Qdrant Hybrid Search

Organization

qdrant

qdrant-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?'. Also 'BM25 on a text index or sparse vectors?'

Overview

Publisher	qdrant
Repository	`skills`
Skill name	`qdrant-hybrid-search`
Stars	262
Forks	31
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by qdrant on GitHub. Read the source before you install it.

Installation

Install the Qdrant Hybrid Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/qdrant/skills/tree/main/skills/qdrant-search-quality/search-strategies/hybrid-search
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/qdrant/skills/tree/main/skills/qdrant-search-quality/search-strategies/hybrid-search

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/qdrant/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/qdrant-search-quality/search-strategies/hybrid-search .claude/skills/qdrant-hybrid-search

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qdrant Hybrid Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qdrant Hybrid Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qdrant Hybrid Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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. 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.

Frequently asked questions

What does the Qdrant Hybrid Search AI skill do?

Why use Qdrant Hybrid Search on TypingMind?

Because you install it once and use it with any model. Qdrant Hybrid Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qdrant Hybrid Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/qdrant/skills/tree/main/skills/qdrant-search-quality/search-strategies/hybrid-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qdrant Hybrid Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qdrant Hybrid Search?

As many as you like. As long as a model supports skills, you can use Qdrant Hybrid Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qdrant Hybrid Search AI skill free?

Yes. It is published on GitHub by qdrant under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Hybrid Search in Qdrant

How Isolated Are Parallel Searches?

What NOT to Do

Frequently asked questions

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