Docling

Docling — Local Document → Markdown Converter

CLI tool that uses AI models (layout detection, OCR, table extraction) to convert documents into clean, structured Markdown.

Supported Formats

Office: PDF, DOCX, PPTX, XLSX
Images: PNG, JPG, TIFF (with OCR)
Web: HTML, Markdown
Data: CSV
Academic: LaTeX, XML (USPTO, JATS, METS)
Media: Audio/video transcripts (VTT)
Other: JSON (docling format), ASCIIDoc

When to Use

Scope boundary: docling handles files on disk. Web content is out of scope — use the right tool for the job.

Prerequisites

Install docling

# China users (recommended) — uses Tsinghua mirror, ~10x faster than default
pip install docling -i https://pypi.tuna.tsinghua.edu.cn/simple

# International users
pip install docling

Verify:

docling --version

⚠️ First-time PDF usage

The first time you convert a PDF, docling downloads 500MB of AI models (layout + table structure) to `/.cache/huggingface/`. Subsequent runs reuse cached models and are much faster.

For users in China: set the HuggingFace mirror environment variable before first run, otherwise model download will timeout:

# Linux / macOS / Git Bash
export HF_ENDPOINT=https://hf-mirror.com
docling file.pdf --to md

# Windows CMD
set HF_ENDPOINT=https://hf-mirror.com
docling file.pdf --to md

# Windows PowerShell
$env:HF_ENDPOINT="https://hf-mirror.com"
docling file.pdf --to md

Tip: If you see connection timeout errors while downloading models, this is almost always the cause.

⚠️ First-time audio/video usage

ASR (Whisper) models are downloaded on demand — only when you actually convert an audio/video file. Processing PDFs/images does not trigger a Whisper download, and vice versa.

• Location: same cache as document models, ~/.cache/huggingface/
• Size by model: whisper_tiny ~75MB (default), whisper_small/base ~250-400MB, whisper_medium ~1GB, whisper_large/turbo ~1.5-3GB
• Reusable: downloaded once, reused on subsequent runs (no re-download)
• China users: the same HF_ENDPOINT=https://hf-mirror.com mirror applies — set it before the first audio run, otherwise the download will time out

First run is slow (downloading the model); subsequent runs are pure local inference, speed depends on CPU/GPU.

Quick Commands

Document → Markdown (most common)

# Auto-detect format (PDF, DOCX, PPTX, XLSX, image)
docling "path/to/file.pdf" --to md --output .molio/docling

# Force a specific format when auto-detect fails
docling "file.docx" --from docx --to md --output .molio/docling
docling "slides.pptx" --from pptx --to md --output .molio/docling
docling "report.xlsx" --from xlsx --to md --output .molio/docling

Output: creates a .md file in the specified output directory. 统一用 .molio/docling 作为输出目录（Molio 工作区，文件树扫描会跳过，不污染 vault 根）。

PDF with specific options

# PDF with OCR (for scanned documents)
docling "scanned.pdf" --ocr --to md --output .molio/docling

# Use pypdfium2 backend (fallback when default parser fails)
docling "problem.pdf" --pdf-backend pypdfium2 --to md --output .molio/docling

# Force OCR even if text layer exists
docling "mixed.pdf" --force-ocr --to md --output .molio/docling

OCR on images

docling "screenshot.png" --from image --to md --output .molio/docling
docling "photo.jpg" --from image --ocr --to md --output .molio/docling

Audio / Video → Markdown (speech-to-text)

Uses Whisper ASR models to transcribe audio/video files into Markdown.

# Audio file (default whisper_tiny — fast, lower accuracy)
docling "recording.mp3" --from audio --to md --output .molio/docling

# Video file with a more accurate model
docling "meeting.mp4" --from audio --pipeline asr --asr-model whisper_large --to md --output .molio/docling

# Apple Silicon: use MLX variant for faster local inference
docling "interview.m4a" --from audio --pipeline asr --asr-model whisper_medium_mlx --to md --output .molio/docling

# Parse an existing VTT subtitle file (no ASR, text-only)
docling "captions.vtt" --from vtt --to md --output .molio/docling

Model size guide:

Use _mlx suffix on Apple Silicon, _native for CPU fallback across platforms.

GPU acceleration

# Use GPU if available (auto-detects CUDA/MPS)
docling "file.pdf" --device auto --to md

# Force CPU (slower but works everywhere)
docling "file.pdf" --device cpu --to md

# Force CUDA GPU
docling "file.pdf" --device cuda --to md

Key Options

Troubleshooting

❌ Error: Inconsistent number of pages: N!=-1

Cause: docling's default PDF parser (docling-parse) cannot handle certain PDFs (especially scanned documents or those with unusual structure).

Fix: Switch to the pypdfium2 backend:

docling "problem.pdf" --pdf-backend pypdfium2 --to md --output .molio/docling

pypdfium2 is more tolerant and will usually succeed.

❌ Error: ConnectTimeout / huggingface_hub.errors.LocalEntryNotFoundError

Cause: Cannot reach HuggingFace Hub to download models (common in mainland China).

Fix: Set the mirror endpoint before running:

# Set for this session
export HF_ENDPOINT=https://hf-mirror.com   # bash/zsh
set HF_ENDPOINT=https://hf-mirror.com       # CMD
$env:HF_ENDPOINT="https://hf-mirror.com"    # PowerShell

# Then retry
docling "file.pdf" --to md --output .molio/docling

Permanent fix (recommended for Chinese users): add to shell profile (~/.bashrc, ~/.zshrc, or system env):

export HF_ENDPOINT=https://hf-mirror.com

❌ Error: ModuleNotFoundError: No module named 'docling'

Cause: docling not installed, or not in PATH.

Fix:

# Install (China users use Tsinghua mirror for speed)
pip install docling -i https://pypi.tuna.tsinghua.edu.cn/simple
# International users
# pip install docling

# Verify it's installed
python -c "import docling; print('OK')"

# Check if docling.exe is in PATH
which docling           # Linux/macOS/Git Bash
where docling           # CMD
Get-Command docling     # PowerShell

Common install locations:

• Windows: C:\Users\<user>\AppData\Roaming\Python\Python3X\Scripts\docling.exe
• Linux/macOS: ~/.local/bin/docling

If installed but not in PATH, use full path or add to PATH.

❌ Slow performance

Cause: Running on CPU without GPU, or processing many pages.

Tips:

• Use --device cuda (NVIDIA) or --device mps (Apple Silicon) if available
• Reduce --page-batch-size for lower memory usage on large PDFs
• For simple text-only PDFs, skip OCR: --no-ocr

❌ Poor Chinese text extraction

Tips:

• Ensure OCR is enabled: --ocr
• Specify OCR language: --ocr-lang ch_sim,en (Simplified Chinese + English)
• For scanned Chinese PDFs, use --force-ocr to override any (possibly wrong) text layer

Workflow Example

# 1. Create output directory (Molio 工作区，不污染 vault 根)
mkdir -p .molio/docling

# 2. Set HF mirror if in China (one-time)
export HF_ENDPOINT=https://hf-mirror.com

# 3. Convert document
docling "合同.pdf" --to md --output .molio/docling

# 4. Read the generated markdown
cat .molio/docling/合同.md

Security Notes

⚠️ Avoid these flags unless you trust the source:

• --enable-remote-services — can send data to remote endpoints
• --allow-external-plugins — loads third-party code
• Custom --headers with untrusted values — can redirect requests

Full CLI Reference

See references/cli-reference.md for complete option list (PDF options, VLM models, enrichment features, debug flags, etc.).