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Knowledge base

A knowledge base gives your agents the facts they need to answer accurately. Upload your documents — product sheets, policies, FAQs — and your agent retrieves the most relevant passages during a call to ground its answers, instead of guessing. This is retrieval-augmented generation (RAG).

Open Knowledge (/knowledge).

How a knowledge base works​

The Knowledge Base The Knowledge Base: documents with indexing status, chunk count, quality score and coverage analysis.

The flow from a raw document to a grounded answer has four steps:

  1. Upload your documents to the knowledge base.
  2. The Portal processes and indexes them, breaking each document into passages it can search.
  3. You link the knowledge base to an agent.
  4. On every call, the agent retrieves the passages that best match what the caller asked and answers from them.

The result is answers that reflect your own products, policies and processes — and far fewer made-up replies.

Upload documents​

Add the documents you want your agents to draw on. Supported formats are PDF, DOC, DOCX and TXT (.pdf, .doc, .docx, .txt), up to 10 MB each.

  1. Upload one or more files.
  2. The content is processed and indexed so it can be retrieved during calls.
tip

Upload focused, well-structured documents. A clear FAQ or a tidy policy doc retrieves far better than a giant, mixed PDF. Split very large or unrelated material into separate files, and give each one a descriptive name so you can tell them apart later.

Use search to check what's in your knowledge base and confirm a document was indexed and is findable. Searching the way a caller might actually ask — in their words, not yours — is a quick way to sanity-check that the right passage comes back before you rely on it in a live call. If your search turns up nothing, the agent won't find it either.

Retrieval settings​

Tune how retrieval behaves so your agent gets the right context — not too little, not too much:

  • Similarity — how closely a passage must match the question to be used. Higher is stricter, so only strong matches come back; lower is more forgiving and surfaces more passages.
  • Top-k — how many of the best-matching passages to pull in for each answer. Smaller keeps answers tight; larger gives the agent more to work with.

Raise top-k when answers are missing context; tighten similarity when the agent pulls in loosely related material. The same two controls also appear per agent on the Knowledge & RAG tab, so you can set a baseline here and adjust for a specific agent there.

Keep your knowledge base current​

A knowledge base is only as good as what's in it. When a policy, price or product detail changes, update the source document so agents stop answering from stale facts. Remove documents you no longer want agents to use, and re-check with search after any change to confirm the new content is what comes back.

A knowledge base is only used by an agent once it's linked to one. On the agent, open the LLM (Model) tab → Add Knowledge Base section to link the knowledge base and set its retrieval behavior — there is no separate Knowledge tab; see Knowledge & RAG. After linking, the agent retrieves from your documents on every call.

Next steps​