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RAG Workflows

llm.port supports Retrieval-Augmented Generation (RAG) for document-grounded responses.

What RAG enables​

  • Ingest enterprise documents
  • Search relevant context at query time
  • Improve answer quality with controlled knowledge sources

Typical lifecycle​

  1. Upload and organize source documents
  2. Publish or activate knowledge for use
  3. Query through the Gateway or chat experiences
  4. Monitor usage and quality outcomes

Public deployment guidance​

  • Start with a focused knowledge scope
  • Define content ownership and refresh cadence
  • Validate permission boundaries before broader rollout

Notes​

The public docs describe RAG at capability level. Deep indexing and processing internals remain internal.

Screenshots​

RAG Knowledge Base

RAG Collectors

Scheduled Publishing

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