> ## 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 & Knowledge bases

> Learn how to use vector databases and Knowledge bases in Langdock to work with large document collections.

## Vector Databases

Vector databases are databases that enable embedding searches. They allow you to store many/long documents and retrieve them later using an LLM.

Langdock currently supports the following providers:

* [Qdrant](/en/using-langdock/integrations/integration-directory)
* [Pinecone](/en/using-langdock/integrations/integration-directory)
* [Azure AI Search](/en/using-langdock/integrations/integration-directory)
* [Milvus](/en/using-langdock/integrations/integration-directory)
* [Vertex AI Vector Search](/en/using-langdock/integrations/integration-directory)

### Setting Up a Connection

When working with vector databases, you first need to set up a connection in the respective integration's settings. Once the vector database is connected, it can be used in agents as an action or in chat with `@`. With each request, the model writes a request to the database and an embedding search is performed.

## Knowledge bases

A Knowledge base is a collection of documents that can be used as knowledge. Knowledge bases use vector search within the product, so smaller use cases do not require setting up a separate vector database, and less technical users can work with many documents.

<Card title="Capacity" icon="files">
  Up to **1,000 files** can be uploaded manually or via API.
</Card>

### Management

Users can manage Knowledge bases through the user interface (**Library → Knowledge bases**), including:

* Uploading files
* Deleting files
* Managing permissions

<Note>
  Automatic synchronization from integrations is not possible.
</Note>

<Tip>
  **Sharing:** You can share Knowledge bases with individual users, groups, or the entire workspace.
</Tip>

### API Access

Alternatively, users can programmatically upload, update, or delete files via the [Knowledge Folder API](/en/developer/knowledge-folder-api/upload-file). A link can be included to reference the original source in responses to users.

## Comparison: Knowledge bases vs. Custom Vector Database

Knowledge bases are suitable for less technical users and smaller use cases that need to be implemented quickly. For very large, valuable use cases with several thousand documents, a custom vector database is recommended because settings can be adjusted to the use case, paragraph length, topics, and metadata.

| Feature              | Knowledge bases (Langdock)                                                     | Custom Vector Database                                                        |
| :------------------- | :----------------------------------------------------------------------------- | :---------------------------------------------------------------------------- |
| **Management**       | Fully managed by Langdock, no infrastructure needed                            | Full control over models, dimensions, retrieval parameters, updates, etc.     |
| **Default Settings** | Optimized defaults (2,000-character chunks, 1536 dimensions, top-50 retrieval) | No predefined settings                                                        |
| **Data Volume**      | Ideal for up to 1,000 files                                                    | Unlimited data volumes and custom data structures possible                    |
| **Setup**            | Quick setup via Langdock UI                                                    | Own infrastructure, maintenance, and connection via Langdock Actions required |
| **Access Control**   | Granular access control and UI management in Langdock                          | Access control must be implemented yourself                                   |

## FAQ

<AccordionGroup>
  <Accordion title="When should I use a vector database instead of a Langdock Knowledge base?">
    Use a vector database when your organization already manages retrieval infrastructure or needs custom indexing, metadata, or search behavior. Use a Langdock Knowledge base when you want Langdock to handle document processing and retrieval.
  </Accordion>

  <Accordion title="Can I use both vector databases and Knowledge bases?">
    Yes. Use them for different retrieval needs, but keep the setup focused. Too many overlapping sources can make answers less predictable and harder to debug.
  </Accordion>
</AccordionGroup>