🚀 Sophisticated FastAPI GPT Backend

A production-ready, scalable FastAPI backend for querying OpenAI's GPT models with professional architecture, comprehensive error handling, and modern development practices.

🏗️ Architecture Overview

This application follows a sophisticated, layered architecture designed for scalability and maintainability:

app/
├── api/                 # API layer with versioning
│   ├── deps.py         # Shared dependencies
│   └── v1/             # API version 1
│       ├── api.py      # Router aggregation
│       └── endpoints/  # Individual endpoint modules
├── core/               # Core functionality
│   ├── config.py       # Configuration management
│   └── logging.py      # Centralized logging
├── middleware/         # Custom middleware
│   ├── cors.py         # CORS configuration
│   └── error_handler.py # Global error handling
├── models/             # Data models and schemas
│   └── schemas.py      # Pydantic models
├── services/           # Business logic layer
│   └── gpt_service.py  # GPT service implementation
└── tests/              # Test suite

✨ Key Features

🔧 Production-Ready Architecture

• Layered Design: Clear separation of concerns with API, service, and data layers
• Dependency Injection: Proper DI pattern for testability and maintainability
• API Versioning: Future-proof API design with version management
• Error Handling: Comprehensive error handling with proper HTTP status codes

📊 Advanced Logging & Monitoring

• Structured Logging: JSON-formatted logs with contextual information
• Request Tracking: Unique request IDs for distributed tracing
• Performance Metrics: Processing time headers and logging
• Health Checks: Comprehensive health monitoring

🛡️ Security & Reliability

• Input Validation: Robust validation with Pydantic models
• Error Sanitization: Safe error responses without sensitive data exposure
• CORS Configuration: Flexible CORS setup for different environments
• Rate Limiting Ready: Structure prepared for rate limiting implementation

🐳 DevOps & Deployment

• Docker Support: Complete containerization with multi-stage builds
• Docker Compose: Development environment setup
• Environment Management: Flexible configuration for different environments
• Health Checks: Built-in health monitoring for container orchestration

🚀 Quick Start

Prerequisites

• Python 3.10+
• OpenAI API key
• Docker (optional, for containerized deployment)

1. Install Dependencies

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Environment Configuration

Create a .env file in the project root:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-3.5-turbo
OPENAI_MAX_TOKENS=1000
OPENAI_TEMPERATURE=0.7

# API Configuration
PROJECT_NAME=GPT Query API
PROJECT_DESCRIPTION=A sophisticated FastAPI backend for querying GPT models
VERSION=1.0.0

# Server Configuration
SERVER_HOST=0.0.0.0
SERVER_PORT=8000
DEBUG=true

# Logging Configuration
LOG_LEVEL=INFO
LOG_FORMAT=json

# CORS Configuration (comma-separated)
BACKEND_CORS_ORIGINS=http://localhost:3000,http://localhost:8080

3. Run the Application

Option A: Direct Python

python main.py

Option B: Using Uvicorn

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Option C: Using Docker

# Build and run with Docker Compose
docker-compose up --build

# Or build and run manually
docker build -t gpt-api .
docker run -p 8000:8000 --env-file .env gpt-api

📚 API Documentation

Available Endpoints

Endpoint	Method	Description
/	GET	System information
/docs	GET	Interactive API documentation (Swagger UI)
/redoc	GET	Alternative API documentation (ReDoc)
/api/v1/health	GET	Health check
/api/v1/query	POST	GPT query endpoint

API Usage Examples

Health Check

curl -X GET "http://localhost:8000/api/v1/health"

GPT Query

curl -X POST "http://localhost:8000/api/v1/query" \
     -H "Content-Type: application/json" \
     -d '{
       "query": "Explain quantum computing in simple terms",
       "model": "gpt-3.5-turbo",
       "temperature": 0.7,
       "max_tokens": 500
     }'

System Information

curl -X GET "http://localhost:8000/"

🧪 Testing

Run Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=app

# Run specific test file
pytest app/tests/test_health.py -v

Test Structure

• app/tests/conftest.py: Pytest configuration and fixtures
• app/tests/test_health.py: Health endpoint tests
• app/tests/test_gpt.py: GPT endpoint tests (create as needed)

🔧 Development Features

Code Quality Tools

# Format code
black app/

# Sort imports
isort app/

# Lint code
flake8 app/

# Type checking
mypy app/

Environment Variables

Variable	Description	Default
OPENAI_API_KEY	OpenAI API key	Required
OPENAI_MODEL	GPT model to use	gpt-3.5-turbo
OPENAI_MAX_TOKENS	Max tokens per response	1000
OPENAI_TEMPERATURE	Response creativity (0-2)	0.7
PROJECT_NAME	API project name	GPT Query API
SERVER_HOST	Server host	0.0.0.0
SERVER_PORT	Server port	8000
DEBUG	Debug mode	false
LOG_LEVEL	Logging level	INFO
LOG_FORMAT	Log format (json/text)	json

🏗️ Advanced Architecture Details

Dependency Injection Pattern

The application uses FastAPI's dependency injection system for clean, testable code:

• Services are injected into endpoints
• Configuration is injected where needed
• Easy mocking for tests

Error Handling Strategy

• Global Exception Handlers: Centralized error handling
• Custom Exceptions: Service-specific error types
• Request ID Tracking: Unique IDs for error tracing
• Safe Error Responses: No sensitive data in error messages

Logging Strategy

• Structured Logging: JSON format for log aggregation
• Contextual Information: Request IDs, processing time, metadata
• Performance Tracking: Built-in performance monitoring
• Environment-Aware: Different log levels for dev/prod

🚀 Production Deployment

Docker Deployment

# Production build
docker build -t gpt-api:latest .

# Run in production mode
docker run -d \
  --name gpt-api \
  -p 8000:8000 \
  --env-file .env.prod \
  --restart unless-stopped \
  gpt-api:latest

Environment-Specific Configuration

Create different .env files for different environments:

• .env.development
• .env.staging
• .env.production

Health Monitoring

The application includes comprehensive health checks:

• Application health at /api/v1/health
• GPT service connectivity verification
• Docker health check configuration

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Related Technologies

• FastAPI: Modern, fast web framework for building APIs
• Pydantic: Data validation and settings management
• OpenAI: GPT model integration
• Docker: Containerization
• Pytest: Testing framework
• Uvicorn: ASGI server implementation