Visiting is a feature to efficiently get or process a set of documents, identified by a| docs.vespa.ai
Read the Vespa grouping guide first, for examples and an introduction to grouping -| docs.vespa.ai
Indexed tensors short form:| docs.vespa.ai
An attribute is a schema keyword,| docs.vespa.ai
This tutorial will guide you through setting up a simple text search application. | docs.vespa.ai
At this point, we assume you have read our Text Search Tutorial and accomplished the following steps.| docs.vespa.ai
This reference documents the full Vespa indexing language.| 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
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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
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Install| docs.vespa.ai
The distribution algorithm decides what nodes should be responsible for a given bucket.| docs.vespa.ai
Content cluster processes are distributor, proton and cluster controller.| docs.vespa.ai
Vespa offers configurable data redundancy with eventual consistency across replicas.| docs.vespa.ai
The content layer splits the document space into chunks called buckets,| docs.vespa.ai
Moving from the minimal quick start to more advanced use cases| docs.vespa.ai
Vespa clusters can be grown and shrunk while serving queries and writes.| docs.vespa.ai
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Refer to Vespa Support for more support options.| docs.vespa.ai
Application packages can contain Java components to be run in container clusters.| docs.vespa.ai
This is the reference for config file definitions.| docs.vespa.ai
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A component is any Java class whose lifetime is controlled by the container,| docs.vespa.ai
A schema defines a document type and what we want to compute over it, the| docs.vespa.ai
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This guide covers the aspects of accessing documents in Vespa.| docs.vespa.ai
Routing is used to configure the paths that documents and updates written| docs.vespa.ai
This document explains the common concepts necessary to develop all types of Container components.| docs.vespa.ai
Refer to the overview.| docs.vespa.ai
Vespa models data as documents.| docs.vespa.ai
An application package is a set of files in a specific structure that defines a deployable application.| docs.vespa.ai
This document describes the Vespa Annotations API; its purpose and use cases along with some usage examples.| docs.vespa.ai
The Container is the home for all global processing of| docs.vespa.ai
This is the reference for the search part of the container config.| docs.vespa.ai
This is the reference for container cluster configuration:| 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
This document describes how to develop and deploy Document Processors,| docs.vespa.ai
This is the reference for the Vespa command-line tools.| docs.vespa.ai
The| docs.vespa.ai
numeric| docs.vespa.ai
This guide is a practical introduction to using Vespa nearest neighbor search query operator and how to combine nearest| docs.vespa.ai
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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
Lucene Linguistics is a custom linguistics implementation of the| 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
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
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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
Vespa CLI is the command-line client for Vespa.| docs.vespa.ai
For an introduction to nearest neighbor search, see nearest neighbor search documentation, | docs.vespa.ai
Refer to the Query API guide for API examples.| docs.vespa.ai