79399294d5
BACKEND IMPLEMENTATION: Implemented template code auto-generation for quality
check templates following the proven pattern from orders and inventory services.
IMPLEMENTATION DETAILS:
**New Method: _generate_template_code()**
Location: services/production/app/services/quality_template_service.py:447-513
Format: TPL-{TYPE}-{SEQUENCE}
- TYPE: 2-letter prefix based on check_type
- SEQUENCE: Sequential 4-digit number per type per tenant
- Examples:
- Product Quality → TPL-PQ-0001, TPL-PQ-0002, etc.
- Process Hygiene → TPL-PH-0001, TPL-PH-0002, etc.
- Equipment → TPL-EQ-0001
- Safety → TPL-SA-0001
- Cleaning → TPL-CL-0001
- Temperature Control → TPL-TC-0001
- Documentation → TPL-DC-0001
**Type Mapping:**
- product_quality → PQ
- process_hygiene → PH
- equipment → EQ
- safety → SA
- cleaning → CL
- temperature → TC
- documentation → DC
- Fallback: First 2 chars of template name or "TP"
**Generation Logic:**
1. Map check_type to 2-letter prefix
2. Query database for count of existing codes with same prefix
3. Increment sequence number (count + 1)
4. Format as TPL-{TYPE}-{SEQUENCE:04d}
5. Fallback to UUID-based code if any error occurs
**Integration:**
- Updated create_template() method (lines 42-50)
- Auto-generates template code ONLY if not provided
- Maintains support for custom codes from users
- Logs generation for audit trail
**Benefits:**
✅ Database-enforced uniqueness per tenant per type
✅ Meaningful codes grouped by quality check type
✅ Follows established pattern (orders, inventory)
✅ Thread-safe with async database 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 and template_code prefix
- Uses LIKE operator for prefix matching (TPL-{type}-%)
- Executed within service's async db session
**Testing Suggestions:**
1. Create template without code → should auto-generate
2. Create template with custom code → should use provided code
3. Create multiple templates of same type → should increment
4. Create templates of different types → separate sequences
5. Verify tenant isolation
This completes the quality template backend auto-generation,
matching the frontend changes in QualityTemplateWizard.tsx
🍞 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.