NVIDIA

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Editor’s note: This post is part of the AI Decoded series, which demystifies AI by making the technology more accessible, and showcases new hardware, software, tools and accelerations for GeForce RTX PC and NVIDIA RTX workstation users.

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. Building a multimodal retrieval augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In our previous post, An Easy Introduction

This blog post was originally published at NVIDIA’s website. It is reprinted here with the permission of NVIDIA. A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts, and diagrams—and not just text. This requires a framework that can understand and generate responses