This blog post describes how your organization can unlock the full potential of multimodal AI-powered vector representations using Vespa -- the industry-leading open-source big data serving engine.
Advances in Vespa features and performance include ANN with configurable filtering, fuzzy matching, and native embedding support. Also see pyvespa’s new experimental ranking module!
Find videos and links from four Vespa-related talks at Berlin Buzzwords 2022, Germany’s most exciting conference on storing, processing, streaming and searching large amounts of digital data.
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.