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Knowledge bases let you ground an agent in your own documents. Documents are chunked, embedded, and stored in a vector index; at call time the most relevant chunks are retrieved and injected into the prompt so the agent answers from your content rather than its training data.

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Creating a knowledge base

  1. Open Agent Framework → Knowledge Bases.
  2. Click Create Knowledge Base.
  3. Fill in the form and click Create.

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Fields

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Adding documents

After creating the knowledge base, open it from the list. The detail view lets you upload documents that will be chunked and embedded according to the configuration above. Subsequent uploads use the same chunking and embedding settings, so all chunks in one knowledge base share the same vector dimension and chunking strategy.

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Attaching to an agent

  1. Open your agent in the Agent Builder.
  2. Go to the Knowledge Bases tab.
  3. Select one or more knowledge bases to attach.
  4. Save the agent.
A knowledge base can be attached to multiple agents — you don’t need to duplicate documents per agent.

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How retrieval works at runtime

When the agent runs:
  1. The user message (and, optionally, recent conversation context) is embedded with the same model used to ingest the knowledge base.
  2. The vector index returns the top-K most similar chunks across all attached knowledge bases.
  3. Those chunks are injected into the agent’s prompt with their source metadata.
  4. The agent responds, and the retrieval step appears as a span in the trace — including which chunks were retrieved, their similarity scores, and the source document — so you can audit how every answer was grounded.

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Tuning

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Relation to other features

  • Skills — skills are agent-mounted instruction bundles read on demand; knowledge bases are document corpora retrieved by semantic similarity. Use skills for “how to do X”; use knowledge bases for “what does our doc say about Y.”
  • Memories — memories are durable per-user facts extracted from conversations. Knowledge bases are shared document corpora retrieved by semantic similarity. Use memories for “what do I know about this user”; use knowledge bases for “what does our doc say about Y.”