Can I implement this using PostgreSQL?

Yes, this skill includes specific templates for using the pgvector extension alongside PostgreSQL's native full-text search (tsvector) capabilities.

What is the benefit of hybrid search over pure vector search?

Hybrid search combines semantic understanding with exact keyword matching, ensuring that technical terms, names, and codes that vector models might miss are still retrieved accurately.

How does Reciprocal Rank Fusion (RRF) work?

RRF is an algorithm that combines multiple ranked result lists by summing the reciprocal of the ranks of each document, providing a robust way to merge disparate search methods without score normalization.

When should I use a cross-encoder reranker?

A cross-encoder should be used as a final step to rerank a small number of top candidates (e.g., top 50) when the highest possible retrieval precision is required for a RAG system.

Hybrid Search Implementation for RAG

Name: Hybrid Search Implementation for RAG
Author: GaitanS

byGaitanS

Data Science & ML

Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in AI-driven applications.

About

This skill provides production-ready implementation patterns for hybrid search, effectively bridging the gap between semantic understanding and exact keyword matching. It offers comprehensive templates for Reciprocal Rank Fusion (RRF), linear scoring, and database-specific implementations using PostgreSQL with pgvector and Elasticsearch. By utilizing this skill, developers can build RAG systems that handle domain-specific vocabulary and technical identifiers with high precision, ensuring that critical information is never missed by the retrieval pipeline.

Key Features

Reciprocal Rank Fusion (RRF) and linear scoring templates
Elasticsearch dense vector and BM25 search patterns
PostgreSQL hybrid search with pgvector and full-text search integration
Standardized score normalization and fusion logic
Cross-encoder reranking workflows for high-quality candidates
0 GitHub stars

Use Cases

Improving retrieval for queries containing specific product codes or technical identifiers
Enhancing search engines that require both semantic and exact keyword matching
Building robust RAG systems for complex enterprise documentation

About

Key Features

Reciprocal Rank Fusion (RRF) and linear scoring templates
Elasticsearch dense vector and BM25 search patterns
PostgreSQL hybrid search with pgvector and full-text search integration
Standardized score normalization and fusion logic
Cross-encoder reranking workflows for high-quality candidates
0 GitHub stars

Use Cases

Improving retrieval for queries containing specific product codes or technical identifiers
Enhancing search engines that require both semantic and exact keyword matching
Building robust RAG systems for complex enterprise documentation