README_OLD.md

Naked Insurance - LLM Chat Client POC

This repository contains the Proof of Concept (POC) for a Large Language Model (LLM) powered chat client for internal use at Naked Insurance. The application is built using the Chainlit framework for both the frontend and backend.

🎯 Project Overview

This POC demonstrates a modern, production-ready approach to building an LLM-powered chat interface that can integrate with multiple AI providers (OpenAI, Google Gemini) while maintaining a clean, maintainable codebase.

Key Features

• Multi-Provider LLM Support: Seamless integration with OpenAI GPT models (including flagship and reasoning models) and Google Gemini
• Latest AI Models: Support for GPT-4.1, GPT-4o, o3 series reasoning models, and Gemini 2.5 Pro/Flash
• Modern Python Architecture: Uses the src layout with proper packaging and dependency management
• Production-Ready Foundation: Comprehensive tooling for linting, testing, and type checking
• Chainlit Framework: Leverages Chainlit for rapid prototyping of chat interfaces
• Environment-Based Configuration: Secure handling of API keys and configuration

🏗️ Architecture

The project follows modern Python best practices with a clear separation of concerns:

naked-chat-poc/
├── .chainlit/              # Chainlit-specific configurations
├── .github/                # GitHub templates and workflows
├── public/                 # Static assets (themes, logos, etc.)
├── src/naked_chat/         # Main application package
├── tests/                  # Test suite
├── pyproject.toml          # Project configuration and dependencies
└── README.md              # This file

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• uv (recommended) or pip

Installation

1. Clone the repository:

   git clone https://github.com/naked-insurance/naked-chat-poc.git
   cd naked-chat-poc
   

2. Set up the environment:

   # Using uv (recommended)
   uv venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   uv pip install -e ".[dev]"
   
   # Or using pip
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e ".[dev]"
   

3. Configure environment variables:

   cp .env.example .env
   # Edit .env with your API keys and configuration
   

4. Run the application:

   chainlit run src/naked_chat/app.py
   

The application will be available at http://localhost:8000.

⚙️ Configuration

Environment Variables

Copy .env.example to .env and configure the following variables:

• OPENAI_API_KEY: Your OpenAI API key
• GOOGLE_API_KEY: Your Google Gemini API key
• DEFAULT_MODEL: Default LLM model to use

Chainlit Configuration

The .chainlit/config.toml file contains Chainlit-specific settings for data persistence, file uploads, and UI customization.

🧪 Development

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=naked_chat

Code Quality

# Linting and formatting with Ruff
ruff check .
ruff format .

# Type checking with mypy
mypy src/naked_chat

Development Workflow

1. Create a feature branch: git checkout -b feature/your-feature-name
2. Make your changes
3. Run tests and quality checks: pytest && ruff check . && mypy src/naked_chat
4. Commit your changes: git commit -m "Add your feature"
5. Push and create a pull request

📁 Project Structure Details

• src/naked_chat/: Main application package following the src layout
  • app.py: Chainlit application entry point
  • models/: LLM integration modules
  • utils/: Utility functions and helpers
• tests/: Comprehensive test suite
  • Unit tests for core functionality
  • Integration tests for LLM providers
• .chainlit/: Chainlit configuration
  • config.toml: Framework settings and customizations
• public/: Static assets
  • theme.json: Custom branding and themes
  • Assets like logos, favicons, etc.

🤝 Contributing

This is an internal POC project. Please follow the established code style and ensure all tests pass before submitting changes.

📄 License

This project is proprietary to Naked Insurance.

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Note: This is a Proof of Concept intended for internal evaluation and demonstration purposes.