> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langdock.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Vector Databases

> Integrate your vector database with Langdock and let your agent perform a semantic search across your data.

You can add large amounts of information in a vector database and integrate it with Langdock. When building an agent, you can connect your vector database and let the agent perform an embedding search across your data. Langdock currently supports:

* [Qdrant](https://qdrant.tech/documentation/)
* [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/)
* [Pinecone](https://docs.pinecone.io/guides/get-started/overview)
* [Milvus](https://milvus.io/docs)
* [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/vector-search/overview)

<Info>
  [Knowledge bases](/en/using-langdock/library/knowledge-bases) use vector search with a simpler interface. If you want to get started quickly without technical setup, Knowledge bases are a good fit. For high volume or specialized use cases where you need custom chunking, metadata, or specific embedding models, a dedicated vector database gives you full control.
</Info>

## FAQ

<AccordionGroup>
  <Accordion title="When should I use a vector database integration?">
    Use a vector database when your organization already stores searchable embeddings outside Langdock or needs custom retrieval infrastructure. Use Langdock Knowledge bases when you want Langdock to manage document processing and retrieval.
  </Accordion>

  <Accordion title="What should I check if vector search results are poor?">
    Check embedding quality, chunking, metadata filters, query wording, and whether the correct index is connected. Retrieval quality often depends on how the source data was prepared before Langdock queries it.
  </Accordion>
</AccordionGroup>