hybrid-search-implementation

Combine vector and keyword search for improved retrieval in RAG systems and search engines. Patterns for combining vector similarity and keyword-based search. - Provides four fusion methods: Reciprocal Rank Fusion (RRF) for general use, linear combination for tunable balance, cross-encoder reranking for highest quality, and cascade filtering for efficiency - Includes production-ready templates for PostgreSQL with pgvector, Elasticsearch with dense vectors, and custom Python pipelines with parallel search execution - Handles score normalization, metadata filtering, and result deduplication across multiple search backends - Supports reranking with cross-encoders and offers practical guidance on tuning weights empirically rather than assuming fixed configurations - Building RAG systems with improved recall - Combining semantic understanding with exact matching - Handling queries with specific terms (names, codes) - Improving search for domain-specific vocabulary - When pure vector search misses keyword matches - Tune weights empirically - Test on your data - Use RRF for simplicity - Works well without tuning - Add reranking - Significant quality improvement - Log both scores - Helps with debugging - A/B test - Measure real user impact - Don't assume one size fits all - Different queries need different weights - Don't skip keyword search - Handles exact matches better - Don't...