A knowledge base is a set of documents an agent searches before it answers. It is the difference between an assistant that improvises your refund policy and one that quotes it.
Video coming soon
When you'd use it #
Policies
Refunds, warranties, delivery terms — the answers that must be exactly right and never invented.
Product detail
Specifications and compatibility that are too long for a prompt and change too often to hard-code.
Internal procedure
The written-down version of what your best agent knows, made available to the AI.
Setting one up #
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Register an embedding model first
Ingestion cannot start without one. You can add it from this page if the dropdown is empty — see Providers & models. -
Create the collection
Name and describe it. Keep separate subjects in separate collections so searches stay sharp. -
Upload documents
Each document is processed into a searchable index. Processing runs in the background; a document is not searchable until it completes. -
Retry anything that failed
Failed documents can be retried individually rather than re-uploading the batch. -
Attach the collection to an agent
In the builder, enable knowledge and pick the collection. You can also tune how many passages are retrieved and how close a match has to be.
Getting good answers out of it #
| Field | What it does |
|---|---|
| Match count | How many passages are pulled in. More context, more cost, and more chance of a distracting passage. |
| Match threshold | How close a passage must be to count. Raise it if the agent quotes irrelevant material; lower it if it says it does not know when the answer is clearly in there. |
| Document structure | Short, well-headed documents retrieve far better than one enormous PDF. Split by subject before uploading. |
| Say so in the prompt | Tell the agent to answer only from retrieved material and to say when it cannot. Retrieval supplies facts; the prompt decides whether it sticks to them. |