Docling: Turn Messy Documents into Clean Data for Your AI App

Docling is an open-source Python library developed by IBM Research and hosted by the LF AI & Data Foundation, designed to simplify the conversion of complex documents into structured formats like Markdown, HTML, or JSON. Addressing the common challenge of extracting clean data from PDFs, Word files, and images for RAG pipelines or LLM applications, Docling uses machine learning to detect layouts, reading order, and tables. It supports a wide range of inputs, including scanned documents via OCR, and integrates seamlessly with popular frameworks like LangChain and LlamaIndex. Because it runs locally, it is ideal for processing sensitive information without external API dependencies. While it requires more computational resources than basic text extractors due to its ML-based approach, it offers a robust, unified solution for developers needing to transform unstructured, messy documents into high-quality, machine-readable data for AI workflows.