qdrant-search-quality

Diagnose Qdrant search quality issues across embedding models, configuration, and query strategies.

17|29|Updated Apr 10, 2026

Search Tags:#embeddings #search #retrieval #diagnosis #qdrant #hnsw #tuning

Ready to run

npx skills add https://github.com/lucifertrj/skills-based-app --skill qdrant-search-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: qdrant-search-quality
Source: https://github.com/lucifertrj/skills-based-app/tree/main/.agents/skills/qdrant-search-quality
Command: npx skills add https://github.com/lucifertrj/skills-based-app --skill qdrant-search-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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.

Core Features & Use Cases

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

Search Strategies

Hybrid search, reranking, relevance feedback, and exploration APIs for improving result quality. Search Strategies

Frequently Asked Questions about qdrant-search-quality

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

Why does Qdrant search return irrelevant matches and low precision?▼

Irrelevant matches often stem from embedding models or chunking strategies, not Qdrant itself. Inspect how chunks are passed to Qdrant, as splitting text mid-sentence can drop search quality by 30-40% before tuning parameters.

How do I diagnose bad search results in Qdrant?▼

Diagnose bad search results by first isolating the problem with exact search to test the baseline. Determine whether the issue originates from the embedding model, Qdrant configuration, or the query strategy across your deployment scenario.

Should I use hybrid search and reranking to improve retrieval relevance?▼

Hybrid search and reranking are effective strategies for improving retrieval relevance. They can be applied alongside relevance feedback and exploration APIs to boost result quality when basic embedding search yields wrong or missing results.

What causes Qdrant search quality to degrade after model changes or data growth?▼

Search quality degrades after quantization, model changes, or data growth due to shifts in embedding distributions or configuration mismatches. Diagnose whether the embedding model or the updated Qdrant configuration requires tuning to restore relevance.

How do I isolate Qdrant configuration issues from embedding model problems?▼

Isolate Qdrant configuration issues by testing with exact search first to establish a baseline. This helps determine whether poor relevance originates from the HNSW configuration or the underlying embedding model and query strategies.

Do I need to tune HNSW parameters when Qdrant search results are missing expected items?▼

Tuning HNSW parameters is secondary when missing expected items. First inspect how data chunks are passed to Qdrant and verify the embedding model, as most quality issues originate from data processing rather than Qdrant configuration.