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Issue 1: Forecasting demand insights not triggered in demo workflow - Created internal ML endpoint: /forecasting/internal/ml/generate-demand-insights - Added trigger_demand_insights_internal() to ForecastServiceClient - Integrated forecasting insights into demo session post-clone workflow - Now triggers 4 AI insight types: price, safety stock, yield, + demand Issue 2: RabbitMQ client cleanup error in procurement service - Fixed: rabbitmq_client.close() → rabbitmq_client.disconnect() - Added proper cleanup in exception handler - Error: "'RabbitMQClient' object has no attribute 'close'" Files modified: - services/forecasting/app/api/ml_insights.py (new internal_router) - services/forecasting/app/main.py (register internal router) - shared/clients/forecast_client.py (new trigger method) - services/demo_session/app/services/clone_orchestrator.py (+ demand insights) - services/procurement/app/api/internal_demo.py (fix disconnect) Expected impact: - Demo sessions will now generate demand forecasting insights - No more RabbitMQ cleanup errors in logs - AI insights count should increase from 1 to 2-3 per session 🤖 Generated with Claude Code Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
🍞 BakeWise - Multi-Service Architecture
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.
🚀 Quick Start
Prerequisites
- Docker Desktop with Kubernetes enabled
- Docker Compose
- Node.js (for frontend development)
Running the Application
-
Clone the repository:
git clone <repository-url> cd bakery-ia -
Set up environment variables:
cp .env.example .env # Edit .env with your specific configuration -
Run with Docker Compose:
docker-compose up --build -
Or run with Kubernetes (Docker Desktop):
# Enable Kubernetes in Docker Desktop # Run the setup script ./scripts/setup-kubernetes-dev.sh
🏗️ Architecture Overview
The project follows a microservices architecture with the following main components:
- Frontend: React-based dashboard for user interaction
- Gateway: API gateway handling authentication and routing
- Services: Multiple microservices handling different business domains
- Infrastructure: Redis, RabbitMQ, PostgreSQL databases
🐳 Kubernetes Infrastructure
🛠️ Services
The project includes multiple services:
- Auth Service: Authentication and authorization
- Tenant Service: Multi-tenancy management
- Sales Service: Sales processing
- External Service: Integration with external systems
- Training Service: AI model training
- Forecasting Service: Demand forecasting
- Notification Service: Notifications and alerts
- Inventory Service: Inventory management
- Recipes Service: Recipe management
- Suppliers Service: Supplier management
- POS Service: Point of sale
- Orders Service: Order management
- Production Service: Production planning
- Alert Processor: Background alert processing
📊 Monitoring
The system includes comprehensive monitoring with:
- Prometheus for metrics collection
- Grafana for visualization
- ELK stack for logging (planned)
🚀 Production Deployment
For production deployment on clouding.io with Kubernetes:
- Set up your clouding.io Kubernetes cluster
- Update image references to your container registry
- Configure production-specific values
- Deploy using the production kustomization:
kubectl apply -k infrastructure/kubernetes/environments/production/
🤝 Contributing
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
📄 License
This project is licensed under the MIT License.