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
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
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
This is the first blog post in a series on hybrid search. This first post focuses on efficient hybrid retrieval and representational approaches in IR| Vespa Blog
expand all| docs.vespa.ai
expand all| 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