Hybrid search

Skill qdrant/skills/skills/qdrant-search-quality/search-strategies/hybrid-search

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?'From its SKILL.md

Install

npx -y skills add qdrant/skills --skill hybrid-search

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SKILL.md

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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 (RRF, DBSF, FormulaQuery, reranking) in Combining Searches

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

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.
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.
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, the idf search param can scope IDF statistics to a payload-filtered corpus (requires a payload index on the filtered field), giving each tenant properly isolated BM25 scoring instead of shard-wide 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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most rag retrieval skills give

Counted across 199 of the 213 authors here whose files we hold, read 2026-09-06

Enable caching for frequent queriesin 14 of 199, across 5 files
Enable MMR for diverse resultsin 12 of 199, across 5 files
Enable binary quantization to reduce memoryin 11 of 199, across 4 files
Initialize the database with dimensions matching the embedding modelin 11 of 199, across 4 files
Start the similarity threshold at 0.7in 11 of 199, across 4 files
Check database statistics when diagnosing slow searchin 11 of 199, across 4 files
Export and import vectors as JSONin 10 of 199, across 3 files
Match index dimension to the embedding modelin 10 of 199, across 9 files
Order filters cheap before expensivein 9 of 199, across 2 files
Generate a runnable scaffold in the user's stackin 9 of 199, across 2 files
Recommend multi-action scoring when frequent tuning is expectedin 9 of 199, across 2 files
Batch store documents for bulk insertsin 9 of 199, across 2 files

Said here and by no other author read

Identify the user's problem and pick search building blocks
Run one search type per prefetch
Combine prefetch results in the outer query
Configure the collection with named vectors, one per representation
Evaluate hybrid search quality on real user data
Explain speed and resource tradeoffs to the user

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.