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skill GitHub Copilot official · GitHub

qdrant-search-quality — GitHub Copilot skill for Qdrant search

Diagnoses why Qdrant returns irrelevant matches and points to ways to improve search quality. Useful after an embedding model change or as your data grows.

Install into GitHub Copilot · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add github/awesome-copilot --skill qdrant-search-quality
Open on GitHub

What qdrant-search-quality does

The skill helps distinguish issues with the embedding model or data from Qdrant configuration and query strategy. It starts with exact search as a way to isolate the problem, then checks how text chunks are passed to Qdrant. Splitting text mid-sentence can reduce search quality by 30-40%.

Its guidance covers HNSW tuning, embedding model selection, hybrid search, reranking, and relevance feedback. The available tools are Read, Grep, and Glob. Install it when you need to troubleshoot Qdrant results; it is not aimed at search systems outside Qdrant.

What it helps with

  • trace irrelevant Qdrant results to their source
  • check whether text chunking hurts search quality
  • choose between hybrid search and reranking
  • tune HNSW settings and embedding models

Example prompts for the agent

  • Find out why my Qdrant search returns irrelevant results.
  • Compare exact search with the current strategy and check how text is chunked.
  • Should I add hybrid search or reranking to this Qdrant setup?

How to install qdrant-search-quality

1Skills CLI (Vercel)

npx skills add github/awesome-copilot --skill qdrant-search-quality

2GitHub CLI

gh skill install github/awesome-copilot qdrant-search-quality

3Manually: put the files in place

.github/skills/qdrant-search-quality/SKILL.md

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Questions about qdrant-search-quality

How do I install qdrant-search-quality for GitHub Copilot?

Run npx skills add github/awesome-copilot --skill qdrant-search-quality or gh skill install github/awesome-copilot qdrant-search-quality.

What does qdrant-search-quality do?

It helps diagnose Qdrant search relevance by examining the embedding model, data, configuration, and query strategy, then suggests areas to tune.

How can I tell whether Qdrant configuration is causing poor results?

The skill recommends starting with exact search to separate configuration issues from problems with the model, data, or query strategy.

Source text

SKILL.md · MIT 
---
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](https://search.qdrant.tech/md/documentation/search/search/?s=search-api)


## Diagnosis and Tuning

Isolate the source of quality issues, tune HNSW parameters, and choose the right embedding model. [Diagnosis and Tuning](diagnosis/SKILL.md)


## Search Strategies

Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. [Search Strategies](search-strategies/SKILL.md)

© github · MIT · github/awesome-copilot

See also

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