Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
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* feat: add support for `ocrmac` OCR engine on macOS - Integrates `ocrmac` as an OCR engine option for macOS users. - Adds configuration options and dependencies for `ocrmac`. - Updates documentation to reflect new engine support. This change allows macOS users to utilize `ocrmac` for improved OCR performance and compatibility. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * updated the poetry lock Signed-off-by: Suhwan Seo <nuridol@gmail.com> * Fix linting issues, update CLI docs, and add error for ocrmac use on non-Mac systems - Resolved formatting and linting issues - Updated `--ocr-engine` CLI option documentation for `ocrmac` - Added RuntimeError for attempts to use `ocrmac` on non-Mac platforms Signed-off-by: Suhwan Seo <nuridol@gmail.com> * feat: add support for `ocrmac` OCR engine on macOS - Integrates `ocrmac` as an OCR engine option for macOS users. - Adds configuration options and dependencies for `ocrmac`. - Updates documentation to reflect new engine support. This change allows macOS users to utilize `ocrmac` for improved OCR performance and compatibility. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * docs: update examples and installation for ocrmac support - Added `OcrMacOptions` to `custom_convert.py` and `full_page_ocr.py` examples. - Included usage comments and examples for `OcrMacOptions` in OCR pipelines. - Updated installation guide to include instructions for installing `ocrmac`, noting macOS version requirements (10.15+). - Highlighted that `ocrmac` leverages Apple's Vision framework as an OCR backend. This enhances documentation for users working on macOS to leverage `ocrmac` effectively. Signed-off-by: Suhwan Seo <nuridol@gmail.com> * fix: update `ocrmac` dependency with macOS-specific marker - Added `sys_platform == 'darwin'` marker to the `ocrmac` dependency in `pyproject.toml` to specify macOS compatibility. - Updated the content hash in `poetry.lock` to reflect the changes. This ensures the `ocrmac` dependency is only installed on macOS systems. Signed-off-by: Suhwan Seo <nuridol@gmail.com> --------- Signed-off-by: Suhwan Seo <nuridol@gmail.com> Co-authored-by: Suhwan Seo <nuridol@gmail.com> |
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Docling
Docling parses documents and exports them to the desired format with ease and speed.
Features
- 🗂️ 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
- 🤖 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!
Coming soon
- ♾️ Equation & code extraction
- 📝 Metadata extraction, including title, authors, references & language
- 🦜🔗 Native LangChain extension
Installation
To use Docling, simply install docling from your package manager, e.g. pip:
pip install docling
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
Getting started
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.
Get help and support
Please feel free to connect with us using the discussion section.
Technical report
For more details on Docling's inner workings, check out the Docling Technical Report.
Contributing
Please read Contributing to Docling for details.
References
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}
}
License
The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
IBM ❤️ Open Source AI
Docling has been brought to you by IBM.