Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to use?', or 'BM25 vs SPLADE?'
安装
npx skills add qdrant/skills --skill qdrant-hybrid-search-prefetchesnpx skills add qdrant/skills --skill qdrant-hybrid-search-prefetches -a claude-code -gnpx skills add qdrant/skills --skill qdrant-hybrid-search-prefetches -a codex -ggemini skills install https://github.com/qdrant/skills.git --path skills/qdrant-search-quality/search-strategies/hybrid-search/search-typesgh skill install qdrant/skills qdrant-hybrid-search-prefetchesgit clone --depth 1 https://github.com/qdrant/skills /tmp/qdrant__skills
mkdir -p ~/.claude/skills ~/.agents/skills
cp -R /tmp/qdrant__skills/skills/qdrant-search-quality/search-strategies/hybrid-search/search-types ~/.claude/skills/qdrant-hybrid-search-prefetches # Claude Code
cp -R /tmp/qdrant__skills/skills/qdrant-search-quality/search-strategies/hybrid-search/search-types ~/.agents/skills/qdrant-hybrid-search-prefetches # Codex, Cursor, Gemini CLI, Copilot, OpenCode