This project provides a structured template for building robust generative AI applications, targeting developers and researchers. It offers modular organization, pre-configured support for multiple LLM providers, and built-in utilities for prompt engineering, rate limiting, and caching, aiming to streamline development and promote best practices.

How It Works

The template employs a modular architecture with distinct directories for configuration, source code, data, examples, and notebooks. Core components include abstract LLM clients with concrete implementations for providers like Anthropic Claude and OpenAI GPT, a prompt engineering module for template management and chaining, and utility functions for essential tasks like rate limiting, token counting, and response caching. This separation of concerns facilitates scalability and maintainability.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Configure API keys and model parameters in config/model_config.yaml.
• Review examples in the examples/ directory and experiment with notebooks in notebooks/.

Highlighted Details

• Modular project structure for scalability.
• Pre-configured support for multiple LLM providers (Claude, GPT).
• Built-in prompt engineering utilities, including few-shot prompting and chaining.
• Rate limiting, token management, and API response caching.

Maintenance & Community

The project is authored by Brij Kishore Pandey (@honestsoul). Contributions are welcome via pull requests.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The README does not detail specific Python version requirements or provide benchmark data for performance claims. It is presented as a template, implying that extensive customization and testing may be required for specific use cases.