NVIDIA's AI Blueprint for RAG is a foundational guide for developers to build powerful data extraction and retrieval pipelines. It leverages NVIDIA NeMo Retriever models to create scalable and customizable RAG (Retrieval-Augmented Generation) applications. This blueprint allows you to connect large language models (LLMs) to a wide range of enterprise data, including text, tables, charts, and infographics within millions of PDFs. The result is context-aware responses that can unlock valuable insights. By using this blueprint, you can achieve 15x faster multimodal PDF data extraction and reduce incorrect answers by 50%. This boost in performance and accuracy helps enterprises drive productivity and get actionable insights from their data.

This environment provides a highly performant environment to test the deployment and tuning of different NVIDIA NIMs and Blueprints.

In this lab a customer will deploy a NVIDIA NIM to the HPE Private Cloud AI instance in the ATC. This lab will not only walk the customer through how to deploy a black-forest-labs/FLUX.1-dev NIM (image generation NIM), but also how to interact with the kubernetes layer (Ezmeral) that is engraved in the HPE solution.