> For the complete documentation index, see [llms.txt](https://docs.spara.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.spara.com/integrations/document-sync-integrations.md).

# Document Sync Integrations

Spara can sync documents from your team's knowledge repositories to train AI agents. Synced documents are added to your [Knowledge](/platform/knowledge.md) base, giving agents the context they need to answer questions accurately.

Connect a document source under [**Settings > Integrations**](https://app.spara.co/organization/integrations).

| Platform                                                                 | Supported content                             |
| ------------------------------------------------------------------------ | --------------------------------------------- |
| [Google Drive](/integrations/document-sync-integrations/google-drive.md) | Google Docs, PDFs, and other text-based files |
| [Sharepoint](/integrations/document-sync-integrations/sharepoint.md)     | SharePoint documents and pages                |
| [Confluence](/integrations/document-sync-integrations/confluence.md)     | Confluence pages and spaces                   |
| [Notion](/integrations/document-sync-integrations/notion.md)             | Notion pages and databases                    |

Spara supports syncing the following document types:

* Google Docs
* Microsoft Word (.docx)
* PDFs
* Rich text and Markdown formats

Some file formats generally result in poor performance when ingested by large language models. Spara does not support the following file formats:

* Slides (ex. Powerpoint, Google Slides) are often mostly visual. Any text ingested by the LLM will be missing the necessary context of slide structure or visual elements.
* Spreadsheets (ex. Excel, Google Sheets) are mostly numerical information without sufficient language context to be useful to a large language model.


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.spara.com/integrations/document-sync-integrations.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.