Search Evaluation

Build an evaluation baseline and measure whether your search system produces useful results.

Find Your Guide

Find practical guidance for the search problem or design decision you are working on.

Build a labeled query set and measure whether retrieved documents answer users' questions, with query-level relevance metrics.

Separate retrieval failures from generation failures and evaluate whether your full pipeline produces supported, useful answers.

Use a worked example to put the guidance into practice.

Compare approximate search with exact results, inspect ANN recall in the Web UI, and add a repeatable Python check.

Use the full reference when you need configuration details or an operating procedure.

Run similarity, hybrid, and multimodal search in Qdrant with filters, multi-stage pipelines, and relevance tuning.

Measuring and improving search relevance with Qdrant: evaluation, relevance feedback, and benchmarking.