Shravan Singh

Docling: Powering Intelligent Document Processing

Harnessing Structured Data for Advanced AI Applications

Introducing Docling

Docling is an open-source Python library that simplifies the complex task of document parsing and understanding, making documents ready for use with advanced AI applications, including Large Language Models (LLMs). It boasts **over 31.3k GitHub stars**, highlighting its growing community and impact.

I understand you'd like to see code snippets for Docling, but as a language model, I cannot directly execute or provide real-time interactive code demonstrations. However, I can show you the examples provided directly on the Docling GitHub page itself, which are ready for you to copy and try.

Key Capabilities

  • Diverse Format Support: Processes various document types like PDFs, DOCX, XLSX, HTML, and images.
  • Advanced PDF Understanding: Extracts layout, reading order, table structures, and more from PDFs.
  • Unified Data Representation: Converts documents into a consistent, structured format (like Markdown or JSON) that AI models can easily consume.
  • Seamless AI Integration: Designed for plug-and-play integration with popular AI frameworks (LangChain, LlamaIndex, Haystack) to build robust RAG (Retrieval-Augmented Generation) and agentic AI systems.
  • Extensive OCR Support: Includes robust Optical Character Recognition (OCR) for scanned documents, ensuring all text is machine-readable.
  • Local Execution: Can be run locally for sensitive data and air-gapped environments.

By transforming unstructured document data into a clean, organized format, Docling significantly enhances the accuracy and efficiency of IDP workflows, allowing your AI to truly understand and act on document content.

Sample Code Snippets

1. Installation:

pip install docling

2. Converting a document (Python):

from docling.document_converter import DocumentConverter

source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"

3. Using the Command Line Interface (CLI):

docling https://arxiv.org/pdf/2206.01062

4. Using CLI with a Visual Language Model (VLM):

docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
Learn more and contribute on the Docling GitHub repository