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A schema defines a document type and what we want to compute over it, the| docs.vespa.ai
Vespa models data as documents.| docs.vespa.ai
This is the reference for the search part of the container config.| docs.vespa.ai
A Query Profile is a named collection of search request parameters given in the configuration.| docs.vespa.ai
The Vespa Container allows multiple sources of data to| docs.vespa.ai
numeric| docs.vespa.ai
Processors,| docs.vespa.ai
add| docs.vespa.ai
Query features| 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
Use the Vespa Query API to query, rank and organize data. Example:| docs.vespa.ai
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
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expand all| 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
Character Sets| www.iana.org