Teaching PyTorch To Read Your Worst PDFs With Docling - Mingxuan Zhao, Peter Staar & Carol Chen

Teaching PyTorch To Read Your Worst PDFs With Docling - Mingxuan Zhao & Peter Staar, IBM & Carol Chen, Red Hat Building production RAG pipelines starts with a problem most teams underestimate: getting clean, structured data out of real-world documents. PDFs lose table structure, figures get separated from captions, and multi-column layouts become unreadable. Before your PyTorch models even see your data, crucial information is already lost. Docling is an open-source, MIT-licensed document parsing library that uses PyTorch-based deep learning models to understand documents the way humans read them. It preserves hierarchy, extracts structured data from tables and figures, and supports over ten common file formats through a consistent API. Because everything runs locally, it integrates cleanly into PyTorch-native workflows with low latency and no data leaving your infrastructure. In this talk, I'll walk through Docling's PyTorch-powered architecture and show how to build document processing pipelines for RAG and other GenAI applications. I'll also share the architecture of real-world applications of Docling and how it has improved workflows. You'll leave with practical patterns for connecting Docling to your own PyTorch-based GenAI stack.