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qdrant-search-quality · qdrant — Claude Code skill: fix Qdrant search relevance

A skill that walks through why Qdrant search returns junk and points at the real culprit: the embedding model, the data, or the config.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add qdrant/skills --skill qdrant-search-quality -a claude-code
Open on GitHub

What qdrant-search-quality does

The skill routes before it answers. You describe the symptom — bad results, low recall, quality dropping after quantization — and the agent opens the matching file and works from there. Three branches sit inside: diagnosis/SKILL.md for diagnosis, search-strategies/hybrid-search/SKILL.md for hybrid search, and search-strategies/relevance-feedback/SKILL.md for relevance feedback. A general search-strategies/SKILL.md covers reranking, MMR and diversity.

Its tool list is short: Read, Grep, Glob. It runs nothing and writes nothing to your database, so calling it on a live codebase is safe.

The point it keeps making: the embedding model or the chunking is usually at fault, not Qdrant's parameters. Splitting chunks mid-sentence alone can cost 30-40% of quality. Rule that out with exact search before touching any setting.

What it helps with

  • find out why search returns irrelevant matches
  • tell an embedding model problem from a data problem
  • build a golden set and score recall@k
  • decide whether hybrid search with fusion and prefetch is worth it
  • check whether an improvement is statistically real

Example prompts for the agent

  • search results in Qdrant went bad after I switched embedding models — dig in
  • help me build a golden set and measure recall@k
  • should I turn on hybrid search with RRF?

How to install qdrant-search-quality

1Skills CLI (Vercel)

npx skills add qdrant/skills --skill qdrant-search-quality -a claude-code

2Manually: put the files in place

~/.claude/skills/qdrant-search-quality/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/qdrant-search-quality/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about qdrant-search-quality

How do I install qdrant-search-quality in Claude?

Run npx skills add qdrant/skills --skill qdrant-search-quality -a claude-code. The skill lands in ~/.claude/skills/qdrant-search-quality/SKILL.md.

What does qdrant-search-quality do?

It matches your symptom to a file and answers from that file. Topics include relevance diagnosis, hybrid search, reranking and relevance feedback.

Does the skill change my Qdrant settings?

No. It is limited to Read, Grep and Glob, so it reads project files and gives recommendations.

Source text

SKILL.md · Apache-2.0
---
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 says | Read |
|---|---|
| Search results are bad or irrelevant, wrong results, missing expected matches | `diagnosis/SKILL.md` |
| Low recall, expected results are missing | `diagnosis/SKILL.md` |
| Low precision, too many wrong matches | `diagnosis/SKILL.md` |
| Which embedding model to use, quality dropped after quantization, model change, or data growth | `diagnosis/SKILL.md` |
| Not sure if the model, the data, or Qdrant is at fault | `diagnosis/SKILL.md` |
| Want to measure recall, build a golden set, ground truth dataset, recall@k | `diagnosis/SKILL.md` |
| Is my improvement real, statistically significant, fine-tune the embedding model | `diagnosis/SKILL.md` |
| Need to combine keyword and semantic search, hybrid search, sparse + dense, fusion / RRF, prefetch | `search-strategies/hybrid-search/SKILL.md` |
| Should I rerank, results too similar, need diversity, MMR, recommendation/discovery API | `search-strategies/SKILL.md` |
| Improving results with relevance feedback or user clicks, cheaper alternative to reranking | `search-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](https://skills.qdrant.tech/md/documentation/search/search/?s=search-api)

© qdrant · Apache-2.0 · qdrant/skills

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