# Knowledge Sources

Knowledge Sources let you connect external content to an LLM node so the model can reference your data when generating responses. Instead of relying solely on the model's training knowledge, you can point it at your own documents, files, and messages.

<figure><img src="/files/YgosWbnkvVNLJFETNuaO" alt=""><figcaption></figcaption></figure>

There are two types of Knowledge Sources you can add to a model node:

### Knowledge Base

A Knowledge Base is an indexed copy of your content stored inside Stack AI. You upload or connect your files, Stack AI ingests and indexes them, and the LLM node queries that index at run time using semantic search.

Use a Knowledge Base when:

* You have a large collection of documents that don't change often
* You need fast, consistent retrieval across many files
* You want to control exactly what content the model has access to

Learn more about Knowledge Bases [here](/workflow-builder/core-nodes/knowledge-bases.md).

### Search Connected Apps

Search Connected Apps queries your external applications — like Google Drive, OneDrive, SharePoint, or Slack — in real time, directly from an LLM node. There is no indexing step: Stack AI searches the live content in your connected accounts and passes the results directly to the model.

Use Search Connected Apps when:

* Your content changes frequently and needs to be current
* You want to start searching immediately without an ingestion step
* You need to search messages or files that are added continuously (e.g. Slack, shared drives)

Learn more about Search Connected Apps [here](https://docs.stackai.com/workflow-builder/llms/llm-node/knowledge-sources/search-connected-apps).

### Search Connected Apps vs. Knowledge Base

Stack AI offers two ways to give an LLM node access to your documents and data. They solve different problems.

A **Knowledge Base** is an indexed copy of your content stored inside Stack AI. When you create one, Stack AI ingests your files, chunks them, and builds a vector index. At run time, the LLM node queries that index — which is fast and handles large document collections well. The tradeoff is that the index is only as current as the last sync: if a file changes in Google Drive after the last ingestion, the Knowledge Base won't reflect it until it syncs again. Knowledge Bases are best for large, stable libraries where you want consistent, low-latency retrieval.

**Search Connected Apps** skips the index entirely. There is no ingestion step — Stack AI queries the live content in your connected accounts directly, every time the workflow runs. This means results are always up to date, but the search depends on the speed and capabilities of the external app's own search API. It works best for content that changes frequently (recent Slack messages, a shared Google Drive folder your team updates daily) or when you want to start searching immediately without waiting for an ingestion job.

|                        | Search Connected Apps                      | Knowledge Base                     |
| ---------------------- | ------------------------------------------ | ---------------------------------- |
| **Indexing required**  | No                                         | Yes                                |
| **Content freshness**  | Always current                             | As fresh as the last sync          |
| **Best for**           | Frequently changing files, recent messages | Large, stable document collections |
| **Setup time**         | Instant                                    | Requires ingestion                 |
| **Search latency**     | Depends on external API                    | Fast (vector index)                |
| **Max content volume** | Limited by API result caps                 | Scales with your index             |

{% hint style="info" %}
If the same Google Drive folder is connected to both a Knowledge Base and a Search Connected Apps node, Stack AI will prefer the cached Knowledge Base content when available, and fall back to live search for anything not yet indexed.
{% endhint %}


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# Agent Instructions: 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:

```
GET https://docs.stackai.com/workflow-builder/core-nodes/ai-agent-node/knowledge-sources.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.