Question

You are building an Azure AI Search index to support a RAG grounding pipeline over a large corpus of policy documents. Each document is chunked, and every chunk carries a text passage, a precomputed embedding vector, and a 'department' value used to scope queries. Requirements: users must be able to run hybrid (keyword + vector) queries, filter results by department, and see the original passage text returned in results, but the raw embedding vector should never be returned in the response payload to reduce bandwidth. How should you configure the field attributes in the index definition?

Accepted Answer

Make the vector field searchable but not retrievable, the passage field searchable and retrievable, and the department field filterable and retrievable.

Answer

Make the vector field searchable and retrievable, the passage field filterable, and the department field searchable.

Answer

Make the vector field filterable, the passage field retrievable only, and the department field searchable and retrievable.

Answer

Make all three fields searchable, retrievable, and filterable so the query engine can decide which to use at runtime.