Individual skill
qdrant-search-strategies
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'BM25 or sparse vectors?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', 'ColBERT reranking', or 'missing keyword matches'
skills/qdrant-search-quality/search-strategies/SKILL.md
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# How to Improve Search Results with Advanced Strategies
These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first.
If the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality.
## Missing Obvious Keyword Matches
Use when: pure vector search misses results that contain obvious keyword matches. Domain terminology not in embedding training data, exact keyword matching critical (brand names, SKUs), acronyms common. Skip when: pure semantic queries, all data in training set, latency budget very tight.
- Dense + sparse with `prefetch` and fusion [Hybrid search](https://search.qdrant.tech/md/documentation/search/hybrid-queries/?s=hybrid-search)
- Prefer learned sparse ([miniCOIL](https://search.qdrant.tech/md/documentation/fastembed/fastembed-minicoil/), SPLADE, GTE) over raw BM25 if applicable (when user needs smart keywords matching and learned sparse models know the vocabulary of the domain)
- For non-English languages, [configure sparse BM25 parameters accordingly](https://search.qdrant.tech/md/documentation/search/text-search/?s=language-specific-settings)
- RRF: good default, supports weighted (v1.17+) [RRF](https://search.qdrant.tech/md/documentation/search/hybrid-queries/?s=reciprocal-rank-fusion-rrf)
- DBSF with asymmetric limits (sparse_limit=250, dense_limit=100) can outperform RRF for technical docs [DBSF](https://search.qdrant.tech/md/documentation/search/hybrid-queries/?s=distribution-based-score-fusion-dbsf)