What is Dense Retrieval?
Dense retrieval is a retrieval technique that represents both queries and documents as dense vector embeddings in a high-dimensional space, then uses similarity measures like cosine similarity to find the most relevant documents. Unlike sparse retrieval methods that rely on exact keyword matching, dense retrieval captures semantic relationships, allowing it to surface relevant content even when query and document don't share the same terms.
The approach leverages neural embedding models that encode text into fixed-length vectors where semantically similar content is positioned closer together in the vector space. When a query is issued, it's encoded into the same embedding space, and the retrieval system searches for the nearest neighbor documents using efficient similarity search algorithms. This enables retrieval based on meaning rather than just lexical overlap, handling synonyms, paraphrases, and conceptual matches that sparse methods would miss.
Dense retrieval has become foundational in modern RAG systems and agent memory architectures. While it excels at semantic matching, it can sometimes struggle with rare terms, proper nouns, or precise keyword requirements where sparse methods excel. This has led to the popularity of hybrid approaches that combine dense and sparse retrieval to leverage the strengths of both. The quality of dense retrieval depends heavily on the embedding model, with domain-specific fine-tuned models often outperforming generic alternatives.