What is hybrid search in RAG?

Hybrid search combines semantic vector search (understanding meaning) with traditional keyword search (matching exact words) to provide the most relevant results for both conceptual queries and specific term matches.

Why would I use a cross-encoder?

A cross-encoder acts as a high-quality reranker. While slower than initial retrieval, it processes a small number of candidates to significantly improve precision by analyzing the query and document text simultaneously.

Does this skill work with PostgreSQL?

Yes, it provides specific implementation templates for PostgreSQL using the pgvector extension combined with built-in full-text search capabilities.

When should I use Reciprocal Rank Fusion (RRF)?

RRF is ideal when you need to combine rankings from different search methods (like BM25 and Vector) that use different scoring scales, as it relies on the position (rank) rather than the raw score.

Hybrid Search Implementation

Name: Hybrid Search Implementation
Author: as4584

byas4584

0•

数据科学与机器学习

Implements optimized hybrid search patterns combining vector similarity and keyword matching to enhance RAG system recall.

This skill provides standardized patterns for building sophisticated search systems that bridge the gap between semantic understanding and exact keyword matching. It includes implementation templates for Reciprocal Rank Fusion (RRF), linear score combination, and cross-encoder reranking, with specific examples for popular data stores like PostgreSQL/pgvector and Elasticsearch. It is particularly useful for developers building RAG applications that require high precision for domain-specific terminology, technical identifiers, or product codes that pure vector search often misses.

主要功能

01Cross-encoder reranking patterns for high-precision retrieval

02PostgreSQL pgvector and full-text search (FTS) integration

03Reciprocal Rank Fusion (RRF) implementation for multi-source ranking

040 GitHub stars

05Score normalization and linear weighting techniques

06Elasticsearch kNN and BM25 hybrid query templates

使用场景

01Improving search accuracy for domain-specific documentation or catalogs

02Implementing multi-stage retrieval pipelines with neural rerankers

03Building RAG systems that require exact matching for technical terms

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add as4584/antigravity-skills hybrid-search-implementation

For use in Claude.ai and ChatGPT

Download Skill