HNSW is a key method for approximate nearest neighbor search in high-dimensional vector databases, for example in the context of embeddings from neural networks in large language models.

Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search. HNSW is a hugely popular technology that time and time again produces state-of …

What is a Hierarchical Navigable Small World? Hierarchical navigable small world (HNSW) is a powerful algorithm used in computer science and information retrieval systems to efficiently search large …

Jun 18, 2025 · Introduced by Yury Malkov and Dmitry Yashunin in 2016, HNSW addresses the critical limitation of existing similarity search methods that struggled to maintain both speed and accuracy as …

Jun 10, 2025 · Hierarchical navigable small world, or HNSW, is a graph-based ANN algorithm that combines navigable small worlds (networks of points where each point is connected to its nearest …

Sep 12, 2025 · What is HNSW? HNSW is an approximate nearest neighbor (ANN) search algorithm designed to efficiently find the closest vectors in high-dimensional spaces (e.g., embeddings from …

HNSW (Hierarchical Navigable Small World) is a data structure designed for efficiently searching high-dimensional data, particularly for approximate nearest neighbor (ANN) searches.

Jul 17, 2024 · Hierarchical Navigable Small World (HNSW) is a graph-based algorithm that performs approximate nearest neighbor (ANN) searches in vector databases.

Hierarchical Navigable Small World (HNSW) is a graph-based Approximate Nearest Neighbor (ANN) search algorithm that offers high speed and scalability, making it a great fit for RAG systems.

Feb 25, 2024 · HNSW is a little complex though, and descriptions often lack a complete and intuitive explanation. This post takes a journey through the history of the HNSW idea to help explain what …