At this point, we assume you have read our Text Search Tutorial and accomplished the following steps.| docs.vespa.ai
The nativeRank text match score is a reasonably good text| docs.vespa.ai
A document summary is the information that is shown for each document in a query result.| docs.vespa.ai
content| 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
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An application package is a set of files in a specific structure that defines a deployable application.| docs.vespa.ai
The| docs.vespa.ai
numeric| docs.vespa.ai
services.xml is the primary configuration file in an| 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 guide demonstrates tokenization, linguistic processing and matching over string | docs.vespa.ai
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Vespa CLI is the command-line client for Vespa.| docs.vespa.ai
Follow @msmarco| microsoft.github.io
Learn how to choose the best method for you to install Docker Engine. This client-server application is available on Linux, Mac, Windows, and as a static binary.| Docker Documentation