> 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

How to sync documents to Spara for AI training.

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

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

#### Why doesn't Spara support spreadsheet or slide 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

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

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.