dense retrieval

**Dense retrieval** uses **learned embedding vectors to find semantically relevant documents** — encoding queries and documents into dense vector representations using bi-encoder models, then finding nearest neighbors in embedding space, enabling semantic search that understands meaning rather than relying on exact keyword matches. **How Dense Retrieval Works** - **Bi-Encoder**: Separate encoders for queries and documents produce independent embeddings. - **Indexing**: Pre-compute document embeddings, store in vector database. - **Search**: Encode query, find nearest document vectors via ANN search. - **Speed**: Sub-millisecond search over millions of documents. **Advantages Over Sparse Retrieval (BM25)** - **Semantic Understanding**: "car" matches "automobile" and "vehicle." - **Zero-Shot**: Works for unseen queries without keyword overlap. - **Multilingual**: Cross-language retrieval with multilingual encoders. **Limitations**: May miss exact keyword matches; hybrid (dense + sparse) retrieval often works best. Dense retrieval **powers modern RAG pipelines** — enabling LLMs to find relevant context through semantic understanding rather than keyword matching.