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Author: Christoph Auer, Michele Dolfi, Maxim Lysak, Nikos Livathinos, Ahmed Nassar, Panos Vagenas, Peter Staar

Requires Python: <4.0,>=3.10

Classifiers

Development Status

5 - Production/Stable

Intended Audience

Developers
Science/Research

Operating System

MacOS :: MacOS X
Microsoft :: Windows
POSIX :: Linux

Programming Language

Python :: 3
Python :: 3.10
Python :: 3.11
Python :: 3.12
Python :: 3.13
Python :: 3.14

Topic

Scientific/Engineering :: Artificial Intelligence

Docling Slim

Lightweight SDK for parsing documents with minimal dependencies and opt-in extras

Docling Slim is a minimal-dependency version of Docling that allows you to install only the components you need. It provides the core document processing functionality with ~50MB of base dependencies, and you can add specific features through optional extras.

When to Use Docling Slim

Use docling (recommended): If you want the full-featured experience with all standard capabilities
Use docling-slim: If you need fine-grained control over dependencies or want to minimize installation size

For Most Users: Use the Main Docling Package

We recommend most users install the full-featured docling package instead:

pip install docling

The docling package includes all standard features, the CLI tools, and is the easiest way to get started. Visit the main Docling documentation for complete guides and examples.

Installation

With Specific Features

# PDF support with local models
pip install docling-slim[format-pdf,models-local]

# Office formats only
pip install docling-slim[format-office]

# PDF + CLI
pip install docling-slim[format-pdf,cli]

# Docling service client for using the Docling Serve API
pip install docling-slim[service-client]

Available Extras

Convenience Bundles

Extra	Description	Use Case
`standard`	All standard features (same as `docling` package)	Full-featured usage
`all`	All available extras	Complete installation

CLI

Extra	Description	Use Case
`cli`	Command-line interface (typer, rich)	CLI tools (docling, docling-tools)

Core Components

Extra	Description	Use Case
`convert-core`	Core conversion components (numpy, pillow, scipy)	Basic document conversion
`extract-core`	Structured information extraction	Data extraction from documents

Format Support

PDF Formats

Extra	Description	Use Case
`format-pdf`	PDF parsing (pypdfium2 + docling-parse)	PDF documents
`format-pdf-pypdfium2`	PDF rendering only	Lightweight PDF support
`format-pdf-docling`	Advanced PDF parsing	Complex PDF layouts

Office Formats (office = docx + pptx + xlsx)

Extra	Description	Use Case
`format-office`	All Office formats	Microsoft Office documents
`format-docx`	Microsoft Word documents	.docx files
`format-pptx`	Microsoft PowerPoint	.pptx files
`format-xlsx`	Microsoft Excel	.xlsx files

Web Formats (web = html + markdown)

Extra	Description	Use Case
`format-web`	HTML and Markdown	Web content
`format-html`	HTML parsing	Web pages and HTML files
`format-markdown`	Markdown parsing	.md files

Other Formats

Extra	Description	Use Case
`format-latex`	LaTeX documents	.tex files
`format-xml-xbrl`	XBRL financial reports	Financial documents
`format-html-render`	HTML rendering with Playwright	Dynamic web content
`format-audio`	Audio transcription (Whisper)	.wav, .mp3 files

OCR Engines

Extra	Description	Use Case
`feat-ocr-rapidocr`	RapidOCR (lightweight)	Fast OCR
`feat-ocr-rapidocr-onnx`	RapidOCR with ONNX runtime	Optimized OCR
`feat-ocr-easyocr`	EasyOCR	Multi-language OCR
`feat-ocr-tesserocr`	Tesseract OCR	High-accuracy OCR
`feat-ocr-mac`	macOS native OCR	macOS only

Models

Extra	Description	Use Case
`models-local`	Local PyTorch models	GPU/CPU inference
`models-remote`	Remote model serving (Triton)	Production deployments
`models-onnxruntime`	ONNX Runtime acceleration	Optimized inference
`models-vlm-inline`	Vision Language Models	Image understanding, inline processing