HybridRAG

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.

Best practices for combining vector and keyword search using RRF and weighted fusion for improved retrieval accuracy in RAG systems.

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.

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.

Retrieval-Augmented Generation extended to handle multiple modalities including text, images, video, and audio. Uses multimodal embeddings like Gemini Embedding 2 or CLIP to enable cross-modal search and generation.

LangChain's hierarchical text chunking strategy achieving 85-90% accuracy by recursively splitting using progressively finer separators to preserve semantic boundaries.