Vamana vs. HNSW - Exploring ANN algorithms Part 1

Vector databases must be able to search through a vast number of vectors at speed, which is becoming more difficult as vector dimensions and dataset sizes increase. Approximate Nearest Neighbor (ANN) algorithms are used to power Weaviate, an open-source vector database written in Go. The current challenge is finding the right ANN algorithm that can efficiently handle large datasets while maintaining performance and user experience. This article explores Vamana, a disk-based solution for vector indexing, and compares it with HNSW, a hierarchical representation of vectors. Both algorithms perform similarly in terms of speed and recall. The future of Weaviate involves exploring other index types besides HNSW to provide cost-effective solutions without sacrificing user experience.