Docling parses documents and exports them to the desired format with ease and speed.
- 🗂️ Reads popular document formats (PDF, DOCX, PPTX, XLSX, Images, HTML, AsciiDoc & Markdown) and exports to HTML, Markdown and JSON (with embedded and referenced images)
- 📑 Advanced PDF document understanding including page layout, reading order & table structures
- 🧩 Unified, expressive DoclingDocument representation format
- 🤖 Easy integration with 🦙 LlamaIndex & 🦜🔗 LangChain 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!
- ♾️ Equation & code extraction
- 📝 Metadata extraction, including title, authors, references & language
- 🦜🔗 Native LangChain extension
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[...]"More advanced usage options are available in the docs.
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 has been brought to you by IBM.
