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Deploying RAG at Scale: Key Questions for Vendors
Retrieval-augmented generation (RAG) has emerged as a vital technology for organizations embracing generative AI.
Announcing support for global significance models
A global significance model improves ranking for streaming search and ensures deterministic search results in multi-node deployments.
Vinted moves from Elasticsearch to Vespa
With Vespa, Vinted managed to halve the number of servers, slash query latency by 2.5x, indexing latency by 3x, and increase ranking depth by more than 3x.
Vespa Newsletter, September 2024
Advances in Vespa features and performance include Optimized MaxSim with Hamming distance, IDE Support, Pyvespa features, and new notebooks with ColPali examples.
Why Danswer - the biggest open source project in Enterprise Search - uses Vespa
Why we as a team decided to migrate to Vespa and why it was worth it even when it meant ripping out the core of our previous stack.
AI Needs More Than a Vector Database
An AI database is a multipurpose platform that manages both structured and unstructured data and applies AI models to various data formats.
Scaling ColPali to billions of PDFs with Vespa
Scaling Vision-Driven Document Retrieval with ColPali to large collections.
Parsing Through the Summer: A Tale of Schemas, Syntax, and Shenanigans
A tale of two interns who dove into the world of Vespa schemas, developing a language server.
Vespa Newsletter, August 2024
Advances in Vespa features and performance include new pyvespa and Vespa CLI features for queries, feeding, and deployment, shared match-phase heap, Chinese tokenization with Jieba, and new ranking framework features....
Beyond Text: The Rise of Vision-Driven Document Retrieval for RAG
Imagine a world where search engines see documents with human-like vision.
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