Docling - Document & Web Content Extraction

CLI tool for parsing documents and web pages into clean, structured text. Uses GPU acceleration for OCR and ML models.

Prerequisites

• docling CLI must be installed (e.g., via pipx install docling)
• For GPU support: NVIDIA GPU with CUDA drivers

When to Use

• Extract content from a URL → Use docling (not web_fetch)
• Search for information → Use web_search (Brave)
• Parse PDFs, DOCX, PPTX → Use docling
• OCR on images → Use docling

Quick Commands

Web Page → Markdown (default)

docling "<URL>" --from html --to md

Output: creates a .md file in current directory (or use --output)

Web Page → Plain Text

docling "<URL>" --from html --to text --output /tmp/docling_out

PDF with OCR

docling "/path/to/file.pdf" --ocr --device cuda --output /tmp/docling_out

Key Options

Option	Values	Description
--from	html, pdf, docx, pptx, image, md, csv, xlsx	Input format
--to	md, text, json, yaml, html	Output format
--device	auto, cuda, cpu	Accelerator (default: auto)
--output	path	Output directory (recommended: use controlled temp dir)
--ocr	flag	Enable OCR for images/scanned PDFs
--tables	flag	Extract tables (default: on)

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

Workflow

1. For web content extraction: Use docling "<URL>" --from html --to text --output /tmp/docling_out
2. Read the output file from the specified output directory
3. Clean up the output directory after reading

GPU Support

Docling supports GPU acceleration via CUDA (NVIDIA). Verify CUDA is available:

python -c "import torch; print(torch.cuda.is_available())"

Full CLI Reference

See references/cli-reference.md for complete option list.