Generative AI Project Template

A structured template for building robust generative AI applications, with modular organization and best practices built-in.

🌟 Features

Modular project structure for scalability
Pre-configured support for multiple LLM providers (Claude, GPT)
Built-in prompt engineering utilities
Rate limiting and token management
Robust error handling
Caching mechanism for API responses
Example implementations and notebooks

📁 Project Structure

generative_ai_project/
├── config/                  # Configuration directory
│   ├── __init__.py
│   ├── model_config.yaml    # Model-specific configurations
│   ├── prompt_templates.yaml # Prompt templates
│   └── logging_config.yaml  # Logging settings
│
├── src/                     # Source code
│   ├── llm/                # LLM clients
│   │   ├── base.py         # Base LLM client
│   │   ├── claude_client.py # Anthropic Claude client
│   │   ├── gpt_client.py   # OpenAI GPT client
│   │   └── utils.py        # Shared utilities
│   │
│   ├── prompt_engineering/ # Prompt engineering tools
│   │   ├── templates.py    # Template management
│   │   ├── few_shot.py    # Few-shot prompt utilities
│   │   └── chain.py       # Prompt chaining logic
│   │
│   ├── utils/             # Utility functions
│   │   ├── rate_limiter.py # API rate limiting
│   │   ├── token_counter.py # Token counting
│   │   ├── cache.py       # Response caching
│   │   └── logger.py      # Logging utilities
│   │
│   └── handlers/          # Error handling
│       └── error_handler.py
│
├── data/                   # Data directory
│   ├── cache/             # Cache storage
│   ├── prompts/           # Prompt storage
│   ├── outputs/           # Output storage
│   └── embeddings/        # Embedding storage
│
├── examples/              # Example implementations
│   ├── basic_completion.py
│   ├── chat_session.py
│   └── chain_prompts.py
│
└── notebooks/            # Jupyter notebooks
    ├── prompt_testing.ipynb
    ├── response_analysis.ipynb
    └── model_experimentation.ipynb

🚀 Getting Started

Clone the repository:

git clone https://github.com/yourusername/generative_ai_project.git
cd generative_ai_project

Install dependencies:

pip install -r requirements.txt

Configure your environment:
- Copy config/model_config.yaml.example to config/model_config.yaml
- Add your API keys and configurations
Review the examples in examples/ directory
Start with the notebooks in notebooks/ for experimentation

📘 Documentation

Configuration

model_config.yaml: Configure API keys and model parameters
prompt_templates.yaml: Define reusable prompt templates
logging_config.yaml: Configure logging behavior

Key Components

LLM Clients (src/llm/)
- Base client with common functionality
- Specific implementations for different providers
- Utility functions for token counting and rate limiting
Prompt Engineering (src/prompt_engineering/)
- Template management system
- Few-shot prompt utilities
- Prompt chaining capabilities
Utilities (src/utils/)
- Rate limiting for API calls
- Token counting
- Response caching
- Logging

🛠️ Development

Best Practices

Keep configuration in YAML files
Implement proper error handling
Use rate limiting for APIs
Maintain separation between model clients
Cache results when appropriate
Document your code
Use notebooks for experimentation

Tips

Follow modular design principles
Write tests for new components
Use proper version control
Keep documentation updated
Monitor API usage and limits

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Brij Kishore Pandey

📧 Contact

For any queries, reach out to:

GitHub: @honestsoul
Email: brij.pydata@gmail.com

⭐ If you find this template useful, please consider giving it a star!

ndkhoa/generative_ai_project