Reciprocal Rank Fusion (RRF)

Method for combining ranked lists from multiple retrieval systems in hybrid search. Standard technique in RAG pipelines for fusing BM25 and dense vector results before reranking, creating diverse high-confidence candidate sets.

Search technique combining BM25 lexical search and semantic vector search using Reciprocal Rank Fusion (RRF) to merge results, balancing precision of keyword matching with contextual understanding of neural embeddings.

Advanced retrieval approach combining dense vectors, sparse vectors, and reranking in a multi-stage pipeline, achieving up to 48% better performance than single-method retrieval.

Advanced algorithm designed to make hybrid searches combining metadata filters and vector similarity more efficient, implemented in Apache Solr and other vector search systems.

Next evolution in RAG systems that combines vector databases for semantic similarity with graph databases for relationship exploration and multi-hop reasoning.

A search approach combining traditional keyword-based BM25 ranking with modern vector similarity search. By leveraging both lexical matching and semantic understanding, hybrid search provides superior retrieval quality through techniques like reciprocal rank fusion (RRF) to merge results from both methods.