Hybrid Search Implementation

About

Provides standardized patterns and implementation templates for hybrid search, merging semantic understanding with keyword precision to create high-performance retrieval systems. It offers implementation guidance for various fusion methods like Reciprocal Rank Fusion (RRF) and linear weighting, alongside concrete code examples for PostgreSQL with pgvector and Elasticsearch. This skill is essential for developers building production-grade Retrieval-Augmented Generation (RAG) pipelines where semantic search alone may miss specific terminology, product codes, or exact matches.

Key Features

• Architectural patterns for combining semantic and keyword search
• 23,139 GitHub stars
• Custom hybrid RAG pipeline templates for multi-stage retrieval
• Multiple fusion methods including RRF, linear weighting, and cross-encoder reranking
• PostgreSQL templates using pgvector and full-text search (tsvector)
• Elasticsearch implementation for dense vector and BM25 retrieval

Use Cases

• Building RAG systems with improved recall and accuracy
• Improving search results for domain-specific technical documentation
• Handling queries with specific terms like names, codes, or technical identifiers