This tutorial will guide you through setting up a simple text search application. | docs.vespa.ai
The nativeRank text match score is a reasonably good text| 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
Improvements made to triple the query performance of lexical search in Vespa.| Vespa Blog
Where should you begin if you plan to implement search functionality but have not yet collected data from user interactions to train ranking models?| Vespa Blog
Connecting the ColPali model with Vespa for complex document format retrieval.| Vespa Blog
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
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
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
Announcing long-context ColBERT, giving it larger context for scoring and simplifying long-document RAG applications.| 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
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