FastAPI-LLM is an advanced, Dockerized Python project that integrates Large Language Models (LLMs) with a FastAPI backend. It demonstrates how to leverage AI to perform intelligent automation tasks and exposes APIs that can be consumed by other applications.

This project is production-ready in structure, containerized with Docker, and designed for deployment on cloud platforms like AWS.

• FastAPI Backend: Lightweight, high-performance web API framework.
• LLM Integration: Uses AI models to perform automation and intelligent processing.
• Dockerized: Easily run the app locally or in cloud environments without dependency conflicts.
• Cloud-Ready: Fully compatible with AWS ECS / EC2 deployment.
• Modular Architecture: Clean folder structure for scalability and maintainability.
• Health Endpoint: Check service status with a simple /health route.

FastAPI-LLM/
│── app/
│   ├── api/          # API route definitions
│   ├── core/         # Core utilities and configurations
│   ├── models/       # Data and ML model classes
│   ├── services/     # Business logic and AI services
│   └── main.py       # FastAPI app entrypoint
│
│── Dockerfile
│── requirements.txt
│── README.md
│── .gitignore

• Python 3.10+
• Docker installed locally
• Optional: OpenAI API key for LLM integration

# Build the Docker image
docker build -t fastapi-llm:latest .

# Run the container
docker run -p 8000:8000 fastapi-llm:latest

# Visit API health check
http://localhost:8000/health

• Ensure your OpenAI API key or other LLM credentials are configured in .env
• Call the AI-powered endpoints via POST requests with your input payload.

• Dockerized project can be deployed on AWS Free Tier using ECS or EC2.
• Cloud storage (like S3) can be used for storing outputs or logs.
• CI/CD (GitHub Actions) can automate building and pushing Docker images to GHCR.

• Add authentication & authorization to API routes
• Implement automatic versioned Docker builds via CI/CD
• Extend AI services with multiple LLM models and pipelines
• Integrate logging & monitoring for production readiness

• Showcases full-stack backend development using Python + FastAPI
• Demonstrates AI/LLM integration in real-world scenarios
• Highlights cloud deployment readiness with Docker & AWS
• Illustrates modern development best practices: modular architecture, containerization, CI/CD

• FastAPI Documentation
• Docker Official Docs
• OpenAI API Documentation
• GitHub Container Registry

MIT License – free to use and modify