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hybrid-search-implementation

Combine vector and keyword search results using RRF, linear combination, or cross-encoder reranking.

2|Updated Jan 18, 2026
Search Tags:#rag#search#retrieval#vector search#keyword search#fusion
One-click install
npx skills add https://github.com/as4584/antigravity-skills --skill hybrid-search-implementation-as4584
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hybrid-search-implementation
Source: https://github.com/as4584/antigravity-skills/tree/main/agents-wshobson/plugins/llm-application-dev/skills/hybrid-search-implementation
Command: npx skills add https://github.com/as4584/antigravity-skills --skill hybrid-search-implementation-as4584

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the limitations of relying on a single search method (either keyword or vector) by providing strategies to combine them for more comprehensive and accurate retrieval.

Core Features & Use Cases

  • Hybrid Search Architectures: Implement patterns combining vector and keyword search for improved recall.
  • Fusion Methods: Utilize techniques like Reciprocal Rank Fusion (RRF), Linear Combination, Cross-encoder reranking, and Cascade filtering.
  • Use Case: Building a RAG system where semantic understanding from vector search needs to be augmented by exact keyword matches for specific product codes or names.

Quick Start

Implement a hybrid search pipeline using the hybrid-search-implementation skill to combine vector and keyword search results for a given query.

Frequently Asked Questions about hybrid-search-implementation

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

FAQPage Schema
How do I combine vector and keyword search to improve RAG retrieval accuracy?▼

Combine vector and keyword search using fusion techniques like Reciprocal Rank Fusion (RRF) or linear combination to enhance retrieval accuracy and recall in RAG systems. This approach merges semantic understanding with exact keyword matching for comprehensive results.

What are the different fusion methods available for hybrid search?▼

Available fusion methods include Reciprocal Rank Fusion (RRF), linear combination, cross-encoder reranking, and cascade filtering. These techniques merge vector and keyword search results to enhance information retrieval accuracy and recall in search applications.

Can I implement hybrid search using PostgreSQL and Elasticsearch?▼

Yes, you can implement hybrid search using PostgreSQL and Elasticsearch. The Skill provides implementation templates for both platforms, combining full-text search engines with vector databases to execute vector and keyword search pipelines.

When should I use hybrid search instead of just vector search?▼

Use hybrid search when vector search alone misses exact keyword matches, such as specific product codes or names. Combining vector and keyword search augments semantic understanding with precise term retrieval to improve overall recall.

Do I need machine learning models to implement cross-encoder reranking for search?▼

Yes, implementing cross-encoder reranking requires integration with machine learning models to score and reorder combined vector and keyword search results, providing an advanced fusion method for enhanced retrieval accuracy.

What's the best way to build a hybrid search pipeline for a search engine?▼

Build a hybrid search pipeline by integrating vector databases with full-text search engines, then apply fusion methods like RRF or cross-encoder reranking. Provided templates for Python, PostgreSQL, and Elasticsearch offer structured implementation patterns.

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