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Configuring a RAG

A RAG deployment grounds model answers in your own documents. Once it's deployed, you configure it from its page in the portal — which models it uses and who can access it. Its vector database is provisioned and managed for you; there's nothing to set up there.

(For the document, retrieval, and chat APIs, see RAG; for programmatic access, see API Keys.)

Models​

Under Settings, choose the models the RAG uses. Each can be any model on the platform — in-cluster or external:

  • Embedding model — required; turns documents and queries into vectors.
  • Completion model — optional; generates answers from retrieved context.
  • Reranker model — optional; reorders retrieved chunks for relevance.
  • Vision model — optional; reads text from images and scanned PDFs.

Behavior​

Also under Settings, tune how the RAG retrieves and handles documents:

  • Smart temporal search — time-aware queries ("latest policy", "what changed last month") automatically prioritize the most time-relevant documents.
  • Require group for retrieval — only return results when the query names a document group, keeping every query scoped to a group.
  • Use PDF OCR by default — run OCR on PDFs and images at upload (requires a vision model).
  • Auto-delete files without a group — discard any uploaded file not assigned to a group.
  • Reranker top N — how many retrieved chunks the reranker considers.
  • Reranker batch size — how many chunks the reranker scores at once.

The defaults suit most deployments; adjust these only for specific needs.

Access​

Under Users, add people and groups as either users or admins:

  • User — use the RAG.
  • Admin — also manage the deployment and who can access it.

Granting a group access gives every member that access — the simplest way to manage a whole team at once. For programmatic access, issue an API key with read and/or write on the RAG.

Updates​

A RAG keeps itself on the platform's recommended version automatically — nothing to do to stay current.