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

Ciencia de Datos y ML

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

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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.

Características Principales

Cross-encoder reranking patterns for high-precision retrieval
PostgreSQL pgvector and full-text search (FTS) integration
Reciprocal Rank Fusion (RRF) implementation for multi-source ranking
0 GitHub stars
Score normalization and linear weighting techniques
Elasticsearch kNN and BM25 hybrid query templates

Casos de Uso

Improving search accuracy for domain-specific documentation or catalogs
Implementing multi-stage retrieval pipelines with neural rerankers
Building RAG systems that require exact matching for technical terms

Acerca de

Características Principales

Cross-encoder reranking patterns for high-precision retrieval
PostgreSQL pgvector and full-text search (FTS) integration
Reciprocal Rank Fusion (RRF) implementation for multi-source ranking
0 GitHub stars
Score normalization and linear weighting techniques
Elasticsearch kNN and BM25 hybrid query templates

Casos de Uso

Improving search accuracy for domain-specific documentation or catalogs
Implementing multi-stage retrieval pipelines with neural rerankers
Building RAG systems that require exact matching for technical terms