> ## Documentation Index > Fetch the complete documentation index at: https://docs.cognee.ai/llms.txt > Use this file to discover all available pages before exploring further. # Qdrant > Use Qdrant as a vector store through a community-maintained adapter 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](/setup-configuration/vector-stores) backend through this [community-maintained](/setup-configuration/community-maintained/overview) [adapter](https://github.com/topoteretes/cognee-community/tree/main/packages/vector/qdrant). ## Installation This adapter is a separate package from core Cognee. Before installing, complete the [Cognee installation](/getting-started/installation) and ensure your environment is configured with [LLM and embedding providers](/setup-configuration/overview). After that, install the adapter package: ```bash theme={null} uv pip install cognee-community-vector-adapter-qdrant ``` ## Configuration Run a local Qdrant instance: ```bash theme={null} docker run -p 6333:6333 -p 6334:6334 \ -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \ qdrant/qdrant ``` Configure in Python: ```python theme={null} 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: ```dotenv theme={null} VECTOR_DB_PROVIDER="qdrant" VECTOR_DB_URL="http://localhost:6333" VECTOR_DB_KEY="" ``` Get your API key and URL from the [Qdrant Cloud](https://qdrant.tech/documentation/cloud/) dashboard. ```python theme={null} 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: ```dotenv theme={null} VECTOR_DB_PROVIDER="qdrant" VECTOR_DB_URL="https://your-cluster.qdrant.io" VECTOR_DB_KEY="your_api_key" ``` ## Important Notes 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: ```python theme={null} 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. Ensure `EMBEDDING_DIMENSIONS` matches your embedding model. See [Embedding Providers](/setup-configuration/embedding-providers) for configuration. Changing dimensions requires recreating collections or running `prune.prune_system()`. ## Resources Official documentation GitHub repository FAQ docs assistant example. Official vector providers All community integrations Configuration guide