An attribute is a schema keyword,| docs.vespa.ai
content| docs.vespa.ai
Vespa can scale in multiple scaling dimensions:| docs.vespa.ai
Advances in Vespa features and performance include Lexical Search Query Performance, Pyvespa Relevance Evaluator, Global-phase rank-score-drop-limit, and Compact tensor representation.| 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
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.| Vespa Blog
Refer to Vespa Support for more support options.| docs.vespa.ai
Using machine-learned models from Vespa Cloud| cloud.vespa.ai
numeric| docs.vespa.ai
Use the Vespa Query API to query, rank and organize data. Example:| docs.vespa.ai
The new IN operator is a shorthand for multiple OR conditions, enabling writing more concise queries with better performance| Vespa Blog
For an introduction to nearest neighbor search, see nearest neighbor search documentation, | docs.vespa.ai
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.| Vespa Blog
Refer to the Query API guide for API examples.| docs.vespa.ai