nuoyi 0.4.17

A simple tool to transform PDF and DOCX to Markdown using marker-pdf

NuoYi

A simple tool to transform PDF and DOCX to Markdown.

中文文档

NuoYi uses marker-pdf for high-quality PDF conversion with OCR and layout detection. All processing is done fully offline after the initial model download.

Features

• 9 PDF Engines: marker, mineru, docling, pymupdf, pdfplumber, llamaparse, mathpix, mineru-cloud, doc2x
• PDF to Markdown: High-quality conversion with multiple engine options
• DOCX to Markdown: Native support for Microsoft Word documents
• Automatic GPU/CPU Selection: Detects available VRAM and falls back to CPU if needed
• Smart Engine Selection: Auto-selects the best engine based on available resources
• Batch Processing: Convert entire directories of documents
• GUI Interface: PySide6-based graphical interface for easy batch conversion
• Image Extraction: Automatically extracts and saves images from PDFs
• Multi-language Support: 10 languages including Chinese, English, Japanese, etc.
• Cloud Engines: LlamaParse, Mathpix, MinerU Cloud, Doc2x for zero-GPU environments

Installation

Requires Python 3.10 or higher (marker-pdf requires Python >= 3.10).

From PyPI

pip install nuoyi

With GUI support

pip install nuoyi[gui]

With NVIDIA CUDA support (IMPORTANT for GPU users)

If you encounter CUBLAS_STATUS_NOT_INITIALIZED errors when using GPU, install the CUDA libraries:

pip install nuoyi[cuda]

Or manually:

pip install nvidia-cublas nvidia-cuda-runtime nvidia-cufft nvidia-cusolver nvidia-cusparse nvidia-curand nvidia-cuda-nvrtc nvidia-nvtx

Why is this needed? PyTorch's CUDA packages sometimes don't include all required NVIDIA libraries. The nvidia-* packages ensure complete CUDA library installation for marker-pdf to work properly.

Full installation with all features

pip install nuoyi[all-cuda]

From source

git clone https://github.com/cycleuser/NuoYi.git
cd NuoYi
pip install -e .

macOS Installation Notes

marker-pdf fully supports macOS (both Intel and Apple Silicon). On macOS, PyTorch is installed automatically without CUDA. Apple Silicon Macs can use MPS acceleration via --device mps.

If you encounter torch installation issues on macOS, install the CPU-only version of PyTorch first:

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install nuoyi

AMD ROCm GPU Setup (Linux)

NuoYi supports AMD Radeon GPUs (RX 5000/6000/7000 series) on Linux via ROCm.

Supported GPUs:

• RX 7900 XTX/XT, RX 7800/7700/7600 (RDNA 3)
• RX 6900/6800/6700/6600 (RDNA 2)
• RX 5700/5600/5500 (RDNA)
• ⚠️ RX 580/590 (Polaris) are NOT supported by ROCm

Step 1: Create a dedicated conda environment

conda create -n rocm python=3.12 -y
conda activate rocm

Step 2: Install ROCm PyTorch

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2

Verify: python -c "import torch; print(torch.version.hip)" should output 6.2.xxxxx

Step 3: Install NuoYi (without touching torch)

⚠️ IMPORTANT: Do NOT use pip install -e ".[dev]" as it will replace ROCm torch with CUDA version.

# Install NuoYi without dependencies
pip install --no-deps -e .

# Install marker-pdf without dependencies
pip install --no-deps marker-pdf

# Install remaining dependencies (no-deps to avoid torch replacement)
pip install pydantic python-docx PyMuPDF Pillow flask pytest ruff \
    python-dotenv rapidfuzz "regex>=2024.4.28,<2025.0.0" \
    "scikit-learn>=1.6.1,<2.0.0" tqdm "transformers>=4.45.2,<5.0.0" \
    "Pillow>=10.1.0,<11.0.0" google-genai markdown2 markdownify \
    "openai>=1.65.2,<2.0.0" pdftext pre-commit pydantic-settings \
    surya-ocr "opencv-python-headless==4.11.0.86" --no-deps

Step 4: Run with ROCm

# Single file
nuoyi input.pdf --device rocm -o output.md

# Batch conversion
nuoyi ./papers --batch --device rocm --output ./output

NuoYi automatically configures ROCm environment variables (HSA_ENABLE_SDMA=0, TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1, and auto-detected HSA_OVERRIDE_GFX_VERSION).

For detailed troubleshooting, see AMD_ROCM_SETUP.md.

PDF Engines

NuoYi supports 9 PDF conversion engines:

Local Engines (Free, Offline)

Engine	Install	GPU	OCR	Models	Best For
marker	pip install marker-pdf	Recommended	Yes	~3GB	Best quality overall
mineru	pip install magic-pdf[full]	Optional	Yes	~1.5GB	Chinese documents
docling	pip install docling	Optional	Yes	~1.5GB	Balanced quality
pymupdf	pip install pymupdf4llm	No	No	None	Fastest, digital PDFs
pdfplumber	pip install pdfplumber	No	No	None	Tables, lightweight

Cloud Engines (API Key Required)

Engine	Install	Best For	API Key
llamaparse	pip install llama-parse	Excellent quality	LLAMA_CLOUD_API_KEY
mathpix	pip install requests	Math/science documents	MATHPIX_APP_ID + MATHPIX_APP_KEY
mineru-cloud	pip install requests	Chinese docs (online)	MINERU_API_KEY
doc2x	pip install requests	Formulas, LaTeX	DOC2X_API_KEY

Engine Selection

# Auto-select (default: best available engine)
nuoyi paper.pdf

# Use specific engine
nuoyi paper.pdf --engine mineru       # Great for Chinese
nuoyi paper.pdf --engine docling     # Balanced quality
nuoyi paper.pdf --engine pymupdf     # Fastest, no GPU
nuoyi paper.pdf --engine doc2x       # Cloud, best formulas
nuoyi paper.pdf --engine mineru-cloud  # Cloud, Chinese docs

# No GPU? Use lightweight engines
nuoyi paper.pdf --engine pymupdf       # Digital PDFs, fastest
nuoyi paper.pdf --engine pdfplumber    # Tables, lightweight
nuoyi paper.pdf --engine doc2x         # Cloud, no local models needed

Usage

Command Line Interface

# Convert a single PDF file
nuoyi paper.pdf

# Specify output file
nuoyi paper.pdf -o output/result.md

# Convert a DOCX file
nuoyi document.docx -o document.md

# Batch convert all files in a directory
nuoyi ./papers --batch

# Batch convert with custom output directory
nuoyi ./papers --batch -o ./output

# Force CPU mode (for low VRAM GPUs)
nuoyi paper.pdf --device cpu

# Force OCR even for digital PDFs
nuoyi paper.pdf --force-ocr

# Specify page range
nuoyi paper.pdf --page-range "0-5,10,15-20"

# Specify languages
nuoyi paper.pdf --langs "zh,en,ja"

# Disable OCR models for digital PDFs (saves ~1.5GB VRAM)
nuoyi paper.pdf --disable-ocr-models

# Low VRAM mode for 4-6GB GPUs
nuoyi paper.pdf --low-vram

GUI Mode

nuoyi --gui

The GUI provides:

• Directory selection for input/output
• File list with status tracking
• Device selection (auto/CPU/CUDA)
• Force OCR option
• Page range and language configuration
• Real-time progress and logging

Startup interface:

Select input directory:

Configure device and options:

Conversion result (viewed in VS Code):

Python API

from nuoyi import MarkerPDFConverter, DocxConverter

# Convert PDF (full models, ~3GB VRAM)
pdf_converter = MarkerPDFConverter(
    force_ocr=False,
    langs="zh,en",
    device="auto"  # or "cpu", "cuda", "mps"
)
markdown_text, images = pdf_converter.convert_file("input.pdf")

# Convert PDF (minimal models for digital PDFs, ~1.5GB VRAM)
pdf_converter_minimal = MarkerPDFConverter(
    disable_ocr_models=True,  # Saves ~1.5GB VRAM
    langs="zh,en",
    device="auto"
)
markdown_text, images = pdf_converter_minimal.convert_file("digital.pdf")

# Convert PDF (low VRAM mode)
pdf_converter_low_vram = MarkerPDFConverter(
    low_vram=True,
    langs="zh,en",
    device="auto"
)
markdown_text, images = pdf_converter_low_vram.convert_file("input.pdf")

# Convert DOCX
docx_converter = DocxConverter()
markdown_text = docx_converter.convert_file("input.docx")

Supported Languages

Code	Language
zh	Chinese / 中文
en	English
ja	Japanese / 日本語
fr	French / Français
ru	Russian / Русский
de	German / Deutsch
es	Spanish / Español
pt	Portuguese / Português
it	Italian / Italiano
ko	Korean / 한국어

Use nuoyi --list-langs to see the full list. Default: zh,en.

Command Line Options

Option	Description
input	Input PDF/DOCX file or directory (with --batch)
-o, --output	Output file path (single file) or directory (batch mode)
--force-ocr	Force OCR even for digital PDFs with embedded text
--page-range	Page range to convert, e.g. '0-5,10,15-20'
--langs	Comma-separated languages (default: zh,en). See --list-langs
--list-langs	List all supported languages and exit
--batch	Process all PDF/DOCX files in the input directory
--device	Device for model inference: auto (default), cpu, cuda, or mps
--low-vram	Enable low VRAM mode for 4-6GB GPUs
--disable-ocr-models	Disable OCR models for digital PDFs (~1.5GB VRAM saved)
--gui	Launch PySide6 GUI mode
-V, --version	Show version and exit

Cloud Engines

NuoYi supports 4 cloud-based PDF engines that require no local GPU or models:

# LlamaParse - LlamaIndex cloud service
export LLAMA_CLOUD_API_KEY=your_key
nuoyi paper.pdf --engine llamaparse

# Mathpix - Best for math/scientific documents
export MATHPIX_APP_ID=your_app_id
export MATHPIX_APP_KEY=your_app_key
nuoyi paper.pdf --engine mathpix

# MinerU Cloud - Excellent for Chinese documents
export MINERU_API_KEY=your_key
nuoyi paper.pdf --engine mineru-cloud

# Doc2x - Best for formulas, supports PDF/DOCX/PPTX
export DOC2X_API_KEY=your_key
nuoyi paper.pdf --engine doc2x

Large PDFs (>50 pages) are automatically split into chunks for cloud processing.

Memory Management

NuoYi automatically manages GPU memory:

• Auto mode (default): Detects available VRAM and uses GPU if sufficient (>6GB free)
• CPU mode: Forces CPU processing (slower but no VRAM limit)
• CUDA mode: Forces GPU processing (may OOM on large PDFs)
• MPS mode: For Apple Silicon Macs

Low VRAM Options

For GPUs with limited VRAM (4-6GB):

1. Use --low-vram flag: Enables aggressive memory optimization

   nuoyi paper.pdf --low-vram
   

2. Disable OCR models (for digital PDFs only): Saves ~1.5GB VRAM

   nuoyi paper.pdf --disable-ocr-models
   

   ⚠️ Warning: This disables OCR features. Only suitable for:

   • Digital PDFs with embedded text (not scanned documents)
   • PDFs without complex tables requiring OCR
   • PDFs without mathematical formulas requiring OCR

3. Use CPU mode: No VRAM limitation but slower

   nuoyi paper.pdf --device cpu
   

4. Use pymupdf engine: Fast, no GPU required

   nuoyi paper.pdf --engine pymupdf
   

If CUDA out of memory occurs during conversion, NuoYi automatically retries with aggressive memory cleanup.

Dependencies

Required

• marker-pdf>=1.0.0 - PDF conversion engine
• PyMuPDF>=1.23.0 - PDF page counting
• python-docx>=0.8.11 - DOCX conversion
• Pillow>=9.0.0 - Image processing

Optional

• PySide6>=6.5.0 - GUI support (install with pip install nuoyi[gui])

Model Download

Download Location

Models are downloaded automatically on first run and stored in:

~/.cache/huggingface/hub/

The models are from Hugging Face and include:

• vikp/surya_det - Layout detection model
• vikp/surya_rec - Text recognition model
• vikp/surya_order - Reading order model
• Other marker-pdf related models

Total size: approximately 2-3 GB.

For Users in China

Hugging Face may be blocked or slow in mainland China due to GFW. You can use a mirror:

# Set Hugging Face mirror (add to ~/.bashrc or run before nuoyi)
export HF_ENDPOINT=https://hf-mirror.com

# Then run nuoyi normally
nuoyi paper.pdf

Alternatively, you can download models manually and place them in the cache directory.

Custom Model Path

The current version does not support custom model paths to keep the tool simple and avoid configuration complexity. Models are always stored in the default Hugging Face cache location.

Notes

• After initial model download, everything works fully offline
• Use --device cpu if you encounter CUDA out of memory errors
• Legacy .doc format is not supported; convert to .docx first

Agent Integration (OpenAI Function Calling)

NuoYi exposes OpenAI-compatible tools for LLM agents:

from nuoyi.tools import TOOLS, dispatch

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=TOOLS,
)

result = dispatch(
    tool_call.function.name,
    tool_call.function.arguments,
)

CLI Help

License

GPL-3.0 License - see LICENSE for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

• marker-pdf - The excellent PDF conversion engine
• surya - OCR and layout detection models