For every tech leader building with generative AI, the real bottleneck isn't the model, it's the messy, unstructured data locked in your documents.
We're building powerful Retrieval-Augmented Generation (RAG) applications, but they often fail because they can't properly understand the structure of PDFs, Office files, and images. Simple text extraction loses crucial context like tables, headings, and reading order.
This is where the Open-Source tool Docling comes in. Born out of IBM Research and now governed by the LF AI & Data Foundation, it's an engine engineered to prepare complex, multimodal documents for AI consumption.
Think of it as a semantic bridge, translating visual layouts into a machine-readable format that preserves context. This means higher-quality data chunks for your RAG pipeline and more accurate, reliable results.
Sources:
Official website: https://docling-project.github.io/docling/
Docling concepts: https://docling-project.github.io/docling/concepts/
Docling application recipes: https://docling-project.github.io/docling/examples/
Docling integrations: https://docling-project.github.io/docling/integrations/
GitHub repository: https://github.com/docling-project/docling
