98 lines
2.7 KiB
Markdown
98 lines
2.7 KiB
Markdown
# 🍞 BakeWise - Multi-Service Architecture
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Welcome to BakeWise, an advanced AI-powered platform for bakery management and optimization. This project implements a microservices architecture with multiple interconnected services to provide comprehensive bakery management solutions.
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## 🚀 Quick Start
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### Prerequisites
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- Docker Desktop with Kubernetes enabled
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- Docker Compose
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- Node.js (for frontend development)
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### Running the Application
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1. **Clone the repository:**
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```bash
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git clone <repository-url>
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cd bakery-ia
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```
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2. **Set up environment variables:**
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```bash
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cp .env.example .env
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# Edit .env with your specific configuration
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```
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3. **Run with Docker Compose:**
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```bash
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docker-compose up --build
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```
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4. **Or run with Kubernetes (Docker Desktop):**
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```bash
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# Enable Kubernetes in Docker Desktop
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# Run the setup script
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./scripts/setup-kubernetes-dev.sh
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```
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## 🏗️ Architecture Overview
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The project follows a microservices architecture with the following main components:
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- **Frontend**: React-based dashboard for user interaction
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- **Gateway**: API gateway handling authentication and routing
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- **Services**: Multiple microservices handling different business domains
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- **Infrastructure**: Redis, RabbitMQ, PostgreSQL databases
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## 🐳 Kubernetes Infrastructure
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## 🛠️ Services
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The project includes multiple services:
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- **Auth Service**: Authentication and authorization
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- **Tenant Service**: Multi-tenancy management
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- **Sales Service**: Sales processing
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- **External Service**: Integration with external systems
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- **Training Service**: AI model training
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- **Forecasting Service**: Demand forecasting
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- **Notification Service**: Notifications and alerts
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- **Inventory Service**: Inventory management
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- **Recipes Service**: Recipe management
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- **Suppliers Service**: Supplier management
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- **POS Service**: Point of sale
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- **Orders Service**: Order management
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- **Production Service**: Production planning
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- **Alert Processor**: Background alert processing
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## 📊 Monitoring
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The system includes comprehensive monitoring with:
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- Prometheus for metrics collection
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- Grafana for visualization
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- ELK stack for logging (planned)
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## 🚀 Production Deployment
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For production deployment on clouding.io with Kubernetes:
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1. Set up your clouding.io Kubernetes cluster
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2. Update image references to your container registry
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3. Configure production-specific values
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4. Deploy using the production kustomization:
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```bash
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kubectl apply -k infrastructure/environments/prod/k8s-manifests
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```
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## 🤝 Contributing
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1. Fork the repository
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2. Create a feature branch
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3. Make your changes
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4. Submit a pull request
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## 📄 License
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This project is licensed under the MIT License.
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