Question

You are building an Azure AI Search RAG ingestion pipeline for a library of long technical manuals. A skillset splits each manual into text chunks and generates vector embeddings per chunk. You need the index to store one document per chunk for fine-grained retrieval, while also preserving the parent manual's metadata (title, product line, revision date) so it can be returned as grounding context. Which indexer configuration achieves this?

Accepted Answer

Configure index projections in the skillset, defining a selector that maps each chunk to its own index document and includes the parent-level metadata fields.

Answer

Set the indexer's parsingMode to jsonArray so each chunk in the split array becomes a separate search document automatically.

Answer

Enable an output field mapping that flattens the chunk array into a single collection field on one document per manual.

Answer

Use a knowledge store table projection to persist chunks, then point the index at the knowledge store as its data source.