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Custom conversion
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Batch conversion
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VLM pipeline with remote model
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ASR pipeline with Whisper
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Table export
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RAG with Azure AI Search
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RAG with Granite [↗]
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RAG with Milvus
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RAG with OpenSearch
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RAG with Weaviate
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Retrieval with Qdrant
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RAG with MongoDB + VoyageAI
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RAG with Vault PII transform [↗]
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<article class="md-content__inner md-typeset">
<h1>Installation</h1>
<p>To use Docling, simply install <code>docling</code> from your Python package manager, e.g. pip:
<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>docling
</code></pre></div></p>
<p>Works on macOS, Linux, and Windows, with support for both x86_64 and arm64 architectures.</p>
<details>
<summary>Alternative PyTorch distributions</summary>
<p>The Docling models depend on the <a href="https://pytorch.org/">PyTorch</a> library.
Depending on your architecture, you might want to use a different distribution of <code>torch</code>.
For example, you might want support for different accelerator or for a cpu-only version.
All the different ways for installing <code>torch</code> are listed on their website <a href="https://pytorch.org/">https://pytorch.org/</a>.</p>
<p>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</p>
<div class="highlight"><pre><span></span><code><span class="c1"># Example for installing on the Linux cpu-only version</span>
pip<span class="w"> </span>install<span class="w"> </span>docling<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/cpu
</code></pre></div>
</details>
<details>
<summary>Installation on macOS Intel (x86_64)</summary>
<p>When installing Docling on macOS with Intel processors, you might encounter errors with PyTorch compatibility.
This happens because newer PyTorch versions (2.6.0+) no longer provide wheels for Intel-based Macs.</p>
<p>If you're using an Intel Mac, install Docling with compatible PyTorch
<strong>Note:</strong> PyTorch 2.2.2 requires Python 3.12 or lower. Make sure you're not using Python 3.13+.</p>
<div class="highlight"><pre><span></span><code><span class="c1"># For uv users</span>
uv<span class="w"> </span>add<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.2.2<span class="w"> </span><span class="nv">torchvision</span><span class="o">==</span><span class="m">0</span>.17.2<span class="w"> </span>docling
<span class="c1"># For pip users</span>
pip<span class="w"> </span>install<span class="w"> </span><span class="s2">&quot;docling[mac_intel]&quot;</span>
<span class="c1"># For Poetry users</span>
poetry<span class="w"> </span>add<span class="w"> </span>docling
</code></pre></div>
</details>
<h2 id="available-extras">Available extras</h2>
<p>The <code>docling</code> package is designed to offer a working solution for the Docling default options.
Some Docling functionalities require additional third-party packages and are therefore installed only if selected as extras (or installed independently).</p>
<p>The following table summarizes the extras available in the <code>docling</code> package. They can be activated with:
<code>pip install "docling[NAME1,NAME2]"</code></p>
<table>
<thead>
<tr>
<th>Extra</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>asr</code></td>
<td>Installs dependencies for running the ASR pipeline.</td>
</tr>
<tr>
<td><code>vlm</code></td>
<td>Installs dependencies for running the VLM pipeline.</td>
</tr>
<tr>
<td><code>easyocr</code></td>
<td>Installs the <a href="https://github.com/JaidedAI/EasyOCR">EasyOCR</a> OCR engine.</td>
</tr>
<tr>
<td><code>tesserocr</code></td>
<td>Installs the Tesseract binding for using it as OCR engine.</td>
</tr>
<tr>
<td><code>ocrmac</code></td>
<td>Installs the OcrMac OCR engine.</td>
</tr>
<tr>
<td><code>rapidocr</code></td>
<td>Installs the <a href="https://github.com/RapidAI/RapidOCR">RapidOCR</a> OCR engine with <a href="https://github.com/microsoft/onnxruntime/">onnxruntime</a> backend.</td>
</tr>
</tbody>
</table>
<h3 id="ocr-engines">OCR engines</h3>
<p>Docling supports multiple OCR engines for processing scanned documents. The current version provides
the following engines.</p>
<table>
<thead>
<tr>
<th>Engine</th>
<th>Installation</th>
<th>Usage</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://github.com/JaidedAI/EasyOCR">EasyOCR</a></td>
<td><code>easyocr</code> extra or via <code>pip install easyocr</code>.</td>
<td><code>EasyOcrOptions</code></td>
</tr>
<tr>
<td>Tesseract</td>
<td>System dependency. See description for Tesseract and Tesserocr below.</td>
<td><code>TesseractOcrOptions</code></td>
</tr>
<tr>
<td>Tesseract CLI</td>
<td>System dependency. See description below.</td>
<td><code>TesseractCliOcrOptions</code></td>
</tr>
<tr>
<td>OcrMac</td>
<td>System dependency. See description below.</td>
<td><code>OcrMacOptions</code></td>
</tr>
<tr>
<td><a href="https://github.com/RapidAI/RapidOCR">RapidOCR</a></td>
<td><code>rapidocr</code> extra can or via <code>pip install rapidocr onnxruntime</code></td>
<td><code>RapidOcrOptions</code></td>
</tr>
<tr>
<td><a href="https://github.com/felixdittrich92/OnnxTR">OnnxTR</a></td>
<td>Can be installed via the plugin system <code>pip install "docling-ocr-onnxtr[cpu]"</code>. Please take a look at <a href="https://github.com/felixdittrich92/docling-OCR-OnnxTR">docling-OCR-OnnxTR</a>.</td>
<td><code>OnnxtrOcrOptions</code></td>
</tr>
</tbody>
</table>
<p>The Docling <code>DocumentConverter</code> allows to choose the OCR engine with the <code>ocr_options</code> settings. For example</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.base_models</span><span class="w"> </span><span class="kn">import</span> <span class="n">ConversionStatus</span><span class="p">,</span> <span class="n">PipelineOptions</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.datamodel.pipeline_options</span><span class="w"> </span><span class="kn">import</span> <span class="n">PipelineOptions</span><span class="p">,</span> <span class="n">EasyOcrOptions</span><span class="p">,</span> <span class="n">TesseractOcrOptions</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">docling.document_converter</span><span class="w"> </span><span class="kn">import</span> <span class="n">DocumentConverter</span>
<span class="n">pipeline_options</span> <span class="o">=</span> <span class="n">PipelineOptions</span><span class="p">()</span>
<span class="n">pipeline_options</span><span class="o">.</span><span class="n">do_ocr</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">pipeline_options</span><span class="o">.</span><span class="n">ocr_options</span> <span class="o">=</span> <span class="n">TesseractOcrOptions</span><span class="p">()</span> <span class="c1"># Use Tesseract</span>
<span class="n">doc_converter</span> <span class="o">=</span> <span class="n">DocumentConverter</span><span class="p">(</span>
<span class="n">pipeline_options</span><span class="o">=</span><span class="n">pipeline_options</span><span class="p">,</span>
<span class="p">)</span>
</code></pre></div>
<details>
<summary>Tesseract installation</summary>
<p><a href="https://github.com/tesseract-ocr/tesseract">Tesseract</a> 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
<code>TESSDATA_PREFIX</code> environment variable (note that it must terminate with a slash <code>/</code>).</p>
<div class="tabbed-set tabbed-alternate" data-tabs="1:3"><input checked="checked" id="macos-via-homebrew" name="__tabbed_1" type="radio" /><input id="debian-based" name="__tabbed_1" type="radio" /><input id="rhel" name="__tabbed_1" type="radio" /><div class="tabbed-labels"><label for="macos-via-homebrew">macOS (via <a href="https://brew.sh/">Homebrew</a>)</label><label for="debian-based">Debian-based</label><label for="rhel">RHEL</label></div>
<div class="tabbed-content">
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="go">brew install tesseract leptonica pkg-config</span>
<span class="go">TESSDATA_PREFIX=/opt/homebrew/share/tessdata/</span>
<span class="go">echo &quot;Set TESSDATA_PREFIX=${TESSDATA_PREFIX}&quot;</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="go">apt-get install tesseract-ocr tesseract-ocr-eng libtesseract-dev libleptonica-dev pkg-config</span>
<span class="go">TESSDATA_PREFIX=$(dpkg -L tesseract-ocr-eng | grep tessdata$)</span>
<span class="go">echo &quot;Set TESSDATA_PREFIX=${TESSDATA_PREFIX}&quot;</span>
</code></pre></div>
</div>
<div class="tabbed-block">
<div class="highlight"><pre><span></span><code><span class="go">dnf install tesseract tesseract-devel tesseract-langpack-eng tesseract-osd leptonica-devel</span>
<span class="go">TESSDATA_PREFIX=/usr/share/tesseract/tessdata/</span>
<span class="go">echo &quot;Set TESSDATA_PREFIX=${TESSDATA_PREFIX}&quot;</span>
</code></pre></div>
</div>
</div>
</div>
<p><h4>Linking to Tesseract</h4>
The most efficient usage of the Tesseract library is via linking. Docling is using
the <a href="https://github.com/sirfz/tesserocr">Tesserocr</a> package for this.</p>
<p>If you get into installation issues of Tesserocr, we suggest using the following
installation options:</p>
<div class="highlight"><pre><span></span><code><span class="go">pip uninstall tesserocr</span>
<span class="go">pip install --no-binary :all: tesserocr</span>
</code></pre></div>
</details>
<h2 id="development-setup">Development setup</h2>
<p>To develop Docling features, bugfixes etc., install as follows from your local clone's root dir:</p>
<div class="highlight"><pre><span></span><code>uv<span class="w"> </span>sync<span class="w"> </span>--all-extras
</code></pre></div>
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