The outcome

Operate a compact edge RAG service whose vector configuration, sources, access rules, and refresh lifecycle are controlled.

Step by step

A workflow you can repeat.

  1. 01

    Define corpus ownership, permissions, update and deletion rules, benchmark queries, source citations, and the approved Worker and storage boundaries.

  2. 02

    Choose an embedding model, dimensions, distance metric, namespaces, and metadata schema before creating separate development and production indexes.

  3. 03

    Chunk approved documents, create stable vector IDs linked to source objects, generate embeddings, and upsert records with minimal searchable metadata.

  4. 04

    Embed queries with the same model, apply namespace and metadata filters, retrieve candidates, and pass only verified context to generation.

  5. 05

    Evaluate recall and answer support, handle asynchronous index updates, test deletion and model migration, and monitor query latency and inference spend.

Working standard

What good use looks like.

  • Fix dimensions and metric deliberately.
  • Map every vector to a source object.
  • Test asynchronous updates and deletions.

Official references

Check the current product documentation.