UDPChat-AI

UDPChat-AI is a secure, real-time chat system that uses a custom UDP-based protocol enhanced with modern cryptography. It supports secure messaging, user sessions, and AI-generated responses through a modular three-part architecture:

---

Project Structure

client/     → Frontend React app (chat interface)
connector/  → Node.js bridge (WebSocket ↔ UDP)
server/     → Python backend (UDP server + AI logic)
deploy/     → Terraform + Helm + scripts for AKS deploys

---

Features

• Secure Messaging: All communication uses AES-GCM encryption over a custom UDP protocol.
• AI-Powered Chat: Optionally allows AI-generated responses using OpenAI or Ollama backends.
• Session Management: RSA handshake with AES session key, fingerprint verification, and session resumption.
• Chat Rooms: Join, create, and chat in rooms with live member lists and message history.
• Bridged Access: WebSocket-to-UDP bridge enables frontend to communicate with the backend securely.

---

Module READMEs

• Server (Python)
• Connector (Node.js)
• Client (React)
• Terraform
• Deploy Scripts
• GitHub Actions
• Helm Charts
• Dockerfiles
• Kubernetes Manifests

---

Getting Started

To run the project, you need to set up three components: the server (Python), the connector (Node.js), and the client (React). Each component has its own directory with a README file for detailed instructions. Before running the project, ensure you have python3, Node.js, and npm installed on your system. You can check if you have them installed by running:

python3 --version
node --version
npm --version

If you don't have them installed, you can download and install them from their official websites:

• Python
• Node.js and npm

Server Setup

The server is built using Python and requires a few dependencies. You can set up the server by following these steps:

1. Navigate to the server directory.
2. Create a virtual environment and activate it:

   cd server
   python3 -m venv venv # Create a virtual environment
   source venv/bin/activate # Activate the virtual environment

3. Install the required dependencies:

   pip install -r requirements.txt

4. Create a .env file based on the .env.sample file and set your OpenAI key or local Ollama setup.
5. Initialize the database:

   python main.py init_db

6. Start the server:

   python main.py start

Connector Setup

The connector is a Node.js application that acts as a bridge between the WebSocket frontend and the UDP server. To set it up, follow these steps:

1. Navigate to the connector directory.
2. Install the required dependencies:

   cd connector
   npm install

3. Create a .env file based on the .env.sample file and set your server's IP address and port.
4. Start the connector:

   npm run dev

Client Setup

The client is a React application that provides the chat interface. To set it up, follow these steps:

1. Navigate to the client directory.
2. Install the required dependencies:

   cd client
   npm install

3. Create a .env file based on the .env.sample file and set your connector's WebSocket URL.
4. Start the client:

npm run dev

5. Open your browser and navigate to the client URL to access the chat interface.

Author

Andrej Kudriavcev

---

Licensed under the MIT License.