Announcing Matryoshka (dimension flexibility) and binary quantization in Vespa and how these features slashes costs.| Vespa Blog
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 Blog
numeric| docs.vespa.ai
Query features| docs.vespa.ai
Announcing multi-vector indexing support in Vespa, which allows you to index multiple vectors per document and retrieve documents by the closest vector in each document.| Vespa Blog
This is the first blog post in a series on hybrid search. This first post focuses on efficient hybrid retrieval and representational approaches in IR| Vespa Blog
For an introduction to nearest neighbor search, see nearest neighbor search documentation, | docs.vespa.ai
A new search experience for Vespa-related content - powered by Vespa, LangChain, and OpenAI’s chatGPT model - our motivation for building it, features, limitations, and how we made it.| Vespa Blog