# Docling
[](https://arxiv.org/abs/2408.09869)
[](https://pypi.org/project/docling/)
[](https://python-poetry.org/)
[](https://github.com/psf/black)
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[](https://opensource.org/licenses/MIT)
Docling bundles PDF document conversion to JSON and Markdown in an easy, self-contained package.
## Features
* ⚡ Converts any PDF document to JSON or Markdown format, stable and lightning fast
* 📑 Understands detailed page layout, reading order and recovers table structures
* 📝 Extracts metadata from the document, such as title, authors, references and language
* 🔍 Includes OCR support for scanned PDFs
* 🤖 Integrates easily with LLM app / RAG frameworks like 🦙 LlamaIndex and 🦜🔗 LangChain
* 💻 Provides a simple and convenient CLI
## Installation
To use Docling, simply install `docling` from your package manager, e.g. pip:
```bash
pip install docling
```
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
Alternative PyTorch distributions
The Docling models depend on the [PyTorch](https://pytorch.org/) library.
Depending on your architecture, you might want to use a different distribution of `torch`.
For example, you might want support for different accelerator or for a cpu-only version.
All the different ways for installing `torch` are listed on their website .
One common situation is the installation on Linux systems with cpu-only support.
In this case, we suggest the installation of Docling with the following options
```bash
# Example for installing on the Linux cpu-only version
pip install docling --extra-index-url https://download.pytorch.org/whl/cpu
```
Alternative OCR engines
Docling supports multiple OCR engines for processing scanned documents. The current version provides
the following engines.
| Engine | Installation | Usage |
| ------ | ------------ | ----- |
| [EasyOCR](https://github.com/JaidedAI/EasyOCR) | Default in Docling or via `pip install easyocr`. | `EasyOcrOptions` |
| Tesseract | System dependency. See description for Tesseract and Tesserocr below. | `TesseractOcrOptions` |
| Tesseract CLI | System dependency. See description below. | `TesseractCliOcrOptions` |
The Docling `DocumentConverter` allows to choose the OCR engine with the `ocr_options` settings. For example
```python
from docling.datamodel.base_models import ConversionStatus, PipelineOptions
from docling.datamodel.pipeline_options import PipelineOptions, EasyOcrOptions, TesseractOcrOptions
from docling.document_converter import DocumentConverter
pipeline_options = PipelineOptions()
pipeline_options.do_ocr = True
pipeline_options.ocr_options = TesseractOcrOptions() # Use Tesseract
doc_converter = DocumentConverter(
pipeline_options=pipeline_options,
)
```
#### Tesseract installation
[Tesseract](https://github.com/tesseract-ocr/tesseract) is a popular OCR engine which is available
on most operating systems. For using this engine with Docling, Tesseract must be installed on your
system, using the packaging tool of your choice. Below we provide example commands.
After installing Tesseract you are expected to provide the path to its language files using the
`TESSDATA_PREFIX` environment variable (note that it must terminate with a slash `/`).
For macOS, we reccomend using [Homebrew](https://brew.sh/).
```console
brew install tesseract leptonica pkg-config
TESSDATA_PREFIX=/opt/homebrew/share/tessdata/
echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}"
```
For Debian-based systems.
```console
apt-get install tesseract-ocr tesseract-ocr-eng libtesseract-dev libleptonica-dev pkg-config
TESSDATA_PREFIX=$(dpkg -L tesseract-ocr-eng | grep tessdata$)
echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}"
```
For RHEL systems.
```console
dnf install tesseract tesseract-devel tesseract-langpack-eng leptonica-devel
TESSDATA_PREFIX=/usr/share/tesseract/tessdata/
echo "Set TESSDATA_PREFIX=${TESSDATA_PREFIX}"
```
#### Linking to Tesseract
The most efficient usage of the Tesseract library is via linking. Docling is using
the [Tesserocr](https://github.com/sirfz/tesserocr) package for this.
If you get into installation issues of Tesserocr, we suggest using the following
installation options:
```console
pip uninstall tesserocr
pip install --no-binary :all: tesserocr
```
Docling development setup
To develop for Docling (features, bugfixes etc.), install as follows from your local clone's root dir:
```bash
poetry install --all-extras
```
## Getting started
### Convert a single document
To convert invidual PDF documents, use `convert_single()`, for example:
```python
from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL
converter = DocumentConverter()
result = converter.convert_single(source)
print(result.render_as_markdown()) # output: "## Docling Technical Report[...]"
print(result.render_as_doctags()) # output: "<page_1><loc_20>..."
```
### Convert a batch of documents
For an example of batch-converting documents, see [batch_convert.py](https://github.com/DS4SD/docling/blob/main/examples/batch_convert.py).
From a local repo clone, you can run it with:
```
python examples/batch_convert.py
```
The output of the above command will be written to `./scratch`.
### CLI
You can also use Docling directly from your command line to convert individual files —be it local or by URL— or whole directories.
A simple example would look like this:
```console
docling https://arxiv.org/pdf/2206.01062
```
To see all available options (export formats etc.) run `docling --help`.
<details>
<summary><b>CLI reference</b></summary>
Here are the available options as of this writing (for an up-to-date listing, run `docling --help`):
```console
$ docling --help
Usage: docling [OPTIONS] source
╭─ Arguments ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * input_sources source PDF files to convert. Can be local file / directory paths or URL. [default: None] [required] │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --json --no-json If enabled the document is exported as JSON. [default: no-json] │
│ --md --no-md If enabled the document is exported as Markdown. [default: md] │
│ --txt --no-txt If enabled the document is exported as Text. [default: no-txt] │
│ --doctags --no-doctags If enabled the document is exported as Doc Tags. [default: no-doctags] │
│ --ocr --no-ocr If enabled, the bitmap content will be processed using OCR. [default: ocr] │
│ --backend [pypdfium2|docling] The PDF backend to use. [default: docling] │
│ --output PATH Output directory where results are saved. [default: .] │
│ --version Show version information. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
```
</details>
### RAG
Check out the following examples showcasing RAG using Docling with standard LLM application frameworks:
- [Basic RAG pipeline with 🦙 LlamaIndex](https://github.com/DS4SD/docling/tree/main/examples/rag_llamaindex.ipynb)
- [Basic RAG pipeline with 🦜🔗 LangChain](https://github.com/DS4SD/docling/tree/main/examples/rag_langchain.ipynb)
## Advanced features
### Adjust pipeline features
The example file [custom_convert.py](https://github.com/DS4SD/docling/blob/main/examples/custom_convert.py) contains multiple ways
one can adjust the conversion pipeline and features.
#### Control pipeline options
You can control if table structure recognition or OCR should be performed by arguments passed to `DocumentConverter`:
```python
doc_converter = DocumentConverter(
artifacts_path=artifacts_path,
pipeline_options=PipelineOptions(
do_table_structure=False, # controls if table structure is recovered
do_ocr=True, # controls if OCR is applied (ignores programmatic content)
),
)
```
#### Control table extraction options
You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself.
This can improve output quality if you find that multiple columns in extracted tables are erroneously merged into one.
```python
from docling.datamodel.pipeline_options import PipelineOptions
pipeline_options = PipelineOptions(do_table_structure=True)
pipeline_options.table_structure_options.do_cell_matching = False # uses text cells predicted from table structure model
doc_converter = DocumentConverter(
artifacts_path=artifacts_path,
pipeline_options=pipeline_options,
)
```
Since docling 1.16.0: You can control which TableFormer mode you want to use. Choose between `TableFormerMode.FAST` (default) and `TableFormerMode.ACCURATE` (better, but slower) to receive better quality with difficult table structures.
```python
from docling.datamodel.pipeline_options import PipelineOptions, TableFormerMode
pipeline_options = PipelineOptions(do_table_structure=True)
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE # use more accurate TableFormer model
doc_converter = DocumentConverter(
artifacts_path=artifacts_path,
pipeline_options=pipeline_options,
)
```
### Impose limits on the document size
You can limit the file size and number of pages which should be allowed to process per document:
```python
conv_input = DocumentConversionInput.from_paths(
paths=[Path("./test/data/2206.01062.pdf")],
limits=DocumentLimits(max_num_pages=100, max_file_size=20971520)
)
```
### Convert from binary PDF streams
You can convert PDFs from a binary stream instead of from the filesystem as follows:
```python
buf = BytesIO(your_binary_stream)
docs = [DocumentStream(filename="my_doc.pdf", stream=buf)]
conv_input = DocumentConversionInput.from_streams(docs)
results = doc_converter.convert(conv_input)
```
### Limit resource usage
You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads.
### Chunking
You can perform a hierarchy-aware chunking of a Docling document as follows:
```python
from docling.document_converter import DocumentConverter
from docling_core.transforms.chunker import HierarchicalChunker
doc = DocumentConverter().convert_single("https://arxiv.org/pdf/2206.01062").output
chunks = list(HierarchicalChunker().chunk(doc))
# > [
# > ChunkWithMetadata(
# > path='$.main-text[0]',
# > text='DocLayNet: A Large Human-Annotated Dataset [...]',
# > page=1,
# > bbox=[107.30, 672.38, 505.19, 709.08]
# > ),
# > [...]
# > ]
```
## Technical report
For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).
## Contributing
Please read [Contributing to Docling](https://github.com/DS4SD/docling/blob/main/CONTRIBUTING.md) for details.
## References
If you use Docling in your projects, please consider citing the following:
```bib
@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.