Vector Stores

Configure vector databases for embedding storage and semantic search in Cognee

Vector stores hold embeddings for semantic similarity search. They enable Cognee to find conceptually related content based on meaning rather than exact text matches.

New to configuration?

See the Setup Configuration Overview for the complete workflow:

install extras → create .env → choose providers → handle pruning.

Supported Providers

Cognee supports multiple vector store options:

• LanceDB — File-based vector store, works out of the box (default)
• PGVector — Postgres-backed vector storage with pgvector extension
• Qdrant — High-performance vector database and similarity search engine
• Redis — Fast vector similarity search via Redis Search module
• ChromaDB — HTTP server-based vector database
• FalkorDB — Hybrid graph + vector database
• Neptune Analytics — Amazon Neptune Analytics hybrid solution

Configuration

Environment Variables

Set these environment variables in your .env file:

• VECTOR_DB_PROVIDER — The vector store provider (lancedb, pgvector, qdrant, redis, chromadb, falkordb, neptune_analytics)
• VECTOR_DB_URL — Database URL or connection string
• VECTOR_DB_KEY — Authentication key (provider-specific)
• VECTOR_DB_PORT — Database port (for some providers)

Setup Guides

LanceDB (Default)

LanceDB is file-based and requires no additional setup. It’s perfect for local development and single-user scenarios.

VECTOR_DB_PROVIDER="lancedb"
# Optional, can be a path or URL. Defaults to <SYSTEM_ROOT_DIRECTORY>/databases/cognee.lancedb
# VECTOR_DB_URL=/absolute/or/relative/path/to/cognee.lancedb

Installation: LanceDB is included by default with Cognee. No additional installation required.

Data Location: Vectors are stored in a local directory. Defaults under the Cognee system path if VECTOR_DB_URL is empty.

PGVector

PGVector stores vectors inside your Postgres database using the pgvector extension.

VECTOR_DB_PROVIDER="pgvector"
# Uses the same Postgres connection as your relational DB (DB_HOST, DB_PORT, DB_NAME, DB_USERNAME, DB_PASSWORD)

Installation: Install the Postgres extras:

pip install "cognee[postgres]"
# or for binary version
pip install "cognee[postgres-binary]"

Docker Setup: Use the built-in Postgres with pgvector:

docker compose --profile postgres up -d

Note: If using your own Postgres, ensure CREATE EXTENSION IF NOT EXISTS vector; is available in the target database.

Qdrant

Qdrant requires a running instance of the Qdrant server.

VECTOR_DB_PROVIDER="qdrant"
VECTOR_DB_URL="http://localhost:6333"

Installation: Since Qdrant is a community adapter, you have to install the community package:

pip install cognee-community-vector-adapter-qdrant

Configuration: To make sure Cognee uses Qdrant, you have to register it beforehand with the following line:

from cognee_community_vector_adapter_qdrant import register

For more details on setting up Qdrant, visit the more detailed description of this adapter.

Docker Setup: Start the Qdrant service:

docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant

Access: Default port is 6333 for the database, and you can access the Qdrant dashboard at “localhost:6333/dashboard”.

Redis

Redis can be used as a vector store through the Redis Search module, providing fast vector similarity search capabilities.

VECTOR_DB_PROVIDER="redis"
VECTOR_DB_URL="redis://localhost:6379"
# VECTOR_DB_KEY is optional and not used by Redis

Installation: Since Redis is a community adapter, you have to install the community package:

pip install cognee-community-vector-adapter-redis

Configuration: To make sure Cognee uses Redis, you have to register it beforehand with the following line:

from cognee_community_vector_adapter_redis import register

You can also configure Redis programmatically:

from cognee import config

config.set_vector_db_config({
    "vector_db_provider": "redis",
    "vector_db_url": "redis://localhost:6379",
})

For more details on setting up Redis, visit the more detailed description of this adapter.

Docker Setup: Start a Redis instance with Search module enabled:

docker run -d --name redis -p 6379:6379 redis:8.0.2

Or use Redis Cloud with the Search module enabled: Redis Cloud

Connection URL Examples:

• Local: redis://localhost:6379
• With authentication: redis://user:password@localhost:6379
• With SSL: rediss://localhost:6380

ChromaDB

ChromaDB requires a running Chroma server and authentication token.

VECTOR_DB_PROVIDER="chromadb"
VECTOR_DB_URL="http://localhost:3002"
VECTOR_DB_KEY="<your_token>"

Installation: Install ChromaDB extras:

pip install "cognee[chromadb]"
# or directly
pip install chromadb

Docker Setup: Start the bundled ChromaDB server:

docker compose --profile chromadb up -d

FalkorDB

FalkorDB can serve as both graph and vector store, providing a hybrid solution.

VECTOR_DB_PROVIDER="falkordb"
VECTOR_DB_URL="localhost"
VECTOR_DB_PORT="6379"

Installation: Since FalkorDB is a community adapter, you have to install the community package:

pip install cognee-community-hybrid-adapter-falkor

Configuration: To make sure Cognee uses FalkorDB, you have to register it beforehand with the following line:

from cognee_community_hybrid_adapter_falkor import register

For more details on setting up FalkorDB, visit the more detailed description of this adapter.

Docker Setup: Start the FalkorDB service:

docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:edge

Access: Default ports are 6379 (DB) and 3000 (UI).

Neptune Analytics

Use Amazon Neptune Analytics as a hybrid vector + graph backend.

VECTOR_DB_PROVIDER="neptune_analytics"
VECTOR_DB_URL="neptune-graph://<GRAPH_ID>"
# AWS credentials via environment or default SDK chain

Installation: Install Neptune extras:

pip install "cognee[neptune]"

Note: URL must start with neptune-graph:// and AWS credentials should be configured via environment variables or AWS SDK.

Important Considerations

Dimension Consistency

Ensure EMBEDDING_DIMENSIONS matches your vector store collection/table schemas:

• PGVector column size
• LanceDB Vector size
• ChromaDB collection schema

Changing dimensions requires recreating collections.

Provider Comparison

Provider	Setup	Performance	Use Case
LanceDB	Zero setup	Good	Local development
PGVector	Postgres required	Excellent	Production with Postgres
Qdrant	Server required	Excellent	High-performance vector search
Redis	Server required	Excellent	Low-latency in-memory search
ChromaDB	Server required	Good	Dedicated vector store
FalkorDB	Server required	Good	Hybrid graph + vector
Neptune Analytics	AWS required	Excellent	Cloud hybrid solution

Community-Maintained Providers

Additional vector stores are available through community-maintained adapters:

• Qdrant — Vector search engine with cloud and self-hosted options
• Redis — Fast vector similarity search
• FalkorDB — Hybrid vector and graph store
• Milvus, Pinecone, Weaviate, and more — See all community adapters

Notes

• Embedding Integration: Vector stores use your embedding engine from the Embeddings section
• Dimension Matching: Keep EMBEDDING_DIMENSIONS consistent between embedding provider and vector store
• Performance: Local providers (LanceDB) are simpler but cloud providers offer better scalability

Embedding Providers

Configure embedding providers for vector generation

Graph Stores

Set up graph databases for knowledge graphs

Overview

Return to setup configuration overview