Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search[1]. HNSW is a hugely popular techno

Hierarchical Navigable Small Worlds (HNSW)

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2024-12-26 09:30:06

Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search[1]. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super fast search speeds and fantastic recall.

Yet despite being a popular and robust algorithm for approximate nearest neighbors (ANN) searches, understanding how it works is far from easy.

Note: Pinecone lets you build scalable, performant vector search into applications without knowing anything about HNSW or vector indexing libraries. But we know you like seeing how things work, so enjoy the guide!

This article helps demystify HNSW and explains this intelligent algorithm in an easy-to-understand way. Towards the end of the article, we’ll look at how to implement HNSW using Faiss and which parameter settings give us the performance we need.

We can split ANN algorithms into three distinct categories; trees, hashes, and graphs. HNSW slots into the graph category. More specifically, it is a proximity graph, in which two vertices are linked based on their proximity (closer vertices are linked) — often defined in Euclidean distance.