> ## 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>
  Our [folders](/en/using-langdock/library/folders) are actually vector databases under the hood, just with a simpler interface. If you want to get started quickly without technical setup, folders are perfect. 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>