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Learn how we built Mockingbird V3 MM, a multimodal model for semiconductor AI agents that can produce grounded answers and work through multi-step engineering tasks.
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Vectara adds new powerful capabilities to allow rerankers to be “chained” together to give you a balance of business rules and neural reranking
How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix
Vectara sponsors The AI Conference in San Francisco where they presented on an Advanced RAG Panel and hosted a meetup on Build vs. Buy (RAG solution)
In this blog post, we share the results of our initial experiments aimed at correcting hallucinations generated by Large Language Models (LLMs). Our focus is on the open-book setting, which encompasses tasks such as summarization and Retrieval-Augmented Generation (RAG).
Before we tackle the question at hand, let me provide some background on my journey to Vectara and how I reached this conclusion
A no-code environment for chat with your documents, powered by Vectara
Today, we’re incredibly excited to announce user-defined sorting functions for Vectara!
Enabling advanced RAG applications with Vectara Agentic
The upgraded HHEM-2.1 outperforms both GPT-3.5-Turbo and GPT-4 for hallucination detection and is powering our updated HHEM leaderboard.
