Contextual Retrieval

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.

A two-stage retrieval process where initial candidates from vector search are reordered using more sophisticated models like cross-encoders. Reranking significantly improves result quality by applying computationally expensive models to a small set of candidates, commonly used in RAG systems and search applications.

A RAG enhancement technique from Anthropic that adds chunk-specific explanatory context to each document chunk before embedding. Contextual Retrieval reduces retrieval failure rates by 49% and improves accuracy by 67% compared to traditional RAG methods.

Techniques to improve retrieval by expanding user queries with synonyms, related terms, and reformulations including HyDE, query rewriting, and multi-query approaches.

A RAG technique that indexes small chunks for precise matching but retrieves larger parent documents for LLM context. Balances retrieval precision with comprehensive context by separating indexing granularity from context size.

An intelligent retrieval technique where an LLM decomposes natural language queries into semantic search components and metadata filters. Enables more precise retrieval by automatically extracting structured filters from unstructured queries.