Setup Vector and Graph Databases Backends for Cognee

Cognee supports multiple database backends for different storage needs. This guide covers all supported databases and their configuration methods. You can configure databases using either environment variables or Python configuration.

Overview

Cognee uses three types of databases:

Vector Databases: Store and query embeddings for semantic search
Graph Databases: Store knowledge graphs and relationships
Relational Databases: Store metadata and structured data

Vector Databases

1. LanceDB (Default)

Environment Variables:

VECTOR_DB_PROVIDER=lancedb

Python Configuration:

cognee.config.set_vector_db_config({
    "vector_db_provider": "lancedb"
})

2. ChromaDB

Environment Variables:

VECTOR_DB_PROVIDER=chromadb
VECTOR_DB_URL=http://localhost:8000

Python Configuration:

cognee.config.set_vector_db_config({
    "vector_db_url": "http://localhost:8000",
    "vector_db_provider": "chromadb"
})

Docker Setup:

# Run ChromaDB server
docker run -p 8000:8000 chromadb/chroma

3. Qdrant

Environment Variables:

VECTOR_DB_PROVIDER=qdrant
VECTOR_DB_URL=https://your-cluster-url
VECTOR_DB_KEY=your-api-key

Python Configuration:

cognee.config.set_vector_db_config({
    "vector_db_url": "https://your-cluster-url",
    "vector_db_key": "your-api-key",
    "vector_db_provider": "qdrant"
})

4. Weaviate

Environment Variables:

VECTOR_DB_PROVIDER=weaviate
VECTOR_DB_URL=https://your-cluster.weaviate.network
VECTOR_DB_KEY=your-api-key

Python Configuration:

cognee.config.set_vector_db_config({
    "vector_db_url": "https://your-cluster.weaviate.network",
    "vector_db_key": "your-api-key",
    "vector_db_provider": "weaviate"
})

5. Milvus

Environment Variables:

VECTOR_DB_PROVIDER=milvus
VECTOR_DB_URL=local_milvus_db_path  # Or your Milvus endpoint
VECTOR_DB_KEY=your-token

Python Configuration:

import os
cognee_directory_path = "path/to/your/cognee/system"
local_milvus_db_path = os.path.join(cognee_directory_path, "databases", "milvus.db")

cognee.config.set_vector_db_config({
    "vector_db_url": local_milvus_db_path,  # or remote endpoint
    "vector_db_key": "", 
    "vector_db_provider": "milvus"
})

6. PGVector (PostgreSQL)

Environment Variables:

VECTOR_DB_PROVIDER=pgvector
DB_PROVIDER=postgres
DB_HOST=127.0.0.1
DB_PORT=5432
DB_USERNAME=cognee
DB_PASSWORD=cognee
DB_NAME=cognee_db

Python Configuration:

# Configure PGVector
cognee.config.set_vector_db_config({
    "vector_db_provider": "pgvector"
})

# Configure PostgreSQL connection
cognee.config.set_relational_db_config({
    "db_provider": "postgres",
    "db_host": ""127.0.0.1"",
    "db_port": "5432",
    "db_username": "cognee",
    "db_password": "cognee",
    "db_name": "cognee_db"
})

PostgreSQL Setup:

# Using Docker with PGVector extension
docker run -d \
  --name postgres \
  -e POSTGRES_USER=cognee \
  -e POSTGRES_PASSWORD=cognee \
  -e POSTGRES_DB=cognee_db \
  -p 5432:5432 \
  pgvector/pgvector:pg17

Graph Databases

1. Kuzu (Default)

Environment Variables:

GRAPH_DATABASE_PROVIDER=kuzu

Python Configuration:

cognee.config.set_graph_db_config({
    "graph_database_provider": "kuzu"
})

2. Neo4j

Environment Variables:

GRAPH_DATABASE_PROVIDER=neo4j
GRAPH_DATABASE_URL=bolt://localhost:7687
GRAPH_DATABASE_USERNAME=neo4j
GRAPH_DATABASE_PASSWORD=your-password

Python Configuration:

import os

cognee.config.set_graph_db_config({
    "graph_database_url": "bolt://localhost:7687",
    "graph_database_provider": "neo4j",
    "graph_database_username": "neo4j",
    "graph_database_password": "your-password"
})

Docker Setup:

docker run \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/password \
  neo4j:latest

3. NetworkX

Environment Variables:

GRAPH_DATABASE_PROVIDER=networkx
# Uses local file path automatically

Python Configuration:

cognee.config.set_graph_db_config({
    "graph_database_provider": "networkx"
})

Hybrid Databases

1. FalkorDB

FalkorDB can be used as both the vector store and the graph database. Environment Variables:

VECTOR_DB_PROVIDER=falkordb
VECTOR_DB_URL=localhost
VECTOR_DB_PORT=6379

GRAPH_DATABASE_PROVIDER=falkordb
GRAPH_DATABASE_URL=localhost
GRAPH_DATABASE_PORT=6379

Python Configuration:

# Vector store configuration
cognee.config.set_vector_db_config({
    "vector_db_provider": "falkordb",
    "vector_db_url": "localhost",
    "vector_db_port": 6379
})

# Graph database configuration
cognee.config.set_graph_db_config({
    "graph_database_provider": "falkordb",
    "graph_database_url": "localhost",
    "graph_database_port": 6379
})

Docker Setup:

# Run FalkorDB 
docker run -p 6379:6379 falkordb/falkordb:latest

2. Amazon Neptune Analytics

Amazon Neptune Analytics provides both graph storage/traversals and vector search in a single backend. Environment Variables:

GRAPH_DATABASE_PROVIDER=neptune_analytics
GRAPH_DATABASE_URL=neptune-graph://g-your-graph
VECTOR_DB_PROVIDER=neptune_analytics
VECTOR_DB_URL=neptune-graph://g-your-graph

AWS_DEFAULT_REGION=us-east-1
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_SESSION_TOKEN=your-session-token

Python Configuration:


export AWS_NEPTUNE_ANALYTICS_GRAPH_ID=g-your-graph

from cognee import config

graph_identifier = os.getenv("AWS_NEPTUNE_ANALYTICS_GRAPH_ID", "")

config.set_graph_db_config(
    {
        "graph_database_provider": "neptune_analytics",
        "graph_database_url": f"neptune-graph://{graph_identifier}",
    }
)
config.set_vector_db_config(
    {
        "vector_db_provider": "neptune_analytics",
        "vector_db_url": f"neptune-graph://{graph_identifier}",
    }
)

Note: Ensure that the Neptune Analytics vector dimension matches your embedding model (for example, 1536 for OpenAI text-embedding-3-small).

Relational Databases

1. SQLite (Default)

Environment Variables:

DB_PROVIDER=sqlite
DB_NAME=cognee_db
# DB_PATH is optional - defaults to .cognee_system/databases

Python Configuration:

cognee.config.set_relational_db_config({
    "db_provider": "sqlite",
    "db_name": "cognee_db"
})

2. PostgreSQL

Environment Variables:

DB_PROVIDER=postgres
DB_HOST=localhost
DB_PORT=5432
DB_USERNAME=cognee
DB_PASSWORD=cognee
DB_NAME=cognee_db

Python Configuration:

cognee.config.set_relational_db_config({
    "db_provider": "postgres",
    "db_host": "localhost",
    "db_port": "5432",
    "db_username": "cognee",
    "db_password": "cognee",
    "db_name": "cognee_db"
})

127.0.0.1 host is when Postgres is used in a local setting. Set DB_HOST=host.docker.internal otherwise, so Cognee docker and Postgres would be on the same network

Complete Configuration Examples

Local Setup

Environment Variables (.env file):

# Vector Database
VECTOR_DB_PROVIDER=lancedb

# Graph Database  
GRAPH_DATABASE_PROVIDER=networkx

# Relational Database
DB_PROVIDER=sqlite
DB_NAME=cognee_db


### Remote Setup
**Environment Variables (.env file):**
```bash
# Vector Database - Qdrant Cloud
VECTOR_DB_PROVIDER=qdrant
VECTOR_DB_URL=https://your-cluster.qdrant
VECTOR_DB_KEY=your-api-key

# Graph Database - Neo4j Aura
GRAPH_DATABASE_PROVIDER=neo4j
GRAPH_DATABASE_URL=neo4j+s://your-instance.databases.neo4j.io
GRAPH_DATABASE_USERNAME=neo4j
GRAPH_DATABASE_PASSWORD=your-password

# Relational Database - PostgreSQL
DB_PROVIDER=postgres
DB_HOST=your-postgres-host.com
DB_PORT=5432
DB_USERNAME=cognee
DB_PASSWORD=your-password
DB_NAME=cognee_db

Installation Requirements

Some databases require additional packages. If you didn’t install cognee with all extras:

# For specific databases, check poetry extras
poetry install -E postgres -E neo4j -E kuzu

# With pip
pip install cognee[neo4j,postgres]

Multi-Tenant Setup

For isolated multi-tenant deployments, Cognee supports automatic database isolation: Environment Variables:

ENABLE_BACKEND_ACCESS_CONTROL=true

When enabled, Cognee automatically uses:

Kuzu for graph data (file-based isolation)
LanceDB for vector data (file-based isolation)

This ensures complete data separation between tenants. Please note that user defined graph and vector database settings will be ignored in this case.

Next Steps

Choose your database backends based on your requirements
Set up the necessary infrastructure (Docker containers, cloud services)
Install cognee with extra requirements for your database choice
Configure Cognee using environment variables or Python configuration
Test your setup with a simple example
Scale your deployment as needed

For example scripts, check the /examples/database_examples/ directory in the Cognee repository.

Getting Started

Core Concepts

Examples

CLI

Python SDK

UI

Setup Any Database

Setup Vector and Graph Databases Backends for Cognee

Overview

Vector Databases

1. LanceDB (Default)

2. ChromaDB

3. Qdrant

4. Weaviate

5. Milvus

6. PGVector (PostgreSQL)

Graph Databases

1. Kuzu (Default)

2. Neo4j

3. NetworkX

Hybrid Databases

1. FalkorDB

2. Amazon Neptune Analytics

Relational Databases

1. SQLite (Default)

2. PostgreSQL

Complete Configuration Examples

Local Setup

Installation Requirements

Multi-Tenant Setup

Next Steps

Getting Started

Core Concepts

Examples

CLI

Python SDK

UI

​Setup Vector and Graph Databases Backends for Cognee

​Overview

​Vector Databases

​1. LanceDB (Default)

​2. ChromaDB

​3. Qdrant

​4. Weaviate

​5. Milvus

​6. PGVector (PostgreSQL)

​Graph Databases

​1. Kuzu (Default)

​2. Neo4j

​3. NetworkX

​Hybrid Databases

​1. FalkorDB

​2. Amazon Neptune Analytics

​Relational Databases

​1. SQLite (Default)

​2. PostgreSQL

​Complete Configuration Examples

​Local Setup

​Installation Requirements

​Multi-Tenant Setup

​Next Steps

Setup Vector and Graph Databases Backends for Cognee

Overview

Vector Databases

1. LanceDB (Default)

2. ChromaDB

3. Qdrant

4. Weaviate

5. Milvus

6. PGVector (PostgreSQL)

Graph Databases

1. Kuzu (Default)

2. Neo4j

3. NetworkX

Hybrid Databases

1. FalkorDB

2. Amazon Neptune Analytics

Relational Databases

1. SQLite (Default)

2. PostgreSQL

Complete Configuration Examples

Local Setup

Installation Requirements

Multi-Tenant Setup

Next Steps