qdrant-hybrid-search-prefetches
De la autor
Constructing prefetch queries for hybrid retrieval, including BM25/dense and multi-field setups, and choosing a lexical retrieval method. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'BM25 on a full-text index or sparse vectors?', 'which sparse embedding model to use?', 'BM25 vs SPLADE?', or 'how to migrate BM25 sparse vectors to a text index?'
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npx skills add qdrant/skills --skill qdrant-hybrid-search-prefetches -a claude-code
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- qdrant-hybrid-searchqdrant/skillsExplains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to combine results from multiple searches?' and 'combining multiple representations'. Also use for how a hybrid query is scoped: 'how is IDF scoped?', 'can one tenant's data contaminate another tenant's scoring?'. Also 'BM25 on a text index or sparse vectors?'Altele★ 263
- qdrant-hybrid-search-combiningqdrant/skillsFusing scores from multiple searches into a single ranked result (RRF, DBSF, custom fusion). Use when someone asks 'RRF or DBSF?', 'how to combine sparse and dense', 'how to combine scores from multiple searches?', 'custom fusion', 'fusion is not producing good results', 'how do I tune RRF', 'what k should I use', or 'how do I set fusion weights'Altele★ 263
- agentsop-hybrid-retrievalagentsope/SkillAlchemyEnhancement-overlay SOP for adding sparse (BM25 / keyword) retrieval alongside dense (embedding) retrieval. Activate when a calling agent is building, reviewing, or debugging a retrieval pipeline whose corpus contains exact-match tokens — identifiers, error codes, SKUs, API/function names, proper nouns, citations, rare jargon — that pure dense embedding silently misses. Encodes the single decision rule (**hybrid is traffic-driven, not theoretical: add sparse only when the query share that depends on exact tokens is non-trivial**), the wiring of QueryFusionRetriever-style fusion (RRF vs alpha-weighted), and per-query-type alpha tuning. Frame the work as recovering lexical identity that dense pooling destroys, not as "add keyword search for completeness". Cross-links [[llamaindex]].Inginerie★ 446
- postgres-hybrid-text-searchtimescale/pg-aiguideUse this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.Date★ 1.9K
- qdrant-search-strategiesqdrant/skillsGuides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', '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', or 'missing keyword matches'Inginerie★ 263
- embeddings-searchericrisco/rsc-harnessUse when choosing an embedding model, chunk size, or query form, when semantic search returns irrelevant results, when adding hybrid BM25+vector or a reranker, or when a retrieval change needs a number (recall@k, nDCG, MRR). NOT operating the store — index tuning, quantization (that is `vector-db`) — nor the retrieve-to-answer loop (that is `rag`).Altele★ 255
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