Qdrant

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.

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

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()  # registers "qdrant" as a vector provider — must run before any cognee call

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=""

Get your API key and URL from the Qdrant Cloud dashboard.

from cognee_community_vector_adapter_qdrant import register
from cognee import config

register()  # registers "qdrant" as a vector provider — must run before any cognee call

config.set_vector_db_config({
    "vector_db_provider": "qdrant",
    "vector_db_url": "https://your-cluster.qdrant.io",
    "vector_db_key": "your_api_key",
})

Or via environment variables:

VECTOR_DB_PROVIDER="qdrant"
VECTOR_DB_URL="https://your-cluster.qdrant.io"
VECTOR_DB_KEY="your_api_key"

Important Notes

Calling register() (fixes 'Unsupported vector database provider: qdrant')

Qdrant is not built into core Cognee — it is a community adapter. Simply setting VECTOR_DB_PROVIDER="qdrant" is not enough; core Cognee only knows about qdrant once the adapter registers itself. If you skip this step you will hit:

OSError: Unsupported vector database provider: qdrant. Supported providers are: LanceDB, PGVector, neptune_analytics

Registration happens by calling register() (importing it is not enough) from the installed adapter package. Add the call in your application’s entrypoint — the same Python process that later calls add, cognify, or search — and run it once, before any Cognee operation:

from cognee_community_vector_adapter_qdrant import register
import cognee

register()  # must run before add/cognify/search

async def main():
    await cognee.add("Cognee turns your data into an AI memory.")
    await cognee.cognify()
    results = await cognee.search("What does cognee do?")
    print(results)

In Docker or any long-running server deployment the same rule applies: register() must run inside the container at startup (for example at the top of the module that boots your app), because registration lives in process memory and is not persisted. Setting the environment variable alone will not register the adapter.

Embedding Dimensions

Ensure EMBEDDING_DIMENSIONS matches your embedding model. See Embedding Providers for configuration.Changing dimensions requires recreating collections or running prune.prune_system().

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

Getting Started

Core Concepts

Setup Configuration

Guides

Examples

CLI

Rust SDK

TypeScript SDK

OSS

Installation

Configuration

Important Notes

Resources

Qdrant Docs

Adapter Source

Extended Example

Vector Stores

Community Overview

Setup Overview

​Installation

​Configuration

​Important Notes

​Resources

Qdrant Docs

Adapter Source

Extended Example

Vector Stores

Community Overview

Setup Overview

Installation

Configuration

Important Notes

Resources