Hybrid Search Techniques

Comprehensive guide to combining BM25 keyword search with vector semantic search using reciprocal rank fusion and reranking. Essential pattern for production RAG systems in 2026.

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.

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

Advanced document chunking approaches that combine multiple chunking methods (fixed-size, semantic, structural) to optimize retrieval in RAG systems. Hybrid strategies adapt to document characteristics for superior performance.

An advanced RAG architecture where an AI agent autonomously decides which questions to ask, which tools to use, when to retrieve information, and how to aggregate results. Represents a major trend in 2026 for more intelligent and adaptive retrieval systems.

A search architecture that combines dense vector embeddings (semantic search) with sparse representations like BM25 (lexical search) to achieve better overall search quality. The industry standard approach for production RAG systems in 2026.