Billion-scale vector search with Vespa - part two | Vespa Blog
Part two in a blog post series on billion-scale vector search with Vespa. This post explores the many trade-offs related to nearest neighbor search.| Vespa Blog
A Query Profile is a named collection of search request parameters given in the configuration.| docs.vespa.ai
The| docs.vespa.ai
numeric| docs.vespa.ai
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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
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expand all| docs.vespa.ai
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
Part two in a blog post series on billion-scale vector search with Vespa. This post explores the many trade-offs related to nearest neighbor search.| Vespa Blog
Refer to the Query API guide for API examples.| docs.vespa.ai
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
