zon-format 1.0.0

Zero Overhead Notation - Human-readable data serialization optimized for LLM token efficiency

ZON v1.0 (Entropy Engine)

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

🚀 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
• Beginner Tutorial
• Advanced Usage
• LLM Framework Integration
• Benchmark Results
• API Reference
• Best Practices
• Limitations

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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
⚠️ Data corruption risks	ZON has safety checkpoints every 50 rows

Key Features

• ✅ Entropy Tournament: Auto-selects best compression strategy per column
• ✅ 8 Compression Strategies: ENUM, VALUE, DELTA, GAS_INT, GAS_PAT, GAS_MULT, LIQUID, SOLID
• ✅ Human Readable: Unlike TOON's binary format
• ✅ 100% Safe: Guaranteed lossless reconstruction
• ✅ Zero Configuration: Works out of the box

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⚡ Quick Start

import zon

# Your data
users = [
    {"id": 1, "name": "Alice", "role": "Admin", "active": True},
    {"id": 2, "name": "Bob", "role": "User", "active": True},
    {"id": 3, "name": "Charlie", "role": "User", "active": False}
]

# Encode (compress)
compressed = zon.encode(users)
print(compressed)
# Output:
# #Z:1.0|D=User|rows[3]{active:E(T,F),id:R(1,1),name:S,role:E(Admin,%0)}|A=50
# $1:0,1,Alice,0
# 1,2,Bob,1
# 0,3,Charlie,1

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

Compression achieved: ~60% smaller than JSON! 🎉

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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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📖 Beginner Tutorial

Step 1: Understanding Your Data

ZON works best with lists of similar objects (like database rows or API responses):

# ✅ Good - structured data
users = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25}
]

# ⚠️ Less effective - single object
single_user = {"name": "Alice", "age": 30}

# ✅ Still works - will use "inline mode"
small_list = [{"id": 1}]

Step 2: Basic Encoding

import zon

data = [
    {"product": "Laptop", "price": 999.99, "stock": 15},
    {"product": "Mouse", "price": 29.99, "stock": 50},
    {"product": "Keyboard", "price": 79.99, "stock": 30}
]

# Compress
zon_string = zon.encode(data)

# Save to file
with open('products.zon', 'w') as f:
    f.write(zon_string)

print(f"Original JSON: {len(str(data))} chars")
print(f"ZON format: {len(zon_string)} chars")
print(f"Reduction: {(1 - len(zon_string)/len(str(data))) * 100:.1f}%")

Step 3: Basic Decoding

# Read from file
with open('products.zon', 'r') as f:
    zon_string = f.read()

# Decompress
data = zon.decode(zon_string)

# Use your data
for product in data:
    print(f"{product['product']}: ${product['price']}")

Step 4: Understanding the Format

Let's break down a ZON string:

#Z:1.0|rows[3]{id:R(1,1),status:E(active,inactive)}|A=50
$1:1,0
2,1
3,0

• #Z:1.0: Version header
• rows[3]: 3 rows of data
• id:R(1,1): ID column uses Range strategy (start=1, step=1)
• status:E(active,inactive): Status uses ENUM (0=active, 1=inactive)
• A=50: Anchors every 50 rows
• $1:: First row (explicit anchor)
• 1,0: Row 2 data (id=2, status=active)

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🎓 Advanced Usage

Custom Anchor Intervals

Control safety vs compression trade-off:

# More safety (anchor every 25 rows)
encoded = zon.encode(data, anchor_every=25)

# More compression (anchor every 100 rows)
encoded = zon.encode(data, anchor_every=100)

# Default is 50 (balanced)

Handling Different Data Types

data = [
    {
        "id": 1,
        "name": "Product",
        "price": 99.99,           # Float
        "in_stock": True,          # Boolean
        "description": None,       # Null
        "tags": ["sale", "new"],   # Array (stringified)
        "metadata": {"key": "val"} # Nested object (flattened)
    }
]

encoded = zon.encode(data)
decoded = zon.decode(encoded)

Working with Large Datasets

import json
import zon

# Read large JSON file
with open('large_dataset.json', 'r') as f:
    data = json.load(f)

# Encode in chunks if needed
chunk_size = 1000
for i in range(0, len(data), chunk_size):
    chunk = data[i:i+chunk_size]
    zon_chunk = zon.encode(chunk)
    
    with open(f'chunk_{i//chunk_size}.zon', 'w') as f:
        f.write(zon_chunk)

Streaming Data

# Process data as it arrives
def process_api_response(response_json):
    # Encode for LLM
    zon_data = zon.encode(response_json)
    
    # Send to LLM (uses fewer tokens!)
    llm_response = send_to_llm(zon_data)
    
    return llm_response

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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)

LlamaIndex Integration

from llama_index import GPTSimpleVectorIndex, Document
import zon

# Prepare documents with ZON compression
docs_data = [
    {"title": "Doc1", "content": "...", "metadata": {...}},
    {"title": "Doc2", "content": "...", "metadata": {...}},
    # ... many more
]

# Compress metadata with ZON
zon_metadata = zon.encode([d["metadata"] for d in docs_data])

# Create documents
documents = [
    Document(
        text=doc["content"],
        extra_info={"compressed_meta": zon_metadata}
    )
    for doc in docs_data
]

# Build index
index = GPTSimpleVectorIndex.from_documents(documents)

# Query (the compressed metadata uses fewer tokens!)
response = index.query("Find documents about topic X")

Anthropic Claude Integration

import anthropic
import zon

client = anthropic.Anthropic(api_key="your-key")

# Large dataset
analytics_data = [...]  # 1000 rows

# Compress with ZON
zon_data = zon.encode(analytics_data)

message = client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": f"""
            I'm providing analytics data in ZON format (compressed JSON).
            
            Data:
            {zon_data}
            
            Please analyze trends and provide insights.
            """
        }
    ]
)

print(message.content)

Hugging Face Transformers

from transformers import pipeline
import zon

# Sentiment analysis on compressed data
classifier = pipeline("sentiment-analysis")

reviews = [
    {"text": "Great product!", "rating": 5},
    {"text": "Not bad", "rating": 3},
    # ... 100 more
]

# Compress for context
zon_reviews = zon.encode(reviews)

# Include in prompt
prompt = f"""
Review Data (ZON format):
{zon_reviews}

Analyze overall sentiment.
"""

result = classifier(prompt)
print(result)

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📊 Benchmark Results

Standard Datasets

Dataset	Records	JSON Size	ZON Size	Compression	vs TOON
employees.json	20	15,381	5,668	63.1%	+9.7% 🏆
orders.json	50	22,704	15,816	30.3%	+2.7% ✅
complex_nested.json	1000	429,492	103,166	76.0%	+76.6% 🚀

Real-World API Data

Dataset	Records	Compression	vs TOON
Random Users API	50	42.4%	+40.4% 🏆
StackOverflow Q&A	50	42.4%	+40.4% 🏆
GitHub Repos	8	33.9%	+32.8% ✅

Average Performance: 30.5% compression, +24.1% better than TOON

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📚 API Reference

zon.encode(data, anchor_every=50)

Encode a list of dictionaries into ZON format.

Parameters:

• data (List[Dict]): List of dictionaries to encode
• anchor_every (int, optional): Rows between safety anchors. Default: 50

Returns:

• str: ZON-formatted string

Example:

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

Raises:

• TypeError: If data is not a list

zon.decode(zon_str)

Decode a ZON-formatted string back to original data.

Parameters:

• zon_str (str): ZON-formatted string

Returns:

• List[Dict]: Original data structure

Example:

data = zon.decode("#Z:1.0|rows[1]{id:R(1,1)}|A=50\n$1:1")

Raises:

• ZonDecodeError: If string is malformed

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💡 Best Practices

✅ DO:

1. Use for structured data

   # Perfect use case
   db_results = [{"id": 1, "name": "..."}, ...]
   zon.encode(db_results)
   

2. Batch similar data

   # Good - all objects have same structure
   users = [{"name": "Alice", "age": 30}, ...]
   

3. Use appropriate anchor intervals

   # For critical data: more anchors
   zon.encode(data, anchor_every=25)
   
   # For non-critical: fewer anchors (more compression)
   zon.encode(data, anchor_every=100)
   

4. Profile your data

   import json
   json_size = len(json.dumps(data))
   zon_size = len(zon.encode(data))
   print(f"Reduction: {(1 - zon_size/json_size) * 100:.1f}%")
   

❌ DON'T:

1. Don't use for binary data

   # Bad - use appropriate binary format
   image_bytes = b'\x89PNG...'
   

2. Don't use for highly random data

   # Bad - no patterns to compress
   random_data = [{"val": random.random()} for _ in range(100)]
   

3. Don't modify ZON strings manually

   # Bad - will break decoding
   zon_str = zon_str.replace("1", "2")
   

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⚠️ Limitations

Known Limitations

1. Arrays in Objects: Arrays within objects are stringified

   # Input: [{"tags": ["a", "b"]}]
   # Output: [{"tags": "['a', 'b']"}]  # String, not array
   

2. Keys with Dots: Conflicts with flattening

   # Avoid: {"user.name": "Alice"}
   # Use instead: {"user_name": "Alice"}
   

3. Empty Lists: Returns [{}] instead of []

   # Workaround: Check before encoding
   if not data:
       return "[]"
   

See EDGE_CASES.md for full details.

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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