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Docling: turning documents into AI-ready data

Docling converts PDFs, Office files and images into structured, AI-ready formats, with local processing. An alternative to Unstructured, Textract and Document Intelligence.

Published on June 15, 20264 min readView on GitHub

The first step of every RAG pipeline is turning messy documents into clean data. Docling does exactly that: it converts PDFs, Office files and images into structured, AI-ready formats, all with local processing. It is an alternative to Unstructured, AWS Textract and Azure Document Intelligence.

What is Docling?

With advanced PDF understanding, layout, reading order, tables, formulas and images, Docling exports to Markdown, HTML and lossless JSON, and integrates with LangChain and LlamaIndex for ingestion. It was born at IBM Research and runs locally, without sending data out.

Key features

  • Understanding of layout, reading order, tables and formulas in PDFs
  • Export to Markdown, HTML and lossless JSON
  • Integration with LangChain and LlamaIndex for RAG
  • Local processing with OCR, without sending data out

How Reche uses it

Ingestion quality defines answer quality. Reche treats the document-extraction step with the same care as the rest of the system, because in RAG, poorly read data is a poorly given answer, no matter how good the model is.

Want to implement this in your product?

Reche's initial diagnosis defines scope, timeline, and budget. Credited to the project if you move forward.