• What if your AI agent could instantly parse complex PDFs, extract nested tables, and “see” data within charts as easily as reading a text file? • With NVIDIA Nemotron RAG, you can build a high-throughput intelligent document processing pipeline that handles massive document workloads with precision and accuracy. • This post walks you through the core components of a multimodal retrieval pipeline step-by-step. • First, we show you how to use the open source NVIDIA NeMo Retriever library to decompose complex documents into structured data using GPU-accelerated microservices. • Then, we demonstrate how to wire that data into Nemotron RAG models to ensure your assistant provides grounded, accurate answers with full traceability back to the source. • Quick links to the model and code Access the following resources for the tutorial: 🧠 Models on Hugging Face: - nvidia/llama-nemotron-embed-vl-1b-v2 multimodal embedding - nvidia/llama-nemotron-rerank-vl-1b-v2 cross-encoder reranker - Extraction models from the Nemotron RAG collection ☁️ Cloud endpoints: - Nemotron OCR document extraction - nvidia/llama-3.3-nemotron-super-49b-v1.5 answer generation model - More from NIM models 🛠️ Code and documentation: - NeMo Retriever Library (GitHub) - Tutorial Notebook Jupyter notebook available on GitHub Prerequisites To follow this tutorial, you need the following: System requirements: - Python 3.10 to 3.12 (tested on 3.12) - NVIDIA GPU with at least 24 GB VRAM for local model deployment - 2

Article Summaries:

  • NVIDIA has released a guide for building a high‑throughput, multimodal Retrieval‑Augmented Generation (RAG) pipeline using its Nemotron models. The tutorial shows how to employ the open‑source NeMo Retriever library to decompose complex PDFs-extracting nested tables, charts, and other visual data-via GPU‑accelerated microservices. The extracted content is then fed into Nemotron RAG models, enabling an AI assistant to provide accurate, source‑grounded answers with traceable citations. The post supplies links to Hugging Face models, cloud endpoints, and a GitHub Jupyter notebook, and outlines system requirements (Python 3.10‑3.12, 24 GB GPU, 250 GB storage) for local deployment.

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