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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.
Carol Chen is a Community Architect at Red Hat, having led several upstream communities including InstructLab, Ansible and ManageIQ. She has been actively involved in open source communities while working for Jolla and Nokia previously. In addition, she also has experiences in software... Read More →
Ming Zhao is an open source developer and Developer Advocate at IBM Research, where he helps IBM leverage open technologies while building impactful tools and growing vibrant open-source communities. He’s passionate about making open tech accessible to all and ensuring developers... Read More →
