Agentic RAG

Imagine sitting in front of your computer, asking a complex question that pulls information from reports, meeting notes, videos and even audio or video snippets. Instead of receiving a fragmented set of responses, you get a single coherent answer—a response backed by traceable sources, enriched with context, and supported by everything your organization knows. This isn't a dream of the future. It's what the NucliaDB database makes possible today.

Introducing Llama-REMi v1: Faster, smarter RAG evaluation with improved alignment, speed, and usability. Now available via Nuclia’s Understanding API! 🚀

Extracting structured data from unstructured text is a common need in many applications. It allows you to get insights from your resources that are not directly accessible, and to use them in your applications.

REMi, the first ever open-source RAG evaluation model. This article provides a comprehensive understanding of metrics for evaluating RAG systems