Turning Unstructured Files into Actionable Knowledge

Every organization that works with documents—whether legal contracts, research papers, or multimedia reports—spends countless hours extracting, cleaning, and structuring data before it can be used. Commercial AI‑powered document services promise to automate this, but they often come with hefty subscription fees, vendor lock‑in, and the risk of sending sensitive information to the cloud.

Docling flips the script. It is a fully open‑source library that parses a massive variety of formats (PDF, DOCX, PPTX, XLSX, HTML, images, audio, and more) locally, delivering rich, hierarchical representations that can be fed directly into LLM pipelines, vector stores, or traditional analytics tools. No data leaves your environment, and you keep full control over costs, customization, and deployment.

Why Choose Docling?

• Privacy‑First Execution – All processing runs on‑premise or in an air‑gapped environment, eliminating the need to upload confidential files to third‑party clouds.
• Broad Format Coverage – From classic office files to images, audio (ASR), and even complex visual content like charts and chemical structures, Docling handles them all under a single API.
• Advanced PDF Understanding – Beyond simple text extraction, Docling detects page layout, reading order, tables, code blocks, formulas, and classifies images, delivering a faithful reconstruction of the original document.
• Plug‑and‑Play AI Integration – Ready‑made adapters for LangChain, LlamaIndex, Crew AI, and Haystack let you enrich documents with VLMs (SmolDocling) or OCR models (Tesseract, RapidOCR) in a single line of code.
• Extensible Export Options – Export to Markdown, HTML, lossless JSON, or custom DoclingDocument formats, making downstream processing painless.
• Zero Licensing Costs – Fully MIT‑licensed, you can use Docling in commercial products without worrying about per‑page fees or usage quotas.

Spotlight on Key Features

Getting Started

Installation

Docling is distributed via PyPI and works on macOS, Linux, and Windows (both x86_64 and arm64). Install with a single command:

pip install docling

For GPU‑accelerated OCR or VLM models, consult the installation guide for optional dependencies.

Quick Python Example

from docling.document_converter import DocumentConverter

# You can pass a local path, a URL, or a BytesIO object
source = "https://arxiv.org/pdf/2408.09869.pdf"

converter = DocumentConverter()
result = converter.convert(source)

# Export to Markdown for easy reading or further processing
print(result.document.export_to_markdown())

The snippet above fetches a PDF from arXiv, parses its full layout (including tables and figures), and prints a clean Markdown version.

Using the CLI

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

The command writes a docling_output folder containing the chosen export format(s) and a JSON dump of the full document graph.

Comparison Table: Open‑Source vs. Proprietary Document AI

Bottom line: If you need privacy, multimodal support, or want to avoid per‑page fees, Docling offers a compelling, cost‑free alternative without sacrificing core capabilities.

Advanced Use Cases

• RAG Pipelines – Combine Docling’s chunked output with vector stores (Milvus, Weaviate, Qdrant) and LLMs for enterprise search over internal knowledge bases.
• Compliance Audits – Run Docling on confidential contracts in an air‑gapped environment, extract clauses, and feed them into rule‑based engines.
• Multimedia Summarization – Convert meeting recordings to text, enrich with VLM‑generated image captions, and generate concise summaries for stakeholders.
• Scientific Data Extraction – Parse PDFs containing charts, formulas, and chemical structures, then export to JSON for downstream analytics.

Contributing & Community

Docling welcomes contributions ranging from bug fixes to new model integrations. The repository includes a detailed contribution guide, issue templates, and a vibrant Discord channel where developers share pipelines and custom plugins.

• Report Issues: GitHub Issues
• Join the Discussion: Discord community (link on the homepage)
• Submit Pull Requests: Follow the dev branch workflow outlined in the repo’s CONTRIBUTING.md.

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Call to Action

Docling proves that you don’t need to sacrifice privacy or spend a fortune to unlock the full potential of your documents. Give it a spin, integrate it into your AI stack, and help shape the future of open‑source document intelligence.

• Try the project: https://docling-project.github.io/docling
• Star on GitHub: https://github.com/docling-project/docling
• Read the full docs: Installation Guide

Empower your data workflows with a tool that puts you—and your data—first.