Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
- 🗂️ Parsing of multiple document formats incl. PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, images (PNG, TIFF, JPEG, ...), and more
- 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
- 🧬 Unified, expressive DoclingDocument representation format
- ↪️ Various export formats and options, including Markdown, HTML, DocTags and lossless JSON
- 🔒 Local execution capabilities for sensitive data and air-gapped environments
- 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- 🔍 Extensive OCR support for scanned PDFs and images
- 👓 Support of several Visual Language Models (SmolDocling)
- 🎙️ Support for Audio with Automatic Speech Recognition (ASR) models
- 💻 Simple and convenient CLI
- 📝 Metadata extraction, including title, authors, references & language
- 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
- 📝 Complex chemistry understanding (Molecular structures)
To use Docling, simply install docling from your package manager, e.g. pip:
pip install doclingWorks on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
To convert individual documents with python, use convert(), for example:
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[...]"More advanced usage options are available in the docs.
Docling has a built-in CLI to run conversions.
docling https://arxiv.org/pdf/2206.01062You can also use 🥚SmolDocling and other VLMs via Docling CLI:
docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062This will use MLX acceleration on supported Apple Silicon hardware.
Read more here
Check out Docling's documentation, for details on installation, usage, concepts, recipes, extensions, and more.
Go hands-on with our examples, demonstrating how to address different application use cases with Docling.
To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.
Please feel free to connect with us using the discussion section.
For more details on Docling's inner workings, check out the Docling Technical Report.
Please read Contributing to Docling for details.
If you use Docling in your projects, please consider citing the following:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {Docling Technical Report},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
doi = {10.48550/arXiv.2408.09869},
version = {1.0.0},
year = {2024}
}The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
Docling is hosted as a project in the LF AI & Data Foundation.
The project was started by the AI for knowledge team at IBM Research Zurich.