2026-06-05 –, Doddington Forum
Unlocking the full potential of AI starts with your data, but real-world documents come in countless formats and levels of complexity. This session will give you hands-on experience with Docling, an open-source Python library designed to convert complex documents into AI-ready formats. Learn how Docling simplifies document processing, enabling you to efficiently harness all your data for downstream AI and analytics applications.
Most organizational knowledge is still locked inside complex documents, making it difficult to extract and use the information effectively. Traditional tools often fail when working with real-world document formats, particularly PDFs. Tables lose their structure, figures get separated from captions, and multi-column layouts become unreadable text. These failures make it difficult to bring AI to document-heavy workflows.
This workshop will give you hands on experience with Docling, an open-source project that takes a different approach, using deep learning models to parse documents the way humans read them. It preserves hierarchy, extracts structured data through a consistent API, and supports 15+ file formats out of the box. All of Docling is MIT-licensed, enabling fully local execution, allowing you to keep sensitive data on-premise while delivering low-latency processing and ingestion.
You'll be building a complete document intelligence pipeline from the ground up. We'll work through three progressive modules: first, converting documents and exploring Docling's enrichment features like table detection and image classification; second, chunking strategies that preserve document semantics for retrieval; and finally, building on all our other components using Docling, we will build a multimodal RAG pipeline with visual grounding, creating an application that can cite the exact page and location where it found an answer.
No prior experience with Docling is required. Colab notebooks with hosted model endpoints will be provided, so you can follow along with just a browser. Attendees who prefer local execution should have Jupyter Notebook installed and the ability to download models from Hugging Face. Bring your own documents to experiment with, or use the samples provided.
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 have the tools they need to succeed in the rapidly developing AI space. Ming now leads community efforts around Docling, IBM’s fastest-growing open source project, recently welcomed into the LF AI & Data Foundation.
Abby Tse is Chair of PyData NYC, where she has led a community of over 8,000 data professionals since 2022. She organizes the annual PyData NYC/Boston Conference, a three-day event that brings together 600+ attendees from around the world to explore the latest in data science, machine learning, and AI. Abby is currently an MBA student at Columbia Business School, where she focuses on entrepreneurship and innovation. Previously, she worked at IBM, where she built enterprise AI systems, including large-scale generative AI applications to improve knowledge access.
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 development/integration in her 12 years in the mobile industry. Carol has spoken at events around the world, including AI_Dev in Paris and OpenSearchCon in Shanghai. On a personal note, Carol plays the Timpani in an orchestra in Tampere, Finland, where she now calls home.