LLM Chatbot with Microservices Architecture and Docker Containerization.

Overview

This project demonstrates building a simple yet scalable LLM chat application using microservices architecture and Docker for containerization. It provides a user-friendly web interface to interact with OpenAI's GPT models through a chat interface.

Technical Blog or Article explaninig the project

Technical Blog Link: Code

Technical Blog Link: AWS

Files information

Components

• Frontend Service

  • Built with Gradio for an interactive chat interface
  • Runs on port 7860
  • Communicates with the backend service

• Backend Service

  • Built with FastAPI
  • Handles API requests to OpenAI
  • Runs on port 8000
  • Requires OpenAI API key

Prerequisites

1. Docker and Docker Compose installed on your system
2. OpenAI API key
3. Create a .env file in the root directory with:

   OPENAI_API_KEY=your_api_key_here
   

Running the Application

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create the .env file with your OpenAI API key as shown above

3. Build and start the containers:

   docker-compose up --build

4. Access the application:

   • Open your browser and go to: http://localhost:7860
   • The chat interface will be ready to use

Testing the Application

1. The frontend will be available at: http://localhost:7860
2. The backend API will be available at: http://localhost:8000
3. Try the example prompts provided in the interface:
   • "Tell me a joke"
   • "What is the meaning of life?"
   • "Write a short poem"

Development

• Frontend modifications can be made in frontend/app.py
• Backend modifications can be made in backend/app.py
• After making changes, rebuild the containers:

  docker-compose down
  docker-compose up --build

Troubleshooting

1. If you see API key errors, ensure your .env file is properly configured
2. If containers fail to start, check if ports 7860 and 8000 are available
3. For connection issues, ensure both services are running:

   docker-compose ps