Deep Lake HNSW Index: Rapidly Query 35M Vectors, Save 80%

Deep Lake 3.7.1 introduces an improved implementation of the HNSW Approximate Nearest Neighbor (ANN) search algorithm, enhancing speed and affordability for production-grade Retrieval Augmented Generation (RAG) applications. The new index implementation allows sub-second vector search for over 35 million embeddings while significantly reducing costs compared to other vector databases. Deep Lake's efficient memory architecture minimizes RAM usage without compromising performance, making it ideal for building large-scale LLM applications.