PyData Berlin
Docling: Get your documents ready for gen AI
Talk
Abstract
Docling, an open source package, is rapidly becoming the de facto standard for document parsing and export in the Python community. Earning close to 30,000 GitHub in less than one year and now part of the Linux AI & Data Foundation. Docling is redefining document AI with its ease and speed of use. In this session, we’ll introduce Docling and its features, including usages with various generative AI frameworks and protocols (e.g. MCP).
Prerequisites
The introduction to Docling has no prerequisite. Going deeper in the talk we will show examples of generative AI applications, it is advised to have previous knowledge of: - Retrieval Augmented Generation (RAG) applications and frameworks. - Model Context Protocol (MCP) and building applications with it.
Description
Docling, an open source package, is rapidly becoming the de facto standard for document parsing and export in the Python community. Earning close to 30,000 GitHub in less than one year and now part of the Linux AI & Data Foundation. Docling is redefining document AI with its ease and speed of use. In this session, we’ll introduce Docling and its features, including how:
- Support for a wide array of formats—such as PDFs, DOCX, PPTX, HTML, images, and Markdown—and easy conversion to structured Markdown or JSON.
- Advanced document understanding through capture of intricate page layouts, reading order, and table structures—ideal for complex analysis.
- Integration of the DoclingDocument format with popular AI frameworks—such as LlamaIndex. LangChain, LlamaStack for retrieval-augmented generation (RAG) and QA applications.
- Optical character recognition (OCR) support for scanned documents.
- Support of Visual Language Models like SmolDocling created in collaboration with Hugging Face.
- A user-friendly command line interface (CLI) and MCP connectors for developers.
- How to use it as-a-service and at scale by deploy your own docling-serve.
Speakers
Michele Dolfi
Senior Technical Staff Member
Dr. Michele Dolfi is a technical lead in the AI for Knowledge group at IBM Research, focusing on knowledge engineering and understanding. Michele is one of the researchers who created the Deep Search platform and the Docling open source project. His expertise spans from artificial intelligence to high performance computing and quantum systems.
Christoph Auer
Senior Research Scientist