An attribute is a schema keyword,| docs.vespa.ai
This tutorial will guide you through setting up a simple text search application. | docs.vespa.ai
Get started with Vespa and set up your first application. Build your first Vespa instance using Python.| Vespa Blog
Vespa can scale in multiple scaling dimensions:| 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
A guide on implementing advanced video retrieval at scale using Vespa and TwelveLabs’ multi-modal embedding models.| 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
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 a technical blog post on developing an end-to-end Visual RAG application powered by Vespa. It has link to a live demo application, and will walk you through why and how we built it, as well as give you the code to build your own Visual RAG application with your own data.| Vespa Blog
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
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
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
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