Combine vector and keyword search for improved retrieval in RAG systems and search engines.

• 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