Extracting PDF Text for LLMs

This skill provides tools and guidance for extracting text from PDFs in formats suitable for language model consumption.

Quick Decision Guide

PDF Type	Best Approach	Script
Simple text PDF	PyMuPDF	scripts/extract_pymupdf.py
PDF with tables	pdfplumber	scripts/extract_pdfplumber.py
Scanned/image PDF (local)	pytesseract	scripts/extract_with_ocr.py
Complex layout, highest accuracy	Mistral OCR API	scripts/extract_mistral_ocr.py
End-to-end RAG pipeline	marker-pdf	pip install marker-pdf

Recommended Workflow

1. Try PyMuPDF first - fastest, handles most text-based PDFs well
2. If tables are mangled - switch to pdfplumber
3. If scanned/image-based - use Mistral OCR API (best accuracy) or local OCR (free but slower)

Local Extraction (No API Required)

PyMuPDF - Fast General Extraction

Best for: Text-heavy PDFs, speed-critical workflows, basic structure preservation.

uv run scripts/extract_pymupdf.py input.pdf output.md

The script outputs markdown with preserved headings and paragraphs. For LLM-optimized output, it uses pymupdf4llm which formats text for RAG systems.

pdfplumber - Table Extraction

Best for: PDFs with tables, financial documents, structured data.

uv run scripts/extract_pdfplumber.py input.pdf output.md

Tables are converted to markdown format. Note: pdfplumber works best on machine-generated PDFs, not scanned documents.

Local OCR - Scanned Documents

Best for: Scanned PDFs when API access is unavailable.

uv run scripts/extract_with_ocr.py input.pdf output.txt

Requires: pytesseract, pdf2image, and Tesseract installed (brew install tesseract on macOS).

API-Based Extraction

Mistral OCR API

Best for: Complex layouts, scanned documents, highest accuracy, multilingual content, math formulas.

Pricing: ~1000 pages per dollar (very cost-effective)

export MISTRAL_API_KEY="your-key"
uv run scripts/extract_mistral_ocr.py input.pdf output.md

Features:

• Outputs clean markdown
• Preserves document structure (headings, lists, tables)
• Handles images, math equations, multilingual text
• 95%+ accuracy on complex documents

For detailed API options and other services, see references/api-services.md.

Output Format Recommendations

For LLM consumption, markdown is preferred:

• Preserves semantic structure (headings become context boundaries)
• Tables remain readable
• Compatible with most RAG chunking strategies

For detailed comparisons of local tools, see references/local-tools.md.