs. It raises exceptions if the context + retrieval fails or if the model encounters issues in generating outputs. + + Parameters: + ----------- + + - query (str): The user's query to be processed and answered. + - context (Optional[Any]): Optional context that may assist in answering the query. + If not provided, it will be fetched based on the query. (default None) + - max_iter: The maximum number of iterations to refine the answer and generate + follow-up questions. (default 4) + + Returns: + -------- + + - List[str]: A list containing the generated answer to the user's query. + """ + llm_client = get_llm_client() + followup_question = "" + triplets = [] + answer = [""] + + for round_idx in range(max_iter + 1): + if round_idx == 0: + if context is None: + context = await self.get_context(query) + else: + triplets += await self.get_triplets(followup_question) + context = await self.resolve_edges_to_text(list(set(triplets))) + + answer = await generate_completion( + query=query, + context=context, + user_prompt_path=self.user_prompt_path, + system_prompt_path=self.system_prompt_path, + ) + logger.info(f"Chain-of-thought: round {round_idx} - answer: {answer}") + if round_idx < max_iter: + valid_args = {"query": query, "answer": answer, "context": context} + valid_user_prompt = render_prompt( + filename=self.validation_user_prompt_path, context=valid_args + ) + valid_system_prompt = read_query_prompt( + prompt_file_name=self.validation_system_prompt_path + ) + + reasoning = await llm_client.acreate_structured_output( + text_input=valid_user_prompt, + system_prompt=valid_system_prompt, + response_model=str, + ) + followup_args = {"query": query, "answer": answer, "reasoning": reasoning} + followup_prompt = render_prompt( + filename=self.followup_user_prompt_path, context=followup_args + ) + followup_system = read_query_prompt( + prompt_file_name=self.followup_system_prompt_path + ) + + followup_question = await llm_client.acreate_structured_output( + text_input=followup_prompt, system_prompt=followup_system, response_model=str + ) + logger.info( + f"Chain-of-thought: round {round_idx} - follow-up question: {followup_question}" + ) + + return [answer] diff --git a/cognee/modules/retrieval/graph_completion_retriever.py b/cognee/modules/retrieval/graph_completion_retriever.py index 98ac5dd61..3c5e85089 100644 --- a/cognee/modules/retrieval/graph_completion_retriever.py +++ b/cognee/modules/retrieval/graph_completion_retriever.py @@ -1,4 +1,4 @@ -from typing import Any, Optional +from typing import Any, Optional, Type, List from collections import Counter import string @@ -11,18 +11,32 @@ from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS class GraphCompletionRetriever(BaseRetriever): - """Retriever for handling graph-based completion searches.""" + """ + Retriever for handling graph-based completion searches. + + This class provides methods to retrieve graph nodes and edges, resolve them into a + human-readable format, and generate completions based on graph context. Public methods + include: + - resolve_edges_to_text + - get_triplets + - get_context + - get_completion + """ def __init__( self, user_prompt_path: str = "graph_context_for_question.txt", system_prompt_path: str = "answer_simple_question.txt", top_k: Optional[int] = 5, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ): """Initialize retriever with prompt paths and search parameters.""" self.user_prompt_path = user_prompt_path self.system_prompt_path = system_prompt_path self.top_k = top_k if top_k is not None else 5 + self.node_type = node_type + self.node_name = node_name def _get_nodes(self, retrieved_edges: list) -> dict: """Creates a dictionary of nodes with their names and content.""" @@ -41,7 +55,19 @@ class GraphCompletionRetriever(BaseRetriever): return nodes async def resolve_edges_to_text(self, retrieved_edges: list) -> str: - """Converts retrieved graph edges into a human-readable string format.""" + """ + Converts retrieved graph edges into a human-readable string format. + + Parameters: + ----------- + + - retrieved_edges (list): A list of edges retrieved from the graph. + + Returns: + -------- + + - str: A formatted string representation of the nodes and their connections. + """ nodes = self._get_nodes(retrieved_edges) node_section = "\n".join( f"Node: {info['name']}\n__node_content_start__\n{info['content']}\n__node_content_end__\n" @@ -54,7 +80,19 @@ class GraphCompletionRetriever(BaseRetriever): return f"Nodes:\n{node_section}\n\nConnections:\n{connection_section}" async def get_triplets(self, query: str) -> list: - """Retrieves relevant graph triplets.""" + """ + Retrieves relevant graph triplets based on a query string. + + Parameters: + ----------- + + - query (str): The query string used to search for relevant triplets in the graph. + + Returns: + -------- + + - list: A list of found triplets that match the query. + """ subclasses = get_all_subclasses(DataPoint) vector_index_collections = [] @@ -68,13 +106,30 @@ class GraphCompletionRetriever(BaseRetriever): vector_index_collections.append(f"{subclass.__name__}_{field_name}") found_triplets = await brute_force_triplet_search( - query, top_k=self.top_k, collections=vector_index_collections or None + query, + top_k=self.top_k, + collections=vector_index_collections or None, + node_type=self.node_type, + node_name=self.node_name, ) return found_triplets async def get_context(self, query: str) -> str: - """Retrieves and resolves graph triplets into context.""" + """ + Retrieves and resolves graph triplets into context based on a query. + + Parameters: + ----------- + + - query (str): The query string used to retrieve context from the graph triplets. + + Returns: + -------- + + - str: A string representing the resolved context from the retrieved triplets, or an + empty string if no triplets are found. + """ triplets = await self.get_triplets(query) if len(triplets) == 0: @@ -83,7 +138,21 @@ class GraphCompletionRetriever(BaseRetriever): return await self.resolve_edges_to_text(triplets) async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Generates a completion using graph connections context.""" + """ + Generates a completion using graph connections context based on a query. + + Parameters: + ----------- + + - query (str): The query string for which a completion is generated. + - context (Optional[Any]): Optional context to use for generating the completion; if + not provided, context is retrieved based on the query. (default None) + + Returns: + -------- + + - Any: A generated completion based on the query and context provided. + """ if context is None: context = await self.get_context(query) diff --git a/cognee/modules/retrieval/graph_summary_completion_retriever.py b/cognee/modules/retrieval/graph_summary_completion_retriever.py index 76ed5f5d4..803fb5993 100644 --- a/cognee/modules/retrieval/graph_summary_completion_retriever.py +++ b/cognee/modules/retrieval/graph_summary_completion_retriever.py @@ -1,11 +1,20 @@ -from typing import Optional +from typing import Optional, Type, List from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever from cognee.modules.retrieval.utils.completion import summarize_text class GraphSummaryCompletionRetriever(GraphCompletionRetriever): - """Retriever for handling graph-based completion searches with summarized context.""" + """ + Retriever for handling graph-based completion searches with summarized context. + + This class inherits from the GraphCompletionRetriever and is intended to manage the + retrieval of graph edges with an added functionality to summarize the retrieved + information efficiently. Public methods include: + + - __init__() + - resolve_edges_to_text() + """ def __init__( self, @@ -13,16 +22,37 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever): system_prompt_path: str = "answer_simple_question.txt", summarize_prompt_path: str = "summarize_search_results.txt", top_k: Optional[int] = 5, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ): """Initialize retriever with default prompt paths and search parameters.""" super().__init__( user_prompt_path=user_prompt_path, system_prompt_path=system_prompt_path, top_k=top_k, + node_type=node_type, + node_name=node_name, ) self.summarize_prompt_path = summarize_prompt_path async def resolve_edges_to_text(self, retrieved_edges: list) -> str: - """Converts retrieved graph edges into a summary without redundancies.""" + """ + Convert retrieved graph edges into a summary without redundancies. + + This asynchronous method processes a list of retrieved edges and summarizes their + content using a specified prompt path. It relies on the parent's implementation to + convert the edges to text before summarizing. Raises an error if the summarization fails + due to an invalid prompt path. + + Parameters: + ----------- + + - retrieved_edges (list): List of graph edges retrieved for summarization. + + Returns: + -------- + + - str: A summary string representing the content of the retrieved edges. + """ direct_text = await super().resolve_edges_to_text(retrieved_edges) return await summarize_text(direct_text, self.summarize_prompt_path) diff --git a/cognee/modules/retrieval/insights_retriever.py b/cognee/modules/retrieval/insights_retriever.py index e34280f99..ee8865544 100644 --- a/cognee/modules/retrieval/insights_retriever.py +++ b/cognee/modules/retrieval/insights_retriever.py @@ -9,7 +9,17 @@ from cognee.infrastructure.databases.vector.exceptions.exceptions import Collect class InsightsRetriever(BaseRetriever): - """Retriever for handling graph connection-based insights.""" + """ + 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: int = 5): """Initialize retriever with exploration levels and search parameters.""" @@ -17,7 +27,24 @@ class InsightsRetriever(BaseRetriever): self.top_k = top_k async def get_context(self, query: str) -> list: - """Find the neighbours of a given node in the graph.""" + """ + 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 [] @@ -67,7 +94,24 @@ class InsightsRetriever(BaseRetriever): return unique_node_connections async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Returns the graph connections context.""" + """ + 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/natural_language_retriever.py b/cognee/modules/retrieval/natural_language_retriever.py index ae7d40afa..9211c286b 100644 --- a/cognee/modules/retrieval/natural_language_retriever.py +++ b/cognee/modules/retrieval/natural_language_retriever.py @@ -12,7 +12,15 @@ logger = logging.getLogger("NaturalLanguageRetriever") class NaturalLanguageRetriever(BaseRetriever): - """Retriever for handling natural language search""" + """ + Retriever for handling natural language search. + + Public methods include: + + - get_context: Retrieves relevant context using a natural language query converted to + Cypher. + - get_completion: Returns a completion based on the query and context. + """ def __init__( self, @@ -97,7 +105,24 @@ class NaturalLanguageRetriever(BaseRetriever): return [] async def get_context(self, query: str) -> Optional[Any]: - """Retrieves relevant context using a natural language query converted to Cypher.""" + """ + Retrieves relevant context using a natural language query converted to Cypher. + + This method raises a SearchTypeNotSupported exception if the graph engine does not + support natural language search. It also logs errors if the execution of the retrieval + fails. + + Parameters: + ----------- + + - query (str): The natural language query used to retrieve context. + + Returns: + -------- + + - Optional[Any]: Returns the context retrieved from the graph database based on the + query. + """ try: graph_engine = await get_graph_engine() @@ -110,7 +135,25 @@ class NaturalLanguageRetriever(BaseRetriever): raise e async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Returns a completion based on the query and context.""" + """ + Returns a completion based on the query and context. + + If context is not provided, it retrieves the context using the given query. No + exceptions are explicitly raised from this method, but it relies on the get_context + method for possible exceptions. + + Parameters: + ----------- + + - query (str): The natural language query to get a completion from. + - context (Optional[Any]): The context in which to base the completion; if not + provided, it will be retrieved using the query. (default None) + + Returns: + -------- + + - Any: Returns the completion derived from the given query and context. + """ if context is None: context = await self.get_context(query) diff --git a/cognee/modules/retrieval/summaries_retriever.py b/cognee/modules/retrieval/summaries_retriever.py index 1e7547258..537a3c222 100644 --- a/cognee/modules/retrieval/summaries_retriever.py +++ b/cognee/modules/retrieval/summaries_retriever.py @@ -7,14 +7,39 @@ from cognee.infrastructure.databases.vector.exceptions.exceptions import Collect class SummariesRetriever(BaseRetriever): - """Retriever for handling summary-based searches.""" + """ + Retriever for handling summary-based searches. + + Public methods: + - __init__ + - get_context + - get_completion + + Instance variables: + - top_k: int - Number of top summaries to retrieve. + """ def __init__(self, top_k: int = 5): """Initialize retriever with search parameters.""" self.top_k = top_k async def get_context(self, query: str) -> Any: - """Retrieves summary context based on the query.""" + """ + Retrieves summary context based on the query. + + On encountering a missing collection, raises NoDataError with a message to add data + first. + + Parameters: + ----------- + + - query (str): The search query for which to retrieve summary context. + + Returns: + -------- + + - Any: A list of payloads from the retrieved summaries. + """ vector_engine = get_vector_engine() try: @@ -27,7 +52,24 @@ class SummariesRetriever(BaseRetriever): return [summary.payload for summary in summaries_results] async def get_completion(self, query: str, context: Optional[Any] = None) -> Any: - """Generates a completion using summaries context.""" + """ + Generates a completion using summaries context. + + If no context is provided, retrieves context using the query. Returns the provided + context or the retrieved context if none was given. + + Parameters: + ----------- + + - query (str): The search query for generating the completion. + - context (Optional[Any]): Optional context for the completion; if not provided, + will be retrieved based on the query. (default None) + + Returns: + -------- + + - Any: The generated completion context, which is either provided or retrieved. + """ if context is None: context = await self.get_context(query) return context diff --git a/cognee/modules/retrieval/utils/brute_force_triplet_search.py b/cognee/modules/retrieval/utils/brute_force_triplet_search.py index 0a08fbd00..06f11ab26 100644 --- a/cognee/modules/retrieval/utils/brute_force_triplet_search.py +++ b/cognee/modules/retrieval/utils/brute_force_triplet_search.py @@ -1,5 +1,5 @@ import asyncio -from typing import List, Optional +from typing import List, Optional, Type from cognee.shared.logging_utils import get_logger, ERROR from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError @@ -55,6 +55,8 @@ def format_triplets(edges): async def get_memory_fragment( properties_to_project: Optional[List[str]] = None, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> CogneeGraph: """Creates and initializes a CogneeGraph memory fragment with optional property projections.""" graph_engine = await get_graph_engine() @@ -68,6 +70,8 @@ async def get_memory_fragment( graph_engine, node_properties_to_project=properties_to_project, edge_properties_to_project=["relationship_name"], + node_type=node_type, + node_name=node_name, ) except EntityNotFoundError: pass @@ -82,6 +86,8 @@ async def brute_force_triplet_search( collections: List[str] = None, properties_to_project: List[str] = None, memory_fragment: Optional[CogneeGraph] = None, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> list: if user is None: user = await get_default_user() @@ -93,6 +99,8 @@ async def brute_force_triplet_search( collections=collections, properties_to_project=properties_to_project, memory_fragment=memory_fragment, + node_type=node_type, + node_name=node_name, ) return retrieved_results @@ -104,6 +112,8 @@ async def brute_force_search( collections: List[str] = None, properties_to_project: List[str] = None, memory_fragment: Optional[CogneeGraph] = None, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> list: """ Performs a brute force search to retrieve the top triplets from the graph. @@ -125,7 +135,9 @@ async def brute_force_search( raise ValueError("top_k must be a positive integer.") if memory_fragment is None: - memory_fragment = await get_memory_fragment(properties_to_project) + memory_fragment = await get_memory_fragment( + properties_to_project, node_type=node_type, node_name=node_name + ) if collections is None: collections = [ diff --git a/cognee/modules/search/methods/search.py b/cognee/modules/search/methods/search.py index 5901001d9..4b43ebf31 100644 --- a/cognee/modules/search/methods/search.py +++ b/cognee/modules/search/methods/search.py @@ -1,5 +1,5 @@ import json -from typing import Callable +from typing import Callable, Optional, List, Type from cognee.exceptions import InvalidValueError from cognee.infrastructure.engine.utils import parse_id @@ -11,6 +11,10 @@ from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionR from cognee.modules.retrieval.graph_summary_completion_retriever import ( GraphSummaryCompletionRetriever, ) +from cognee.modules.retrieval.graph_completion_cot_retriever import GraphCompletionCotRetriever +from cognee.modules.retrieval.graph_completion_context_extension_retriever import ( + GraphCompletionContextExtensionRetriever, +) from cognee.modules.retrieval.code_retriever import CodeRetriever from cognee.modules.retrieval.cypher_search_retriever import CypherSearchRetriever from cognee.modules.retrieval.natural_language_retriever import NaturalLanguageRetriever @@ -19,7 +23,7 @@ from cognee.modules.storage.utils import JSONEncoder from cognee.modules.users.models import User from cognee.modules.users.permissions.methods import get_document_ids_for_user from cognee.shared.utils import send_telemetry -from ..operations import log_query, log_result +from cognee.modules.search.operations import log_query, log_result async def search( @@ -29,12 +33,20 @@ async def search( user: User, system_prompt_path="answer_simple_question.txt", top_k: int = 10, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ): query = await log_query(query_text, query_type.value, user.id) own_document_ids = await get_document_ids_for_user(user.id, datasets) search_results = await specific_search( - query_type, query_text, user, system_prompt_path=system_prompt_path, top_k=top_k + query_type, + query_text, + user, + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, ) filtered_search_results = [] @@ -57,21 +69,39 @@ async def specific_search( user: User, system_prompt_path="answer_simple_question.txt", top_k: int = 10, + node_type: Optional[Type] = None, + node_name: Optional[List[str]] = None, ) -> list: search_tasks: dict[SearchType, Callable] = { SearchType.SUMMARIES: SummariesRetriever(top_k=top_k).get_completion, SearchType.INSIGHTS: InsightsRetriever(top_k=top_k).get_completion, SearchType.CHUNKS: ChunksRetriever(top_k=top_k).get_completion, SearchType.RAG_COMPLETION: CompletionRetriever( - system_prompt_path=system_prompt_path, - top_k=top_k, + system_prompt_path=system_prompt_path, top_k=top_k ).get_completion, SearchType.GRAPH_COMPLETION: GraphCompletionRetriever( system_prompt_path=system_prompt_path, top_k=top_k, + node_type=node_type, + node_name=node_name, + ).get_completion, + SearchType.GRAPH_COMPLETION_COT: GraphCompletionCotRetriever( + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, + ).get_completion, + SearchType.GRAPH_COMPLETION_CONTEXT_EXTENSION: GraphCompletionContextExtensionRetriever( + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, ).get_completion, SearchType.GRAPH_SUMMARY_COMPLETION: GraphSummaryCompletionRetriever( - system_prompt_path=system_prompt_path, top_k=top_k + system_prompt_path=system_prompt_path, + top_k=top_k, + node_type=node_type, + node_name=node_name, ).get_completion, SearchType.CODE: CodeRetriever(top_k=top_k).get_completion, SearchType.CYPHER: CypherSearchRetriever().get_completion, diff --git a/cognee/modules/search/types/SearchType.py b/cognee/modules/search/types/SearchType.py index 9c6a84483..1c672f0f0 100644 --- a/cognee/modules/search/types/SearchType.py +++ b/cognee/modules/search/types/SearchType.py @@ -11,3 +11,5 @@ class SearchType(Enum): CODE = "CODE" CYPHER = "CYPHER" NATURAL_LANGUAGE = "NATURAL_LANGUAGE" + GRAPH_COMPLETION_COT = "GRAPH_COMPLETION_COT" + GRAPH_COMPLETION_CONTEXT_EXTENSION = "GRAPH_COMPLETION_CONTEXT_EXTENSION" diff --git a/cognee/modules/visualization/cognee_network_visualization.py b/cognee/modules/visualization/cognee_network_visualization.py index b0f2f0a1a..83a0f237e 100644 --- a/cognee/modules/visualization/cognee_network_visualization.py +++ b/cognee/modules/visualization/cognee_network_visualization.py @@ -21,6 +21,7 @@ async def cognee_network_visualization(graph_data, destination_file_path: str = "TextSummary": "#1077f4", "TableRow": "#f47710", "TableType": "#6510f4", + "ColumnValue": "#13613a", "default": "#D3D3D3", } diff --git a/cognee/shared/data_models.py b/cognee/shared/data_models.py index e0e90e322..fb9386dcf 100644 --- a/cognee/shared/data_models.py +++ b/cognee/shared/data_models.py @@ -46,9 +46,6 @@ else: name: str type: str description: str - properties: Optional[Dict[str, Any]] = Field( - None, description="A dictionary of properties associated with the node." - ) class Edge(BaseModel): """Edge in a knowledge graph.""" @@ -56,9 +53,6 @@ else: source_node_id: str target_node_id: str relationship_name: str - properties: Optional[Dict[str, Any]] = Field( - None, description="A dictionary of properties associated with the edge." - ) class KnowledgeGraph(BaseModel): """Knowledge graph.""" diff --git a/cognee/shared/logging_utils.py b/cognee/shared/logging_utils.py index bfd4828f8..26bea77d9 100644 --- a/cognee/shared/logging_utils.py +++ b/cognee/shared/logging_utils.py @@ -4,8 +4,12 @@ import threading import logging import structlog import traceback +import platform from datetime import datetime from pathlib import Path +import importlib.metadata + +from cognee import __version__ as cognee_version # Export common log levels DEBUG = logging.DEBUG @@ -36,6 +40,13 @@ LOGS_DIR.mkdir(exist_ok=True) # Create logs dir if it doesn't exist # Maximum number of log files to keep MAX_LOG_FILES = 10 +# Version information +PYTHON_VERSION = platform.python_version() +STRUCTLOG_VERSION = structlog.__version__ +COGNEE_VERSION = cognee_version + +OS_INFO = f"{platform.system()} {platform.release()} ({platform.version()})" + class PlainFileHandler(logging.FileHandler): """A custom file handler that writes simpler plain text log entries.""" @@ -326,8 +337,18 @@ def setup_logging(log_level=None, name=None): # Clean up old log files, keeping only the most recent ones cleanup_old_logs(LOGS_DIR, MAX_LOG_FILES) - # Return a configured logger - return structlog.get_logger(name if name else __name__) + # Get a configured logger and log system information + logger = structlog.get_logger(name if name else __name__) + logger.info( + "Logging initialized", + python_version=PYTHON_VERSION, + structlog_version=STRUCTLOG_VERSION, + cognee_version=COGNEE_VERSION, + os_info=OS_INFO, + ) + + # Return the configured logger + return logger def get_log_file_location(): diff --git a/cognee/tasks/chunks/chunk_by_paragraph.py b/cognee/tasks/chunks/chunk_by_paragraph.py index d4b434eee..1668276c5 100644 --- a/cognee/tasks/chunks/chunk_by_paragraph.py +++ b/cognee/tasks/chunks/chunk_by_paragraph.py @@ -10,14 +10,23 @@ def chunk_by_paragraph( batch_paragraphs: bool = True, ) -> Iterator[Dict[str, Any]]: """ - Chunks text by paragraph while preserving exact text reconstruction capability. - When chunks are joined with empty string "", they reproduce the original text exactly. + Chunk the input text by paragraph while enabling exact text reconstruction. - Notes: - - Tokenization is handled using our tokenization adapters, ensuring compatibility with the vector engine's embedding model. - - If `batch_paragraphs` is False, each paragraph will be yielded as a separate chunk. - - Handles cases where paragraphs exceed the specified token or word limits by splitting them as needed. - - Remaining text at the end of the input will be yielded as a final chunk. + This function divides the given text data into smaller chunks based on the specified + maximum chunk size. It ensures that when the generated chunks are concatenated, they + reproduce the original text accurately. The tokenization process is handled by adapters + compatible with the vector engine's embedding model, and the function can operate in + either batch mode or paragraph mode, based on the `batch_paragraphs` flag. + + Parameters: + ----------- + + - data (str): The input text to be chunked. + - max_chunk_size: The maximum allowed size for each chunk, in terms of tokens or + words. + - batch_paragraphs (bool): Flag indicating whether to yield each paragraph as a + separate chunk. If set to False, individual paragraphs are yielded as they are + processed. (default True) """ current_chunk = "" chunk_index = 0 diff --git a/cognee/tasks/chunks/chunk_by_sentence.py b/cognee/tasks/chunks/chunk_by_sentence.py index 75ffc39e8..d37aa2a66 100644 --- a/cognee/tasks/chunks/chunk_by_sentence.py +++ b/cognee/tasks/chunks/chunk_by_sentence.py @@ -5,6 +5,23 @@ from cognee.infrastructure.databases.vector.embeddings import get_embedding_engi def get_word_size(word: str) -> int: + """ + Calculate the size of a given word in terms of tokens. + + If an embedding engine's tokenizer is available, count the tokens for the provided word. + If the tokenizer is not available, assume the word counts as one token. + + Parameters: + ----------- + + - word (str): The word for which the token size is to be calculated. + + Returns: + -------- + + - int: The number of tokens representing the word, typically an integer, depending + on the tokenizer's output. + """ embedding_engine = get_embedding_engine() if embedding_engine.tokenizer: return embedding_engine.tokenizer.count_tokens(word) @@ -16,12 +33,22 @@ def chunk_by_sentence( data: str, maximum_size: Optional[int] = None ) -> Iterator[Tuple[UUID, str, int, Optional[str]]]: """ - Splits the input text into sentences based on word-level processing, with optional sentence length constraints. + Splits text into sentences while preserving word and paragraph boundaries. - Notes: - - Relies on the `chunk_by_word` function for word-level tokenization and classification. - - Ensures sentences within paragraphs are uniquely identifiable using UUIDs. - - Handles cases where the text ends mid-sentence by appending a special "sentence_cut" type. + This function processes the input string, dividing it into sentences based on word-level + tokenization. Each sentence is identified with a unique UUID, and it handles scenarios + where the text may end mid-sentence by tagging it with a specific type. If a maximum + sentence length is specified, the function ensures that sentences do not exceed this + length, raising a ValueError if an individual word surpasses it. The function utilizes + an external word processing function `chunk_by_word` to determine the structure of the + text. + + Parameters: + ----------- + + - data (str): The input text to be split into sentences. + - maximum_size (Optional[int]): An optional limit on the maximum size of sentences + generated. (default None) """ sentence = "" paragraph_id = uuid4() diff --git a/cognee/tasks/chunks/chunk_by_word.py b/cognee/tasks/chunks/chunk_by_word.py index f0782cd9c..51ffd39d3 100644 --- a/cognee/tasks/chunks/chunk_by_word.py +++ b/cognee/tasks/chunks/chunk_by_word.py @@ -8,15 +8,23 @@ PARAGRAPH_ENDINGS = r"[\n\r]" def is_real_paragraph_end(last_char: str, current_pos: int, text: str) -> bool: """ - Determines if the current position represents a real paragraph ending. + Determine if the current position represents a valid paragraph end. - Args: - last_char: The last processed character - current_pos: Current position in the text - text: The input text + The function checks if the last character indicates a possible sentence ending, then + verifies if the subsequent characters lead to a valid paragraph end based on specific + conditions. + + Parameters: + ----------- + + - last_char (str): The last processed character + - current_pos (int): Current position in the text + - text (str): The input text Returns: - bool: True if this is a real paragraph end, False otherwise + -------- + + - bool: True if this is a real paragraph end, False otherwise """ if re.match(SENTENCE_ENDINGS, last_char): return True @@ -38,9 +46,16 @@ def is_real_paragraph_end(last_char: str, current_pos: int, text: str) -> bool: def chunk_by_word(data: str) -> Iterator[Tuple[str, str]]: """ - Chunks text into words and endings while preserving whitespace. - Whitespace is included with the preceding word. - Outputs can be joined with "" to recreate the original input. + Chunk text into words and sentence endings, preserving whitespace. + + Whitespace is included with the preceding word. Outputs can be joined with "" to + recreate the original input. + + Parameters: + ----------- + + - data (str): The input string of text to be chunked into words and sentence + endings. """ current_chunk = "" i = 0 diff --git a/cognee/tasks/code/enrich_dependency_graph_checker.py b/cognee/tasks/code/enrich_dependency_graph_checker.py index 7b04e0357..1b3a80210 100644 --- a/cognee/tasks/code/enrich_dependency_graph_checker.py +++ b/cognee/tasks/code/enrich_dependency_graph_checker.py @@ -6,6 +6,14 @@ from cognee.tasks.repo_processor.enrich_dependency_graph import enrich_dependenc def main(): + """ + Execute the main logic of the dependency graph processor. + + This function sets up argument parsing to retrieve the repository path, checks the + existence of the specified path, and processes the repository to produce a dependency + graph. If the repository path does not exist, it logs an error message and terminates + without further execution. + """ parser = argparse.ArgumentParser() parser.add_argument("repo_path", help="Path to the repository") args = parser.parse_args() diff --git a/cognee/tasks/code/get_repo_dependency_graph_checker.py b/cognee/tasks/code/get_repo_dependency_graph_checker.py index 3a393d3f3..0e68cf7fe 100644 --- a/cognee/tasks/code/get_repo_dependency_graph_checker.py +++ b/cognee/tasks/code/get_repo_dependency_graph_checker.py @@ -5,6 +5,14 @@ from cognee.tasks.repo_processor.get_repo_file_dependencies import get_repo_file def main(): + """ + Parse the command line arguments and print the repository file dependencies. + + This function sets up an argument parser to retrieve the path of a repository. It checks + if the provided path exists and if it doesn’t, it prints an error message and exits. If + the path is valid, it calls an asynchronous function to get the dependencies and prints + the nodes and their relations in the dependency graph. + """ parser = argparse.ArgumentParser() parser.add_argument("repo_path", help="Path to the repository") args = parser.parse_args() diff --git a/cognee/tasks/completion/exceptions/exceptions.py b/cognee/tasks/completion/exceptions/exceptions.py index aebece145..ac105a966 100644 --- a/cognee/tasks/completion/exceptions/exceptions.py +++ b/cognee/tasks/completion/exceptions/exceptions.py @@ -3,6 +3,21 @@ from fastapi import status class NoRelevantDataError(CogneeApiError): + """ + Represents an error when no relevant data is found during a search. This class is a + subclass of CogneeApiError. + + Public methods: + + - __init__ + + Instance variables: + + - message + - name + - status_code + """ + def __init__( self, message: str = "Search did not find any data.", diff --git a/cognee/tasks/documents/classify_documents.py b/cognee/tasks/documents/classify_documents.py index 97ff3e483..673e17c75 100644 --- a/cognee/tasks/documents/classify_documents.py +++ b/cognee/tasks/documents/classify_documents.py @@ -52,7 +52,21 @@ EXTENSION_TO_DOCUMENT_CLASS = { def update_node_set(document): - """Extracts node_set from document's external_metadata.""" + """ + Extracts node_set from document's external_metadata. + + Parses the external_metadata of the given document and updates the document's + belongs_to_set attribute with NodeSet objects generated from the node_set found in the + external_metadata. If the external_metadata is not valid JSON, is not a dictionary, does + not contain the 'node_set' key, or if node_set is not a list, the function has no effect + and will return early. + + Parameters: + ----------- + + - document: The document object which contains external_metadata from which the + node_set will be extracted. + """ try: external_metadata = json.loads(document.external_metadata) except json.JSONDecodeError: @@ -76,11 +90,26 @@ def update_node_set(document): async def classify_documents(data_documents: list[Data]) -> list[Document]: """ - Classifies a list of data items into specific document types based on file extensions. + Classifies a list of data items into specific document types based on their file + extensions. - Notes: - - The function relies on `get_metadata` to retrieve metadata information for each data item. - - Ensure the `Data` objects and their attributes (e.g., `extension`, `id`) are valid before calling this function. + This function processes each item in the provided list of data documents, retrieves + relevant metadata, and creates instances of document classes mapped to their extensions. + It ensures that the data items are valid before performing the classification and + invokes `update_node_set` to extract and set relevant node information from the + document's external metadata. + + Parameters: + ----------- + + - data_documents (list[Data]): A list of Data objects representing the documents to + be classified. + + Returns: + -------- + + - list[Document]: A list of Document objects created based on the classified data + documents. """ documents = [] for data_item in data_documents: diff --git a/cognee/tasks/graph/infer_data_ontology.py b/cognee/tasks/graph/infer_data_ontology.py index bec7d6fb3..23ede6656 100644 --- a/cognee/tasks/graph/infer_data_ontology.py +++ b/cognee/tasks/graph/infer_data_ontology.py @@ -33,6 +33,25 @@ logger = get_logger("task:infer_data_ontology") async def extract_ontology(content: str, response_model: Type[BaseModel]): + """ + Extracts structured ontology from the provided content using a pre-defined LLM client. + + This asynchronous function retrieves a system prompt from a file and utilizes an LLM + client to create a structured output based on the input content and specified response + model. + + Parameters: + ----------- + + - content (str): The content from which to extract the ontology. + - response_model (Type[BaseModel]): The model that defines the structure of the + output ontology. + + Returns: + -------- + + The structured ontology extracted from the content. + """ llm_client = get_llm_client() system_prompt = read_query_prompt("extract_ontology.txt") @@ -43,10 +62,38 @@ async def extract_ontology(content: str, response_model: Type[BaseModel]): class OntologyEngine: + """ + Manage ontology data and operations for graph structures, providing methods for data + loading, flattening models, and adding ontological relationships to a graph database. + + Public methods: + + - flatten_model + - recursive_flatten + - load_data + - add_graph_ontology + """ + async def flatten_model( self, model: NodeModel, parent_id: Optional[str] = None ) -> Dict[str, Any]: - """Flatten the model to a dictionary.""" + """ + Flatten the model to a dictionary including optional parent ID and relationship details + if available. + + Parameters: + ----------- + + - model (NodeModel): The NodeModel instance to flatten. + - parent_id (Optional[str]): An optional ID of the parent node for hierarchical + purposes. (default None) + + Returns: + -------- + + - Dict[str, Any]: A dictionary representation of the model with flattened + attributes. + """ result = model.dict() result["parent_id"] = parent_id if model.default_relationship: @@ -62,7 +109,23 @@ class OntologyEngine: async def recursive_flatten( self, items: Union[List[Dict[str, Any]], Dict[str, Any]], parent_id: Optional[str] = None ) -> List[Dict[str, Any]]: - """Recursively flatten the items.""" + """ + Recursively flatten a hierarchical structure of models into a flat list of dictionaries. + + Parameters: + ----------- + + - items (Union[List[Dict[str, Any]], Dict[str, Any]]): A list or dictionary + containing models to flatten. + - parent_id (Optional[str]): An optional ID of the parent node to maintain hierarchy + during flattening. (default None) + + Returns: + -------- + + - List[Dict[str, Any]]: A flat list of dictionaries representing the hierarchical + model structure. + """ flat_list = [] if isinstance(items, list): @@ -76,7 +139,20 @@ class OntologyEngine: return flat_list async def load_data(self, file_path: str) -> Union[List[Dict[str, Any]], Dict[str, Any]]: - """Load data from a JSON or CSV file.""" + """ + Load data from a specified JSON or CSV file and return it in a structured format. + + Parameters: + ----------- + + - file_path (str): The path to the file to load data from. + + Returns: + -------- + + - Union[List[Dict[str, Any]], Dict[str, Any]]: Parsed data from the file as either a + list of dictionaries or a single dictionary depending on content type. + """ try: if file_path.endswith(".json"): async with aiofiles.open(file_path, mode="r") as f: @@ -96,7 +172,18 @@ class OntologyEngine: ) async def add_graph_ontology(self, file_path: str = None, documents: list = None): - """Add graph ontology from a JSON or CSV file or infer from documents content.""" + """ + Add graph ontology from a JSON or CSV file, or infer relationships from provided + document content. Raise exceptions for invalid file types or missing entities. + + Parameters: + ----------- + + - file_path (str): Optional path to a file containing data to be loaded. (default + None) + - documents (list): Optional list of document objects for content extraction if no + file path is provided. (default None) + """ if file_path is None: initial_chunks_and_ids = [] @@ -202,6 +289,17 @@ class OntologyEngine: async def infer_data_ontology(documents, ontology_model=KnowledgeGraph, root_node_id=None): + """ + Infer data ontology from provided documents and optionally add it to a graph. + + Parameters: + ----------- + + - documents: The documents from which to infer the ontology. + - ontology_model: The ontology model to use for the inference, defaults to + KnowledgeGraph. (default KnowledgeGraph) + - root_node_id: An optional root node identifier for the ontology. (default None) + """ if ontology_model == KnowledgeGraph: ontology_engine = OntologyEngine() root_node_id = await ontology_engine.add_graph_ontology(documents=documents) diff --git a/cognee/tasks/graph/models.py b/cognee/tasks/graph/models.py index bfffb0262..e7429824d 100644 --- a/cognee/tasks/graph/models.py +++ b/cognee/tasks/graph/models.py @@ -3,12 +3,40 @@ from pydantic import BaseModel, Field class RelationshipModel(BaseModel): + """ + Represents a relationship between two entities in a model. + + This class holds the type of the relationship and the identifiers for the source and + target entities. It includes the following public instance variables: + + - type: A string indicating the type of relationship. + - source: A string representing the source entity of the relationship. + - target: A string representing the target entity of the relationship. + """ + type: str source: str target: str class NodeModel(BaseModel): + """ + Represents a node in a hierarchical model structure with relationships to other nodes. + + Public methods: + + - __init__(self, node_id: str, name: str, default_relationship: + Optional[RelationshipModel] = None, children: List[Union[Dict[str, Any], NodeModel]] = + Field(default_factory=list)) + + Instance variables: + + - node_id: Unique identifier for the node. + - name: Name of the node. + - default_relationship: Default relationship associated with the node, if any. + - children: List of child nodes or dictionaries representing children for this node. + """ + node_id: str name: str default_relationship: Optional[RelationshipModel] = None @@ -19,12 +47,28 @@ NodeModel.model_rebuild() class OntologyNode(BaseModel): + """ + Represents a node in an ontology with a unique identifier, name, and description. + """ + id: str = Field(..., description="Unique identifier made from node name.") name: str description: str class OntologyEdge(BaseModel): + """ + Represent an edge in an ontology, connecting a source and target with a specific + relationship type. + + The class includes the following instance variables: + - id: A unique identifier for the edge. + - source_id: The identifier of the source node. + - target_id: The identifier of the target node. + - relationship_type: The type of relationship represented by this edge, defining how the + source and target are related. + """ + id: str source_id: str target_id: str @@ -32,5 +76,14 @@ class OntologyEdge(BaseModel): class GraphOntology(BaseModel): + """ + Represents a graph-based structure of ontology consisting of nodes and edges. + + The GraphOntology class contains a collection of OntologyNode instances representing the + nodes of the graph and OntologyEdge instances representing the relationships between + them. Public methods include the management of nodes and edges as well as any relevant + graph operations. Instance variables include a list of nodes and a list of edges. + """ + nodes: list[OntologyNode] edges: list[OntologyEdge] diff --git a/cognee/tasks/ingestion/get_dlt_destination.py b/cognee/tasks/ingestion/get_dlt_destination.py index 2de0d7f0d..131834f0b 100644 --- a/cognee/tasks/ingestion/get_dlt_destination.py +++ b/cognee/tasks/ingestion/get_dlt_destination.py @@ -10,11 +10,19 @@ from cognee.infrastructure.databases.relational import get_relational_config @lru_cache def get_dlt_destination() -> Union[type[dlt.destinations.sqlalchemy], None]: """ - Handles propagation of the cognee database configuration to the dlt library + Handle the propagation of the cognee database configuration to the dlt library. + + This function determines the appropriate sqlalchemy destination based on the database + provider specified in the relational configuration. It constructs the destination + credentials for either sqlite or postgres databases accordingly. If the database + provider is neither sqlite nor postgres, it returns None. Returns: - sqlachemy: sqlachemy destination used by the dlt library + -------- + - Union[type[dlt.destinations.sqlalchemy], None]: An instance of sqlalchemy + destination used by the dlt library, or None if the database provider is + unsupported. """ relational_config = get_relational_config() diff --git a/cognee/tasks/ingestion/migrate_relational_database.py b/cognee/tasks/ingestion/migrate_relational_database.py index 946b5d721..936ea59e0 100644 --- a/cognee/tasks/ingestion/migrate_relational_database.py +++ b/cognee/tasks/ingestion/migrate_relational_database.py @@ -8,12 +8,12 @@ from cognee.infrastructure.databases.relational.get_migration_relational_engine 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.models import TableRow, TableType +from cognee.modules.engine.models import TableRow, TableType, ColumnValue logger = logging.getLogger(__name__) -async def migrate_relational_database(graph_db, schema): +async def migrate_relational_database(graph_db, schema, migrate_column_data=True): """ Migrates data from a relational database into a graph database. @@ -95,6 +95,42 @@ async def migrate_relational_database(graph_db, schema): ) ) + # Migrate data stored in columns of table rows + if migrate_column_data: + # Get foreign key columns to filter them out from column migration + foreign_keys = [] + for fk in details.get("foreign_keys", []): + foreign_keys.append(fk["ref_column"]) + + for key, value in row_properties.items(): + # Skip mapping primary key information to itself and mapping of foreign key information (as it will be mapped bellow) + if key is primary_key_col or key in foreign_keys: + continue + + # Create column value node + column_node_id = f"{table_name}:{key}:{value}" + column_node = ColumnValue( + id=uuid5(NAMESPACE_OID, name=column_node_id), + name=column_node_id, + properties=f"{key} {value} {table_name}", + description=f"Column name={key} and value={value} from column from table={table_name}", + ) + node_mapping[column_node_id] = column_node + + # Create relationship between column value of table row and table row + edge_mapping.append( + ( + row_node.id, + column_node.id, + key, + dict( + relationship_name=key, + source_node_id=row_node.id, + target_node_id=column_node.id, + ), + ) + ) + # Process foreign key relationships after all nodes are created for table_name, details in schema.items(): # Process foreign key relationships for the current table diff --git a/cognee/tasks/ingestion/transform_data.py b/cognee/tasks/ingestion/transform_data.py index cc75c7a65..cd35364d7 100644 --- a/cognee/tasks/ingestion/transform_data.py +++ b/cognee/tasks/ingestion/transform_data.py @@ -5,6 +5,26 @@ from typing import Union def get_data_from_llama_index(data_point: Union[Document, ImageDocument], dataset_name: str) -> str: + """ + Retrieve the file path based on the data point type. + + Ensure the data point is an instance of either Document or ImageDocument. If the data + point has a metadata or image path file path, return it; otherwise, save the data + point's text to a file and return the newly created file path. + + Parameters: + ----------- + + - data_point (Union[Document, ImageDocument]): An instance of Document or + ImageDocument to extract data from. + - dataset_name (str): The name of the dataset associated with the data point. + + Returns: + -------- + + - str: The file path as a string where the data is stored or the existing path from + the data point. + """ # Specific type checking is used to ensure it's not a child class from Document if isinstance(data_point, Document) and type(data_point) is Document: file_path = data_point.metadata.get("file_path") diff --git a/cognee/tasks/repo_processor/get_local_dependencies.py b/cognee/tasks/repo_processor/get_local_dependencies.py index a406d6023..ed8e4e14b 100644 --- a/cognee/tasks/repo_processor/get_local_dependencies.py +++ b/cognee/tasks/repo_processor/get_local_dependencies.py @@ -19,10 +19,34 @@ logger = get_logger() class FileParser: + """ + Handles the parsing of files into source code and an abstract syntax tree + representation. Public methods include: + + - parse_file: Parses a file and returns its source code and syntax tree representation. + """ + def __init__(self): self.parsed_files = {} async def parse_file(self, file_path: str) -> tuple[str, Tree]: + """ + Parse a file and return its source code along with its syntax tree representation. + + If the file has already been parsed, retrieve the result from memory instead of reading + the file again. + + Parameters: + ----------- + + - file_path (str): The path of the file to parse. + + Returns: + -------- + + - tuple[str, Tree]: A tuple containing the source code of the file and its + corresponding syntax tree representation. + """ PY_LANGUAGE = Language(tspython.language()) source_code_parser = Parser(PY_LANGUAGE) @@ -35,6 +59,24 @@ class FileParser: async def get_source_code(file_path: str): + """ + Read source code from a file asynchronously. + + This function attempts to open a file specified by the given file path, read its + contents, and return the source code. In case of any errors during the file reading + process, it logs an error message and returns None. + + Parameters: + ----------- + + - file_path (str): The path to the file from which to read the source code. + + Returns: + -------- + + Returns the contents of the file as a string if successful, or None if an error + occurs. + """ try: async with aiofiles.open(file_path, "r", encoding="utf-8") as f: source_code = await f.read() @@ -45,7 +87,22 @@ async def get_source_code(file_path: str): def resolve_module_path(module_name): - """Find the file path of a module.""" + """ + Find the file path of a module. + + Return the file path of the specified module if found, or return None if the module does + not exist or cannot be located. + + Parameters: + ----------- + + - module_name: The name of the module whose file path is to be resolved. + + Returns: + -------- + + The file path of the module as a string or None if the module is not found. + """ try: spec = importlib.util.find_spec(module_name) if spec and spec.origin: @@ -58,7 +115,23 @@ def resolve_module_path(module_name): def find_function_location( module_path: str, function_name: str, parser: FileParser ) -> Optional[tuple[str, str]]: - """Find the function definition in the module.""" + """ + Find the location of a function definition in a specified module. + + Parameters: + ----------- + + - module_path (str): The path to the module where the function is defined. + - function_name (str): The name of the function whose location is to be found. + - parser (FileParser): An instance of FileParser used to parse the module's source + code. + + Returns: + -------- + + - Optional[tuple[str, str]]: Returns a tuple containing the module path and the + start point of the function if found; otherwise, returns None. + """ if not module_path or not os.path.exists(module_path): return None @@ -78,6 +151,24 @@ def find_function_location( async def get_local_script_dependencies( repo_path: str, script_path: str, detailed_extraction: bool = False ) -> CodeFile: + """ + Retrieve local script dependencies and create a CodeFile object. + + Parameters: + ----------- + + - repo_path (str): The path to the repository that contains the script. + - script_path (str): The path of the script for which dependencies are being + extracted. + - detailed_extraction (bool): A flag indicating whether to perform a detailed + extraction of code components. + + Returns: + -------- + + - CodeFile: Returns a CodeFile object containing information about the script, + including its dependencies and definitions. + """ code_file_parser = FileParser() source_code, source_code_tree = await code_file_parser.parse_file(script_path) @@ -113,6 +204,24 @@ async def get_local_script_dependencies( def find_node(nodes: list[Node], condition: callable) -> Node: + """ + Find and return the first node that satisfies the given condition. + + Iterate through the provided list of nodes and return the first node for which the + condition callable returns True. If no such node is found, return None. + + Parameters: + ----------- + + - nodes (list[Node]): A list of Node objects to search through. + - condition (callable): A callable that takes a Node and returns a boolean + indicating if the node meets specified criteria. + + Returns: + -------- + + - Node: The first Node that matches the condition, or None if no such node exists. + """ for node in nodes: if condition(node): return node @@ -123,6 +232,30 @@ def find_node(nodes: list[Node], condition: callable) -> Node: async def extract_code_parts( tree_root: Node, script_path: str, existing_nodes: list[DataPoint] = {} ) -> AsyncGenerator[DataPoint, None]: + """ + Extract code parts from a given AST node tree asynchronously. + + Iteratively yields DataPoint nodes representing import statements, function definitions, + and class definitions found in the children of the specified tree root. The function + checks + if nodes are already present in the existing_nodes dictionary to prevent duplicates. + This function has to be used in an asynchronous context, and it requires a valid + tree_root + and proper initialization of existing_nodes. + + Parameters: + ----------- + + - tree_root (Node): The root node of the AST tree containing code parts to extract. + - script_path (str): The file path of the script from which the AST was generated. + - existing_nodes (list[DataPoint]): A dictionary that holds already extracted + DataPoint nodes to avoid duplicates. (default {}) + + Returns: + -------- + + Yields DataPoint nodes representing imported modules, functions, and classes. + """ for child_node in tree_root.children: if child_node.type == "import_statement" or child_node.type == "import_from_statement": parts = child_node.text.decode("utf-8").split() diff --git a/cognee/tasks/repo_processor/get_non_code_files.py b/cognee/tasks/repo_processor/get_non_code_files.py index 5117f261d..b9ab1d4c6 100644 --- a/cognee/tasks/repo_processor/get_non_code_files.py +++ b/cognee/tasks/repo_processor/get_non_code_files.py @@ -2,7 +2,24 @@ import os async def get_non_py_files(repo_path): - """Get files that are not .py files and their contents""" + """ + Get files that are not .py files and their contents. + + Check if the specified repository path exists and if so, traverse the directory, + collecting the paths of files that do not have a .py extension and meet the + criteria set in the allowed and ignored patterns. Return a list of paths to + those files. + + Parameters: + ----------- + + - repo_path: The file system path to the repository to scan for non-Python files. + + Returns: + -------- + + A list of file paths that are not Python files and meet the specified criteria. + """ if not os.path.exists(repo_path): return {} @@ -111,6 +128,22 @@ async def get_non_py_files(repo_path): } def should_process(path): + """ + Determine if a file should be processed based on its extension and path patterns. + + This function checks if the file extension is in the allowed list and ensures that none + of the ignored patterns are present in the provided file path. + + Parameters: + ----------- + + - path: The file path to check for processing eligibility. + + Returns: + -------- + + Returns True if the file should be processed; otherwise, False. + """ _, ext = os.path.splitext(path) return ext in ALLOWED_EXTENSIONS and not any( pattern in path for pattern in IGNORED_PATTERNS diff --git a/cognee/tasks/repo_processor/get_repo_file_dependencies.py b/cognee/tasks/repo_processor/get_repo_file_dependencies.py index 92e2d7910..232850936 100644 --- a/cognee/tasks/repo_processor/get_repo_file_dependencies.py +++ b/cognee/tasks/repo_processor/get_repo_file_dependencies.py @@ -11,7 +11,24 @@ from cognee.shared.CodeGraphEntities import CodeFile, Repository async def get_source_code_files(repo_path): - """Get .py files and their source code""" + """ + Retrieve Python source code files from the specified repository path. + + This function scans the given repository path for files that have the .py extension + while excluding test files and files within a virtual environment. It returns a list of + absolute paths to the source code files that are not empty. + + Parameters: + ----------- + + - repo_path: The file path to the repository to search for Python source files. + + Returns: + -------- + + A list of absolute paths to .py files that contain source code, excluding empty + files, test files, and files from a virtual environment. + """ if not os.path.exists(repo_path): return {} @@ -40,6 +57,26 @@ async def get_source_code_files(repo_path): def run_coroutine(coroutine_func, *args, **kwargs): + """ + Run a coroutine function until it completes. + + This function creates a new asyncio event loop, sets it as the current loop, and + executes the given coroutine function with the provided arguments. Once the coroutine + completes, the loop is closed. Intended for use in environments where an existing event + loop is not available or desirable. + + Parameters: + ----------- + + - coroutine_func: The coroutine function to be run. + - *args: Positional arguments to pass to the coroutine function. + - **kwargs: Keyword arguments to pass to the coroutine function. + + Returns: + -------- + + The result returned by the coroutine after completion. + """ loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) result = loop.run_until_complete(coroutine_func(*args, **kwargs)) @@ -50,7 +87,21 @@ def run_coroutine(coroutine_func, *args, **kwargs): async def get_repo_file_dependencies( repo_path: str, detailed_extraction: bool = False ) -> AsyncGenerator[DataPoint, None]: - """Generate a dependency graph for Python files in the given repository path.""" + """ + Generate a dependency graph for Python files in the given repository path. + + Check the validity of the repository path and yield a repository object followed by the + dependencies of Python files within that repository. Raise a FileNotFoundError if the + provided path does not exist. The extraction of detailed dependencies can be controlled + via the `detailed_extraction` argument. + + Parameters: + ----------- + + - repo_path (str): The file path to the repository where Python files are located. + - detailed_extraction (bool): A flag indicating whether to perform a detailed + extraction of dependencies (default is False). (default False) + """ if not os.path.exists(repo_path): raise FileNotFoundError(f"Repository path {repo_path} does not exist.") diff --git a/cognee/tasks/summarization/mock_summary.py b/cognee/tasks/summarization/mock_summary.py index f60ce2d82..6d288b927 100644 --- a/cognee/tasks/summarization/mock_summary.py +++ b/cognee/tasks/summarization/mock_summary.py @@ -2,6 +2,19 @@ from cognee.shared.data_models import SummarizedCode, SummarizedClass, Summarize def get_mock_summarized_code() -> SummarizedCode: + """ + Return a summarized representation of mock code. + + This function constructs and returns a `SummarizedCode` object that includes various + components such as file name, high-level summary, key features, imports, constants, + classes, and functions, all described with placeholders for mock data. + + Returns: + -------- + + - SummarizedCode: A `SummarizedCode` object containing mock data for file summary, + features, imports, constants, classes, and functions. + """ return SummarizedCode( file_name="mock_file.py", high_level_summary="This is a mock high-level summary.", diff --git a/cognee/tasks/summarization/models.py b/cognee/tasks/summarization/models.py index aac1d4178..75ed82d50 100644 --- a/cognee/tasks/summarization/models.py +++ b/cognee/tasks/summarization/models.py @@ -6,6 +6,15 @@ from cognee.shared.CodeGraphEntities import CodeFile, CodePart class TextSummary(DataPoint): + """ + Represent a text summary derived from a document chunk. + + This class encapsulates a text summary as well as its associated metadata. The public + instance variables include 'text' for the summary content and 'made_from' which + indicates the source document chunk. The 'metadata' instance variable contains + additional information such as indexed fields. + """ + text: str made_from: DocumentChunk @@ -13,6 +22,15 @@ class TextSummary(DataPoint): class CodeSummary(DataPoint): + """ + Summarizes code and its components. + + This class inherits from DataPoint and contains a text representation alongside the + summarized content, which can either be a full code file or a part of it. The metadata + dictionary defines index fields for the class's instances, particularly focusing on the + 'text' attribute. Public attributes include 'text', 'summarizes', and 'metadata'. + """ + text: str summarizes: Union[CodeFile, CodePart] diff --git a/cognee/tasks/summarization/summarize_text.py b/cognee/tasks/summarization/summarize_text.py index cca41ae88..9a8b7cbd7 100644 --- a/cognee/tasks/summarization/summarize_text.py +++ b/cognee/tasks/summarization/summarize_text.py @@ -11,6 +11,29 @@ from .models import TextSummary async def summarize_text( data_chunks: list[DocumentChunk], summarization_model: Type[BaseModel] = None ): + """ + Summarize the text contained in the provided data chunks. + + If no summarization model is provided, the function retrieves the default model from the + configuration. It processes the data chunks asynchronously and returns summaries for + each chunk. If the provided list of data chunks is empty, it simply returns the list as + is. + + Parameters: + ----------- + + - data_chunks (list[DocumentChunk]): A list of DocumentChunk objects containing text + to be summarized. + - summarization_model (Type[BaseModel]): An optional model used for summarizing + text. If not provided, the default is fetched from the configuration. (default + None) + + Returns: + -------- + + A list of TextSummary objects, each containing the summary of a corresponding + DocumentChunk. + """ if len(data_chunks) == 0: return data_chunks diff --git a/cognee/tasks/temporal_awareness/graphiti_model.py b/cognee/tasks/temporal_awareness/graphiti_model.py index 89aef540b..c2a2eeb69 100644 --- a/cognee/tasks/temporal_awareness/graphiti_model.py +++ b/cognee/tasks/temporal_awareness/graphiti_model.py @@ -3,6 +3,18 @@ from typing import Optional class GraphitiNode(DataPoint): + """ + Represent a node in a graph with optional content, name, and summary attributes. + + This class extends DataPoint and includes a metadata dictionary that specifies the index + fields for the node's data. The public instance variables are: + + - content: an optional string representing the content of the node. + - name: an optional string representing the name of the node. + - summary: an optional string providing a summary of the node. + - metadata: a dictionary outlining the fields used for indexing. + """ + content: Optional[str] = None name: Optional[str] = None summary: Optional[str] = None diff --git a/cognee/tests/test_kuzu.py b/cognee/tests/test_kuzu.py index d0b03057e..c2a2389ab 100644 --- a/cognee/tests/test_kuzu.py +++ b/cognee/tests/test_kuzu.py @@ -2,6 +2,9 @@ import os import shutil import cognee import pathlib + +from cognee.modules.engine.models import NodeSet +from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever from cognee.shared.logging_utils import get_logger from cognee.modules.search.types import SearchType from cognee.modules.search.operations import get_history @@ -84,6 +87,30 @@ async def main(): history = await get_history(user.id) assert len(history) == 6, "Search history is not correct." + nodeset_text = "Neo4j is a graph database that supports cypher." + + await cognee.add([nodeset_text], dataset_name, node_set=["first"]) + + await cognee.cognify([dataset_name]) + + context_nonempty = await GraphCompletionRetriever( + node_type=NodeSet, + node_name=["first"], + ).get_context("What is in the context?") + + context_empty = await GraphCompletionRetriever( + node_type=NodeSet, + node_name=["nonexistent"], + ).get_context("What is in the context?") + + assert isinstance(context_nonempty, str) and context_nonempty != "", ( + f"Nodeset_search_test:Expected non-empty string for context_nonempty, got: {context_nonempty!r}" + ) + + assert context_empty == "", ( + f"Nodeset_search_test:Expected empty string for context_empty, got: {context_empty!r}" + ) + await cognee.prune.prune_data() assert not os.path.isdir(data_directory_path), "Local data files are not deleted" diff --git a/cognee/tests/test_neo4j.py b/cognee/tests/test_neo4j.py index e87c656a0..1d4af2b2c 100644 --- a/cognee/tests/test_neo4j.py +++ b/cognee/tests/test_neo4j.py @@ -1,10 +1,12 @@ import os import pathlib import cognee +from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever 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.modules.engine.models import NodeSet logger = get_logger() @@ -89,6 +91,30 @@ async def main(): assert len(history) == 6, "Search history is not correct." + nodeset_text = "Neo4j is a graph database that supports cypher." + + await cognee.add([nodeset_text], dataset_name, node_set=["first"]) + + await cognee.cognify([dataset_name]) + + context_nonempty = await GraphCompletionRetriever( + node_type=NodeSet, + node_name=["first"], + ).get_context("What is in the context?") + + context_empty = await GraphCompletionRetriever( + node_type=NodeSet, + node_name=["nonexistent"], + ).get_context("What is in the context?") + + assert isinstance(context_nonempty, str) and context_nonempty != "", ( + f"Nodeset_search_test:Expected non-empty string for context_nonempty, got: {context_nonempty!r}" + ) + + assert context_empty == "", ( + f"Nodeset_search_test:Expected empty string for context_empty, got: {context_empty!r}" + ) + await cognee.prune.prune_data() assert not os.path.isdir(data_directory_path), "Local data files are not deleted" diff --git a/cognee/tests/test_relational_db_migration.py b/cognee/tests/test_relational_db_migration.py index b9c77716f..8a9670a7c 100644 --- a/cognee/tests/test_relational_db_migration.py +++ b/cognee/tests/test_relational_db_migration.py @@ -112,10 +112,10 @@ async def relational_db_migration(): else: raise ValueError(f"Unsupported graph database provider: {graph_db_provider}") - assert len(distinct_node_names) == 8, ( - f"Expected 8 distinct node references, found {len(distinct_node_names)}" + assert len(distinct_node_names) == 12, ( + f"Expected 12 distinct node references, found {len(distinct_node_names)}" ) - assert len(found_edges) == 7, f"Expected 7 {relationship_label} edges, got {len(found_edges)}" + assert len(found_edges) == 15, f"Expected 15 {relationship_label} edges, got {len(found_edges)}" expected_edges = { ("Employee:5", "Employee:2"), @@ -158,8 +158,8 @@ async def relational_db_migration(): # NOTE: Because of the different size of the postgres and sqlite databases, # different number of nodes and edges are expected - assert node_count == 227, f"Expected 227 nodes, got {node_count}" - assert edge_count == 580, f"Expected 580 edges, got {edge_count}" + assert node_count == 543, f"Expected 543 nodes, got {node_count}" + assert edge_count == 1317, f"Expected 1317 edges, got {edge_count}" elif migration_db_provider == "postgresql": if graph_db_provider == "neo4j": @@ -189,8 +189,8 @@ async def relational_db_migration(): # NOTE: Because of the different size of the postgres and sqlite databases, # different number of nodes and edges are expected - assert node_count == 115, f"Expected 115 nodes, got {node_count}" - assert edge_count == 356, f"Expected 356 edges, got {edge_count}" + assert node_count == 522, f"Expected 522 nodes, got {node_count}" + assert edge_count == 961, f"Expected 961 edges, got {edge_count}" print(f"Node & edge count validated: node_count={node_count}, edge_count={edge_count}.") diff --git a/cognee/tests/unit/modules/retrieval/graph_completion_retriever_context_extension_test.py b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_context_extension_test.py new file mode 100644 index 000000000..2e2f2e197 --- /dev/null +++ b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_context_extension_test.py @@ -0,0 +1,185 @@ +import os +import pytest +import pathlib +from typing import Optional, Union + +import cognee +from cognee.low_level import setup, DataPoint +from cognee.tasks.storage import add_data_points +from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError +from cognee.modules.retrieval.graph_completion_context_extension_retriever import ( + GraphCompletionContextExtensionRetriever, +) + + +class TestGraphCompletionRetriever: + @pytest.mark.asyncio + async def test_graph_completion_extension_context_simple(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + await setup() + + class Company(DataPoint): + name: str + + class Person(DataPoint): + name: str + works_for: Company + + company1 = Company(name="Figma") + company2 = Company(name="Canva") + person1 = Person(name="Steve Rodger", works_for=company1) + person2 = Person(name="Ike Loma", works_for=company1) + person3 = Person(name="Jason Statham", works_for=company1) + person4 = Person(name="Mike Broski", works_for=company2) + person5 = Person(name="Christina Mayer", works_for=company2) + + entities = [company1, company2, person1, person2, person3, person4, person5] + + await add_data_points(entities) + + retriever = GraphCompletionContextExtensionRetriever() + + context = await retriever.get_context("Who works at Canva?") + + assert "Mike Broski --[works_for]--> Canva" in context, "Failed to get Mike Broski" + assert "Christina Mayer --[works_for]--> Canva" in context, "Failed to get Christina Mayer" + + answer = await retriever.get_completion("Who works at Canva?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + @pytest.mark.asyncio + async def test_graph_completion_extension_context_complex(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_completion_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_completion_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + await setup() + + class Company(DataPoint): + name: str + metadata: dict = {"index_fields": ["name"]} + + class Car(DataPoint): + brand: str + model: str + year: int + + class Location(DataPoint): + country: str + city: str + + class Home(DataPoint): + location: Location + rooms: int + sqm: int + + class Person(DataPoint): + name: str + works_for: Company + owns: Optional[list[Union[Car, Home]]] = None + + company1 = Company(name="Figma") + company2 = Company(name="Canva") + + person1 = Person(name="Mike Rodger", works_for=company1) + person1.owns = [Car(brand="Toyota", model="Camry", year=2020)] + + person2 = Person(name="Ike Loma", works_for=company1) + person2.owns = [ + Car(brand="Tesla", model="Model S", year=2021), + Home(location=Location(country="USA", city="New York"), sqm=80, rooms=4), + ] + + person3 = Person(name="Jason Statham", works_for=company1) + + person4 = Person(name="Mike Broski", works_for=company2) + person4.owns = [Car(brand="Ford", model="Mustang", year=1978)] + + person5 = Person(name="Christina Mayer", works_for=company2) + person5.owns = [Car(brand="Honda", model="Civic", year=2023)] + + entities = [company1, company2, person1, person2, person3, person4, person5] + + await add_data_points(entities) + + retriever = GraphCompletionContextExtensionRetriever(top_k=20) + + context = await retriever.get_context("Who works at Figma?") + + print(context) + + assert "Mike Rodger --[works_for]--> Figma" in context, "Failed to get Mike Rodger" + assert "Ike Loma --[works_for]--> Figma" in context, "Failed to get Ike Loma" + assert "Jason Statham --[works_for]--> Figma" in context, "Failed to get Jason Statham" + + answer = await retriever.get_completion("Who works at Figma?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + @pytest.mark.asyncio + async def test_get_graph_completion_extension_context_on_empty_graph(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_completion_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_completion_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + retriever = GraphCompletionContextExtensionRetriever() + + with pytest.raises(DatabaseNotCreatedError): + await retriever.get_context("Who works at Figma?") + + await setup() + + context = await retriever.get_context("Who works at Figma?") + assert context == "", "Context should be empty on an empty graph" + + answer = await retriever.get_completion("Who works at Figma?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + +if __name__ == "__main__": + from asyncio import run + + test = TestGraphCompletionRetriever() + + async def main(): + await test.test_graph_completion_context_simple() + await test.test_graph_completion_context_complex() + await test.test_get_graph_completion_context_on_empty_graph() + + run(main()) diff --git a/cognee/tests/unit/modules/retrieval/graph_completion_retriever_cot_test.py b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_cot_test.py new file mode 100644 index 000000000..5723afd4a --- /dev/null +++ b/cognee/tests/unit/modules/retrieval/graph_completion_retriever_cot_test.py @@ -0,0 +1,183 @@ +import os +import pytest +import pathlib +from typing import Optional, Union + +import cognee +from cognee.low_level import setup, DataPoint +from cognee.tasks.storage import add_data_points +from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError +from cognee.modules.retrieval.graph_completion_cot_retriever import GraphCompletionCotRetriever + + +class TestGraphCompletionRetriever: + @pytest.mark.asyncio + async def test_graph_completion_cot_context_simple(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + await setup() + + class Company(DataPoint): + name: str + + class Person(DataPoint): + name: str + works_for: Company + + company1 = Company(name="Figma") + company2 = Company(name="Canva") + person1 = Person(name="Steve Rodger", works_for=company1) + person2 = Person(name="Ike Loma", works_for=company1) + person3 = Person(name="Jason Statham", works_for=company1) + person4 = Person(name="Mike Broski", works_for=company2) + person5 = Person(name="Christina Mayer", works_for=company2) + + entities = [company1, company2, person1, person2, person3, person4, person5] + + await add_data_points(entities) + + retriever = GraphCompletionCotRetriever() + + context = await retriever.get_context("Who works at Canva?") + + assert "Mike Broski --[works_for]--> Canva" in context, "Failed to get Mike Broski" + assert "Christina Mayer --[works_for]--> Canva" in context, "Failed to get Christina Mayer" + + answer = await retriever.get_completion("Who works at Canva?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + @pytest.mark.asyncio + async def test_graph_completion_cot_context_complex(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_completion_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_completion_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + await setup() + + class Company(DataPoint): + name: str + metadata: dict = {"index_fields": ["name"]} + + class Car(DataPoint): + brand: str + model: str + year: int + + class Location(DataPoint): + country: str + city: str + + class Home(DataPoint): + location: Location + rooms: int + sqm: int + + class Person(DataPoint): + name: str + works_for: Company + owns: Optional[list[Union[Car, Home]]] = None + + company1 = Company(name="Figma") + company2 = Company(name="Canva") + + person1 = Person(name="Mike Rodger", works_for=company1) + person1.owns = [Car(brand="Toyota", model="Camry", year=2020)] + + person2 = Person(name="Ike Loma", works_for=company1) + person2.owns = [ + Car(brand="Tesla", model="Model S", year=2021), + Home(location=Location(country="USA", city="New York"), sqm=80, rooms=4), + ] + + person3 = Person(name="Jason Statham", works_for=company1) + + person4 = Person(name="Mike Broski", works_for=company2) + person4.owns = [Car(brand="Ford", model="Mustang", year=1978)] + + person5 = Person(name="Christina Mayer", works_for=company2) + person5.owns = [Car(brand="Honda", model="Civic", year=2023)] + + entities = [company1, company2, person1, person2, person3, person4, person5] + + await add_data_points(entities) + + retriever = GraphCompletionCotRetriever(top_k=20) + + context = await retriever.get_context("Who works at Figma?") + + print(context) + + assert "Mike Rodger --[works_for]--> Figma" in context, "Failed to get Mike Rodger" + assert "Ike Loma --[works_for]--> Figma" in context, "Failed to get Ike Loma" + assert "Jason Statham --[works_for]--> Figma" in context, "Failed to get Jason Statham" + + answer = await retriever.get_completion("Who works at Figma?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + @pytest.mark.asyncio + async def test_get_graph_completion_cot_context_on_empty_graph(self): + system_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".cognee_system/test_graph_completion_context" + ) + cognee.config.system_root_directory(system_directory_path) + data_directory_path = os.path.join( + pathlib.Path(__file__).parent, ".data_storage/test_graph_completion_context" + ) + cognee.config.data_root_directory(data_directory_path) + + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + retriever = GraphCompletionCotRetriever() + + with pytest.raises(DatabaseNotCreatedError): + await retriever.get_context("Who works at Figma?") + + await setup() + + context = await retriever.get_context("Who works at Figma?") + assert context == "", "Context should be empty on an empty graph" + + answer = await retriever.get_completion("Who works at Figma?") + + assert isinstance(answer, list), f"Expected list, got {type(answer).__name__}" + assert all(isinstance(item, str) and item.strip() for item in answer), ( + "Answer must contain only non-empty strings" + ) + + +if __name__ == "__main__": + from asyncio import run + + test = TestGraphCompletionRetriever() + + async def main(): + await test.test_graph_completion_context_simple() + await test.test_graph_completion_context_complex() + await test.test_get_graph_completion_context_on_empty_graph() + + run(main()) diff --git a/cognee/tests/unit/modules/search/search_methods_test.py b/cognee/tests/unit/modules/search/search_methods_test.py index f8e440ca4..7a76d5f7c 100644 --- a/cognee/tests/unit/modules/search/search_methods_test.py +++ b/cognee/tests/unit/modules/search/search_methods_test.py @@ -3,6 +3,7 @@ import uuid from unittest.mock import AsyncMock, MagicMock, patch import pytest +from pylint.checkers.utils import node_type from cognee.exceptions import InvalidValueError from cognee.modules.search.methods.search import search, specific_search @@ -68,7 +69,13 @@ async def test_search( mock_log_query.assert_called_once_with(query_text, query_type.value, mock_user.id) mock_get_document_ids.assert_called_once_with(mock_user.id, datasets) mock_specific_search.assert_called_once_with( - query_type, query_text, mock_user, system_prompt_path="answer_simple_question.txt", top_k=10 + query_type, + query_text, + mock_user, + system_prompt_path="answer_simple_question.txt", + top_k=10, + node_type=None, + node_name=None, ) # Only the first two results should be included (doc_id3 is filtered out) diff --git a/cognee/version.py b/cognee/version.py new file mode 100644 index 000000000..db242cb72 --- /dev/null +++ b/cognee/version.py @@ -0,0 +1,24 @@ +import os +from contextlib import suppress +import importlib.metadata +from pathlib import Path + + +def get_cognee_version() -> str: + """Returns either the version of installed cognee package or the one + found in nearby pyproject.toml""" + with suppress(FileNotFoundError, StopIteration): + with open( + os.path.join(Path(__file__).parent.parent, "pyproject.toml"), encoding="utf-8" + ) as pyproject_toml: + version = ( + next(line for line in pyproject_toml if line.startswith("version")) + .split("=")[1] + .strip("'\"\n ") + ) + # Mark the version as a local Cognee library by appending “-dev” + return f"{version}-dev" + try: + return importlib.metadata.version("cognee") + except importlib.metadata.PackageNotFoundError: + return "unknown" diff --git a/helm/Chart.yaml b/deployment/helm/Chart.yaml similarity index 100% rename from helm/Chart.yaml rename to deployment/helm/Chart.yaml diff --git a/helm/Dockerfile b/deployment/helm/Dockerfile similarity index 100% rename from helm/Dockerfile rename to deployment/helm/Dockerfile diff --git a/helm/README.md b/deployment/helm/README.md similarity index 100% rename from helm/README.md rename to deployment/helm/README.md diff --git a/helm/docker-compose-helm.yml b/deployment/helm/docker-compose-helm.yml similarity index 100% rename from helm/docker-compose-helm.yml rename to deployment/helm/docker-compose-helm.yml diff --git a/helm/templates/cognee_deployment.yaml b/deployment/helm/templates/cognee_deployment.yaml similarity index 100% rename from helm/templates/cognee_deployment.yaml rename to deployment/helm/templates/cognee_deployment.yaml diff --git a/helm/templates/cognee_service.yaml b/deployment/helm/templates/cognee_service.yaml similarity index 100% rename from helm/templates/cognee_service.yaml rename to deployment/helm/templates/cognee_service.yaml diff --git a/helm/templates/postgres_deployment.yaml b/deployment/helm/templates/postgres_deployment.yaml similarity index 100% rename from helm/templates/postgres_deployment.yaml rename to deployment/helm/templates/postgres_deployment.yaml diff --git a/helm/templates/postgres_pvc.yaml b/deployment/helm/templates/postgres_pvc.yaml similarity index 100% rename from helm/templates/postgres_pvc.yaml rename to deployment/helm/templates/postgres_pvc.yaml diff --git a/helm/templates/postgres_service.yaml b/deployment/helm/templates/postgres_service.yaml similarity index 100% rename from helm/templates/postgres_service.yaml rename to deployment/helm/templates/postgres_service.yaml diff --git a/helm/values.yaml b/deployment/helm/values.yaml similarity index 100% rename from helm/values.yaml rename to deployment/helm/values.yaml diff --git a/docker-compose.yml b/docker-compose.yml index 91e3291b7..f6c1b4dae 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -13,7 +13,7 @@ services: - DEBUG=false # Change to true if debugging - HOST=0.0.0.0 - ENVIRONMENT=local - - PYTHONPATH=. + - LOG_LEVEL=ERROR extra_hosts: # Allows the container to reach your local machine using "host.docker.internal" instead of "localhost" - "host.docker.internal:host-gateway" diff --git a/entrypoint.sh b/entrypoint.sh index b2f2191f9..9cd81939c 100755 --- a/entrypoint.sh +++ b/entrypoint.sh @@ -13,15 +13,19 @@ echo "Environment: $ENVIRONMENT" # inconsistencies and should cause the startup to fail. This check allows for # smooth redeployments and container restarts while maintaining data integrity. echo "Running database migrations..." -MIGRATION_OUTPUT=$(alembic upgrade head 2>&1) || { - if [[ $MIGRATION_OUTPUT == *"UserAlreadyExists"* ]] || [[ $MIGRATION_OUTPUT == *"User default_user@example.com already exists"* ]]; then + +MIGRATION_OUTPUT=$(alembic upgrade head 2>&1) +MIGRATION_EXIT_CODE=$? + +if [[ $MIGRATION_EXIT_CODE -ne 0 ]]; then + if [[ "$MIGRATION_OUTPUT" == *"UserAlreadyExists"* ]] || [[ "$MIGRATION_OUTPUT" == *"User default_user@example.com already exists"* ]]; then echo "Warning: Default user already exists, continuing startup..." else - echo "Migration failed with unexpected error:" - echo "$MIGRATION_OUTPUT" + echo "Migration failed with unexpected error." exit 1 fi -} +fi + echo "Database migrations done." echo "Starting server..." diff --git a/.data/multimedia/example.png b/examples/data/multimedia/example.png similarity index 100% rename from .data/multimedia/example.png rename to examples/data/multimedia/example.png diff --git a/.data/multimedia/text_to_speech.mp3 b/examples/data/multimedia/text_to_speech.mp3 similarity index 100% rename from .data/multimedia/text_to_speech.mp3 rename to examples/data/multimedia/text_to_speech.mp3 diff --git a/examples/database_examples/chromadb_example.py b/examples/database_examples/chromadb_example.py new file mode 100644 index 000000000..defa7f78d --- /dev/null +++ b/examples/database_examples/chromadb_example.py @@ -0,0 +1,87 @@ +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 ChromaDB + + This example: + 1. Configures Cognee to use ChromaDB as vector database + 2. Sets up data directories + 3. Adds sample data to Cognee + 4. Processes (cognifies) the data + 5. Performs different types of searches + """ + # Configure ChromaDB as the vector database provider + cognee.config.set_vector_db_config( + { + "vector_db_url": "http://localhost:8000", # Default ChromaDB server URL + "vector_db_key": "", # ChromaDB doesn't require an API key by default + "vector_db_provider": "chromadb", # Specify ChromaDB as provider + } + ) + + # 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 = "chromadb_example" + + # Add sample text to the dataset + sample_text = """ChromaDB is an open-source embedding database. + It allows users to store and query embeddings and their associated metadata. + ChromaDB can be deployed in various ways: in-memory, on disk via sqlite, or as a persistent service. + It is designed to be fast, scalable, and easy to use, making it a popular choice for AI applications. + The database is built to handle vector search efficiently, which is essential for semantic search applications. + ChromaDB supports multiple distance metrics for vector similarity search and can be integrated with various ML frameworks.""" + + # 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 "ChromaDB" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="ChromaDB") + print("\nInsights about ChromaDB:") + for result in insights_results: + print(f"- {result}") + + # 2. Search for text chunks related to "vector search" + chunks_results = await cognee.search( + query_type=SearchType.CHUNKS, query_text="vector search", datasets=[dataset_name] + ) + print("\nChunks about vector search:") + 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/falkordb_example.py b/examples/database_examples/falkordb_example.py new file mode 100644 index 000000000..9e096fd81 --- /dev/null +++ b/examples/database_examples/falkordb_example.py @@ -0,0 +1,87 @@ +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 new file mode 100644 index 000000000..a31404cbc --- /dev/null +++ b/examples/database_examples/kuzu_example.py @@ -0,0 +1,85 @@ +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 KuzuDB + + This example: + 1. Configures Cognee to use KuzuDB 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 KuzuDB as the graph database provider + cognee.config.set_graph_db_config( + { + "graph_database_provider": "kuzu", # Specify KuzuDB as provider + } + ) + + # 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 = "kuzu_example" + + # Add sample text to the dataset + sample_text = """KuzuDB is a graph database system optimized for running complex graph analytics. + It is designed to be a high-performance graph database for data science workloads. + KuzuDB is built with modern hardware optimizations in mind. + It provides support for property graphs and offers a Cypher-like query language. + KuzuDB can handle both transactional and analytical graph workloads. + The database now includes vector search capabilities for AI applications and semantic search.""" + + # 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 "KuzuDB" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="KuzuDB") + print("\nInsights about KuzuDB:") + 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/milvus_example.py b/examples/database_examples/milvus_example.py new file mode 100644 index 000000000..7a8b74c71 --- /dev/null +++ b/examples/database_examples/milvus_example.py @@ -0,0 +1,89 @@ +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 Milvus + + This example: + 1. Configures Cognee to use Milvus as vector database + 2. Sets up data directories + 3. Adds sample data to Cognee + 4. Processes (cognifies) the data + 5. Performs different types of searches + """ + + # 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) + + local_milvus_db_path = os.path.join(cognee_directory_path, "databases", "milvus.db") + + # Configure Milvus as the vector database provider + cognee.config.set_vector_db_config( + { + "vector_db_url": local_milvus_db_path, # Enter Milvus Endpoint if exist + "vector_db_key": "", # Enter Token + "vector_db_provider": "milvus", # Specify Milvus as provider + } + ) + # Clean any existing data (optional) + await cognee.prune.prune_data() + await cognee.prune.prune_system(metadata=True) + + # Create a dataset + dataset_name = "milvus_example" + + # Add sample text to the dataset + sample_text = """Milvus is an open-source vector database built to power AI applications. + It is designed for storing, indexing, and querying large-scale vector datasets. + Milvus implements efficient approximate nearest neighbor search algorithms. + It features advanced indexing techniques like HNSW, IVF, PQ, and more. + Milvus supports hybrid searches combining vector similarity with scalar filtering. + The system can be deployed standalone, in clusters, or through a cloud service.""" + + # 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 "Milvus" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Milvus") + print("\nInsights about Milvus:") + for result in insights_results: + print(f"- {result}") + + # 2. Search for text chunks related to "vector similarity" + chunks_results = await cognee.search( + query_type=SearchType.CHUNKS, query_text="vector similarity", datasets=[dataset_name] + ) + print("\nChunks about vector similarity:") + 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/neo4j_example.py b/examples/database_examples/neo4j_example.py new file mode 100644 index 000000000..45985610f --- /dev/null +++ b/examples/database_examples/neo4j_example.py @@ -0,0 +1,94 @@ +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 Neo4j + + This example: + 1. Configures Cognee to use Neo4j 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 + """ + + # Set up Neo4j credentials in .env file and get the values from environment variables + neo4j_url = os.getenv("GRAPH_DATABASE_URL") + neo4j_user = os.getenv("GRAPH_DATABASE_USERNAME") + neo4j_pass = os.getenv("GRAPH_DATABASE_PASSWORD") + + # Configure Neo4j as the graph database provider + cognee.config.set_graph_db_config( + { + "graph_database_url": neo4j_url, # Neo4j Bolt URL + "graph_database_provider": "neo4j", # Specify Neo4j as provider + "graph_database_username": neo4j_user, # Neo4j username + "graph_database_password": neo4j_pass, # Neo4j password + } + ) + + # 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 = "neo4j_example" + + # Add sample text to the dataset + sample_text = """Neo4j is a graph database management system. + It stores data in nodes and relationships rather than tables as in traditional relational databases. + Neo4j provides a powerful query language called Cypher for graph traversal and analysis. + It now supports vector indexing for similarity search with the vector index plugin. + Neo4j allows embedding generation and vector search to be combined with graph operations. + Applications can use Neo4j to connect vector search with graph context for more meaningful results.""" + + # 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 "Neo4j" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Neo4j") + print("\nInsights about Neo4j:") + 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/pgvector_example.py b/examples/database_examples/pgvector_example.py new file mode 100644 index 000000000..19dbb44e9 --- /dev/null +++ b/examples/database_examples/pgvector_example.py @@ -0,0 +1,99 @@ +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 PGVector + + This example: + 1. Configures Cognee to use PostgreSQL with PGVector extension as vector database + 2. Sets up data directories + 3. Adds sample data to Cognee + 4. Processes (cognifies) the data + 5. Performs different types of searches + """ + # Configure PGVector as the vector database provider + cognee.config.set_vector_db_config( + { + "vector_db_provider": "pgvector", # Specify PGVector as provider + } + ) + + # Configure PostgreSQL connection details + # These settings are required for PGVector + cognee.config.set_relational_db_config( + { + "db_path": "", + "db_name": "cognee_db", + "db_host": "127.0.0.1", + "db_port": "5432", + "db_username": "cognee", + "db_password": "cognee", + "db_provider": "postgres", + } + ) + + # 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 = "pgvector_example" + + # Add sample text to the dataset + sample_text = """PGVector is an extension for PostgreSQL that adds vector similarity search capabilities. + It supports multiple indexing methods, including IVFFlat, HNSW, and brute-force search. + PGVector allows you to store vector embeddings directly in your PostgreSQL database. + It provides distance functions like L2 distance, inner product, and cosine distance. + Using PGVector, you can perform both metadata filtering and vector similarity search in a single query. + The extension is often used for applications like semantic search, recommendations, and image similarity.""" + + # 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 "PGVector" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="PGVector") + print("\nInsights about PGVector:") + for result in insights_results: + print(f"- {result}") + + # 2. Search for text chunks related to "vector similarity" + chunks_results = await cognee.search( + query_type=SearchType.CHUNKS, query_text="vector similarity", datasets=[dataset_name] + ) + print("\nChunks about vector similarity:") + 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/qdrant_example.py b/examples/database_examples/qdrant_example.py new file mode 100644 index 000000000..b2d2eef7d --- /dev/null +++ b/examples/database_examples/qdrant_example.py @@ -0,0 +1,93 @@ +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 Qdrant + + This example: + 1. Configures Cognee to use Qdrant as vector database + 2. Sets up data directories + 3. Adds sample data to Cognee + 4. Processes (cognifies) the data + 5. Performs different types of searches + """ + + # Set up Qdrant credentials in .env file and get the values from environment variables + qdrant_url = os.getenv("VECTOR_DB_URL") + qdrant_key = os.getenv("VECTOR_DB_KEY") + + # Configure Qdrant as the vector database provider + + cognee.config.set_vector_db_config( + { + "vector_db_url": qdrant_url, # Enter Qdrant URL + "vector_db_key": qdrant_key, # API key needed + "vector_db_provider": "qdrant", # Specify Qdrant as provider + } + ) + + # 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 = "qdrant_example" + + # Add sample text to the dataset + sample_text = """Qdrant is a vector similarity search engine and vector database. + It provides a production-ready service with a convenient API for storing, searching, and managing vectors. + Qdrant supports filtering during vector search, which is essential for real-world applications. + The database implements various performance optimizations, including HNSW index for approximate nearest neighbor search. + Qdrant can be deployed via Docker, as a managed cloud service, or directly on bare metal. + It also supports payload and metadata storage alongside the vectors, allowing for rich data retrieval.""" + + # 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 "Qdrant" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Qdrant") + print("\nInsights about Qdrant:") + for result in insights_results: + print(f"- {result}") + + # 2. Search for text chunks related to "vector search" + chunks_results = await cognee.search( + query_type=SearchType.CHUNKS, query_text="vector search", datasets=[dataset_name] + ) + print("\nChunks about vector search:") + 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/weaviate_example.py b/examples/database_examples/weaviate_example.py new file mode 100644 index 000000000..b14cc3c97 --- /dev/null +++ b/examples/database_examples/weaviate_example.py @@ -0,0 +1,92 @@ +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 Weaviate + + This example: + 1. Configures Cognee to use Weaviate as vector database + 2. Sets up data directories + 3. Adds sample data to Cognee + 4. Processes (cognifies) the data + 5. Performs different types of searches + """ + + # Set up Weaviate credentials in .env file and get the values from environment variables + weaviate_url = os.getenv("VECTOR_DB_URL") + weaviate_key = os.getenv("VECTOR_DB_KEY") + + # Configure Weaviate as the vector database provider + cognee.config.set_vector_db_config( + { + "vector_db_url": weaviate_url, # Set your Weaviate Endpoint + "vector_db_key": weaviate_key, # Set your Weaviate API key + "vector_db_provider": "weaviate", # Specify Weaviate as provider + } + ) + + # 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 = "weaviate_example" + + # Add sample text to the dataset + sample_text = """Weaviate is an open-source vector database that stores both objects and vectors. + It enables vector search with GraphQL-based filtering capabilities. + Weaviate can be deployed in the cloud, on-premise, or embedded in your application. + It allows users to search through vectors using different algorithms and metrics. + Weaviate supports various modules for text2vec transformations, including BERT, OpenAI, and other models. + It can index data in multiple ways and offers features like semantic search, classification, and contextualization.""" + + # 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 "Weaviate" + insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Weaviate") + print("\nInsights about Weaviate:") + for result in insights_results: + print(f"- {result}") + + # 2. Search for text chunks related to "vector search" + chunks_results = await cognee.search( + query_type=SearchType.CHUNKS, query_text="vector search", datasets=[dataset_name] + ) + print("\nChunks about vector search:") + 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/python/dynamic_steps_example.py b/examples/python/dynamic_steps_example.py index 8a39ce72c..fd61be9ca 100644 --- a/examples/python/dynamic_steps_example.py +++ b/examples/python/dynamic_steps_example.py @@ -1,7 +1,6 @@ import cognee import asyncio from cognee.shared.logging_utils import get_logger, ERROR -from cognee.modules.metrics.operations import get_pipeline_run_metrics from cognee.api.v1.search import SearchType @@ -180,15 +179,10 @@ async def main(enable_steps): # Step 3: Create knowledge graph if enable_steps.get("cognify"): - pipeline_run = await cognee.cognify() + await cognee.cognify() print("Knowledge graph created.") - # Step 4: Calculate descriptive metrics - if enable_steps.get("graph_metrics"): - await get_pipeline_run_metrics(pipeline_run, include_optional=True) - print("Descriptive graph metrics saved to database.") - - # Step 5: Query insights + # Step 4: Query insights if enable_steps.get("retriever"): search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="Who has experience in design tools?" diff --git a/examples/python/multimedia_example.py b/examples/python/multimedia_example.py index 228ea0f04..c38b746ef 100644 --- a/examples/python/multimedia_example.py +++ b/examples/python/multimedia_example.py @@ -21,11 +21,11 @@ async def main(): # and description of these files mp3_file_path = os.path.join( pathlib.Path(__file__).parent.parent.parent, - ".data/multimedia/text_to_speech.mp3", + "examples/data/multimedia/text_to_speech.mp3", ) png_file_path = os.path.join( pathlib.Path(__file__).parent.parent.parent, - ".data/multimedia/example.png", + "examples/data/multimedia/example.png", ) # Add the files, and make it available for cognify diff --git a/examples/python/ontology_demo_example.py b/examples/python/ontology_demo_example.py index dbd41e356..fcc6d9373 100644 --- a/examples/python/ontology_demo_example.py +++ b/examples/python/ontology_demo_example.py @@ -1,6 +1,5 @@ import cognee import asyncio -from cognee.modules.metrics.operations import get_pipeline_run_metrics from cognee.shared.logging_utils import get_logger import os @@ -62,14 +61,10 @@ async def main(): os.path.dirname(os.path.abspath(__file__)), "ontology_input_example/basic_ontology.owl" ) - pipeline_run = await cognee.cognify(ontology_file_path=ontology_path) + await cognee.cognify(ontology_file_path=ontology_path) print("Knowledge with ontology created.") - # Step 4: Calculate descriptive metrics - await get_pipeline_run_metrics(pipeline_run, include_optional=True) - print("Descriptive graph metrics saved to database.") - - # Step 5: Query insights + # Step 4: Query insights search_results = await cognee.search( query_type=SearchType.GRAPH_COMPLETION, query_text="What are the exact cars and their types produced by Audi?", diff --git a/notebooks/cognee_multimedia_demo.ipynb b/notebooks/cognee_multimedia_demo.ipynb index 9dde1aec1..d27ce069b 100644 --- a/notebooks/cognee_multimedia_demo.ipynb +++ b/notebooks/cognee_multimedia_demo.ipynb @@ -21,10 +21,10 @@ ] }, { - "cell_type": "code", - "execution_count": 23, "metadata": {}, + "cell_type": "code", "outputs": [], + "execution_count": null, "source": [ "import os\n", "import pathlib\n", @@ -34,12 +34,12 @@ "mp3_file_path = os.path.join(\n", " os.path.abspath(\"\"),\n", " \"../\",\n", - " \".data/multimedia/text_to_speech.mp3\",\n", + " \"examples/data/multimedia/text_to_speech.mp3\",\n", ")\n", "png_file_path = os.path.join(\n", " os.path.abspath(\"\"),\n", " \"../\",\n", - " \".data/multimedia/example.png\",\n", + " \"examples/data/multimedia/example.png\",\n", ")" ] }, diff --git a/notebooks/cognee_openai_compatable_demo.ipynb b/notebooks/cognee_openai_compatable_demo.ipynb new file mode 100644 index 000000000..99e0c15c1 --- /dev/null +++ b/notebooks/cognee_openai_compatable_demo.ipynb @@ -0,0 +1,109 @@ +{ + "cells": [ + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-03T09:53:28.509066Z", + "start_time": "2025-05-03T09:52:53.464324Z" + } + }, + "source": [ + "from openai import OpenAI\n", + "\n", + "# Use /api/v1/auth/login to get JWT\n", + "\n", + "client = OpenAI(api_key=\"COGNEE_API_KEY\", base_url=\"http://localhost:8000/api/v1/\")\n", + "\n", + "client.responses.create(\n", + " model=\"cognee-v1\",\n", + " input=\"Cognify: Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.\",\n", + ")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses \"HTTP/1.1 307 Temporary Redirect\"\u001B[0m\n", + "\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses/ \"HTTP/1.1 200 OK\"\u001B[0m" + ] + }, + { + "data": { + "text/plain": [ + "Response(id='resp_6815e775ed1c8191a7c9e5cd7d18dd6d01678e9c98ad3fea', created_at=None, error=None, incomplete_details=None, instructions=None, metadata=None, model='cognee-v1', object='response', output=None, parallel_tool_calls=None, temperature=None, tool_choice=None, tools=None, top_p=None, max_output_tokens=None, previous_response_id=None, reasoning=None, status='completed', text=None, truncation=None, usage=ResponseUsage(input_tokens=None, output_tokens=None, output_tokens_details=None, total_tokens=242, prompt_tokens=207, completion_tokens=35), user=None, created=1746266008, toolCalls=[{'id': 'call_VPk6HLbJClOEeznVbLe5l5zJ', 'type': 'function', 'function': {'name': 'cognify', 'arguments': '{\"text\":\"Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval.\"}'}, 'output': {'status': 'success', 'data': {'result': 'Text successfully converted into knowledge graph.'}}}])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 29 + }, + { + "cell_type": "code", + "metadata": { + "ExecuteTime": { + "end_time": "2025-05-03T09:53:35.039433Z", + "start_time": "2025-05-03T09:53:31.791117Z" + } + }, + "source": [ + "from openai import OpenAI\n", + "\n", + "client = OpenAI(api_key=\"COGNEE_API_KEY\", base_url=\"http://localhost:8000/api/v1/\")\n", + "\n", + "client.responses.create(\n", + " model=\"cognee-v1\",\n", + " input=\"Search insights about NLP\",\n", + ")" + ], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses \"HTTP/1.1 307 Temporary Redirect\"\u001B[0m\n", + "\u001B[1mHTTP Request: POST http://localhost:8000/api/v1/responses/ \"HTTP/1.1 200 OK\"\u001B[0m" + ] + }, + { + "data": { + "text/plain": [ + "Response(id='resp_6815e79c2e5c8191a2b2279408544f560bbe226c4ebfeb1f', created_at=None, error=None, incomplete_details=None, instructions=None, metadata=None, model='cognee-v1', object='response', output=None, parallel_tool_calls=None, temperature=None, tool_choice=None, tools=None, top_p=None, max_output_tokens=None, previous_response_id=None, reasoning=None, status='completed', text=None, truncation=None, usage=ResponseUsage(input_tokens=None, output_tokens=None, output_tokens_details=None, total_tokens=215, prompt_tokens=188, completion_tokens=27), user=None, created=1746266015, toolCalls=[{'id': 'call_lP8Le7A76id2MIKqVYa3gvI8', 'type': 'function', 'function': {'name': 'search', 'arguments': '{\"search_query\":\"NLP\",\"search_type\":\"INSIGHTS\",\"top_k\":5}'}, 'output': {'status': 'success', 'data': {'result': 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'02bdab9a-0981-518c-a0d4-1684e0329447'}]]}}}])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 30 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/profiling/graph_pydantic_conversion/benchmark_function.py b/profiling/graph_pydantic_conversion/benchmark_function.py deleted file mode 100644 index a4f5c839b..000000000 --- a/profiling/graph_pydantic_conversion/benchmark_function.py +++ /dev/null @@ -1,62 +0,0 @@ -import statistics -import time -import tracemalloc -from typing import Any, Callable, Dict - -import psutil - - -def benchmark_function(func: Callable, *args, num_runs: int = 5) -> Dict[str, Any]: - """ - Benchmark a function for memory usage and computational performance. - - Args: - func: Function to benchmark - *args: Arguments to pass to the function - num_runs: Number of times to run the benchmark - - Returns: - Dictionary containing benchmark metrics - """ - execution_times = [] - peak_memory_usages = [] - cpu_percentages = [] - - process = psutil.Process() - - for _ in range(num_runs): - # Start memory tracking - tracemalloc.start() - - # Measure execution time and CPU usage - start_time = time.perf_counter() - start_cpu_time = process.cpu_times() - - end_cpu_time = process.cpu_times() - end_time = time.perf_counter() - - # Calculate metrics - execution_time = end_time - start_time - cpu_time = (end_cpu_time.user + end_cpu_time.system) - ( - start_cpu_time.user + start_cpu_time.system - ) - current, peak = tracemalloc.get_traced_memory() - - # Store results - execution_times.append(execution_time) - peak_memory_usages.append(peak / 1024 / 1024) # Convert to MB - cpu_percentages.append((cpu_time / execution_time) * 100) - - tracemalloc.stop() - - analysis = { - "mean_execution_time": statistics.mean(execution_times), - "mean_peak_memory_mb": statistics.mean(peak_memory_usages), - "mean_cpu_percent": statistics.mean(cpu_percentages), - "num_runs": num_runs, - } - - if num_runs > 1: - analysis["std_execution_time"] = statistics.stdev(execution_times) - - return analysis diff --git a/profiling/graph_pydantic_conversion/profile_graph_pydantic_conversion.py b/profiling/graph_pydantic_conversion/profile_graph_pydantic_conversion.py deleted file mode 100644 index c1c0b6756..000000000 --- a/profiling/graph_pydantic_conversion/profile_graph_pydantic_conversion.py +++ /dev/null @@ -1,63 +0,0 @@ -import argparse -import asyncio - -from .benchmark_function import benchmark_function - -from cognee.modules.graph.utils import get_graph_from_model -from cognee.tests.unit.interfaces.graph.util import ( - PERSON_NAMES, - create_organization_recursive, -) - -# Example usage: -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Benchmark graph model with configurable recursive depth" - ) - parser.add_argument( - "--recursive-depth", - type=int, - default=3, - help="Recursive depth for graph generation (default: 3)", - ) - parser.add_argument("--runs", type=int, default=5, help="Number of benchmark runs (default: 5)") - args = parser.parse_args() - - society = create_organization_recursive( - "society", "Society", PERSON_NAMES, args.recursive_depth - ) - added_nodes = {} - added_edges = {} - visited_properties = {} - nodes, edges = asyncio.run( - get_graph_from_model( - society, - added_nodes=added_nodes, - added_edges=added_edges, - visited_properties=visited_properties, - ) - ) - - def get_graph_from_model_sync(model): - added_nodes = {} - added_edges = {} - visited_properties = {} - - return asyncio.run( - get_graph_from_model( - model, - added_nodes=added_nodes, - added_edges=added_edges, - visited_properties=visited_properties, - ) - ) - - results = benchmark_function(get_graph_from_model_sync, society, num_runs=args.runs) - print("\nBenchmark Results:") - print(f"N nodes: {len(nodes)}, N edges: {len(edges)}, Recursion depth: {args.recursive_depth}") - print(f"Mean Peak Memory: {results['mean_peak_memory_mb']:.2f} MB") - print(f"Mean CPU Usage: {results['mean_cpu_percent']:.2f}%") - print(f"Mean Execution Time: {results['mean_execution_time']:.4f} seconds") - - if "std_execution_time" in results: - print(f"Execution Time Std: {results['std_execution_time']:.4f} seconds") diff --git a/profiling/util/DummyEmbeddingEngine.py b/profiling/util/DummyEmbeddingEngine.py deleted file mode 100644 index 0ba742182..000000000 --- a/profiling/util/DummyEmbeddingEngine.py +++ /dev/null @@ -1,10 +0,0 @@ -import numpy as np -from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine - - -class DummyEmbeddingEngine(EmbeddingEngine): - async def embed_text(self, text: list[str]) -> list[list[float]]: - return list(list(np.random.randn(3072))) - - def get_vector_size(self) -> int: - return 3072 diff --git a/profiling/util/DummyLLMAdapter.py b/profiling/util/DummyLLMAdapter.py deleted file mode 100644 index b28261665..000000000 --- a/profiling/util/DummyLLMAdapter.py +++ /dev/null @@ -1,59 +0,0 @@ -from typing import Type -from uuid import uuid4 - -import spacy -import textacy -from pydantic import BaseModel - -from cognee.infrastructure.llm.llm_interface import LLMInterface -from cognee.shared.data_models import Edge, KnowledgeGraph, Node, SummarizedContent - - -class DummyLLMAdapter(LLMInterface): - nlp = spacy.load("en_core_web_sm") - - async def acreate_structured_output( - self, text_input: str, system_prompt: str, response_model: Type[BaseModel] - ) -> BaseModel: - if str(response_model) == "": - return dummy_summarize_content(text_input) - elif str(response_model) == "": - return dummy_extract_knowledge_graph(text_input, self.nlp) - else: - raise Exception( - "Currently dummy acreate_structured_input is only implemented for SummarizedContent and KnowledgeGraph" - ) - - -def dummy_extract_knowledge_graph(text, nlp): - doc = nlp(text) - triples = list(textacy.extract.subject_verb_object_triples(doc)) - - nodes = {} - edges = [] - for triple in triples: - source = "_".join([str(e) for e in triple.subject]) - target = "_".join([str(e) for e in triple.object]) - nodes[source] = nodes.get( - source, Node(id=str(uuid4()), name=source, type="object", description="") - ) - nodes[target] = nodes.get( - target, Node(id=str(uuid4()), name=target, type="object", description="") - ) - edge_type = "_".join([str(e) for e in triple.verb]) - edges.append( - Edge( - source_node_id=nodes[source].id, - target_node_id=nodes[target].id, - relationship_name=edge_type, - ) - ) - return KnowledgeGraph(nodes=list(nodes.values()), edges=edges) - - -def dummy_summarize_content(text): - words = [(word, len(word)) for word in set(text.split(" "))] - words = sorted(words, key=lambda x: x[1], reverse=True) - summary = " ".join([word for word, _ in words[:50]]) - description = " ".join([word for word, _ in words[:10]]) - return SummarizedContent(summary=summary, description=description) diff --git a/pyproject.toml b/pyproject.toml index 675ee101a..4a878cfad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "cognee" -version = "0.1.40" +version = "0.1.41" description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning." authors = [ { name = "Vasilije Markovic" }, @@ -136,6 +136,27 @@ Repository = "https://github.com/topoteretes/cognee" requires = ["hatchling"] build-backend = "hatchling.build" +[tool.hatch.build] +exclude = [ + "/bin", + "/dist", + "/.data", + "/.github", + "/alembic", + "/distributed", + "/deployment", + "/cognee-mcp", + "/cognee-frontend", + "/examples", + "/helm", + "/licenses", + "/logs", + "/notebooks", + "/profiling", + "/tests", + "/tools", +] + [tool.ruff] line-length = 100 exclude = [ diff --git a/uv.lock b/uv.lock index 0e6236625..ff16015d0 100644 --- a/uv.lock +++ b/uv.lock @@ -860,7 +860,7 @@ wheels = [ [[package]] name = "cognee" -version = "0.1.39" +version = "0.1.41" source = { editable = "." } dependencies = [ { name = "aiofiles" },