Setting Up a Knowledge Base

A knowledge base (KB) gives an agent private, searched context. When an agent has a KB attached, the platform retrieves the most relevant passages for each run and adds them to the agent's context — this is retrieval-augmented generation (RAG). This guide covers creating a KB, getting documents into it, and attaching it to an agent.

Knowledge bases are scoped to a single AI Employee (AIE): every KB belongs to exactly one AI Employee, and its documents are only searched by that AI Employee's agents.

Two ways to add documents

A knowledge base gets its content one of two ways:

• File upload — drag files (or a folder) straight into the KB. No credentials, no schedule. These are "local file" knowledge bases.
• Connected source — point the KB at an external system so documents sync in automatically. Supported connectors are SharePoint, Google Drive, Confluence, Box, and ServiceNow.

A connection holds the credentials and configuration for an external source. Connections are created and owned by admins, separately from KBs, so one connection can back several KBs scoped to different folders or sites.

Step 1 — create the knowledge base

1. Open the AI Employee and go to its Knowledge bases.
2. Create a knowledge base. Give it a clear name and description — builders see these when attaching it to an agent.
3. Choose its source: a local-file KB (upload), or a connected source backed by a connection.

When you create a KB you also choose its intent, which selects the ingestion pipeline:

• Search (the default) — documents are chunked, embedded, and indexed for retrieval. This is what you want for almost every KB.
• Extraction — the document's full extracted text is stored as a single LLM-ready artifact and is not chunked or indexed for search. Use this only for the file-upload-and-extract pattern, where an agent reads a whole document rather than searching across many.

Step 2 — add documents

By upload

In the KB detail view, drag files or a folder onto the upload area, or use the file picker. You can upload a single file or a batch. Each file kicks off ingestion in the background; the document list shows its status as it moves from queued to processed.

Uploads accept .txt, .md, .csv, .html, .pdf, .docx, .pptx, .xlsx, .png, .jpg, .webp, and .gif, up to 50 MB each. Files of other types are skipped during ingestion.

By connecting a source

For a connected KB, an admin configures the connection's scope — for example, which SharePoint sites, Google Drive folders, Confluence spaces, Box folders, or ServiceNow knowledge bases to pull from — and a sync configuration that controls how often the KB re-syncs (manual or on an interval). You can trigger a sync on demand from the KB's Sync config tab, and review past syncs in Sync logs.

Incremental syncs only re-ingest documents that changed, so repeated syncs are cheap.

Step 3 — let ingestion process the documents

Once a document arrives — by upload or sync — it runs through the ingestion pipeline:

1. Text extraction — text is pulled from the file. PDFs, Office documents (.docx, .xlsx, .pptx), HTML, and images are all supported; images are described and OCR'd by a vision model.
2. Chunking — the extracted text is split into passages sized for retrieval.
3. Embedding — each chunk is embedded into a vector so it can be matched against a query.
4. Storage — chunks and their embeddings are stored and indexed.

You don't configure chunk size or the embedding model per KB — the embedding model is a platform-level setting shared across the tenant. What you control is the source content and, for connected KBs, the sync scope and schedule.

Watch the document list for status:

• Processed — chunked, embedded, and searchable.
• In progress — still ingesting.
• Failed — extraction or processing errored. You can retry a failed document from its row.

The KB shows a processed-document count, which is what builders see when attaching the KB.

Step 4 — attach the knowledge base to an agent

KBs are searched only when an agent references them. To attach one:

1. Open the agent in the AI Employee builder. Knowledge base support appears for the agent types that use retrieval — search and respond, document agent, and custom.
2. In the Knowledge bases section, open the dropdown and select one or more KBs. Each option shows the KB's name, description, and processed-document count.
3. Save the agent configuration.

At run time the agent's search-and-respond loop retrieves relevant passages from the attached KBs and uses them to answer. For text-output agents, the platform also tracks which sources were used and surfaces citations on the response.

Scoping retrieval

You can narrow what an agent searches without changing the KB itself:

• Tag filters — if a KB's documents are tagged (via its taxonomy), select specific tags so the agent only searches documents carrying them.
• KB filtering — when an agent has more than one KB attached, you can enable KB filtering and give the agent natural-language instructions for choosing which KB to search per query, plus a per-KB label.
• Search settings — under Advanced settings, tune retrieval behavior (such as how many passages to retrieve) for the agent.

What's next

• Writing effective Instructions — tell the agent how to use the searched knowledge.
• Build your first workflow — put a knowledge-backed agent into a workflow.
• Core concepts — how knowledge bases fit into the platform.