FeaturedHallucination
Vectara launches the open source Hughes Hallucination Evaluation Model (HHEM) and uses it to compare hallucination rates across top LLMs including OpenAI, Cohere, PaLM, Anthropic’s Claude 2 and more.
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RAG with User-Defined Functions Based Reranking
Using UDF-based reranking for fine-grained control over your search results with Vectara
Building a RAG Pipeline is Difficult
Overview The best RAG systems utilize many different types of models (embedding model, generative LLM) to achieve the best, and highest quality results. When you build a small RAG POC,…
Introducing Vectara’s Chain Rerankers
Vectara adds new powerful capabilities to allow rerankers to be “chained” together to give you a balance of business rules and neural reranking
Building AI Assistants with Vectara-agentic and Arize
How to add Observability to your Vectara AI Assistants and Agents with Arize Phoenix
Introducing Vectara Portal
A no-code environment for chat with your documents, powered by Vectara
Introducing User-Defined Functions for Vectara
Today, we’re incredibly excited to announce user-defined sorting functions for Vectara!
Introducing Vectara-Agentic
Enabling advanced RAG applications with Vectara Agentic
Mockingbird is a RAG-Specific LLM that Beats GPT 4, Gemini 1.5 Pro in RAG Output Quality
In response to growing enterprise concerns over data security and the quality of retrieval-augmented generation (RAG), Vectara is proud to introduce Mockingbird, an LLM fine-tuned specifically for RAG. Mockingbird achieves the world’s leading RAG output quality and hallucination mitigation, making it perfect for enterprise RAG and autonomous agent use cases.
RAGTime – A RAG-Powered Bot for Slack and Discord
Enhance your Slack and Discord communities with RAGTime – a RAG-bot from Vectara.
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