# ChatSBT

ChatSBT is a full-stack chat application that allows users to interact with multiple AI models through a web interface. The application features real-time streaming responses, chat history persistence, and support for various language models.

## Features

- Real-time chat with multiple AI models
- Server-Sent Events (SSE) for streaming responses
- Chat history persistence
- Responsive web interface built with Svelte
- Docker support for easy deployment

## Technology Stack

### Backend
- **Python 3.11+**
- **Starlette** - ASGI framework for building asynchronous web applications
- **Langchain** - Framework for developing applications with LLMs
- **Masonite ORM** - Database ORM for Python
- **SQLite** - Default database (configurable)

### Frontend
- **Svelte 5** - Reactive UI framework
- **Vite** - Next-generation frontend tooling
- **DaisyUI** - Tailwind CSS components
- **Marked** - Markdown parser

### Infrastructure
- **Deno** - JavaScript/TypeScript runtime for frontend build
- **uv** - Python package installer and resolver

## Prerequisites

Before you begin, ensure you have the following installed:
- Python 3.11 or higher
- Deno 2.4 or higher
- uv (Python package installer)
- Docker (optional, for containerized deployment)

## Installation

### Backend Setup

1. Install Python dependencies using uv:
   ```bash
   uv sync
   ```

2. Set up environment variables:
   Create a `.env` file in the project root with the necessary configuration:
   ```env
   # Database configuration
   DB_CONNECTION=sqlite
   DB_DATABASE=chatsbt.db
   
   # AI Provider API keys (add as needed)
   OPENAI_API_KEY=your_openai_api_key
   ANTHROPIC_API_KEY=your_anthropic_api_key
   ```

### Frontend Setup

1. Navigate to the frontend directory:
   ```bash
   cd frontend
   ```

2. Install frontend dependencies:
   ```bash
   deno install --allow-scripts
   ```

3. Build the frontend:
   ```bash
   deno run build
   ```

## Running the Application

### Development Mode

To run the application in development mode with hot reloading:

1. Start the backend server:
   ```bash
   uv run app.py
   ```
   The backend will be available at http://localhost:8000

2. In a separate terminal, start the frontend development server:
   ```bash
   cd frontend
   deno run dev
   ```
   The frontend will be available at http://localhost:5173

### Production Mode

To run the application in production mode:

1. Build the frontend:
   ```bash
   cd frontend
   deno run build
   cd ..
   ```

2. Start the backend server:
   ```bash
   uv run app.py
   ```

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

### Using Docker

To run the application using Docker:

1. Build the Docker image:
   ```bash
   docker build -t chatsbt .
   ```

2. Run the container:
   ```bash
   docker run -p 8000:8000 chatsbt
   ```

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


## API Endpoints

The backend exposes the following RESTful API endpoints:

### Models

- `GET /api/models` - Retrieve list of available AI models
  - Response: `{"models": ["model1", "model2", ...]}`

### Chats

- `POST /api/chats` - Create a new chat session
  - Request: `{"model": "model_name"}`
  - Response: `{"id": "chat_id", "model": "model_name"}`

- `GET /api/chats/{chat_id}` - Retrieve chat history
  - Response: `{"messages": [{"role": "human|assistant", "content": "message_text"}, ...]}`

### Messages

- `POST /api/chats/{chat_id}/messages` - Send a message to a chat
  - Request: `{"message": "user_message", "model": "model_name"}`
  - Response: `{"status": "queued", "message_id": "message_id"}`

- `GET /api/chats/{chat_id}/stream?message_id={message_id}` - Stream AI response
  - Server-Sent Events (SSE) endpoint that streams the AI response token by token
  - Events:
    - `data: token_content` - Individual tokens from the AI response
    - `event: done` - Indicates the response is complete