Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in RAG systems.
The hybrid-search-implementation skill provides standardized patterns for merging semantic vector search with traditional lexical matching. It is specifically designed for developers building Retrieval-Augmented Generation (RAG) systems or search engines where neither method alone provides sufficient recall. By utilizing fusion techniques like Reciprocal Rank Fusion (RRF) and linear weighting, this skill ensures Claude can implement robust search architectures that handle both conceptual queries and specific technical terminology, including specialized templates for PostgreSQL and Elasticsearch.
Key Features
01Elasticsearch hybrid query configurations with dense vector support
02Cross-encoder re-ranking patterns for high-precision result filtering
03Normalization strategies for combining disparate search scoring systems
0423,194 GitHub stars
05Reciprocal Rank Fusion (RRF) and linear combination scoring templates
06PostgreSQL integration utilizing pgvector and full-text search indexes
Use Cases
01Building production-grade RAG systems that require high recall for technical terms
02Implementing efficient 'search-then-rerank' pipelines for large-scale datasets
03Improving search relevance for domain-specific vocabulary and exact-match codes
