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* adding rapidocr engine for ocr in docling Signed-off-by: swayam-singhal <swayam.singhal@inito.com> * fixing styling format Signed-off-by: Swaymaw <swaymaw@gmail.com> * updating pyproject.toml and poetry.lock to fix ci bugs Signed-off-by: Swaymaw <swaymaw@gmail.com> * help poetry pinning for python3.9 Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * simplifying rapidocr options so that device can be changed using a single option for all models Signed-off-by: Swaymaw <swaymaw@gmail.com> * fix styling issues and small bug in rapidOcrOptions Signed-off-by: Swaymaw <swaymaw@gmail.com> * use default device until we enable global management Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: swayam-singhal <swayam.singhal@inito.com> Signed-off-by: Swaymaw <swaymaw@gmail.com> Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Co-authored-by: swayam-singhal <swayam.singhal@inito.com> Co-authored-by: Michele Dolfi <dol@zurich.ibm.com> |
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| CHANGELOG.md | ||
| CITATION.cff | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
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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, XLSX, 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.