Read the Vespa grouping guide first, for examples and an introduction to grouping -| docs.vespa.ai
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
Proton is Vespa's search core and runs on each content node as the vespa-proton-bin process.| docs.vespa.ai
Install| docs.vespa.ai
Vespa functionality from a Solr user’s perspective. Where it overlaps and where it differs. Why would you migrate and what challenges to expect.| Vespa Blog
Advances in Vespa features and performance include Lexical Search Query Performance, Pyvespa Relevance Evaluator, Global-phase rank-score-drop-limit, and Compact tensor representation.| Vespa Blog
Improvements made to triple the query performance of lexical search in 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
Part one in a blog post series on billion-scale vector search. This post covers using nearest neighbor search with compact binary representations and bitwise hamming distance.| Vespa Blog
Refer to Vespa Support for more support options.| docs.vespa.ai
A schema defines a document type and what we want to compute over it, the| 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
This guide is a practical introduction to using Vespa nearest neighbor search query operator and how to combine nearest| docs.vespa.ai
add| docs.vespa.ai
Query features| 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
Using the “shortening” properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality| Vespa Blog
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
For an introduction to nearest neighbor search, see nearest neighbor search documentation, | docs.vespa.ai