We Make AI Work| Vespa Blog
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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
An application package is a set of files in a specific structure that defines a deployable application.| 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
Three comprehensive guides to using the Cohere Embed v3 binary embeddings with Vespa.| Vespa Blog
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
Announcing multi-vector indexing support in Vespa, which allows you to index multiple vectors per document and retrieve documents by the closest vector in each document.| Vespa Blog
Using the “shortening” properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality| Vespa Blog
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
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
We’re on a journey to advance and democratize artificial intelligence through open source and open science.| huggingface.co
