Indexed tensors short form:| docs.vespa.ai
Introducing layered ranking: The missing piece for context engineering at scale.| Vespa Blog
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
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
ColPali simplifies and enhances information retrieval from complex, visually rich documents, transforming retrieval-augmented generation| 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
numeric| docs.vespa.ai
Query features| docs.vespa.ai
Use the Vespa Query API to query, rank and organize data. Example:| 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
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
Refer to the Query API guide for API examples.| docs.vespa.ai
A new search experience for Vespa-related content - powered by Vespa, LangChain, and OpenAI’s chatGPT model - our motivation for building it, features, limitations, and how we made it.| Vespa Blog