Visiting is a feature to efficiently get or process a set of documents, identified by a| 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
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
Content cluster processes are distributor, proton and cluster controller.| docs.vespa.ai
Moving from the minimal quick start to more advanced use cases| docs.vespa.ai
We Make AI Work| Vespa Blog
Example of an end-to-end implementation of an agentic retail chatbot assistant that provides an advanced conversational search experience through an agentic workflow encapsulating tool usage.| Vespa Blog
AI search requires more than a vector database. A search platform bridges the gaps.| Vespa Blog
Fastest way to get your data into Vespa. Logstash generates the schema. Then deploys the application package to Vespa. Next Logstash run does the actual writes.| Vespa Blog
Mediumish is a free Jekyll theme for blogging, Medium style, built with Bootstrap v4.x. Mediumish is compatible with Github pages and it is modern, clean and lightweight. Download Mediumish here.| Wow Themes
Vespa clusters can be grown and shrunk while serving queries and writes.| docs.vespa.ai
Tutorials on feeding data to Vespa from CSV files, PostgreSQL, Kafka, Elasticsearch and another Vespa.| Vespa Blog
This blog post describes Vespa’s industry leading support for combining approximate nearest neighbor search, or vector search, with query constraints to solve real-world search and recommendation problems at scale.| Vespa Blog
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A schema defines a document type and what we want to compute over it, the| docs.vespa.ai
This document explains the common concepts necessary to develop all types of Container components.| 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 is a technical blog post on developing an end-to-end Visual RAG application powered by Vespa. It has link to a live demo application, and will walk you through why and how we built it, as well as give you the code to build your own Visual RAG application with your own data.| Vespa Blog
The Container is the home for all global processing of| docs.vespa.ai
This document describes how to develop and deploy Document Processors,| docs.vespa.ai
The| docs.vespa.ai
numeric| docs.vespa.ai
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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
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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
