qdrant-search-quality-diagnosis

Diagnose Qdrant search quality by comparing exact KNN ground truth with approximate HNSW results to measure recall. This baseline comparison isolates whether missing matches stem from quantization, payload filtering, or HNSW parameter misconfiguration.

Recall degrades because quantization compresses vector precision and payload filtering narrows the HNSW graph traversal path. Diagnose this by contrasting exact KNN results with filtered or quantized HNSW outputs to identify the specific recall drop.

Tune HNSW parameters by establishing a baseline recall score and iteratively adjusting hnsw_ef for search, and ef_construct with m for indexing. Validate each parameter change against exact KNN ground truth to ensure recall improvements.

Validate Qdrant search relevance fixes by establishing exact KNN baselines and running reproducible tests against HNSW results. This ensures that parameter tuning and filtering adjustments yield measurable recall improvements before production deployment.

Yes, you can use exact KNN search to establish a ground truth baseline for Qdrant HNSW recall evaluation. Comparing exact KNN results against approximate HNSW outputs provides the definitive recall metric needed to guide parameter tuning.

HNSW approximate search trades exact recall for speed, meaning results may miss true nearest neighbors compared to exact KNN. Limitations become pronounced with aggressive quantization or strict payload filtering, requiring careful hnsw_ef tuning to maintain acceptable recall.