FeaturedRetrieval Augmented Generation
Think building your own Retrieval-Augmented Generation (RAG) system will give you a competitive edge? Think again. Companies are sinking months and hundreds of thousands of dollars into in-house AI projects, only to end up with sluggish, unsecure, and overpriced systems that can't hold a candle to existing solutions.
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When building something new and testing the art of the possible, crashes are bound to happen. Not just for Ferraris but for all new technology, and especially with Generative AI…
The Hacker News startup community website deserves better search
The top large language models in the Summer of 2024, along with recommendations for when to use each based upon needs like API, tunable, or fully hosted.
Enhance your Slack and Discord communities with RAGTime – a RAG-bot from Vectara.
Vectara’s Boomerang stands out as the optimal choice for production use cases, balancing precision, embedding size, and storage costs effectively.
Today, we’re excited to announce that we’re introducing a brand new REST API for Vectara: API v2.
We are so proud of our Built By Vectara Contestants.
In the ever-evolving landscape of RAG and information retrieval, a balance of precision, recall, and latency will make or break the applications. We are excited to introduce our latest innovation, the Multilingual Reranker_v1. This state-of-the-art reranker, significantly enhances the precision of retrieved results across both English and multilingual datasets.
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