Diagnosis

---
name: diagnosis
description: "Use when Qdrant search results are irrelevant or incomplete and the agent must isolate embedding, HNSW, quantization, filtering, or pipeline causes."
version: "1.0.2"
source: "https://github.com/qdrant/skills"
attribution: "qdrant/skills by Qdrant"
---

> **Attribution:** Sourced from [qdrant/skills](https://github.com/qdrant/skills) by [Qdrant](https://qdrant.tech).

# How to Diagnose Bad Search Quality

Before tuning, establish baselines. Use exact KNN as ground truth, compare against approximate HNSW. Target >95% recall@K for production.

## Don't Know What's Wrong Yet

Use when: results are irrelevant or missing expected matches and you need to isolate the cause.

- Test with `exact=true` to bypass HNSW approximation [Search API](https://qdrant.tech/documentation/tutorials-search-engineering/retrieval-quality/#standard-mode-vs-exact-search)
- Exact search bad = model or search pipeline problem. Exact good, approximate bad = tune HNSW.
- Check if quantization degrades quality (compare with and without)
- Check if filters are too restrictive (then you might need to use ACORN)
- If duplicate results from chunked documents, use Grouping API to deduplicate [Grouping](https://search.qdrant.tech/md/documentation/search/search/?s=grouping-api)

Payload filtering and sparse vector search are different things. Metadata (dates, categories, tags) goes in payload for filtering. Text content goes in sparse vectors for search.

## Approximate Search Worse Than Exact

Use when: exact search returns good results but HNSW approximation misses them.

- Increase `hnsw_ef` at query time [Search params](https://qdrant.tech/documentation/operations/optimize/#fine-tuning-search-parameters)
- Increase `ef_construct` (200+ for high quality) [HNSW config](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=vector-index)
- Increase `m` (16 default, 32 for high recall) [HNSW config](https://search.qdrant.tech/md/documentation/manage-data/indexing/?s=vector-index)
- Enable oversampling + rescore with quantization [Search with quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/?s=searching-with-quantization)
- ACORN for filtered queries (v1.16+) [ACORN](https://search.qdrant.tech/md/documentation/search/search/?s=acorn-search-algorithm)

Binary quantization requires rescore. Without it, quality loss is severe. Use oversampling (3-5x minimum for binary) to recover recall. Always test quantization impact on your data before production. [Quantization](https://search.qdrant.tech/md/documentation/manage-data/quantization/)

## Wrong Embedding Model

Use when: exact search also returns bad results.

Test top 3 MTEB models on 100-1000 sample queries, measure recall@10. Domain-specific models often outperform general models. [Hosted inference](https://search.qdrant.tech/md/documentation/inference/)

## Unoptimized Search Pipeline

Use when: exact search also returns bad results and model choice is confirmed by user.

Optimize search according to advanced search-strategies skill.

## What NOT to Do

- Tune Qdrant before verifying the model is right for the task (most quality issues are model issues)
- Use binary quantization without rescore (severe quality loss)
- Set `hnsw_ef` lower than results requested (guaranteed bad recall)
- Skip payload indexes on filtered fields then blame quality (HNSW can't traverse filtered-out nodes, and filterable HNSW is built only if payload indexes were set up prior)
- Deploy without baseline recall or other search relevance metrics (no way to measure regressions)
- Confuse payload filtering with sparse vector search (different things, different config)