qdrant-search-quality

指导Qdrant搜索策略选择。适用于有人问“要不要用混合搜索？”、“BM25还是稀疏向量？”、“如何重排序？”、“结果不相关”、“我从数据集中得不到所需结果但它们确实存在”、“检索质量不够好”、“结果太相似”、“需要多样性”、“MMR”、“相关性反馈”、“推荐API”、“发现API”、“ColBERT重排序”或“遗漏关键词匹配”时使用。

SKILL.md

--- frontmatter

name: qdrant-search-quality
description: "Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?'. Also use when search quality degrades after quantization, model change, or data growth."
allowed-tools:
  - Read
  - Grep
  - Glob

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, 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