An attribute is a schema keyword,| docs.vespa.ai
A document summary is the information that is shown for each document in a query result.| docs.vespa.ai
Proton is Vespa's search core and runs on each content node as the vespa-proton-bin process.| docs.vespa.ai
Vespa can scale in multiple scaling dimensions:| docs.vespa.ai
A schema defines a document type and what we want to compute over it, the| docs.vespa.ai
numeric| docs.vespa.ai
add| docs.vespa.ai
Vespa ranks documents retrieved by a query by performing computations or inference that produces a score for each document. | docs.vespa.ai
Vespa uses a linguistics module to process text in queries and documents during indexing and searching.| docs.vespa.ai
This guide demonstrates tokenization, linguistic processing and matching over string | docs.vespa.ai
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
expand all| docs.vespa.ai
Vespa allows expressing multi-phased retrieval and ranking of documents. The retrieval phase is done close to the data in the content nodes,| docs.vespa.ai
For an introduction to nearest neighbor search, see nearest neighbor search documentation, | docs.vespa.ai