qdrant-search-quality
Helps your agent improve the accuracy of Qdrant search results. It's useful when results aren't as expected or seem wrong.
What it does
This tool helps understand and improve the quality of search results in a system called Qdrant. It checks if the problem lies with the language model, system settings, or how queries are made.
When to use it
- ✓When search results seem wrong or irrelevant.
- ✓When you want to improve the accuracy or amount of useful results.
- ✓When search quality has worsened after a system change.
Example of what to ask your AI
“My search results are bad, what can I do?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It analyzes the language model, Qdrant settings, and search strategy. It starts by testing exact search to identify the root cause of issues.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill qdrant-search-qualityOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/qdrant-search-quality/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
FAQ
What are 'precision' and 'recall'?
Precision is the percentage of results that are actually useful. Recall is the percentage of all useful results that were found.
Where do quality problems usually occur?
Most of the time, problems come from the language model or the data, not from Qdrant itself.
Why is how text is split important?
Splitting text mid-sentence can significantly reduce search quality because the information becomes incomplete.
Details
Original description by the author
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 da
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5315
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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