🎯 Toonify Token Optimization

Reduce LLM API costs by 30-60% using TOON (Token-Oriented Object Notation) format for structured data serialization.

📋 Overview

This app demonstrates how to use Toonify to dramatically reduce token usage when passing structured data to Large Language Models. TOON format achieves CSV-like compactness while maintaining explicit structure and human readability.

Key Benefits

• 💰 63.9% average token reduction compared to JSON
• 🎯 Up to 73.4% savings for optimal use cases (tabular data)
• 💵 Saves $2,147 per million API requests at GPT-4 pricing
• 📖 Human-readable format
• ⚡ Minimal overhead (<1ms for typical payloads)

🚀 Features

• JSON vs TOON Comparison: See the size difference in action
• Token Cost Calculator: Calculate savings for your use cases
• LLM Integration Example: Pass optimized data to GPT/Claude
• Real-world Examples: Product catalogs, surveys, analytics data
• Benchmarking: Measure compression ratios for your data

📦 Installation

1. Install required dependencies:

pip install -r requirements.txt

2. Set up your API key (optional, for LLM integration demo):

export OPENAI_API_KEY='your-api-key-here'

💻 Usage

Basic Example

Run the basic comparison demo:

python toonify_demo.py

Interactive Demo

Run the interactive Streamlit app:

streamlit run toonify_app.py

📊 Format Comparison

JSON (247 bytes)

{
  "products": [
    {"id": 101, "name": "Laptop Pro", "price": 1299},
    {"id": 102, "name": "Magic Mouse", "price": 79},
    {"id": 103, "name": "USB-C Cable", "price": 19}
  ]
}

TOON (98 bytes, 60% reduction)

products[3]{id,name,price}:
  101,Laptop Pro,1299
  102,Magic Mouse,79
  103,USB-C Cable,19

🎯 Best Use Cases

Use TOON when:

• ✅ Passing data to LLM APIs (reduce token costs)
• ✅ Working with uniform tabular data
• ✅ Context window is limited
• ✅ Human readability matters

Use JSON when:

• ❌ Maximum compatibility is required
• ❌ Data is highly irregular/nested
• ❌ Working with existing JSON-only tools

💡 Example: E-commerce Product Analysis

from toonify import encode
import openai

# Your product data (could be hundreds of products)
products = [
    {"id": 1, "name": "Laptop", "price": 1299, "stock": 45},
    {"id": 2, "name": "Mouse", "price": 79, "stock": 120},
    # ... many more products
]

# Convert to TOON format (saves 60% tokens)
toon_data = encode(products)

# Send to LLM with reduced token cost
response = openai.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "user",
        "content": f"Analyze this product data:\n{toon_data}"
    }]
)

📈 Performance

Benchmarked across 50 real-world datasets:

• 63.9% average size reduction vs JSON
• 54.1% average token reduction
• 98% of datasets achieve 40%+ savings
• Minimal overhead (<1ms encoding/decoding)

🔗 Resources

• Toonify GitHub: https://github.com/ScrapeGraphAI/toonify
• PyPI: https://pypi.org/project/toonify/
• Documentation: https://docs.scrapegraphai.com/services/toonify
• Format Spec: https://github.com/toon-format/toon

🤝 Contributing

Contributions are welcome! Feel free to:

• Report bugs
• Suggest new examples
• Add benchmarks
• Improve documentation

📄 License

This example is provided as-is for educational purposes. Toonify library is licensed under MIT License.

🙏 Credits

Built with Toonify by the ScrapeGraphAI team.