docs: document CLI, minor README revamp (#100)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
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README.md
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README.md
@ -22,8 +22,9 @@ Docling bundles PDF document conversion to JSON and Markdown in an easy, self-co
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* ⚡ Converts any PDF document to JSON or Markdown format, stable and lightning fast
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* 📑 Understands detailed page layout, reading order and recovers table structures
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* 📝 Extracts metadata from the document, such as title, authors, references and language
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* 🔍 Optionally applies OCR (use with scanned PDFs)
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* 🔍 Includes OCR support for scanned PDFs
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* 🤖 Integrates easily with LLM app / RAG frameworks like 🦙 LlamaIndex and 🦜🔗 LangChain
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* 💻 Provides a simple and convenient CLI
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## Installation
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@ -35,31 +36,33 @@ pip install docling
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> [!NOTE]
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> Works on macOS and Linux environments. Windows platforms are currently not tested.
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<details>
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<summary><b>Alternative PyTorch distributions</b></summary>
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### Use alternative PyTorch distributions
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The Docling models depend on the [PyTorch](https://pytorch.org/) library.
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Depending on your architecture, you might want to use a different distribution of `torch`.
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For example, you might want support for different accelerator or for a cpu-only version.
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All the different ways for installing `torch` are listed on their website <https://pytorch.org/>.
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The Docling models depend on the [PyTorch](https://pytorch.org/) library.
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Depending on your architecture, you might want to use a different distribution of `torch`.
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For example, you might want support for different accelerator or for a cpu-only version.
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All the different ways for installing `torch` are listed on their website <https://pytorch.org/>.
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One common situation is the installation on Linux systems with cpu-only support.
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In this case, we suggest the installation of Docling with the following options
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One common situation is the installation on Linux systems with cpu-only support.
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In this case, we suggest the installation of Docling with the following options
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```bash
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# Example for installing on the Linux cpu-only version
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pip install docling --extra-index-url https://download.pytorch.org/whl/cpu
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```
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</details>
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```bash
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# Example for installing on the Linux cpu-only version
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pip install docling --extra-index-url https://download.pytorch.org/whl/cpu
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```
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<details>
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<summary><b>Docling development setup</b></summary>
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To develop for Docling (features, bugfixes etc.), install as follows from your local clone's root dir:
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```bash
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poetry install --all-extras
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```
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</details>
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### Development setup
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To develop for Docling, you need Python 3.10 / 3.11 / 3.12 and Poetry. You can then install from your local clone's root dir:
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```bash
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poetry install --all-extras
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```
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## Usage
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## Getting started
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### Convert a single document
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@ -70,7 +73,6 @@ from docling.document_converter import DocumentConverter
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source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL
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converter = DocumentConverter()
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result = converter.convert_single(source)
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print(result.render_as_markdown()) # output: "## Docling Technical Report[...]"
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print(result.render_as_doctags()) # output: "<document><title><page_1><loc_20>..."
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```
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@ -86,6 +88,51 @@ python examples/batch_convert.py
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```
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The output of the above command will be written to `./scratch`.
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### CLI
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You can also use Docling directly from your command line to convert individual files —be it local or by URL— or whole directories.
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A simple example would look like this:
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```console
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docling https://arxiv.org/pdf/2206.01062
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```
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To see all available options (export formats etc.) run `docling --help`.
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<details>
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<summary><b>CLI reference</b></summary>
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Here are the available options as of this writing (for an up-to-date listing, run `docling --help`):
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```console
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$ docling --help
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Usage: docling [OPTIONS] source
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╭─ Arguments ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
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│ * input_sources source PDF files to convert. Can be local file / directory paths or URL. [default: None] [required] │
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╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
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│ --json --no-json If enabled the document is exported as JSON. [default: no-json] │
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│ --md --no-md If enabled the document is exported as Markdown. [default: md] │
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│ --txt --no-txt If enabled the document is exported as Text. [default: no-txt] │
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│ --doctags --no-doctags If enabled the document is exported as Doc Tags. [default: no-doctags] │
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│ --ocr --no-ocr If enabled, the bitmap content will be processed using OCR. [default: ocr] │
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│ --backend [pypdfium2|docling] The PDF backend to use. [default: docling] │
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│ --output PATH Output directory where results are saved. [default: .] │
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│ --version Show version information. │
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│ --help Show this message and exit. │
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╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
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```
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</details>
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### RAG
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Check out the following examples showcasing RAG using Docling with standard LLM application frameworks:
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- [Basic RAG pipeline with 🦙 LlamaIndex](https://github.com/DS4SD/docling/tree/main/examples/rag_llamaindex.ipynb)
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- [Basic RAG pipeline with 🦜🔗 LangChain](https://github.com/DS4SD/docling/tree/main/examples/rag_langchain.ipynb)
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## Advanced features
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### Adjust pipeline features
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The example file [custom_convert.py](https://github.com/DS4SD/docling/blob/main/examples/custom_convert.py) contains multiple ways
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@ -144,11 +191,6 @@ results = doc_converter.convert(conv_input)
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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.
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### RAG
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Check out the following examples showcasing RAG using Docling with standard LLM application frameworks:
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- [Basic RAG pipeline with 🦙 LlamaIndex](https://github.com/DS4SD/docling/tree/main/examples/rag_llamaindex.ipynb)
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- [Basic RAG pipeline with 🦜🔗 LangChain](https://github.com/DS4SD/docling/tree/main/examples/rag_langchain.ipynb)
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## Technical report
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For more details on Docling's inner workings, check out the [Docling Technical Report](https://arxiv.org/abs/2408.09869).
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