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4
cognee/api/.env.example → .env.example
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@ -3,3 +3,7 @@
# CORS_ALLOWED_ORIGINS="https://yourdomain.com,https://another.com" # CORS_ALLOWED_ORIGINS="https://yourdomain.com,https://another.com"
# For local development, you might use: # For local development, you might use:
# CORS_ALLOWED_ORIGINS="http://localhost:3000" # CORS_ALLOWED_ORIGINS="http://localhost:3000"
LLM_API_KEY="your-openai-api-key"
LLM_MODEL="openai/gpt-4o-mini"
LLM_PROVIDER="openai"

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.github/ISSUE_TEMPLATE/documentation.yml vendored
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@ -71,4 +71,3 @@ body:
required: true required: true
- label: I have specified the location of the documentation issue - label: I have specified the location of the documentation issue
required: true required: true

1
.github/ISSUE_TEMPLATE/feature_request.yml vendored
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@ -75,4 +75,3 @@ body:
required: true required: true
- label: I have described my specific use case - label: I have described my specific use case
required: true required: true

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.github/core-team.txt vendored
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@ -8,5 +8,3 @@ lxobr
pazone pazone
siillee siillee
vasilije1990 vasilije1990

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.github/pull_request_template.md vendored
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@ -10,26 +10,21 @@ DO NOT use AI-generated descriptions. We want to understand your thought process
<!-- <!--
* Key requirements to the new feature or modification; * Key requirements to the new feature or modification;
* Proof that the changes work and meet the requirements; * Proof that the changes work and meet the requirements;
* Include instructions on how to verify the changes. Describe how to test it locally;
* Proof that it's sufficiently tested.
--> -->
## Type of Change ## Type of Change
<!-- Please check the relevant option --> <!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue) - [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality) - [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to change)
- [ ] Documentation update
- [ ] Code refactoring - [ ] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify): - [ ] Other (please specify):
## Screenshots/Videos (if applicable) ## Screenshots
<!-- Add screenshots or videos to help explain your changes --> <!-- ADD SCREENSHOT OF LOCAL TESTS PASSING-->
## Pre-submission Checklist ## Pre-submission Checklist
<!-- Please check all boxes that apply before submitting your PR --> <!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **I have tested my changes thoroughly before submitting this PR** (See `CONTRIBUTING.md`)
- [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] **This PR contains minimal changes necessary to address the issue/feature**
- [ ] My code follows the project's coding standards and style guidelines - [ ] My code follows the project's coding standards and style guidelines
- [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added tests that prove my fix is effective or that my feature works

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.github/workflows/basic_tests.yml vendored
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@ -34,43 +34,6 @@ env:
ENV: 'dev' ENV: 'dev'
jobs: jobs:
lint:
name: Run Linting
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run Linting
uses: astral-sh/ruff-action@v2
format-check:
name: Run Formatting Check
runs-on: ubuntu-22.04
steps:
- name: Check out repository
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Cognee Setup
uses: ./.github/actions/cognee_setup
with:
python-version: ${{ inputs.python-version }}
- name: Run Formatting Check
uses: astral-sh/ruff-action@v2
with:
args: "format --check"
unit-tests: unit-tests:
name: Run Unit Tests name: Run Unit Tests
runs-on: ubuntu-22.04 runs-on: ubuntu-22.04

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.github/workflows/label-core-team.yml vendored
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@ -72,5 +72,3 @@ jobs:
} catch (error) { } catch (error) {
core.warning(`Failed to add label: ${error.message}`); core.warning(`Failed to add label: ${error.message}`);
} }

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.github/workflows/load_tests.yml vendored
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AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }} AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
run: uv run python ./cognee/tests/test_load.py run: uv run python ./cognee/tests/test_load.py

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.github/workflows/pre_test.yml vendored
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@ -5,7 +5,7 @@ permissions:
contents: read contents: read
jobs: jobs:
check-uv-lock: check-uv-lock:
name: Validate uv lockfile and project metadata name: Lockfile and Pre-commit Hooks
runs-on: ubuntu-22.04 runs-on: ubuntu-22.04
steps: steps:
- name: Check out repository - name: Check out repository
@ -20,3 +20,6 @@ jobs:
- name: Validate uv lockfile and project metadata - name: Validate uv lockfile and project metadata
run: uv lock --check || { echo "'uv lock --check' failed."; echo "Run 'uv lock' and push your changes."; exit 1; } run: uv lock --check || { echo "'uv lock --check' failed."; echo "Run 'uv lock' and push your changes."; exit 1; }
- name: Run pre-commit hooks
uses: pre-commit/action@v3.0.1

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.github/workflows/release_test.yml vendored
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@ -15,4 +15,3 @@ jobs:
name: Load Tests name: Load Tests
uses: ./.github/workflows/load_tests.yml uses: ./.github/workflows/load_tests.yml
secrets: inherit secrets: inherit

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.gitignore vendored
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@ -147,6 +147,8 @@ venv/
ENV/ ENV/
env.bak/ env.bak/
venv.bak/ venv.bak/
mise.toml
deployment/helm/values-local.yml
# Spyder project settings # Spyder project settings
.spyderproject .spyderproject

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.pre-commit-config.yaml
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- id: trailing-whitespace - id: trailing-whitespace
- id: end-of-file-fixer - id: end-of-file-fixer
- id: check-yaml - id: check-yaml
exclude: ^deployment/helm/templates/
- id: check-added-large-files - id: check-added-large-files
- repo: https://github.com/astral-sh/ruff-pre-commit - repo: https://github.com/astral-sh/ruff-pre-commit
# Ruff version. # Ruff version.

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AGENTS.md
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@ -128,5 +128,3 @@ MCP server and Frontend:
## CI Mirrors Local Commands ## CI Mirrors Local Commands
Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises. Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.

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CLAUDE.md Normal file
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# CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Project Overview
Cognee is an open-source AI memory platform that transforms raw data into persistent knowledge graphs for AI agents. It replaces traditional RAG (Retrieval-Augmented Generation) with an ECL (Extract, Cognify, Load) pipeline combining vector search, graph databases, and LLM-powered entity extraction.
**Requirements**: Python 3.9 - 3.12
## Development Commands
### Setup
```bash
# Create virtual environment (recommended: uv)
uv venv && source .venv/bin/activate
# Install with pip, poetry, or uv
uv pip install -e .
# Install with dev dependencies
uv pip install -e ".[dev]"
# Install with specific extras
uv pip install -e ".[postgres,neo4j,docs,chromadb]"
# Set up pre-commit hooks
pre-commit install
```
### Available Installation Extras
- **postgres** / **postgres-binary** - PostgreSQL + PGVector support
- **neo4j** - Neo4j graph database support
- **neptune** - AWS Neptune support
- **chromadb** - ChromaDB vector database
- **docs** - Document processing (unstructured library)
- **scraping** - Web scraping (Tavily, BeautifulSoup, Playwright)
- **langchain** - LangChain integration
- **llama-index** - LlamaIndex integration
- **anthropic** - Anthropic Claude models
- **gemini** - Google Gemini models
- **ollama** - Ollama local models
- **mistral** - Mistral AI models
- **groq** - Groq API support
- **llama-cpp** - Llama.cpp local inference
- **huggingface** - HuggingFace transformers
- **aws** - S3 storage backend
- **redis** - Redis caching
- **graphiti** - Graphiti-core integration
- **baml** - BAML structured output
- **dlt** - Data load tool (dlt) integration
- **docling** - Docling document processing
- **codegraph** - Code graph extraction
- **evals** - Evaluation tools
- **deepeval** - DeepEval testing framework
- **posthog** - PostHog analytics
- **monitoring** - Sentry + Langfuse observability
- **distributed** - Modal distributed execution
- **dev** - All development tools (pytest, mypy, ruff, etc.)
- **debug** - Debugpy for debugging
### Testing
```bash
# Run all tests
pytest
# Run with coverage
pytest --cov=cognee --cov-report=html
# Run specific test file
pytest cognee/tests/test_custom_model.py
# Run specific test function
pytest cognee/tests/test_custom_model.py::test_function_name
# Run async tests
pytest -v cognee/tests/integration/
# Run unit tests only
pytest cognee/tests/unit/
# Run integration tests only
pytest cognee/tests/integration/
```
### Code Quality
```bash
# Run ruff linter
ruff check .
# Run ruff formatter
ruff format .
# Run both linting and formatting (pre-commit)
pre-commit run --all-files
# Type checking with mypy
mypy cognee/
# Run pylint
pylint cognee/
```
### Running Cognee
```bash
# Using Python SDK
python examples/python/simple_example.py
# Using CLI
cognee-cli add "Your text here"
cognee-cli cognify
cognee-cli search "Your query"
cognee-cli delete --all
# Launch full stack with UI
cognee-cli -ui
```
## Architecture Overview
### Core Workflow: add → cognify → search/memify
1. **add()** - Ingest data (files, URLs, text) into datasets
2. **cognify()** - Extract entities/relationships and build knowledge graph
3. **search()** - Query knowledge using various retrieval strategies
4. **memify()** - Enrich graph with additional context and rules
### Key Architectural Patterns
#### 1. Pipeline-Based Processing
All data flows through task-based pipelines (`cognee/modules/pipelines/`). Tasks are composable units that can run sequentially or in parallel. Example pipeline tasks: `classify_documents`, `extract_graph_from_data`, `add_data_points`.
#### 2. Interface-Based Database Adapters
Multiple backends are supported through adapter interfaces:
- **Graph**: Kuzu (default), Neo4j, Neptune via `GraphDBInterface`
- **Vector**: LanceDB (default), ChromaDB, PGVector via `VectorDBInterface`
- **Relational**: SQLite (default), PostgreSQL
Key files:
- `cognee/infrastructure/databases/graph/graph_db_interface.py`
- `cognee/infrastructure/databases/vector/vector_db_interface.py`
#### 3. Multi-Tenant Access Control
User → Dataset → Data hierarchy with permission-based filtering. Enable with `ENABLE_BACKEND_ACCESS_CONTROL=True`. Each user+dataset combination can have isolated graph/vector databases (when using supported backends: Kuzu, LanceDB, SQLite, Postgres).
### Layer Structure
```
API Layer (cognee/api/v1/)
↓
Main Functions (add, cognify, search, memify)
↓
Pipeline Orchestrator (cognee/modules/pipelines/)
↓
Task Execution Layer (cognee/tasks/)
↓
Domain Modules (graph, retrieval, ingestion, etc.)
↓
Infrastructure Adapters (LLM, databases)
↓
External Services (OpenAI, Kuzu, LanceDB, etc.)
```
### Critical Data Flow Paths
#### ADD: Data Ingestion
`add()` → `resolve_data_directories` → `ingest_data` → `save_data_item_to_storage` → Create Dataset + Data records in relational DB
Key files: `cognee/api/v1/add/add.py`, `cognee/tasks/ingestion/ingest_data.py`
#### COGNIFY: Knowledge Graph Construction
`cognify()` → `classify_documents` → `extract_chunks_from_documents` → `extract_graph_from_data` (LLM extracts entities/relationships using Instructor) → `summarize_text` → `add_data_points` (store in graph + vector DBs)
Key files:
- `cognee/api/v1/cognify/cognify.py`
- `cognee/tasks/graph/extract_graph_from_data.py`
- `cognee/tasks/storage/add_data_points.py`
#### SEARCH: Retrieval
`search(query_text, query_type)` → route to retriever type → filter by permissions → return results
Available search types (from `cognee/modules/search/types/SearchType.py`):
- **GRAPH_COMPLETION** (default) - Graph traversal + LLM completion
- **GRAPH_SUMMARY_COMPLETION** - Uses pre-computed summaries with graph context
- **GRAPH_COMPLETION_COT** - Chain-of-thought reasoning over graph
- **GRAPH_COMPLETION_CONTEXT_EXTENSION** - Extended context graph retrieval
- **TRIPLET_COMPLETION** - Triplet-based (subject-predicate-object) search
- **RAG_COMPLETION** - Traditional RAG with chunks
- **CHUNKS** - Vector similarity search over chunks
- **CHUNKS_LEXICAL** - Lexical (keyword) search over chunks
- **SUMMARIES** - Search pre-computed document summaries
- **CYPHER** - Direct Cypher query execution (requires `ALLOW_CYPHER_QUERY=True`)
- **NATURAL_LANGUAGE** - Natural language to structured query
- **TEMPORAL** - Time-aware graph search
- **FEELING_LUCKY** - Automatic search type selection
- **FEEDBACK** - User feedback-based refinement
- **CODING_RULES** - Code-specific search rules
Key files:
- `cognee/api/v1/search/search.py`
- `cognee/modules/retrieval/context_providers/TripletSearchContextProvider.py`
- `cognee/modules/search/types/SearchType.py`
### Core Data Models
#### Engine Models (`cognee/infrastructure/engine/models/`)
- **DataPoint** - Base class for all graph nodes (versioned, with metadata)
- **Edge** - Graph relationships (source, target, relationship type)
- **Triplet** - (Subject, Predicate, Object) representation
#### Graph Models (`cognee/shared/data_models.py`)
- **KnowledgeGraph** - Container for nodes and edges
- **Node** - Entity (id, name, type, description)
- **Edge** - Relationship (source_node_id, target_node_id, relationship_name)
### Key Infrastructure Components
#### LLM Gateway (`cognee/infrastructure/llm/LLMGateway.py`)
Unified interface for multiple LLM providers: OpenAI, Anthropic, Gemini, Ollama, Mistral, Bedrock. Uses Instructor for structured output extraction.
#### Embedding Engines
Factory pattern for embeddings: `cognee/infrastructure/databases/vector/embeddings/get_embedding_engine.py`
#### Document Loaders
Support for PDF, DOCX, CSV, images, audio, code files in `cognee/infrastructure/files/`
## Important Configuration
### Environment Setup
Copy `.env.template` to `.env` and configure:
```bash
# Minimal setup (defaults to OpenAI + local file-based databases)
LLM_API_KEY="your_openai_api_key"
LLM_MODEL="openai/gpt-4o-mini" # Default model
```
**Important**: If you configure only LLM or only embeddings, the other defaults to OpenAI. Ensure you have a working OpenAI API key, or configure both to avoid unexpected defaults.
Default databases (no extra setup needed):
- **Relational**: SQLite (metadata and state storage)
- **Vector**: LanceDB (embeddings for semantic search)
- **Graph**: Kuzu (knowledge graph and relationships)
All stored in `.venv` by default. Override with `DATA_ROOT_DIRECTORY` and `SYSTEM_ROOT_DIRECTORY`.
### Switching Databases
#### Relational Databases
```bash
# PostgreSQL (requires postgres extra: pip install cognee[postgres])
DB_PROVIDER=postgres
DB_HOST=localhost
DB_PORT=5432
DB_USERNAME=cognee
DB_PASSWORD=cognee
DB_NAME=cognee_db
```
#### Vector Databases
Supported: lancedb (default), pgvector, chromadb, qdrant, weaviate, milvus
```bash
# ChromaDB (requires chromadb extra)
VECTOR_DB_PROVIDER=chromadb
# PGVector (requires postgres extra)
VECTOR_DB_PROVIDER=pgvector
VECTOR_DB_URL=postgresql://cognee:cognee@localhost:5432/cognee_db
```
#### Graph Databases
Supported: kuzu (default), neo4j, neptune, kuzu-remote
```bash
# Neo4j (requires neo4j extra: pip install cognee[neo4j])
GRAPH_DATABASE_PROVIDER=neo4j
GRAPH_DATABASE_URL=bolt://localhost:7687
GRAPH_DATABASE_NAME=neo4j
GRAPH_DATABASE_USERNAME=neo4j
GRAPH_DATABASE_PASSWORD=yourpassword
# Remote Kuzu
GRAPH_DATABASE_PROVIDER=kuzu-remote
GRAPH_DATABASE_URL=http://localhost:8000
GRAPH_DATABASE_USERNAME=your_username
GRAPH_DATABASE_PASSWORD=your_password
```
### LLM Provider Configuration
Supported providers: OpenAI (default), Azure OpenAI, Google Gemini, Anthropic, AWS Bedrock, Ollama, LM Studio, Custom (OpenAI-compatible APIs)
#### OpenAI (Recommended - Minimal Setup)
```bash
LLM_API_KEY="your_openai_api_key"
LLM_MODEL="openai/gpt-4o-mini" # or gpt-4o, gpt-4-turbo, etc.
LLM_PROVIDER="openai"
```
#### Azure OpenAI
```bash
LLM_PROVIDER="azure"
LLM_MODEL="azure/gpt-4o-mini"
LLM_ENDPOINT="https://YOUR-RESOURCE.openai.azure.com/openai/deployments/gpt-4o-mini"
LLM_API_KEY="your_azure_api_key"
LLM_API_VERSION="2024-12-01-preview"
```
#### Google Gemini (requires gemini extra)
```bash
LLM_PROVIDER="gemini"
LLM_MODEL="gemini/gemini-2.0-flash-exp"
LLM_API_KEY="your_gemini_api_key"
```
#### Anthropic Claude (requires anthropic extra)
```bash
LLM_PROVIDER="anthropic"
LLM_MODEL="claude-3-5-sonnet-20241022"
LLM_API_KEY="your_anthropic_api_key"
```
#### Ollama (Local - requires ollama extra)
```bash
LLM_PROVIDER="ollama"
LLM_MODEL="llama3.1:8b"
LLM_ENDPOINT="http://localhost:11434/v1"
LLM_API_KEY="ollama"
EMBEDDING_PROVIDER="ollama"
EMBEDDING_MODEL="nomic-embed-text:latest"
EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
```
#### Custom / OpenRouter / vLLM
```bash
LLM_PROVIDER="custom"
LLM_MODEL="openrouter/google/gemini-2.0-flash-lite-preview-02-05:free"
LLM_ENDPOINT="https://openrouter.ai/api/v1"
LLM_API_KEY="your_api_key"
```
#### AWS Bedrock (requires aws extra)
```bash
LLM_PROVIDER="bedrock"
LLM_MODEL="anthropic.claude-3-sonnet-20240229-v1:0"
AWS_REGION="us-east-1"
AWS_ACCESS_KEY_ID="your_access_key"
AWS_SECRET_ACCESS_KEY="your_secret_key"
# Optional for temporary credentials:
# AWS_SESSION_TOKEN="your_session_token"
```
#### LLM Rate Limiting
```bash
LLM_RATE_LIMIT_ENABLED=true
LLM_RATE_LIMIT_REQUESTS=60 # Requests per interval
LLM_RATE_LIMIT_INTERVAL=60 # Interval in seconds
```
#### Instructor Mode (Structured Output)
```bash
# LLM_INSTRUCTOR_MODE controls how structured data is extracted
# Each LLM has its own default (e.g., gpt-4o models use "json_schema_mode")
# Override if needed:
LLM_INSTRUCTOR_MODE="json_schema_mode" # or "tool_call", "md_json", etc.
```
### Structured Output Framework
```bash
# Use Instructor (default, via litellm)
STRUCTURED_OUTPUT_FRAMEWORK="instructor"
# Or use BAML (requires baml extra: pip install cognee[baml])
STRUCTURED_OUTPUT_FRAMEWORK="baml"
BAML_LLM_PROVIDER=openai
BAML_LLM_MODEL="gpt-4o-mini"
BAML_LLM_API_KEY="your_api_key"
```
### Storage Backend
```bash
# Local filesystem (default)
STORAGE_BACKEND="local"
# S3 (requires aws extra: pip install cognee[aws])
STORAGE_BACKEND="s3"
STORAGE_BUCKET_NAME="your-bucket-name"
AWS_REGION="us-east-1"
AWS_ACCESS_KEY_ID="your_access_key"
AWS_SECRET_ACCESS_KEY="your_secret_key"
DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data"
SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system"
```
## Extension Points
### Adding New Functionality
1. **New Task Type**: Create task function in `cognee/tasks/`, return Task object, register in pipeline
2. **New Database Backend**: Implement `GraphDBInterface` or `VectorDBInterface` in `cognee/infrastructure/databases/`
3. **New LLM Provider**: Add configuration in LLM config (uses litellm)
4. **New Document Processor**: Extend loaders in `cognee/modules/data/processing/`
5. **New Search Type**: Add to `SearchType` enum and implement retriever in `cognee/modules/retrieval/`
6. **Custom Graph Models**: Define Pydantic models extending `DataPoint` in your code
### Working with Ontologies
Cognee supports ontology-based entity extraction to ground knowledge graphs in standardized semantic frameworks (e.g., OWL ontologies).
Configuration:
```bash
ONTOLOGY_RESOLVER=rdflib # Default: uses rdflib and OWL files
MATCHING_STRATEGY=fuzzy # Default: fuzzy matching with 80% similarity
ONTOLOGY_FILE_PATH=/path/to/your/ontology.owl # Full path to ontology file
```
Implementation: `cognee/modules/ontology/`
## Branching Strategy
**IMPORTANT**: Always branch from `dev`, not `main`. The `dev` branch is the active development branch.
```bash
git checkout dev
git pull origin dev
git checkout -b feature/your-feature-name
```
## Code Style
- Ruff for linting and formatting (configured in `pyproject.toml`)
- Line length: 100 characters
- Pre-commit hooks run ruff automatically
- Type hints encouraged (mypy checks enabled)
## Testing Strategy
Tests are organized in `cognee/tests/`:
- `unit/` - Unit tests for individual modules
- `integration/` - Full pipeline integration tests
- `cli_tests/` - CLI command tests
- `tasks/` - Task-specific tests
When adding features, add corresponding tests. Integration tests should cover the full add → cognify → search flow.
## API Structure
FastAPI application with versioned routes under `cognee/api/v1/`:
- `/add` - Data ingestion
- `/cognify` - Knowledge graph processing
- `/search` - Query interface
- `/memify` - Graph enrichment
- `/datasets` - Dataset management
- `/users` - Authentication (if `REQUIRE_AUTHENTICATION=True`)
- `/visualize` - Graph visualization server
## Python SDK Entry Points
Main functions exported from `cognee/__init__.py`:
- `add(data, dataset_name)` - Ingest data
- `cognify(datasets)` - Build knowledge graph
- `search(query_text, query_type)` - Query knowledge
- `memify(extraction_tasks, enrichment_tasks)` - Enrich graph
- `delete(data_id)` - Remove data
- `config()` - Configuration management
- `datasets()` - Dataset operations
All functions are async - use `await` or `asyncio.run()`.
## Security Considerations
Several security environment variables in `.env`:
- `ACCEPT_LOCAL_FILE_PATH` - Allow local file paths (default: True)
- `ALLOW_HTTP_REQUESTS` - Allow HTTP requests from Cognee (default: True)
- `ALLOW_CYPHER_QUERY` - Allow raw Cypher queries (default: True)
- `REQUIRE_AUTHENTICATION` - Enable API authentication (default: False)
- `ENABLE_BACKEND_ACCESS_CONTROL` - Multi-tenant isolation (default: True)
For production deployments, review and tighten these settings.
## Common Patterns
### Creating a Custom Pipeline Task
```python
from cognee.modules.pipelines.tasks.Task import Task
async def my_custom_task(data):
# Your logic here
processed_data = process(data)
return processed_data
# Use in pipeline
task = Task(my_custom_task)
```
### Accessing Databases Directly
```python
from cognee.infrastructure.databases.graph import get_graph_engine
from cognee.infrastructure.databases.vector import get_vector_engine
graph_engine = await get_graph_engine()
vector_engine = await get_vector_engine()
```
### Using LLM Gateway
```python
from cognee.infrastructure.llm.get_llm_client import get_llm_client
llm_client = get_llm_client()
response = await llm_client.acreate_structured_output(
text_input="Your prompt",
system_prompt="System instructions",
response_model=YourPydanticModel
)
```
## Key Concepts
### Datasets
Datasets are project-level containers that support organization, permissions, and isolated processing workflows. Each user can have multiple datasets with different access permissions.
```python
# Create/use a dataset
await cognee.add(data, dataset_name="my_project")
await cognee.cognify(datasets=["my_project"])
```
### DataPoints
Atomic knowledge units that form the foundation of graph structures. All graph nodes extend the `DataPoint` base class with versioning and metadata support.
### Permissions System
Multi-tenant architecture with users, roles, and Access Control Lists (ACLs):
- Read, write, delete, and share permissions per dataset
- Enable with `ENABLE_BACKEND_ACCESS_CONTROL=True`
- Supports isolated databases per user+dataset (Kuzu, LanceDB, SQLite, Postgres)
### Graph Visualization
Launch visualization server:
```bash
# Via CLI
cognee-cli -ui # Launches full stack with UI at http://localhost:3000
# Via Python
from cognee.api.v1.visualize import start_visualization_server
await start_visualization_server(port=8080)
```
## Debugging & Troubleshooting
### Debug Configuration
- Set `LITELLM_LOG="DEBUG"` for verbose LLM logs (default: "ERROR")
- Enable debug mode: `ENV="development"` or `ENV="debug"`
- Disable telemetry: `TELEMETRY_DISABLED=1`
- Check logs in structured format (uses structlog)
- Use `debugpy` optional dependency for debugging: `pip install cognee[debug]`
### Common Issues
**Ollama + OpenAI Embeddings NoDataError**
- Issue: Mixing Ollama with OpenAI embeddings can cause errors
- Solution: Configure both LLM and embeddings to use the same provider, or ensure `HUGGINGFACE_TOKENIZER` is set when using Ollama
**LM Studio Structured Output**
- Issue: LM Studio requires explicit instructor mode
- Solution: Set `LLM_INSTRUCTOR_MODE="json_schema_mode"` (or appropriate mode)
**Default Provider Fallback**
- Issue: Configuring only LLM or only embeddings defaults the other to OpenAI
- Solution: Always configure both LLM and embedding providers, or ensure valid OpenAI API key
**Permission Denied on Search**
- Behavior: Returns empty list rather than error (prevents information leakage)
- Solution: Check dataset permissions and user access rights
**Database Connection Issues**
- Check: Verify database URLs, credentials, and that services are running
- Docker users: Use `DB_HOST=host.docker.internal` for local databases
**Rate Limiting Errors**
- Enable client-side rate limiting: `LLM_RATE_LIMIT_ENABLED=true`
- Adjust limits: `LLM_RATE_LIMIT_REQUESTS` and `LLM_RATE_LIMIT_INTERVAL`
## Resources
- [Documentation](https://docs.cognee.ai/)
- [Discord Community](https://discord.gg/NQPKmU5CCg)
- [GitHub Issues](https://github.com/topoteretes/cognee/issues)
- [Example Notebooks](examples/python/)
- [Research Paper](https://arxiv.org/abs/2505.24478) - Optimizing knowledge graphs for LLM reasoning

19
CONTRIBUTING.md
View file

@ -62,6 +62,11 @@ Looking for a place to start? Try filtering for [good first issues](https://gith
## 2. 🛠️ Development Setup ## 2. 🛠️ Development Setup
### Required tools
* [Python](https://www.python.org/downloads/)
* [uv](https://docs.astral.sh/uv/getting-started/installation/)
* pre-commit: `uv run pip install pre-commit && pre-commit install`
### Fork and Clone ### Fork and Clone
1. Fork the [**cognee**](https://github.com/topoteretes/cognee) repository 1. Fork the [**cognee**](https://github.com/topoteretes/cognee) repository
@ -93,29 +98,31 @@ git checkout -b feature/your-feature-name
4. **Commits**: Write clear commit messages 4. **Commits**: Write clear commit messages
### Running Tests ### Running Tests
Rename `.env.example` into `.env` and provide your OPENAI_API_KEY as LLM_API_KEY
```shell ```shell
python cognee/cognee/tests/test_library.py uv run python cognee/tests/test_library.py
``` ```
### Running Simple Example ### Running Simple Example
Change .env.example into .env and provide your OPENAI_API_KEY as LLM_API_KEY Rename `.env.example` into `.env` and provide your OPENAI_API_KEY as LLM_API_KEY
Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee
```shell ```shell
python cognee/cognee/examples/python/simple_example.py python examples/python/simple_example.py
``` ```
or or
```shell ```shell
uv run python cognee/cognee/examples/python/simple_example.py uv run python examples/python/simple_example.py
``` ```
## 4. 📤 Submitting Changes ## 4. 📤 Submitting Changes
1. Install ruff on your system 1. Make sure that `pre-commit` and hooks are installed. See `Required tools` section for more information. Try executing `pre-commit run` if you are not sure.
2. Run ```ruff format .``` and ``` ruff check ``` and fix the issues
3. Push your changes: 3. Push your changes:
```shell ```shell
git add . git add .

3
Dockerfile
View file

@ -16,9 +16,6 @@ ARG DEBUG
# Set environment variable based on the build argument # Set environment variable based on the build argument
ENV DEBUG=${DEBUG} ENV DEBUG=${DEBUG}
# if you located in China, you can use aliyun mirror to speed up
#RUN sed -i 's@deb.debian.org@mirrors.ustc.edu.cn@g' /etc/apt/sources.list.d/debian.sources
# Install system dependencies # Install system dependencies
RUN apt-get update && apt-get install -y \ RUN apt-get update && apt-get install -y \
gcc \ gcc \

2
alembic/versions/1a58b986e6e1_enable_delete_for_old_tutorial_notebooks.py
View file

@ -14,7 +14,7 @@ import sqlalchemy as sa
# revision identifiers, used by Alembic. # revision identifiers, used by Alembic.
revision: str = "1a58b986e6e1" revision: str = "1a58b986e6e1"
down_revision: Union[str, None] = "46a6ce2bd2b2" down_revision: Union[str, None] = "e1ec1dcb50b6"
branch_labels: Union[str, Sequence[str], None] = None branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None depends_on: Union[str, Sequence[str], None] = None

2
cognee-frontend/package.json
View file

@ -13,7 +13,7 @@
"classnames": "^2.5.1", "classnames": "^2.5.1",
"culori": "^4.0.1", "culori": "^4.0.1",
"d3-force-3d": "^3.0.6", "d3-force-3d": "^3.0.6",
"next": "^16.1.0", "next": "^16.1.7",
"react": "^19.2.3", "react": "^19.2.3",
"react-dom": "^19.2.3", "react-dom": "^19.2.3",
"react-force-graph-2d": "^1.27.1", "react-force-graph-2d": "^1.27.1",

1
cognee-frontend/src/ui/elements/Modal/index.ts
View file

@ -1,3 +1,2 @@
export { default as Modal } from "./Modal"; export { default as Modal } from "./Modal";
export { default as useModal } from "./useModal"; export { default as useModal } from "./useModal";

1
cognee-frontend/src/ui/elements/Notebook/MarkdownPreview.tsx
View file

@ -74,4 +74,3 @@ function MarkdownPreview({ content, className = "" }: MarkdownPreviewProps) {
} }
export default memo(MarkdownPreview); export default memo(MarkdownPreview);

15
cognee/api/v1/search/routers/get_search_router.py
View file

@ -8,12 +8,14 @@ from fastapi.encoders import jsonable_encoder
from cognee.modules.search.types import SearchType, SearchResult, CombinedSearchResult from cognee.modules.search.types import SearchType, SearchResult, CombinedSearchResult
from cognee.api.DTO import InDTO, OutDTO from cognee.api.DTO import InDTO, OutDTO
from cognee.modules.users.exceptions.exceptions import PermissionDeniedError from cognee.modules.users.exceptions.exceptions import PermissionDeniedError, UserNotFoundError
from cognee.modules.users.models import User from cognee.modules.users.models import User
from cognee.modules.search.operations import get_history from cognee.modules.search.operations import get_history
from cognee.modules.users.methods import get_authenticated_user from cognee.modules.users.methods import get_authenticated_user
from cognee.shared.utils import send_telemetry from cognee.shared.utils import send_telemetry
from cognee import __version__ as cognee_version from cognee import __version__ as cognee_version
from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError
from cognee.exceptions import CogneeValidationError
# Note: Datasets sent by name will only map to datasets owned by the request sender # Note: Datasets sent by name will only map to datasets owned by the request sender
@ -138,6 +140,17 @@ def get_search_router() -> APIRouter:
) )
return jsonable_encoder(results) return jsonable_encoder(results)
except (DatabaseNotCreatedError, UserNotFoundError, CogneeValidationError) as e:
# Return a clear 422 with actionable guidance instead of leaking a stacktrace
status_code = getattr(e, "status_code", 422)
return JSONResponse(
status_code=status_code,
content={
"error": "Search prerequisites not met",
"detail": str(e),
"hint": "Run `await cognee.add(...)` then `await cognee.cognify()` before searching.",
},
)
except PermissionDeniedError: except PermissionDeniedError:
return [] return []
except Exception as error: except Exception as error:

16
cognee/api/v1/search/search.py
View file

@ -11,6 +11,9 @@ from cognee.modules.data.methods import get_authorized_existing_datasets
from cognee.modules.data.exceptions import DatasetNotFoundError from cognee.modules.data.exceptions import DatasetNotFoundError
from cognee.context_global_variables import set_session_user_context_variable from cognee.context_global_variables import set_session_user_context_variable
from cognee.shared.logging_utils import get_logger from cognee.shared.logging_utils import get_logger
from cognee.infrastructure.databases.exceptions import DatabaseNotCreatedError
from cognee.exceptions import CogneeValidationError
from cognee.modules.users.exceptions.exceptions import UserNotFoundError
logger = get_logger() logger = get_logger()
@ -176,7 +179,18 @@ async def search(
datasets = [datasets] datasets = [datasets]
if user is None: if user is None:
user = await get_default_user() try:
user = await get_default_user()
except (DatabaseNotCreatedError, UserNotFoundError) as error:
# Provide a clear, actionable message instead of surfacing low-level stacktraces
raise CogneeValidationError(
message=(
"Search prerequisites not met: no database/default user found. "
"Initialize Cognee before searching by:\n"
"• running `await cognee.add(...)` followed by `await cognee.cognify()`."
),
name="SearchPreconditionError",
) from error
await set_session_user_context_variable(user) await set_session_user_context_variable(user)

4
cognee/infrastructure/llm/prompts/generate_graph_prompt.txt
View file

@ -19,8 +19,8 @@ The aim is to achieve simplicity and clarity in the knowledge graph.
- **Naming Convention**: Use snake_case for relationship names, e.g., `acted_in`. - **Naming Convention**: Use snake_case for relationship names, e.g., `acted_in`.
# 3. Coreference Resolution # 3. Coreference Resolution
- **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency. - **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency.
If an entity, such as "John Doe", is mentioned multiple times in the text but is referred to by different names or pronouns (e.g., "Joe", "he"), If an entity, is mentioned multiple times in the text but is referred to by different names or pronouns,
always use the most complete identifier for that entity throughout the knowledge graph. In this example, use "John Doe" as the Persons ID. always use the most complete identifier for that entity throughout the knowledge graph.
Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial. Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial.
# 4. Strict Compliance # 4. Strict Compliance
Adhere to the rules strictly. Non-compliance will result in termination Adhere to the rules strictly. Non-compliance will result in termination

2
cognee/infrastructure/llm/prompts/generate_graph_prompt_guided.txt
View file

@ -22,7 +22,7 @@ You are an advanced algorithm designed to extract structured information to buil
3. **Coreference Resolution**: 3. **Coreference Resolution**:
- Maintain one consistent node ID for each real-world entity. - Maintain one consistent node ID for each real-world entity.
- Resolve aliases, acronyms, and pronouns to the most complete form. - Resolve aliases, acronyms, and pronouns to the most complete form.
- *Example*: Always use "John Doe" even if later referred to as "Doe" or "he". - *Example*: Always use full identifier even if later referred to as in a similar but slightly different way
**Property & Data Guidelines**: **Property & Data Guidelines**:

4
cognee/infrastructure/llm/prompts/generate_graph_prompt_oneshot.txt
View file

@ -42,10 +42,10 @@ You are an advanced algorithm designed to extract structured information from un
- **Rule**: Resolve all aliases, acronyms, and pronouns to one canonical identifier. - **Rule**: Resolve all aliases, acronyms, and pronouns to one canonical identifier.
> **One-Shot Example**: > **One-Shot Example**:
> **Input**: "John Doe is an author. Later, Doe published a book. He is well-known." > **Input**: "X is an author. Later, Doe published a book. He is well-known."
> **Output Node**: > **Output Node**:
> ``` > ```
> John Doe (Person) > X (Person)
> ``` > ```
--- ---

2
cognee/infrastructure/llm/prompts/generate_graph_prompt_simple.txt
View file

@ -15,7 +15,7 @@ You are an advanced algorithm that extracts structured data into a knowledge gra
- Properties are key-value pairs; do not use escaped quotes. - Properties are key-value pairs; do not use escaped quotes.
3. **Coreference Resolution** 3. **Coreference Resolution**
- Use a single, complete identifier for each entity (e.g., always "John Doe" not "Joe" or "he"). - Use a single, complete identifier for each entity
4. **Relationship Labels**: 4. **Relationship Labels**:
- Use descriptive, lowercase, snake_case names for edges. - Use descriptive, lowercase, snake_case names for edges.

2
cognee/infrastructure/llm/prompts/generate_graph_prompt_strict.txt
View file

@ -26,7 +26,7 @@ Use **basic atomic types** for node labels. Always prefer general types over spe
- Good: "Alan Turing", "Google Inc.", "World War II" - Good: "Alan Turing", "Google Inc.", "World War II"
- Bad: "Entity_001", "1234", "he", "they" - Bad: "Entity_001", "1234", "he", "they"
- Never use numeric or autogenerated IDs. - Never use numeric or autogenerated IDs.
- Prioritize **most complete form** of entity names for consistency (e.g., always use "John Doe" instead of "John" or "he"). - Prioritize **most complete form** of entity names for consistency
2. Dates, Numbers, and Properties 2. Dates, Numbers, and Properties
--------------------------------- ---------------------------------

1
cognee/infrastructure/llm/structured_output_framework/litellm_instructor/llm/get_llm_client.py
View file

@ -194,6 +194,7 @@ def get_llm_client(raise_api_key_error: bool = True):
) )
# Get optional local mode parameters (will be None if not set) # Get optional local mode parameters (will be None if not set)
# TODO: refactor llm_config to include these parameters, currently they cannot be defined and defaults are used
model_path = getattr(llm_config, "llama_cpp_model_path", None) model_path = getattr(llm_config, "llama_cpp_model_path", None)
n_ctx = getattr(llm_config, "llama_cpp_n_ctx", 2048) n_ctx = getattr(llm_config, "llama_cpp_n_ctx", 2048)
n_gpu_layers = getattr(llm_config, "llama_cpp_n_gpu_layers", 0) n_gpu_layers = getattr(llm_config, "llama_cpp_n_gpu_layers", 0)

1
cognee/modules/notebooks/tutorials/python-development-with-cognee/data/copilot_conversations.json
View file

@ -105,4 +105,3 @@
} }
} }
} }

1
cognee/modules/notebooks/tutorials/python-development-with-cognee/data/pep_style_guide.md
View file

@ -72,4 +72,3 @@ profile = "black"
- E501: line too long -> break with parentheses - E501: line too long -> break with parentheses
- E225: missing whitespace around operator - E225: missing whitespace around operator
- E402: module import not at top of file - E402: module import not at top of file

1
cognee/modules/notebooks/tutorials/python-development-with-cognee/data/zen_principles.md
View file

@ -72,4 +72,3 @@ Use modules/packages to separate concerns; avoid wildcard imports.
- Is this the simplest working solution? - Is this the simplest working solution?
- Are errors explicit and logged? - Are errors explicit and logged?
- Are modules/namespaces used appropriately? - Are modules/namespaces used appropriately?

1
cognee/modules/retrieval/__init__.py
View file

@ -1 +0,0 @@

5
cognee/modules/users/authentication/get_api_auth_backend.py
View file

@ -16,10 +16,7 @@ def get_api_auth_backend():
def get_jwt_strategy() -> JWTStrategy[models.UP, models.ID]: def get_jwt_strategy() -> JWTStrategy[models.UP, models.ID]:
secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret") secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret")
try: lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
except ValueError:
lifetime_seconds = 3600
return APIJWTStrategy(secret, lifetime_seconds=lifetime_seconds) return APIJWTStrategy(secret, lifetime_seconds=lifetime_seconds)

5
cognee/modules/users/authentication/get_client_auth_backend.py
View file

@ -18,10 +18,7 @@ def get_client_auth_backend():
from .default.default_jwt_strategy import DefaultJWTStrategy from .default.default_jwt_strategy import DefaultJWTStrategy
secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret") secret = os.getenv("FASTAPI_USERS_JWT_SECRET", "super_secret")
try: lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
lifetime_seconds = int(os.getenv("JWT_LIFETIME_SECONDS", "3600"))
except ValueError:
lifetime_seconds = 3600
return DefaultJWTStrategy(secret, lifetime_seconds=lifetime_seconds) return DefaultJWTStrategy(secret, lifetime_seconds=lifetime_seconds)

17
cognee/shared/utils.py
View file

@ -8,7 +8,8 @@ import http.server
import socketserver import socketserver
from threading import Thread from threading import Thread
import pathlib import pathlib
from uuid import uuid4, uuid5, NAMESPACE_OID from typing import Union, Any, Dict, List
from uuid import uuid4, uuid5, NAMESPACE_OID, UUID
from cognee.base_config import get_base_config from cognee.base_config import get_base_config
from cognee.shared.logging_utils import get_logger from cognee.shared.logging_utils import get_logger
@ -58,7 +59,7 @@ def get_anonymous_id():
return anonymous_id return anonymous_id
def _sanitize_nested_properties(obj, property_names: list[str]): def _sanitize_nested_properties(obj: Any, property_names: list[str]) -> Any:
""" """
Recursively replaces any property whose key matches one of `property_names` Recursively replaces any property whose key matches one of `property_names`
(e.g., ['url', 'path']) in a nested dict or list with a uuid5 hash (e.g., ['url', 'path']) in a nested dict or list with a uuid5 hash
@ -78,7 +79,9 @@ def _sanitize_nested_properties(obj, property_names: list[str]):
return obj return obj
def send_telemetry(event_name: str, user_id, additional_properties: dict = {}): def send_telemetry(event_name: str, user_id: Union[str, UUID], additional_properties: dict = {}):
if additional_properties is None:
additional_properties = {}
if os.getenv("TELEMETRY_DISABLED"): if os.getenv("TELEMETRY_DISABLED"):
return return
@ -108,7 +111,7 @@ def send_telemetry(event_name: str, user_id, additional_properties: dict = {}):
print(f"Error sending telemetry through proxy: {response.status_code}") print(f"Error sending telemetry through proxy: {response.status_code}")
def embed_logo(p, layout_scale, logo_alpha, position): def embed_logo(p: Any, layout_scale: float, logo_alpha: float, position: str):
""" """
Embed a logo into the graph visualization as a watermark. Embed a logo into the graph visualization as a watermark.
""" """
@ -138,7 +141,11 @@ def embed_logo(p, layout_scale, logo_alpha, position):
def start_visualization_server( def start_visualization_server(
host="0.0.0.0", port=8001, handler_class=http.server.SimpleHTTPRequestHandler host: str = "0.0.0.0",
port: int = 8001,
handler_class: type[
http.server.SimpleHTTPRequestHandler
] = http.server.SimpleHTTPRequestHandler,
): ):
""" """
Spin up a simple HTTP server in a background thread to serve files. Spin up a simple HTTP server in a background thread to serve files.

1
cognee/tasks/temporal_graph/__init__.py
View file

@ -1 +0,0 @@

18
deployment/helm/README.md
View file

@ -1,6 +1,7 @@
# cognee-infra-helm # Example helm chart
General infrastructure setup for Cognee on Kubernetes using a Helm chart. Example Helm chart fro Cognee with PostgreSQL and pgvector extension
It is not ready for production usage
## Prerequisites ## Prerequisites
Before deploying the Helm chart, ensure the following prerequisites are met:  Before deploying the Helm chart, ensure the following prerequisites are met: 
@ -13,13 +14,22 @@ Before deploying the Helm chart, ensure the following prerequisites are met: 
Clone the Repository Clone this repository to your local machine and navigate to the directory. Clone the Repository Clone this repository to your local machine and navigate to the directory.
## Deploy Helm Chart: ## Example deploy Helm Chart:
```bash ```bash
helm install cognee ./cognee-chart helm upgrade --install cognee deployment/helm \
--namespace cognee --create-namespace \
--set cognee.env.LLM_API_KEY="$YOUR_KEY"
``` ```
**Uninstall Helm Release**: **Uninstall Helm Release**:
```bash ```bash
helm uninstall cognee helm uninstall cognee
``` ```
## Port forwarding
To access cognee, run
```
kubectl port-forward svc/cognee-service -n cognee 8000
```
it will be available at localhost:8000

1
deployment/helm/docker-compose-helm.yml
View file

@ -43,4 +43,3 @@ networks:
volumes: volumes:
postgres_data: postgres_data:

23
deployment/helm/templates/cognee_deployment.yaml
View file

@ -20,12 +20,35 @@ spec:
ports: ports:
- containerPort: {{ .Values.cognee.port }} - containerPort: {{ .Values.cognee.port }}
env: env:
- name: ENABLE_BACKEND_ACCESS_CONTROL
value: "false"
- name: HOST - name: HOST
value: {{ .Values.cognee.env.HOST }} value: {{ .Values.cognee.env.HOST }}
- name: ENVIRONMENT - name: ENVIRONMENT
value: {{ .Values.cognee.env.ENVIRONMENT }} value: {{ .Values.cognee.env.ENVIRONMENT }}
- name: PYTHONPATH - name: PYTHONPATH
value: {{ .Values.cognee.env.PYTHONPATH }} value: {{ .Values.cognee.env.PYTHONPATH }}
- name: VECTOR_DB_PROVIDER
value: pgvector
- name: DB_HOST
value: {{ .Release.Name }}-postgres
- name: DB_PORT
value: "{{ .Values.postgres.port }}"
- name: DB_NAME
value: {{ .Values.postgres.env.POSTGRES_DB }}
- name: DB_USERNAME
value: {{ .Values.postgres.env.POSTGRES_USER }}
- name: DB_PASSWORD
value: {{ .Values.postgres.env.POSTGRES_PASSWORD }}
- name: LLM_API_KEY
valueFrom:
secretKeyRef:
name: {{ .Release.Name }}-llm-api-key
key: LLM_API_KEY
- name: LLM_MODEL
value: {{ .Values.cognee.env.LLM_MODEL }}
- name: LLM_PROVIDER
value: {{ .Values.cognee.env.LLM_PROVIDER }}
resources: resources:
limits: limits:
cpu: {{ .Values.cognee.resources.cpu }} cpu: {{ .Values.cognee.resources.cpu }}

2
deployment/helm/templates/cognee_service.yaml
View file

@ -5,7 +5,7 @@ metadata:
labels: labels:
app: {{ .Release.Name }}-cognee app: {{ .Release.Name }}-cognee
spec: spec:
type: NodePort type: ClusterIP
ports: ports:
- port: {{ .Values.cognee.port }} - port: {{ .Values.cognee.port }}
targetPort: {{ .Values.cognee.port }} targetPort: {{ .Values.cognee.port }}

1
deployment/helm/templates/postgres_service.yaml
View file

@ -11,4 +11,3 @@ spec:
targetPort: {{ .Values.postgres.port }} targetPort: {{ .Values.postgres.port }}
selector: selector:
app: {{ .Release.Name }}-postgres app: {{ .Release.Name }}-postgres

7
deployment/helm/templates/secrets.yml Normal file
View file

@ -0,0 +1,7 @@
apiVersion: v1
kind: Secret
metadata:
name: {{ .Release.Name }}-llm-api-key
type: Opaque
data:
LLM_API_KEY: {{ .Values.cognee.env.LLM_API_KEY | b64enc | quote }}

6
deployment/helm/values.yaml
View file

@ -7,9 +7,11 @@ cognee:
HOST: "0.0.0.0" HOST: "0.0.0.0"
ENVIRONMENT: "local" ENVIRONMENT: "local"
PYTHONPATH: "." PYTHONPATH: "."
LLM_MODEL: "openai/gpt-4o-mini"
LLM_PROVIDER: "openai"
resources: resources:
cpu: "4.0" cpu: "4.0"
memory: "8Gi" memory: "2Gi"
# Configuration for the 'postgres' database service # Configuration for the 'postgres' database service
postgres: postgres:
@ -19,4 +21,4 @@ postgres:
POSTGRES_USER: "cognee" POSTGRES_USER: "cognee"
POSTGRES_PASSWORD: "cognee" POSTGRES_PASSWORD: "cognee"
POSTGRES_DB: "cognee_db" POSTGRES_DB: "cognee_db"
storage: "8Gi" storage: "2Gi"

1
examples/python/weighted_graph_visualization.html
View file

@ -209,4 +209,3 @@
</svg> </svg>
</body> </body>
</html> </html>

2
mise.toml Normal file
View file

@ -0,0 +1,2 @@
[tools]
python = "latest"

1
new-examples/custom_pipelines/organizational_hierarchy/data/companies.json
View file

@ -36,4 +36,3 @@
] ]
} }
] ]

1
new-examples/custom_pipelines/organizational_hierarchy/data/people.json
View file

@ -50,4 +50,3 @@
"department": "HR" "department": "HR"
} }
] ]

1
new-examples/custom_pipelines/product_recommendation/data/customers.json
View file

@ -106,4 +106,3 @@
"action": "purchased" "action": "purchased"
}] }]
}] }]

476
poetry.lock generated
View file

@ -88,132 +88,132 @@ files = [
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[package.dependencies] [package.dependencies]
@ -227,7 +227,7 @@ propcache = ">=0.2.0"
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[package.extras] [package.extras]
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[[package]] [[package]]
name = "aioitertools" name = "aioitertools"
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python-versions = ">=3.9" python-versions = ">=3.9"
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[package.dependencies] [package.dependencies]
@ -5017,6 +5011,7 @@ pydantic = ">=2.7.4,<3.0.0"
pyyaml = ">=5.3.0,<7.0.0" pyyaml = ">=5.3.0,<7.0.0"
tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0" tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10.0.0"
typing-extensions = ">=4.7.0,<5.0.0" typing-extensions = ">=4.7.0,<5.0.0"
uuid-utils = ">=0.12.0,<1.0"
[[package]] [[package]]
name = "langchain-text-splitters" name = "langchain-text-splitters"
@ -5292,6 +5287,36 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "azure-ident
semantic-router = ["semantic-router ; python_version >= \"3.9\""] semantic-router = ["semantic-router ; python_version >= \"3.9\""]
utils = ["numpydoc"] utils = ["numpydoc"]
[[package]]
name = "llama-cpp-python"
version = "0.3.16"
description = "Python bindings for the llama.cpp library"
optional = true
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"llama-cpp\""
files = [
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jinja2 = ">=2.11.3"
numpy = ">=1.20.0"
pydantic-settings = {version = ">=2.0.1", optional = true, markers = "extra == \"server\""}
PyYAML = {version = ">=5.1", optional = true, markers = "extra == \"server\""}
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starlette-context = {version = ">=0.3.6,<0.4", optional = true, markers = "extra == \"server\""}
typing-extensions = ">=4.5.0"
uvicorn = {version = ">=0.22.0", optional = true, markers = "extra == \"server\""}
[package.extras]
all = ["llama_cpp_python[dev,server,test]"]
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[[package]] [[package]]
name = "llama-index-core" name = "llama-index-core"
version = "0.12.52.post1" version = "0.12.52.post1"
@ -12338,6 +12363,29 @@ files = [
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rs = ["sqlglotrs (==0.7.3)"] rs = ["sqlglotrs (==0.7.3)"]
[[package]]
name = "sse-starlette"
version = "3.1.2"
description = "SSE plugin for Starlette"
optional = true
python-versions = ">=3.9"
groups = ["main"]
markers = "extra == \"llama-cpp\""
files = [
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anyio = ">=4.7.0"
starlette = ">=0.49.1"
[package.extras]
daphne = ["daphne (>=4.2.0)"]
examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio] (>=2.0.41)", "uvicorn (>=0.34.0)"]
granian = ["granian (>=2.3.1)"]
uvicorn = ["uvicorn (>=0.34.0)"]
[[package]] [[package]]
name = "stack-data" name = "stack-data"
version = "0.6.3" version = "0.6.3"
@ -12378,6 +12426,22 @@ typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""
[package.extras] [package.extras]
full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"] full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
[[package]]
name = "starlette-context"
version = "0.3.6"
description = "Middleware for Starlette that allows you to store and access the context data of a request. Can be used with logging so logs automatically use request headers such as x-request-id or x-correlation-id."
optional = true
python-versions = ">=3.8,<4.0"
groups = ["main"]
markers = "extra == \"llama-cpp\""
files = [
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starlette = "*"
[[package]] [[package]]
name = "structlog" name = "structlog"
version = "25.5.0" version = "25.5.0"
@ -13520,21 +13584,54 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]] [[package]]
name = "urllib3" name = "urllib3"
version = "2.3.0" version = "2.6.3"
description = "HTTP library with thread-safe connection pooling, file post, and more." description = "HTTP library with thread-safe connection pooling, file post, and more."
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python-versions = ">=3.9" python-versions = ">=3.9"
groups = ["main"] groups = ["main"]
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h2 = ["h2 (>=4,<5)"] h2 = ["h2 (>=4,<5)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"] zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""]
[[package]]
name = "uuid-utils"
version = "0.13.0"
description = "Fast, drop-in replacement for Python's uuid module, powered by Rust."
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python-versions = ">=3.9"
groups = ["main"]
markers = "extra == \"neptune\" or extra == \"langchain\""
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]
[[package]] [[package]]
name = "uvicorn" name = "uvicorn"
@ -14427,7 +14524,8 @@ evals = ["gdown", "matplotlib", "pandas", "plotly", "scikit-learn"]
graphiti = ["graphiti-core"] graphiti = ["graphiti-core"]
groq = ["groq"] groq = ["groq"]
huggingface = ["transformers"] huggingface = ["transformers"]
langchain = ["langchain_text_splitters", "langsmith"] langchain = ["langchain-core", "langchain_text_splitters", "langsmith"]
llama-cpp = ["llama-cpp-python"]
llama-index = ["llama-index-core"] llama-index = ["llama-index-core"]
mistral = ["mistral-common"] mistral = ["mistral-common"]
monitoring = ["langfuse", "sentry-sdk"] monitoring = ["langfuse", "sentry-sdk"]
@ -14444,4 +14542,4 @@ scraping = ["APScheduler", "beautifulsoup4", "lxml", "lxml", "playwright", "prot
[metadata] [metadata]
lock-version = "2.1" lock-version = "2.1"
python-versions = ">=3.10,<3.14" python-versions = ">=3.10,<3.14"
content-hash = "09f7040236a62a2d610e79e92394bb0c23e13ed41ba4de92c064ab4d5430b84e" content-hash = "58cae432164caef56438d23e8d96b1d5778cd3c4b1caaffbefe7ec363a15dd19"

6
pyproject.toml
View file

@ -32,7 +32,7 @@ dependencies = [
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"instructor>=1.9.1,<2.0.0", "instructor>=1.9.1,<2.0.0",
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@ -60,6 +60,9 @@ dependencies = [
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"aiolimiter>=1.2.1", "aiolimiter>=1.2.1",
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"cbor2>=5.8.0"
] ]
[project.optional-dependencies] [project.optional-dependencies]
@ -95,6 +98,7 @@ notebook = ["notebook>=7.1.0,<8"]
langchain = [ langchain = [
"langsmith>=0.2.3,<1.0.0", "langsmith>=0.2.3,<1.0.0",
"langchain_text_splitters>=0.3.2,<1.0.0", "langchain_text_splitters>=0.3.2,<1.0.0",
"langchain-core>=1.2.5"
] ]
llama-index = ["llama-index-core>=0.12.11,<0.13"] llama-index = ["llama-index-core>=0.12.11,<0.13"]
huggingface = ["transformers>=4.46.3,<5"] huggingface = ["transformers>=4.46.3,<5"]

1
tools/check-package.sh
View file

@ -58,4 +58,3 @@ fi
# error in package # error in package
exit 1 exit 1

285
uv.lock generated
View file

@ -1,5 +1,4 @@
version = 1 version = 1
revision = 1
requires-python = ">=3.10, <3.14" requires-python = ">=3.10, <3.14"
resolution-markers = [ resolution-markers = [
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@ -94,7 +93,7 @@ wheels = [
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source = { registry = "https://pypi.org/simple" } source = { registry = "https://pypi.org/simple" }
dependencies = [ dependencies = [
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@ -106,76 +105,76 @@ dependencies = [
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{ name = "yarl" }, { name = "yarl" },
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[[package]] [[package]]
@ -954,6 +949,7 @@ dependencies = [
{ name = "aiolimiter" }, { name = "aiolimiter" },
{ name = "aiosqlite" }, { name = "aiosqlite" },
{ name = "alembic" }, { name = "alembic" },
{ name = "cbor2" },
{ name = "diskcache" }, { name = "diskcache" },
{ name = "fakeredis", extra = ["lua"] }, { name = "fakeredis", extra = ["lua"] },
{ name = "fastapi" }, { name = "fastapi" },
@ -989,6 +985,7 @@ dependencies = [
{ name = "tenacity" }, { name = "tenacity" },
{ name = "tiktoken" }, { name = "tiktoken" },
{ name = "typing-extensions" }, { name = "typing-extensions" },
{ name = "urllib3" },
{ name = "uvicorn" }, { name = "uvicorn" },
{ name = "websockets" }, { name = "websockets" },
] ]
@ -1068,6 +1065,7 @@ huggingface = [
{ name = "transformers" }, { name = "transformers" },
] ]
langchain = [ langchain = [
{ name = "langchain-core" },
{ name = "langchain-text-splitters" }, { name = "langchain-text-splitters" },
{ name = "langsmith" }, { name = "langsmith" },
] ]
@ -1125,7 +1123,7 @@ scraping = [
[package.metadata] [package.metadata]
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