Question

You are building a RAG ingestion pipeline in Azure AI Search for a corpus of long technical manuals (200+ pages each). Retrieval must return small, focused passages for precise grounding, but the agent needs enough surrounding context to reason accurately. During ingestion you must split each document into fine-grained chunks for embedding while still being able to hydrate the full parent section into the agent prompt at query time. Which indexing approach in the skillset should you configure?

Accepted Answer

Configure an index projection that maps each small text chunk to a child index document while preserving a reference to the parent document via a parent key.

Answer

Use the Split skill in pages mode with a large page length so each chunk equals a full section, then embed each large chunk directly.

Answer

Store the entire document text in a single searchable field and rely on semantic ranker to extract the relevant passage at query time.

Answer

Enable integrated vectorization on the whole document field so the embedding model automatically produces one vector per manual.