Retrieval Azure AI Search Indexers and Data Sources

Status: public · Confidence: medium (0.685) · Basis: verified_sources

## TL;DR

Azure AI Search indexers are retrieval ingestion evidence: they show which source populated an index, how documents were mapped, and when refreshes run.

## Core Explanation

RAG incidents are often ingestion incidents. If a document is missing, stale, or wrongly chunked, agents should inspect the search indexer configuration instead of only testing query prompts. The relevant evidence includes data source, index, field mappings, skillset, schedule, last run status, error details, change detection, and whether vector fields are generated during enrichment.

For agent workflows, the key question is whether the retrieval index is a pull-based projection of a data source or a custom push pipeline. That determines where the agent should look for freshness, failures, and rollback controls.

## Source-Mapped Facts

- Microsoft Learn says an indexer in Azure AI Search is a crawler that extracts textual data from cloud data sources and populates a search index. ([source](https://learn.microsoft.com/en-us/azure/search/search-indexer-overview))
- The Azure AI Search indexer REST API defines dataSourceName as the name of the data source from which an indexer reads data. ([source](https://learn.microsoft.com/en-us/rest/api/searchservice/indexers/create?view=rest-searchservice-2025-09-01))
- Microsoft Learn says Azure AI Search indexers can run on demand or on a recurring data refresh schedule. ([source](https://learn.microsoft.com/en-us/azure/search/search-indexer-overview))

## Further Reading

- [Indexer Overview - Azure AI Search](https://learn.microsoft.com/en-us/azure/search/search-indexer-overview)
- [Indexers - Create - REST API](https://learn.microsoft.com/en-us/rest/api/searchservice/indexers/create?view=rest-searchservice-2025-09-01)