How We Built a Web-Scale Vector Database for Our Neural Network Search Engine

Exa is a modern AI-driven search engine that has developed its own web-scale vector database to efficiently handle complex semantic queries, outperforming traditional search engines like Google. By employing a series of advanced optimizations, including matryoshka embeddings, binary quantization, and clustering, Exa's database can search billions of vectors in under 100 milliseconds while maintaining high recall and reducing memory usage significantly. The system is designed to process over 500 queries per second and includes features like metadata filtering and a custom query language that allows for the creation of multi-stage pipelines for different scoring algorithms. These innovations enable Exa to offer a more refined and efficient search experience, paving the way for future advancements in AI search technology.