Author: Pavel Snoj

Source: An Easy Introduction to Multimodal Retrieval-Augmented Generation for Video and Audio | NVIDIA Technical Blog

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 NVIDIA previous post, An Easy Introduction to Multimodal Retrieval-Augmented Generation, authors discussed how to tackle text and images. This post extends this conversation to audio and videos. Specifically, they explore how to build a multimodal RAG pipeline to search information in videos.

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Source: Creating RAG-Based Question-and-Answer LLM Workflows at NVIDIA

The rapid development of solutions using retrieval augmented generation (RAG) for question-and-answer LLM workflows has led to new types of system architectures. NVIDIA work, using AI for internal operations, has led to several important findings for finding alignment between system capabilities and user expectations. 

NVIDIA found that regardless of the intended scope or use case, users generally want to be able to execute non-RAG tasks like performing document translation, editing emails, or even writing code. A vanilla RAG application might be implemented so that it executes a retrieval pipeline on every message, leading to excess usage of tokens and unwanted latency as irrelevant results are included.

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Source: What Is Agentic AI?

Agentic AI uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems.

AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply.

The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries.

Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like supply chain optimization, cybersecurity vulnerability analysis and helping doctors with time-consuming tasks.

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Source: Access to NVIDIA NIM Now Available Free to Developer Program Members

The ability to use simple APIs to integrate pretrained AI foundation models into products and experiences has significantly increased developer usage of LLM endpoints and application development frameworks. NVIDIA NIM enables developers and engineering teams to rapidly deploy their own AI model endpoints for the secure development of accelerated generative AI applications using popular development tools and frameworks.

Developers said they want easier access to NIM for development purposes, so NVIDIA is excited to provide free access to downloadable NIM microservices for development, testing, and research to over 5M NVIDIA Developer Program members. Members of the program are provided comprehensive resources, training, tools, and a community of experts that help build accelerated applications and solutions.

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Source: A Simple Guide to Deploying Generative AI with NVIDIA NIM

Whether you’re working on-premises or in the cloud, NVIDIA NIM microservices provide enterprise developers with easy-to-deploy optimized AI models from the community, partners, and NVIDIA. Part of NVIDIA AI Enterprise, NIM offers a secure, streamlined path forward to iterate quickly and build innovations for world-class generative AI solutions.

Using a single optimized container, you can easily deploy a NIM microservice in under 5 minutes on accelerated NVIDIA GPU systems in the cloud or data center, or on workstations and PCs. Alternatively, if you want to avoid deploying a container, you can begin prototyping your applications with NIM APIs from the NVIDIA API Catalog. 

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Explore the latest optimized AI models, connect applications to data with NVIDIA NIM™ Agent Blueprints, and deploy anywhere with NVIDIA NIM microservices.

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