mgraph-ai-service-cache-client 0.2.0

MGraph-AI__Service__Cache__Client

MGraph AI Service Cache Client

A production-ready FastAPI microservice template for building MGraph-AI services. This template provides a complete scaffold with CI/CD pipeline, AWS Lambda deployment, and type-safe architecture.

🎯 Purpose

This repository serves as the base template for creating new MGraph-AI services. It includes:

• ✅ Complete FastAPI application structure
• ✅ Multi-stage CI/CD pipeline (dev, qa, prod)
• ✅ AWS Lambda deployment configuration
• ✅ Type-safe architecture using OSBot-Utils
• ✅ Comprehensive test coverage
• ✅ API key authentication
• ✅ Health check and monitoring endpoints

Note: This is a template repository. To create your own service, see Creating Services from Template.

📚 Creating a New Service

To create a new service from this template, see Creating Services from MGraph-AI__Service__Cache__Client.

🚀 Features

• Type-Safe Architecture: Built on OSBot-Utils type safety framework
• Multi-Stage Deployment: Automated CI/CD pipeline for dev, QA, and production
• AWS Lambda Ready: Optimized for serverless deployment
• API Key Authentication: Secure access control

📋 Table of Contents

• Quick Start
• Installation
• API Documentation
• Configuration
• Development
• Testing
• Deployment
• Security
• Contributing
• License

🎯 Quick Start

Local Development

# Clone the repository
git clone https://github.com/the-cyber-boardroom/MGraph-AI__Service__Cache__Client.git
cd MGraph-AI__Service__Cache__Client

# Install dependencies
pip install -r requirements-test.txt
pip install -e .

# Set environment variables
export FAST_API__AUTH__API_KEY__NAME="x-api-key"
export FAST_API__AUTH__API_KEY__VALUE="your-secret-key"

# Run locally
./scripts/run-locally.sh
# or
uvicorn mgraph_ai_service_cache__client.fast_api.lambda_handler:app --reload --host 0.0.0.0 --port 10011

Basic Usage

import requests

# Set up authentication
headers = {"x-api-key": "your-secret-key"}
base_url = "http://localhost:10011"

# Check service health
response = requests.get(f"{base_url}/health", headers=headers)
print(response.json())

# Get service info
response = requests.get(f"{base_url}/info/version", headers=headers)
print(response.json())

📦 Installation

Prerequisites

• Python 3.12+
• AWS CLI (for deployment)
• Docker (for LocalStack testing)

Using Poetry

# Install poetry if not already installed
pip install poetry

# Install dependencies
poetry install

# Activate virtual environment
poetry shell

Using pip

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

# Install dependencies
pip install -r requirements-test.txt
pip install -e .

📖 API Documentation

Interactive API Documentation

Once the service is running, access the interactive API documentation at:

• Swagger UI: http://localhost:10011/docs
• ReDoc: http://localhost:10011/redoc

Endpoints Overview

Health Endpoints

Endpoint	Method	Description
/health	GET	Service health check
/health/detailed	GET	Detailed health status

Information Endpoints

Endpoint	Method	Description
/info/version	GET	Get service version
/info/status	GET	Get service status

⚙️ Configuration

Environment Variables

Variable	Description	Required	Default
FAST_API__AUTH__API_KEY__NAME	Header name for API key	Yes	-
FAST_API__AUTH__API_KEY__VALUE	API key value	Yes	-
AWS_REGION	AWS region (triggers Lambda mode)	No	-
DEBUG	Enable debug logging	No	false

Configuration File

Create a .env file for local development:

FAST_API__AUTH__API_KEY__NAME=x-api-key
FAST_API__AUTH__API_KEY__VALUE=development-key-12345

🛠️ Development

Project Structure

mgraph_ai_service_cache__client/
├── fast_api/
│   ├── lambda_handler.py      # AWS Lambda entry point
│   ├── Service__Fast_API.py   # FastAPI application setup
│   └── routes/               # API endpoint definitions
├── service/
│   └── info/               # Service information
├── utils/
│   ├── deploy/             # Deployment utilities
│   └── Version.py          # Version management
└── config.py               # Service configuration

Adding New Endpoints

1. Create a new route class in fast_api/routes/:

from osbot_fast_api.api.Fast_API_Routes import Fast_API_Routes

class Routes__MyFeature(Fast_API_Routes):
    tag = 'my-feature'
    
    def my_endpoint(self, param: str = "default"):
        return {"result": param}
    
    def setup_routes(self):
        self.add_route_get(self.my_endpoint)

2. Register in Service__Fast_API:

def setup_routes(self):
    # ... existing routes
    self.add_routes(Routes__MyFeature)

🧪 Testing

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=mgraph_ai_service_cache__client

# Run specific test file
pytest tests/unit/fast_api/test_Service__Fast_API__client.py

# Run integration tests (requires LocalStack)
pytest tests/integration/

Test Structure

tests/
├── unit/                    # Unit tests
│   ├── fast_api/           # API tests
│   └── service/            # Service tests
└── deploy_aws/             # Deployment tests

🚀 Deployment

AWS Lambda Deployment

The service includes automated deployment scripts for multiple environments:

# Deploy to development
pytest tests/deploy_aws/test_Deploy__Service__to__dev.py

# Deploy to QA
pytest tests/deploy_aws/test_Deploy__Service__to__qa.py

# Deploy to production (manual trigger)
# Use GitHub Actions workflow

CI/CD Pipeline

The project uses GitHub Actions for continuous deployment:

1. Development Branch (dev)

   • Runs tests with LocalStack
   • Deploys to dev environment
   • Increments minor version

2. Main Branch (main)

   • Runs comprehensive test suite
   • Deploys to QA environment
   • Increments major version

3. Production (manual)

   • Requires manual workflow trigger
   • Deploys to production environment

🔒 Security

Authentication

API key authentication is required for all endpoints:

headers = {"x-api-key": "your-secret-key"}

Best Practices

1. Never commit secrets - Use environment variables
2. Rotate API keys - Regular key rotation
3. Use HTTPS - Always encrypt in transit
4. Monitor access - Log and audit API usage

🤝 Contributing

We welcome contributions! Please follow these steps:

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

Development Guidelines

• Write tests for new features
• Update documentation
• Follow existing code style
• Add type annotations
• Consider security implications

🔗 Related Projects

• OSBot-Utils - Core utilities library
• OSBot-AWS - AWS integration layer
• OSBot-Fast-API - FastAPI utilities

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Built with FastAPI
• Deployed on AWS Lambda

📞 Support

• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

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Created and maintained by The Cyber Boardroom team