0086b53fa0
BACKEND IMPLEMENTATION: Implemented SKU auto-generation following the proven
pattern from the orders service (order_number generation).
IMPLEMENTATION DETAILS:
**New Method: _generate_sku()**
Location: services/inventory/app/services/inventory_service.py:1069-1104
Format: SKU-{PREFIX}-{SEQUENCE}
- PREFIX: First 3 characters of product name (uppercase)
- SEQUENCE: Sequential 4-digit number per prefix per tenant
- Examples:
- "Flour" → SKU-FLO-0001, SKU-FLO-0002, etc.
- "Bread" → SKU-BRE-0001, SKU-BRE-0002, etc.
- "Sourdough Starter" → SKU-SOU-0001, etc.
**Generation Logic:**
1. Extract prefix from product name (first 3 chars)
2. Query database for count of existing SKUs with same prefix
3. Increment sequence number (count + 1)
4. Format as SKU-{PREFIX}-{SEQUENCE:04d}
5. Fallback to UUID-based SKU if any error occurs
**Integration:**
- Updated create_ingredient() method (line 52-54)
- Auto-generates SKU ONLY if not provided by frontend
- Maintains support for custom SKUs from users
- Logs generation for audit trail
**Benefits:**
✅ Database-enforced uniqueness per tenant
✅ Meaningful, sequential SKUs grouped by product type
✅ Follows established orders service pattern
✅ Thread-safe with database transaction context
✅ Graceful fallback to UUID on errors
✅ Full audit logging
**Technical Details:**
- Uses SQLAlchemy select with func.count for efficient counting
- Filters by tenant_id for tenant isolation
- Uses LIKE operator for prefix matching (SKU-{prefix}-%)
- Executed within get_db_transaction() context for safety
**Testing Suggestions:**
1. Create ingredient without SKU → should auto-generate
2. Create ingredient with custom SKU → should use provided SKU
3. Create multiple ingredients with same name prefix → should increment
4. Verify tenant isolation (different tenants can have same SKU)
NEXT: Consider adding similar generation for:
- Quality template codes (TPL-{TYPE}-{SEQUENCE})
- Production batch numbers (if not already implemented)
This completes the backend implementation for inventory SKU generation,
matching the frontend changes that delegated generation to backend.
🍞 Bakery IA - Multi-Service Architecture
Welcome to Bakery IA, 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.