Approximate Nearest Neighbor Search in Vespa - Part 1

The blog post discusses the implementation of an approximate nearest neighbor (ANN) search algorithm in Vespa, focusing on the selection and adaptation of the Hierarchical Navigable Small World Graphs (HNSW) algorithm. It highlights the importance of nearest neighbor search in high-dimensional vector spaces, particularly for real-time applications like image recognition and document retrieval, where the document corpus is dynamic and subject to metadata constraints. The Vespa team evaluated three ANN algorithms—Annoy, HNSW, and RPLSH—based on their performance in indexing and search throughput, with HNSW emerging as the preferred choice due to its superior search efficiency, memory usage, and compatibility with Vespa's real-time update requirements. The post also outlines the challenges of integrating ANN algorithms with Vespa's query tree and filter support, and the necessity of approximate solutions when dealing with large document corpora. The blog concludes with an overview of the decision-making process and the promise of further details on HNSW's integration in subsequent entries.