126 lines
No EOL
3.8 KiB
Markdown
126 lines
No EOL
3.8 KiB
Markdown
# Qdrant
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> Use Qdrant as a vector store through a community-maintained adapter
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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.
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<Note>
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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).
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</Note>
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## Installation
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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:
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```bash theme={null}
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uv pip install cognee-community-vector-adapter-qdrant
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```
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## Configuration
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<Tabs>
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<Tab title="Docker (Local)">
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Run a local Qdrant instance:
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```bash theme={null}
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docker run -p 6333:6333 -p 6334:6334 \
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-v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
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qdrant/qdrant
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```
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Configure in Python:
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```python theme={null}
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from cognee_community_vector_adapter_qdrant import register
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from cognee import config
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register()
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config.set_vector_db_config({
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"vector_db_provider": "qdrant",
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"vector_db_url": "http://localhost:6333",
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"vector_db_key": "",
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})
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```
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Or via environment variables:
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```dotenv theme={null}
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VECTOR_DB_PROVIDER="qdrant"
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VECTOR_DB_URL="http://localhost:6333"
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VECTOR_DB_KEY=""
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```
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</Tab>
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<Tab title="Qdrant Cloud">
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Get your API key and URL from the [Qdrant Cloud](https://qdrant.tech/documentation/cloud/) dashboard.
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```python theme={null}
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from cognee_community_vector_adapter_qdrant import register
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from cognee import config
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register()
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config.set_vector_db_config({
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"vector_db_provider": "qdrant",
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"vector_db_url": "https://your-cluster.qdrant.io",
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"vector_db_key": "your_api_key",
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})
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```
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Or via environment variables:
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```dotenv theme={null}
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VECTOR_DB_PROVIDER="qdrant"
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VECTOR_DB_URL="https://your-cluster.qdrant.io"
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VECTOR_DB_KEY="your_api_key"
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```
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</Tab>
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</Tabs>
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## Important Notes
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<Accordion title="Adapter Registration">
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Import and call `register()` from the adapter package before using Qdrant with Cognee. This registers the adapter with Cognee's provider system.
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</Accordion>
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<Accordion title="Embedding Dimensions">
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Ensure `EMBEDDING_DIMENSIONS` matches your embedding model. See [Embedding Providers](/setup-configuration/embedding-providers) for configuration.
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Changing dimensions requires recreating collections or running `prune.prune_system()`.
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</Accordion>
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## Resources
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<CardGroup cols={3}>
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<Card title="Qdrant Docs" icon="book" href="https://qdrant.tech/documentation/">
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Official documentation
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</Card>
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<Card title="Adapter Source" icon="github" href="https://github.com/topoteretes/cognee-community/tree/main/packages/vector/qdrant">
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GitHub repository
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</Card>
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<Card title="Extended Example" icon="lightbulb" href="https://github.com/topoteretes/cognee-community/tree/main/packages/vector/qdrant/example.py">
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FAQ docs assistant example.
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</Card>
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</CardGroup>
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<Columns cols={3}>
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<Card title="Vector Stores" icon="database" href="/setup-configuration/vector-stores">
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Official vector providers
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</Card>
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<Card title="Community Overview" icon="users" href="/setup-configuration/community-maintained/overview">
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All community integrations
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</Card>
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<Card title="Setup Overview" icon="settings" href="/setup-configuration/overview">
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Configuration guide
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</Card>
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</Columns>
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---
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> To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt |