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qdrant-search-quality

qdrant-search-quality

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?', 'how to combine keyword and vector search / fusion / RRF / pref…

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Qqdrant / skillsApache-2.0

Skill-Dokumentation

GitHub

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?', 'how to combine keyword and vector search / fusion / RRF / prefetch?', 'should I use reranking?', 'relevance feedback?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', 'how to score recall@k?', 'is my improvement real / statistically significant?', or 'should I fine-tune my embedding model?'. Also use when search quality degrades after quantization, model change, or data growth." allowed-tools:

  • Read
  • Grep
  • Glob

Qdrant Search Quality

Route first, then answer. Match the user's symptom in the table, Read that file, and answer from it. Do not answer from this page alone: it contains routing only, not the guidance. If two rows match, read both.

The user saysRead
Search results are bad or irrelevant, wrong results, missing expected matchesdiagnosis/SKILL.md
Low recall, expected results are missingdiagnosis/SKILL.md
Low precision, too many wrong matchesdiagnosis/SKILL.md
Which embedding model to use, quality dropped after quantization, model change, or data growthdiagnosis/SKILL.md
Not sure if the model, the data, or Qdrant is at faultdiagnosis/SKILL.md
Want to measure recall, build a golden set, ground truth dataset, recall@kdiagnosis/SKILL.md
Is my improvement real, statistically significant, fine-tune the embedding modeldiagnosis/SKILL.md
Need to combine keyword and semantic search, hybrid search, sparse + dense, fusion / RRF, prefetchsearch-strategies/hybrid-search/SKILL.md
Should I rerank, results too similar, need diversity, MMR, recommendation/discovery APIsearch-strategies/SKILL.md
Improving results with relevance feedback or user clicks, cheaper alternative to rerankingsearch-strategies/relevance-feedback/SKILL.md

Most quality issues come from the embedding model or the data, not from Qdrant's configuration — splitting chunks mid-sentence alone can drop quality 30-40%. Rule that out with exact search before tuning any Qdrant parameter: Search API

Gespeicherte Skills