A document summary is the information that is shown for each document in a query result.| docs.vespa.ai
content| 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
Vespa clusters can be grown and shrunk while serving queries and writes.| docs.vespa.ai
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A schema defines a document type and what we want to compute over it, the| docs.vespa.ai
The Container is the home for all global processing of| 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
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
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Refer to the Query API guide for API examples.| docs.vespa.ai