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Qdrant Hybrid Search

QdrantOfficialPublished by Qdrant

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
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From the author

Explains hybrid search in Qdrant. Use when someone asks 'how do I setup hybrid search?', 'how to combine keyword and semantic search?', 'sparse plus dense vectors?', 'missing keyword matches', 'how to combine results from multiple searches?' and 'combining multiple representations'. Also use for how a hybrid query is scoped: 'how is IDF scoped?', 'can one tenant's data contaminate another tenant's scoring?'. Also 'BM25 on a text index or sparse vectors?'
— the description by Qdrant

AutoGineer summary

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

This skill provides a framework for implementing hybrid search in Qdrant by leveraging the Query API and prefetch operations. It explains how to combine multiple search representations, such as dense and sparse vectors, and offers best practices for tuning fusion methods like RRF. Additionally, it addresses technical considerations for multi-tenant environments, specifically regarding the scoping of IDF statistics and scoring isolation.

What it does

  • Explains the use of the Query API and prefetch operations for parallel search execution.
  • Outlines the process for combining multiple search representations into a single result set.
  • Provides instructions for configuring IDF modifiers to ensure tenant-specific scoring statistics.
  • Offers guidance on selecting appropriate fusion methods for hybrid search results.
  • Lists common pitfalls to avoid when configuring collections and prefetch limits.

When to use it

  • When you need to combine keyword and semantic search results.
  • When implementing hybrid search patterns involving sparse and dense vectors.
  • When you need to ensure search scoring isolation between different tenants in a shared collection.
  • When troubleshooting result quality issues related to prefetch limits or scoring statistics.

Advantages

  • Provides a structured approach to building complex search pipelines.
  • Clarifies how to manage scoring statistics in multi-tenant Qdrant deployments.
  • Helps prevent common configuration errors that degrade search results.
  • Offers clear guidance on the relationship between prefetch limits and fusion quality.

Usage examples

“How do I set up hybrid search in Qdrant?”
The agent explains the use of the Query API with multiple prefetches and suggests using RRF fusion for combining dense and sparse vector results.
“Can one tenant's data contaminate another tenant's scoring in hybrid search?”
The agent explains how to use IDF corpus filters to ensure that scoring statistics are calculated per tenant rather than per shard.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/qdrant/qdrant-hybrid-search/download
  2. Unzip it into the cross-client skills folder of your project:

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

Documentation by Qdrant

Hybrid Search in Qdrant

Hybrid search means running two or more different searches in parallel and combining their results into one.

In Qdrant this is powered by the Query API via prefetch: each prefetch runs exactly one type of search independently, and the outer query combines results from parallel prefetches.
Prefetches can be nested and searches can be multi-stage, all pipeline happening in one request through Query API. See Universal Query API for examples.

Identify the user's problem and pick building blocks:

  • What can go into one prefetch, e.g. power one search, in Search Types

  • How to combine results of these searches and tune the fusion (RRF, DBSF, FormulaQuery, reranking) in Combining Searches

The most common hybrid search pattern is dense + sparse → RRF fusion. For this: create a collection with two named vectors (one dense, one sparse), construct a Query API request with two prefetches (one per vector type using using), and fuse with RRF as the outer query. See Hybrid Queries documentation.

Based on what you've picked, test your approach:

  1. Configure Qdrant collection with named vectors, where each named vector usually corresponds to one representation (different embedding models or different vector types) of a data point.

  2. Construct a hybrid search request with Query API from your building blocks. You can search independently among one type of vectors, with prefetch + using, like shown in examples in Hybrid Queries documentation.

  3. Size prefetch limit generously -- each prefetch must return enough candidates for the outer fusion to work. A prefetch limit of 20-100 is typical when the final limit is 10. Undersized prefetch limits silently degrade result quality.

  4. Evaluate hybrid search quality on real user data and provide user with improvements and tradeoffs (speed/resources).

How Isolated Are Parallel Searches?

Use when: different tenants share one collection and you need to understand hybrid search isolation guarantees.

If user wants to isolate/share hybrid search pipelines between tenants, consider that:

  • Indexes (sparse, payload and dense) and IDF modifier for sparse vectors are computed independently per shard, not per tenant, by default. Payload-based tenant partitioning alone does not isolate IDF statistics. On Qdrant 1.19 or newer, set params.idf.corpus to a payload filter (e.g. group_id = tenant) so IDF is computed over that tenant's documents only. The corpus filter is separate from the retrieval filter and is usually broader. Index the corpus field. If the corpus filter matches nothing, ranking degrades to plain TF. On 1.20 or newer it applies to BM25 text queries on a full-text index too, where it scopes the average document length as well Per-tenant scoring statistics.

  • Prefetch runs independently per shard to retrieve #limit results, so for collection-level prefetches if collection has several shards, Qdrant will always prefetch under the hood #limit * #shard results. Final results are merged based on scores.

  • In nested prefetches (deeper than 1 level), methods described in "Combining Searches" might be done on a shard level first, then per-shards results once again will be merged based on scores.

What NOT to Do

  • Choose a hybrid search pattern based on "vibes" without any hybrid search quality evaluation in-place.

  • Create too many named vectors without a need. An unfilled named vector might take as much resources as a filled one.

  • Set prefetch limit too low (e.g. equal to outer limit). The fusion step needs a surplus of candidates to produce good results.

  • Apply score_threshold on an outer fusion query expecting it to filter by individual prefetch scores -- it filters by the fused score only.

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

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Attribution and licence

Published by
Qdrant
Publisher
Qdrant (Official)
Source
qdrant/skills · skills/qdrant-search-quality/search-strategies/hybrid-search · commit 9f55f6a
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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