What if you could ask questions about complex PDFs—dense tables, charts, and scanned pages—and have an AI agent answer with precise, cited snippets from the original document, without hand-writing brittle parsers? With Nemotron RAG, you can build a multimodal intelligent document processing pipeline from scratch in under an hour, using GPU-accelerated extraction and just a few hundred lines of Python to turn unstructured files into grounded, queryable knowledge.
Curious how it works? Join us for a developer-focused livestream that takes you from first principles to running code, showing exactly how Nemotron RAG powers end-to-end document processing for real-world reports. We’ll cover, step by step:
- A live demo: building and running an intelligent document processing agent built with Nemotron RAGthat “reads” PDFs, preserves tables, and understands charts in real time.
- How to plug NeMo Retriever library and Nemotron RAG models to structure text, tables (as markdown), and chart crops for high‑recall multimodal retrieval.
- How to tune chunking, embeddings, and reranking so Nemotron RAG can reliably surface the few pages or tables that actually answer complex queries.
- Wiring the top results into a Nemotron‑powered assistant that returns grounded, traceable answers with citations to the exact page, table, or figure.
By the end, you’ll understand how Nemotron RAG retrieves information from documents—and you’ll leave with a reusable Python pipeline you can adapt for your own intelligent document processing workloads.