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.

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.

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.

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

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.