Qdrant Search Strategies

QdrantOfficialPublished by Qdrant

Skill
Guidelines
Data and AI
Apache-2.0
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From the author

Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', or 'missing keyword matches'

— the description by Qdrant

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured framework for diagnosing and resolving common search quality issues in Qdrant. It helps users identify specific symptoms—such as missing keyword matches, poor precision, redundant results, or the need for business-logic ranking—and maps them to appropriate technical strategies like hybrid search, reranking, MMR, or relevance feedback. The guide emphasizes a methodical approach, advising users to verify their base embedding models and HNSW configurations before implementing more complex search enhancements.

What it does

Maps common search quality symptoms to specific Qdrant search strategies.
Provides guidance on when to use hybrid search for keyword and domain term matching.
Explains how to improve precision using multistage queries and reranking techniques.
Details the use of MMR to balance search relevance with result diversity.
Outlines how to steer search results using Recommendation and Discovery APIs.
Offers best practices for applying business logic to search results via score boosting.

When to use it

When pure vector search fails to return relevant documents or misses exact keyword matches.
When search results are redundant or lack sufficient diversity.
When you need to incorporate positive and negative examples to refine search results.
When you need to apply business-specific ranking logic, such as recency or distance.
When you have good recall but poor precision in your top-ranked results.

Advantages

Helps isolate and treat distinct search symptoms individually rather than applying blanket fixes.
Provides a clear decision matrix for selecting the right search strategy.
Encourages performance-aware implementation by highlighting the trade-offs of strategies like MMR.
Promotes rigorous evaluation to ensure that added complexity actually improves pipeline output.

Usage examples

“My search results are too similar to each other, how can I get more variety?”: The agent will recommend using the MMR (Maximal Marginal Relevance) parameter to balance relevance and diversity, suggesting a starting diversity value of 0.5.
“I am missing exact keyword matches in my vector search, what should I do?”: The agent will advise implementing hybrid search to combine vector-based retrieval with keyword-based search signals.
“My relevant documents are being found but they are ranked too low in the results.”: The agent will suggest using multistage queries or reranking techniques to improve precision for the top-ranked items.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/qdrant/qdrant-search-strategies/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip qdrant-search-strategies.zip -d .agents/skills

Documentation by Qdrant

How to Improve Search Results with Advanced Strategies

These strategies complement basic vector search. Use them after confirming the embedding model is fitting the task and HNSW config is correct. If exact search returns bad results, verify the selection of the embedding model (retriever) first. If the user wants to use a weaker embedding model because it is small, fast, and cheap, use reranking or relevance feedback to improve search quality.

Each symptom may require a different strategy: diagnose and address them independently. A single project can have multiple symptoms at once, and fixing one (e.g. adding hybrid search for keyword misses) may not fix the others (e.g. redundant results may still need MMR; poor precision may still need reranking).

Symptom	Strategy
Missing exact/keyword matches	Hybrid search
Right documents exist but rank low (good recall, poor precision)	Multistage queries / reranking
Dense retriever misses relevant items entirely, or reranking too costly	Relevance feedback
Results are redundant / near-duplicate	MMR
Need to steer with example points	Recommendation / Discovery API
Need business-logic-based ranking	Score boosting

Missing Keyword Matches or Need to Combine Multiple Search Signals

Use when: pure vector search misses keyword/domain term matches, or the use case benefits from combining searches on multiple representations (including languages and modalities) of the same item.

See how to use hybrid search

Right Documents Found But Not in the Top Results

Use when: good recall but poor precision (right docs in top-100, not top-10).

See how to use Multistage queries, for example with late interaction rerankers through Multivectors.
Cross-encoder rerankers via FastEmbed Rerankers
Before adding a ranking stage: score your candidate pool as if perfectly ordered and compare it to what you return today. A small gap means ranking is near its ceiling, so fix retrieval or candidate depth instead.
See how to choose, test and size a reranker, and how to tell a real gain from a false one

Dense Retriever Misses Relevant Items or Reranking Is Too Costly

Use when: dense retriever misses relevant items you know exist in the collection; relevant documents lie outside the initial ANN retrieval pool; reranking a large candidate pool is too slow or expensive; using a small/cheap embedding model but need quality close to a larger model; or want to improve top-1/3 precision without the full cost of reranking.

See Relevance Feedback in Qdrant

Results Too Similar

Use when: top results are redundant, near-duplicates, or lack diversity. Common in dense content domains (academic papers, product catalogs).

Use MMR (v1.15+) as a query parameter with diversity to balance relevance and diversity MMR
Start with diversity=0.5, lower for more precision, higher for more exploration
MMR is slower than standard search. Only use when redundancy is an actual problem.

Want to improve search results based on examples (positive and negative)

Use when: you can provide positive and negative example points to steer search closer to positive and further from negative.

Recommendation API: positive/negative examples to recommend fitting vectors Recommendation API
- Best score strategy: better for diverse examples, supports negative-only Best score
Discovery API: context pairs (positive/negative) constrain the search region, either around a target (discovery search) or without one (context search) Discovery

Have Business Logic Behind Results Relevance

Use when: results should be additionally ranked according to some business logic based on data, like recency or distance.

Check how to set up in Score Boosting docs

What NOT to Do

Use hybrid search before verifying pure vector search quality (adds complexity, may mask model issues)
Apply one strategy (e.g. hybrid search) as a blanket fix for multiple distinct symptoms, diagnose and treat each symptom separately (see table above)
Skip evaluation when adding relevance feedback: score the end-to-end pipeline to confirm it actually helps Pipeline Output Quality

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools

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Attribution and licence

Published by

Qdrant

Publisher

Qdrant (Official)

Source

qdrant/skills · skills/qdrant-search-quality/search-strategies · commit 9f55f6a

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

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2026-11-09

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