neuronic 0.1.0

Neuronic: AI-powered data transformation and analysis tool.

Neuronic 🧪

Neuronic is a Python library that leverages AI to transform, analyze, and generate data in various formats. Think of it as your Swiss Army knife for data manipulation, powered by OpenAI's GPT models.

🚀 Features

• Data Transformation: Convert between formats (CSV ↔ JSON ↔ XML)
• Smart Analysis: Get insights and answers about your data
• Data Generation: Create realistic test data on demand
• Multiple Output Types: Support for strings, numbers, JSON, lists, booleans, and Python structures
• Context-Aware: Use additional context for more accurate transformations
• Flexible Input: Accept virtually any data type as input

📦 Installation

Install using pip:

pip install neuronic

🔑 Configuration

Create a .env file in your project root:

OPENAI_API_KEY=your-openai-api-key-here

Or pass your API key directly:

neuronic = Neuronic(api_key="your-api-key-here")

💡 Usage Examples

1. Data Transformation

Convert CSV data to JSON format:

from neuronic import Neuronic

neuronic = Neuronic()

customer_data = "John Doe, [e-mail], New York"
contact_card = neuronic.transform(
    data=customer_data,
    instruction="Convert this CSV data into a contact card format",
    output_type="json",
    example='{"name": "Jane Doe", "email": "[e-mail]", "location": "Los Angeles"}'
)

2. Data Analysis

Analyze sales data and get insights:

sales_data = [
    {"month": "Jan", "revenue": 1000},
    {"month": "Feb", "revenue": 1200},
    {"month": "Mar", "revenue": 900}
]
analysis = neuronic.analyze(
    data=sales_data,
    question="What's the trend in revenue and which month performed best?"
)

3. Data Generation

Generate test data with specific requirements:

test_data = neuronic.generate(
    spec="Create realistic user profiles with name, age, occupation, and favorite color",
    n=3
)

4. Context-Aware Transformation

Generate documentation with specific context:

code_snippet = "print('hello world')"
documentation = neuronic.transform(
    data=code_snippet,
    instruction="Generate detailed documentation for this code",
    output_type="json",
    context={
        "language": "Python",
        "audience": "beginners",
        "include_examples": True
    }
)

5. Boolean Decision Making

Make simple yes/no decisions:

sentiment = neuronic.transform(
    data="This product exceeded my expectations! Highly recommended!",
    instruction="Is this review positive?",
    output_type="bool"
)

6. Python Data Structures

Generate complex Python data structures:

data_structure = neuronic.transform(
    data="Create a nested data structure representing a family tree",
    instruction="Generate a Python dictionary with at least 3 generations",
    output_type="python"
)

🎯 Use Cases

Data Processing

• Format conversion (CSV ↔ JSON ↔ XML)
• Data cleaning and normalization
• Schema transformation

Content Generation

• Test data creation
• Sample content generation
• Documentation automation

Analysis

• Data summarization
• Trend analysis
• Pattern recognition
• Sentiment analysis

Development Support

• Code documentation
• API response transformation
• Test data generation
• Data validation

🔧 API Reference

Neuronic Class

Initialize the Neuronic class:

neuronic = Neuronic(api_key: str = None, model: str = "gpt-3.5-turbo")

Methods

transform()

Transform data according to instructions:

result = neuronic.transform(
    data: Any,                    # Input data
    instruction: str,             # What to do with the data
    output_type: str = "string",  # Desired output format
    example: str = None,          # Optional example
    context: dict = None          # Optional context
)

analyze()

Analyze data and get insights:

result = neuronic.analyze(
    data: Any,        # Data to analyze
    question: str     # Question about the data
)

generate()

Generate new data based on specifications:

result = neuronic.generate(
    spec: str,    # What to generate
    n: int = 1    # Number of items
)

🔒 Best Practices

1. API Key Security

   • Use environment variables for API keys
   • Never commit .env files to version control

2. Performance

   • Cache frequently used transformations
   • Batch similar operations when possible

3. Error Handling

   • Always handle potential exceptions
   • Validate output types match expected formats

📝 License

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

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.