qdrant-search-quality-diagnosis

# How to Diagnose Bad Search Quality Before tuning, establish baselines. Use exact KNN as ground truth, compare against approximate HNSW. Target >95% recall@K for production. ## Don't Know What's Wrong Yet Use when: results are irrelevant or missing expected matches and you need to isolate the cause. - Test with `exact=true` to bypass HNSW approximation [Search API](https://search.qdrant.tech/md/documentation/tutorials-search-engineering/retrieval-quality/?s=standard-mode-vs-exact-search) - Exact search bad = model or search pipeline problem. Exact good, approximate bad = tune HNSW. - Check if quantization degrades quality (compare with and without) - Check if filters are too restrictive (then you might need to use ACORN) - If duplicate results from chunked documents, use Grouping API to deduplicate [Grouping](https://search.qdrant.tech/md/documentation/search/search/?s=grouping-api) Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering. Text content goes in sparse vectors for search. ## Approximate Search Worse Than Exact Use when: exact search returns good results but HNSW approximation misses them. - Increase `hnsw_ef` at query time [Search params](https://search.qdrant.tech/md/documentation/operations/optimize/?s=fine-tuning-search-parameters) - Increase `ef_construct` (200+ for high quality) [HNSW config](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=vector-index) - Increase `m` (16 default, 32 for high recall) [HNSW config](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=vector-index) - Enable oversampling + rescore with quantization [Search with quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/?s=searching-with-quantization) - ACORN for filtered queries (v1.16+) [ACORN](https://search.qdrant.tech/md/documentation/search/search/?s=acorn-search-algorithm) Binary quantization requires rescore. Without it, quality loss is severe. Use oversampling (3-5x minimum for binary) to recover recall. Always test quantization impact on your data before production. [Quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/) ## Wrong Embedding Model Use when: exact search also returns bad results. Test top 3 MTEB models on 100-1000 sample queries, measure recall@10. Domain-specific models often outperform general models. [Hosted inference](https://search.qdrant.tech/md/documentation/inference/) ## Unoptimized Search Pipeline Use when: exact search also returns bad results and model choice is confirmed by user. Optimize search according to advanced search-strategies skill. ## What NOT to Do - Tune Qdrant before verifying the model is right for the task (most quality issues are model issues) - Use binary quantization without rescore (severe quality loss) - Set `hnsw_ef` lower than results requested (guaranteed bad recall) - Skip payload indexes on filtered fields then blame quality (HNSW can't traverse filtered-out nodes, and filterable HNSW is built only if payload indexes were set up prior) - Deploy without baseline recall or other search relevance metrics (no way to measure regressions) - Confuse payload filtering with sparse vector search (different things, different config)