neuronic 0.3.0

Neuronic - AI-powered data transformation library

Neuronic 🧠

Imagine Python functions that can think, reason, and create - functions that understand natural language, analyze complex data patterns, and generate creative solutions. Welcome to Neuronic - where we transform ordinary Python code into intelligent, AI-powered modules that bring GPT's cognitive capabilities directly into your codebase, complete with enterprise-grade validation, caching, and error handling.

✨ Magic in Three Lines

from neuronic import Neuronic

@neuronic.function(output_type="json")
def convert_to_vegetarian(recipe: str) -> dict:
    """Convert any food recipe to its vegetarian version while maintaining the flavor profile."""
    pass

# That's it! Your function is now AI-powered 🌱
veggie_recipe = convert_to_vegetarian("Classic Beef Burger with bacon")

🌟 What is Neuronic?

Neuronic is your gateway to building intelligent Python applications powered by GPT-4o. Create functions that can understand context, extract insights, and solve complex problems - all while maintaining the reliability and predictability of traditional programming. With built-in validation, type checking, and caching, Neuronic makes AI as dependable as any other Python module.

🚀 Features

• 🎯 Smart Function Decorator: Transform any Python function into an AI-powered one with a simple decorator
• 🧠 Intelligent Understanding: Create functions that truly understand your data, extracting meaning and insights
• 🗣️ Natural Language Processing: Process text like a human - analyze sentiment, extract key information, and understand context
• ✍️ Creative Generation: Generate human-quality content, from documentation to test data, tailored to your specifications
• 🔍 Pattern Recognition: Uncover hidden patterns and relationships in your data through GPT-powered analysis
• 📦 Multiple Output Types: Get results in any format you need - strings, numbers, JSON, lists, booleans, or Python structures
• 🏢 Enterprise Ready: Built-in validation, type checking, and caching ensure production-ready outputs
• ⚡ Performance Optimized: Automatic chunking for large inputs and smart caching for repeated operations

🛠️ Quick Start

Installation

pip install neuronic

Configuration

# Option 1: Use environment variables (.env file)
OPENAI_API_KEY=your-openai-api-key-here

# Option 2: Pass API key directly
neuronic = Neuronic(api_key="your-api-key-here")

🎮 Cool Examples

🪄 Smart Function Decorator

Transform any Python function into an AI-powered one:

@neuronic.function(output_type="string")
def generate_mini_story(character: str, setting: str, mood: str) -> str:
    """Create a three-sentence story based on the given character, setting, and mood."""
    pass

story = generate_mini_story(
    character="a curious robot",
    setting="ancient library",
    mood="mysterious"
)

🧹 Smart Data Cleaning

Clean and standardize messy data:

@neuronic.function(output_type="json")
def clean_user_data(users: List[Dict]) -> List[Dict]:
    """
    Clean and standardize user data:
    - Properly capitalize names
    - Validate and fix email addresses
    - Convert age to numbers
    """
    pass

clean_data = clean_user_data(messy_data)

🎨 Creative Generation

Generate emoji-based character profiles:

emoji_personas = neuronic.generate(
    spec="""Create unique emoji-based character profiles with:
    - Emoji combination for appearance
    - Personality trait
    - Favorite hobby
    - Life motto""",
    n=3
)

📊 Smart Analysis

Analyze text with context and reasoning:

analysis = neuronic.analyze(
    data=movie_review,
    question="What aspects of the movie did the reviewer focus on?"
)
print(f"Answer: {analysis['answer']}")
print(f"Confidence: {analysis['confidence']}")
print(f"Reasoning: {analysis['reasoning']}")

🎯 Perfect For

• 🔄 Data Processing: Format conversion, cleaning, normalization
• 📝 Content Creation: Documentation, test data, sample content
• 📊 Analysis: Pattern recognition, sentiment analysis, trend detection
• 🛠️ Development: Code transformation, API handling, validation

🧰 API Reference

Core Methods

# Transform data
result = neuronic.transform(
    data=input_data,               # What to transform
    instruction="your instruction", # How to transform it
    output_type="string",          # What format you want
    example=None,                  # Optional example
    context=None                   # Optional context
)

# Analyze data
insights = neuronic.analyze(
    data=your_data,      # What to analyze
    question="your question"  # What to find out
)

# Generate data
new_data = neuronic.generate(
    spec="what to generate",  # What you want
    n=1                      # How many items
)

🎓 Best Practices

1. 🔐 Security First

   • Keep API keys in environment variables
   • Never commit sensitive data

2. ⚡ Performance Tips

   • Use caching for repeated operations
   • Batch similar requests when possible

3. 🛡️ Error Handling

   • Always handle exceptions gracefully
   • Validate outputs match expected formats

📜 License

MIT License - feel free to use in your own projects!

🤝 Contributing

Got ideas? Found a bug? Contributions are welcome! Feel free to:

• Open an issue
• Submit a pull request
• Share your cool use cases

🌟 Star Us!

If you find Neuronic useful, give us a star! It helps others discover the project.