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Vespa at Berlin Buzzwords 2022

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.

Billion-scale vector search using hybrid HNSW-IF

This blog post describes HNSW-IF, a cost-efficient solution for high-accuracy vector search over billion scale vector datasets.

Query Time Constrained Approximate Nearest Neighbor Search

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.

Vespa Newsletter, April 2022

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Vespa Newsletter, April 2022

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!

Upcoming changes in OS support for Vespa

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Upcoming changes in OS support for Vespa

Today we support CentOS Linux 7 as the OS for Vespa release artifacts. This is about to change.

Preview of Vespa on ARM64

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Preview of Vespa on ARM64

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.

Vespa Newsletter, January 2022

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Vespa Newsletter, January 2022

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.

Billion-scale vector search with Vespa - part two

Part two in a blog post series on billion-scale vector search with Vespa. This post explores the many trade-offs related to nearest neighbor search.

Doubling the throughput of data redistribution

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Doubling the throughput of data redistribution

Learn which improvements we made to double the throughput of data redistribution in Vespa.

Machine-learned model serving at scale

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.

Vespa Newsletter, December 2021

Photo by Ilya Pavlov on Unsplash

Vespa Newsletter, December 2021

Advances in Vespa features and performance include tensor performance improvements, match-features and a Vespa IntelliJ plugin.

Log4j vulnerability update (CVE-2021-44228)

Brief update on our current knowledge about the Log4j vulnerability (CVE-2021-44228) and Vespa.