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Qdrant is a vector search engine that stores embeddings and performs similarity searches. It supports both cloud-hosted and self-hosted deployments.
Cognee can use Qdrant as a vector store backend through this community-maintained adapter.

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Installation

This adapter is a separate package from core Cognee. Before installing, complete the Cognee installation and ensure your environment is configured with LLM and embedding providers. After that, install the adapter package: 
uv pip install cognee-community-vector-adapter-qdrant

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Configuration

  • Docker (Local)
  • Qdrant Cloud
Run a local Qdrant instance:
docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant
Configure in Python:
from cognee_community_vector_adapter_qdrant import register
from cognee import config

register()

config.set_vector_db_config({
    "vector_db_provider": "qdrant",
    "vector_db_url": "http://localhost:6333",
    "vector_db_key": "",
})
Or via environment variables:
VECTOR_DB_PROVIDER="qdrant"
VECTOR_DB_URL="http://localhost:6333"
VECTOR_DB_KEY=""

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Important Notes

Import and call register() from the adapter package before using Qdrant with Cognee. This registers the adapter with Cognee’s provider system.
Ensure EMBEDDING_DIMENSIONS matches your embedding model. See Embedding Providers for configuration.Changing dimensions requires recreating collections or running prune.prune_system().

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Resources

Qdrant Docs

Official documentation

Adapter Source

GitHub repository

Extended Example

FAQ docs assistant example.

Vector Stores

Official vector providers

Community Overview

All community integrations

Setup Overview

Configuration guide