Why intelligent RAG systems need both Snowflake (for structured scale) and Vespa (for high-performance retrieval across unstructured text.| Vespa Blog
An attribute is a schema keyword,| docs.vespa.ai
At this point, we assume you have read our Text Search Tutorial and accomplished the following steps.| docs.vespa.ai
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
Advances in Vespa features and performance include Lexical Search Query Performance, Pyvespa Relevance Evaluator, Global-phase rank-score-drop-limit, and Compact tensor representation.| Vespa Blog
Announcing Matryoshka (dimension flexibility) and binary quantization in Vespa and how these features slashes costs.| 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
Advances in Vespa features and performance include Elasticsearch vs Vespa Performance Comparison, Vision RAG and Binarizing Vectors| Vespa Blog
A beginner’s guide to Vespa, exploring its role in information retrieval and its advantages for enterprise AI applications.| Vespa Blog
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
This guide is a practical introduction to using Vespa nearest neighbor search query operator and how to combine nearest| docs.vespa.ai
How to create your own reusable retrieval evaluation dataset for your data and use it to assess your retrieval system’s effectiveness| Vespa Blog
Query features| docs.vespa.ai
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
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
Hybrid Search| pyvespa.readthedocs.io
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