Why intelligent RAG systems need both Snowflake (for structured scale) and Vespa (for high-performance retrieval across unstructured text.| Vespa Blog
Get started with Vespa and set up your first application. Build your first Vespa instance using Python.| Vespa Blog
Introducing layered ranking: The missing piece for context engineering at scale.| Vespa Blog
We Make AI Work| Vespa Blog
Vespa 8.240.5 is available with updated dependencies to address CVE-2023-44487.| Vespa Blog
It is now the age of RAG, semantic + hybrid search, domain reasoning, powering copilots and AI agents. Lets see how - lets build your next Scientific Search engine!| Vespa Blog
We Make AI Work| Vespa Blog
We Make AI Work| Vespa Blog
Learn how how Vespa’s native tensor capabilities are redefining AI-powered search and retrieval in life sciences, enabling faster, more accurate insights across complex, multimodal scientific data.| Vespa Blog
Example of an end-to-end implementation of an agentic retail chatbot assistant that provides an advanced conversational search experience through an agentic workflow encapsulating tool usage.| 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
AI search requires more than a vector database. A search platform bridges the gaps.| Vespa Blog
Perplexity chose to build on Vespa.ai to provide the world’s most used RAG application.| Vespa Blog
Live Webinar: Unlock the Future of eCommerce – May 8, 2 PM CET.| Vespa Blog
Fastest way to get your data into Vespa. Logstash generates the schema. Then deploys the application package to Vespa. Next Logstash run does the actual writes.| Vespa Blog
Document enrichment with LLMs can be used to transform raw text into structured form and expand it with additional contextual information. This helps to improve search relevance and create a more effective search experience.| 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
Introducing Vespa Voice: a podcast on AI infrastructure, hybrid search, and RAG.| Vespa Blog
Improvements made to triple the query performance of lexical search in Vespa.| Vespa Blog
Announcing Matryoshka (dimension flexibility) and binary quantization in Vespa and how these features slashes costs.| Vespa Blog
Learn how the ModernBERT backbone model paves the way for more efficient and effective retrieval pipelines, and how to use ModernBERT in Vespa.| Vespa Blog
A guide on implementing advanced video retrieval at scale using Vespa and TwelveLabs’ multi-modal embedding models.| Vespa Blog
The evolution of language models combined with state-of-the-art information retrieval is reshaping the insurance landscape.| Vespa Blog
Advances in Vespa features and performance include Pyvespa Querybuilder, Vespa input/output plugins for Logstash, ModernBERT models, and Vespa CLI multi-get.| 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
Tutorials on feeding data to Vespa from CSV files, PostgreSQL, Kafka, Elasticsearch and another Vespa.| Vespa Blog
Have you ever wondered how the world’s largest internet and social media companies can deliver algorithmic content to so many users so fast?| Vespa Blog
How MRL and BQL Make AI-Powered Representations Efficient| 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
Learn how to securely store and manage sensitive secrets in your Vespa Cloud applications using Vespa’s built-in secret store.| Vespa Blog
Exploring how Vespa.ai exemplifies Norway’s commitment to sustainability through efficient technology.| Vespa Blog
A beginner’s guide to Vespa, exploring its role in information retrieval and its advantages for enterprise AI applications.| Vespa Blog
Discover how Vespa outperforms Elasticsearch in query efficiency, scalability, and operational costs, making it a robust choice for modern eCommerce search solutions.| 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 AI-driven ecommerce evolution is driven by consumer’s increasing demand for personalized experiences, real-time interactions, and seamless omnichannel integration.| Vespa Blog
Detailed report of a comprehensive performance comparison between Vespa and Elasticsearch for an e-commerce search application.| Vespa Blog
The purpose of using vectors is to improve quality, but that takes much more than a similarity lookup. Search engines are built for that, databases are not.| Vespa Blog
How to create your own reusable retrieval evaluation dataset for your data and use it to assess your retrieval system’s effectiveness| Vespa Blog
Three comprehensive guides to using the Cohere Embed v3 binary embeddings with Vespa.| Vespa Blog
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
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.| 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
The new IN operator is a shorthand for multiple OR conditions, enabling writing more concise queries with better performance| 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
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
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
