Retrieval Query Logs and Search Observability

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

Retrieval query logs and search observability let agents connect bad answers to actual queries, slow searches, empty result sets, and index behavior.

## Core Explanation

RAG failures are often retrieval failures. Query logs can show what the system searched for, how long it took, which filters were applied, and whether search results were too slow or too sparse.

Agents should use this telemetry to diagnose query rewriting, metadata filters, index freshness, and latency regressions. They should also treat logs as sensitive data because raw user prompts and retrieved identifiers may be present.

## Source-Mapped Facts

- Azure AI Search documentation includes guidance for monitoring queries and search traffic. ([source](https://learn.microsoft.com/en-us/azure/search/search-monitor-queries))
- Elasticsearch slow log documentation describes logging slow search and indexing operations. ([source](https://www.elastic.co/docs/deploy-manage/monitor/logging-configuration/slow-logs))
- OpenSearch logging documentation includes search slow logs and indexing slow logs. ([source](https://docs.opensearch.org/latest/install-and-configure/configuring-opensearch/logs/))

## Further Reading

- [Azure AI Search Monitor Queries](https://learn.microsoft.com/en-us/azure/search/search-monitor-queries)
- [Elasticsearch Slow Logs](https://www.elastic.co/docs/deploy-manage/monitor/logging-configuration/slow-logs)
- [OpenSearch Logs](https://docs.opensearch.org/latest/install-and-configure/configuring-opensearch/logs/)