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.
In this post we explore a text-to-image search application on Vespa using approximate nearest neighbor search on vector representations of text and images.
Advances in Vespa features and performance include improved schema inheritance, Intellij plugin for schemas, Hamming distance in ranking, and performance gains in data dump and application deployment.
The new slicing feature in /document/v1 splits visiting across independent HTTP requests, letting throughput scale with the number of container nodes or clients.