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qdrant-hybrid-search

Combine keyword and semantic searches in Qdrant with configurable fusion methods.

220|26|Updated Jan 29, 2026
Search Tags:#vector search#hybrid search#Qdrant#search optimization#query API
One-click install
npx skills add https://github.com/qdrant/skills --skill qdrant-hybrid-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qdrant-hybrid-search
Source: https://github.com/qdrant/skills/tree/main/skills/qdrant-search-quality/search-strategies/hybrid-search
Command: npx skills add https://github.com/qdrant/skills --skill qdrant-hybrid-search

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of search precision and performance by combining keyword and semantic search capabilities within Qdrant, offering a comprehensive solution for complex query requirements.

Core Features & Use Cases

  • Hybrid Search Implementation: Integrates both keyword and semantic search within Qdrant to enhance the precision of search results.
  • Configurable Prefetches: Allows for running multiple parallel searches to refine results and improve the search experience.
  • Fusion Methods: Employs various fusion methods such as RRF and DBSF to blend scores from multiple searches effectively.
  • Use Case: Ideal for scenarios where a combination of exact keyword matching and nuanced semantic understanding is needed, such as when a user needs to search for both the term 'Paris' and the semantic concept of a city.

Quick Start

Apply the qdrant-hybrid-search skill to a Qdrant collection for enhanced search precision, using a query like "Show me information on Paris that includes both the city and the Eiffel Tower."

Frequently Asked Questions about qdrant-hybrid-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I combine keyword and semantic search in Qdrant for better accuracy?▼

Hybrid search in Qdrant combines keyword and semantic search capabilities to deliver precise, blended results. It integrates exact term matching with nuanced semantic understanding to handle complex query scenarios effectively.

What fusion methods are available for blending scores from parallel searches?▼

Score fusion methods like RRF and DBSF blend scores from multiple parallel searches in Qdrant. These configurable prefetches refine results by effectively merging different search types into a single ranked output.

When do I need hybrid search instead of just vector similarity search?▼

You need hybrid search when a query requires both exact keyword matching and semantic understanding, such as searching for the specific term 'Paris' alongside the general concept of a city.

Can I run multiple parallel searches in Qdrant to refine my results?▼

Yes, Qdrant supports configurable prefetches that allow running multiple parallel searches. This capability refines results and improves the search experience by blending scores from these distinct search operations.

How do I implement a query for both exact terms and semantic concepts using the query API?▼

Using the Qdrant query API, you can implement hybrid search by configuring parallel prefetches for exact terms and semantic concepts, then applying fusion methods to blend the scores for precise results.

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