From 0989faa5610dc7bd21e7794e0c837dad9f9cf434 Mon Sep 17 00:00:00 2001
From: Sebarocks <sebastian_orrego_a@outlook.com>
Date: Wed, 27 Aug 2025 13:27:44 -0400
Subject: [PATCH] Readme added

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+# 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
+
+