Docling parses documents and exports them to the desired format with ease and speed.
- 🗂️ Reads popular document formats (PDF, DOCX, PPTX, Images, HTML, AsciiDoc, Markdown) and exports to Markdown and JSON
- 📑 Advanced PDF document understanding including page layout, reading order & table structures
- 🧩 Unified, expressive DoclingDocument representation format
- 📝 Metadata extraction, including title, authors, references & language
- 🤖 Seamless LlamaIndex 🦙 & LangChain 🦜🔗 integration for powerful RAG / QA applications
- 🔍 OCR support for scanned PDFs
- 💻 Simple and convenient CLI
Explore the documentation to discover plenty examples and unlock the full power of Docling!
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, 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[...]"Check out Getting started. You will find lots of tuning options to leverage all the advanced capabilities.
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.