qdrant-search-quality
关于
This skill helps developers diagnose and improve search relevance in Qdrant vector databases. It addresses issues like poor results, low precision/recall, and guides on embedding models, hybrid search, and reranking. Use it when search quality degrades or when needing to measure retrieval performance with techniques like building ground truth datasets.
快速安装
Claude Code
推荐npx skills add qdrant/skills -a claude-code/plugin add https://github.com/qdrant/skillsgit clone https://github.com/qdrant/skills.git ~/.claude/skills/qdrant-search-quality在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Qdrant Search Quality
First determine whether the problem is the embedding model, Qdrant configuration, or the query strategy. Most quality issues come from the model or data, not from Qdrant itself. If search quality is low, inspect how chunks are being passed to Qdrant before tuning any parameters. Splitting mid-sentence can drop quality 30-40%.
- Start by testing with exact search to isolate the problem Search API
Diagnosis and Tuning
Isolate the source of quality issues, establish labeled baselines to measure recall and relevance, tune HNSW parameters, and choose the right embedding model. Diagnosis and Tuning
Search Strategies
Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies
GitHub 仓库
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