# Qdrant Search Quality

> Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

- Skill: `github/qdrant-search-quality` (Agent Skill)
- Install (CLI): `npx skillmds@latest add github/qdrant-search-quality`
- Raw SKILL.md: https://api.skillmd.com/api/skills/github/qdrant-search-quality/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML, Data & Analytics, Marketing & Growth, RAG & Embeddings
- Tags: Embedding Model, Hnsw, Hybrid Search, Qdrant, Reranking, Search Relevance
- Author: GitHub (Microsoft) (https://skillmd.com/u/github), verified publisher
- Updated: 2026-07-06
- Page: https://skillmd.com/skills/github/qdrant-search-quality

---


# 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)