This blog post describes Vespa's industry leading support for combining approximate nearest neighbor search, or vector search, with query constraints to solve real-world search and recommendation problems at scale.
Advances in Vespa features and performance include tensor and ranking configuration improvements, pyvespa usability features and grouping configuration. Also find new guides for performance and ANN. And a podcast!
With the increasing adoption of ARM64 based hardware like the AWS Graviton and Apple M1 MacBooks we are making a preview of Vespa available for this architecture.
Advances in Vespa features, performance and operability improvements include: Improved synonym support, faster node recovery, re-balancing and re-indexing, WeakAnd query type and new pyvespa features and sample applications.
An under-communicated point is that the default tuning for most libraries and platforms for evaluating machine-learned models is unsuitable for serving at scale.
Part one in a blog post series on billion-scale vector search. This post covers using nearest neighbor search with compact binary representations and bitwise hamming distance.