zon-format 1.0.2

Zero Overhead Notation v1.0.2 (ClearText) - Human-readable data format with 30%+ compression over JSON

ZON v1.0 (Entropy Engine)

Zero Overhead Notation - A human-readable data serialization format optimized for LLM token efficiency, JSON for LLMs.

🚀 24-40% better compression than TOON | 📊 30-42% compression vs JSON | 🔍 100% Human Readable

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📚 Table of Contents

• What is ZON?
• Quick Start
• Installation
• LLM Framework Integration
• Benchmark Results
• API Reference

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🚀 What is ZON?

ZON is a smart compression format designed specifically for transmitting structured data to Large Language Models. Unlike traditional compression (which creates binary data), ZON remains 100% human-readable while dramatically reducing token usage.

Why ZON?

Problem	Solution
💸 High LLM costs from verbose JSON	ZON reduces tokens by 30-42%
🔍 Binary formats aren't debuggable	ZON is plain text - you can read it!
🎯 One-size-fits-all compression	ZON auto-selects optimal strategy per column

Key Features

• ✅ Entropy Tournament: Auto-selects best compression strategy per column
• ✅ 100% Safe: Guaranteed lossless reconstruction
• ✅ Zero Configuration: Works out of the box

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⚡ Quick Start

import zon

# Your data
users = {
  "context": {
    "task": "Our favorite hikes together",
    "location": "Boulder",
    "season": "spring_2025"
  },
  "friends": [
    "ana",
    "luis",
    "sam"
  ],
  "hikes": [
    {
      "id": 1,
      "name": "Blue Lake Trail",
      "distanceKm": 7.5,
      "elevationGain": 320,
      "companion": "ana",
      "wasSunny": true
    },
    {
      "id": 2,
      "name": "Ridge Overlook",
      "distanceKm": 9.2,
      "elevationGain": 540,
      "companion": "luis",
      "wasSunny": false
    },
    {
      "id": 3,
      "name": "Wildflower Loop",
      "distanceKm": 5.1,
      "elevationGain": 180,
      "companion": "sam",
      "wasSunny": true
    }
  ]
}

# Encode (compress)
compressed = zon.encode(users)
# Decode (decompress)
original = zon.decode(compressed)
assert original == users  # ✓ Perfect reconstruction!

• ZON (96 tokens, 264 bytes)

context:"{task:Our favorite hikes together,location:Boulder,season:spring_2025}"
friends:"[ana,luis,sam]"

@hikes(3):companion,distanceKm,elevationGain,id,name,wasSunny
ana,7.5,320,1,Blue Lake Trail,T
luis,9.2,540,2,Ridge Overlook,F
sam,5.1,180,3,Wildflower Loop,T

vs TOON Compression comparison:

• TOON (104 tokens, 286 bytes):

context:
  task: Our favorite hikes together
  location: Boulder
  season: spring_2025
friends[3]: ana,luis,sam
hikes[3]{id,name,distanceKm,elevationGain,companion,wasSunny}:
  1,Blue Lake Trail,7.5,320,ana,true
  2,Ridge Overlook,9.2,540,luis,false
  3,Wildflower Loop,5.1,180,sam,true

Compression's:

• JSON (compact) (139 tokens, 451 bytes)
• ZON (96 tokens, 264 bytes)
• TOON (104 tokens, 286 bytes)

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📦 Installation

From PyPI (Recommended)

pip install zon-format

From Source

git clone https://github.com/yourusername/zon-format.git
cd zon-format
pip install -e .

Verify Installation

import zon
print("ZON installed successfully! ✅")

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Format Reference

Metadata (YAML-like)

key:value
nested.key:value
list:[item1,item2,item3]

• No spaces after : for compactness
• Dot notation for nested objects
• Minimal quoting (only when necessary)

Tables (@table syntax)

@tablename(count):col1,col2,col3
val1,val2,val3
val1,val2,val3

• @ marks table start
• (count) shows row count
• Columns separated by commas (no spaces)

Compression Tokens

Token	Meaning	Example
T	Boolean true	T instead of true
F	Boolean false	F instead of false

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🤖 LLM Framework Integration

OpenAI Integration

import zon
import openai

# Prepare your data
users = [{"id": i, "name": f"User{i}", "active": True} for i in range(100)]

# Compress with ZON (saves tokens = saves money!)
zon_data = zon.encode(users)

# Use in prompt
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You will receive data in ZON format. Decode mentally and analyze."},
        {"role": "user", "content": f"Analyze this user data:\n\n{zon_data}\n\nHow many active users?"}
    ]
)

print(response.choices[0].message.content)

Cost Savings: ~30-40% fewer tokens vs JSON!

LangChain Integration

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
import zon

# Prepare data
products = [
    {"name": "Laptop", "price": 999, "rating": 4.5},
    {"name": "Mouse", "price": 29, "rating": 4.2},
    # ... 100 more products
]

# Compress
zon_products = zon.encode(products)

# Create prompt template
template = """
You have access to product data in ZON format (a compressed JSON format).

Product Data:
{zon_data}

Question: {question}

Please analyze the data and answer.
"""

prompt = PromptTemplate(
    input_variables=["zon_data", "question"],
    template=template
)

# Use with LangChain
llm = OpenAI(temperature=0)
chain = prompt | llm

result = chain.invoke({
    "zon_data": zon_products,
    "question": "What's the average price of products with rating > 4?"
})

print(result)

📊 Benchmark Results

Unified Benchmark Results

JSON vs ZON

Dataset	Records	JSON Size	ZON Size	Compression	JSON tk	ZON tk
analytics	60	5.9 KB	2.1 KB	+63.6%	2343	1396
complex_nested	1000	381.3 KB	296.8 KB	+22.2%	121213	108563
employees	100	13.7 KB	5.9 KB	+56.9%	3624	2083
github-repos	100	33.7 KB	21.0 KB	+37.8%	12124	8693
hikes	1	451.0 B	264.0 B	+41.5%	139	96
internet_github_repos	100	411.4 KB	345.6 KB	+16.0%	113357	98980
internet_posts	100	24.0 KB	20.5 KB	+14.6%	6093	5249
internet_random_users	50	53.4 KB	44.5 KB	+16.7%	19860	18637
internet_users	10	4.0 KB	3.1 KB	+23.8%	1225	1093
mongodb_irregular	50	16.0 KB	13.5 KB	+15.6%	5832	5570
orders	50	20.1 KB	14.1 KB	+29.9%	6906	5814

Summary

• Total JSON (compact) size: 963.9 KB
• Total ZON size: 767.3 KB
• Overall compression: 20.4%

TOON Comparison

(datasets with .toon files)

Dataset	Records	JSON Size	ZON Size	TOON Size	vs TOON	JSON tk	ZON tk	TOON tk
hikes	3	451.0 B	264.0 B	286.0 B	+7.7%	139	96	104

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📚 API Reference

zon.encode(data)

Encodes a Python object (dict or list) into a ZON-formatted string.

Parameters:

• data (Any): The input data to encode. Must be JSON-serializable (dict, list, str, int, float, bool, None).

Returns:

• str: The ZON-encoded string.

Example:

import zon
data = {"id": 1, "name": "Alice"}
zon_str = zon.encode(data)

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zon.decode(zon_str)

Decodes a ZON-formatted string back into a Python object.

Parameters:

• zon_str (str): The ZON-encoded string to decode.

Returns:

• Any: The decoded Python object (dict or list).

Example:

import zon
data = zon.decode(zon_str)
print(data["name"])  # "Alice"

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new features
4. Submit a pull request

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📄 License

Proprietary License - Free for Production Use

✅ You CAN:

• Use ZON in production (commercial or non-commercial)
• Integrate into your applications and services
• Deploy at any scale

❌ You CANNOT:

• Redistribute or sell the source code
• Modify and redistribute
• Create competing products

Copyright (c) 2025 Roni Bhakta. All Rights Reserved.

See LICENSE for full terms. For custom licensing: [e-mail]

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🙏 Acknowledgments

• Inspired by TOON format for LLM token efficiency
• Benchmark datasets from JSONPlaceholder, GitHub API, Random User Generator, StackExchange API
• Community feedback and testing

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✉️ Support

• Documentation: Full Docs
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Made with ❤️ for the LLM community

ZON v1.0+ - Compression that scales with complexity