Claude ddc4928d78 feat: Implement structured reasoning_data generation for i18n support
Implemented proper reasoning data generation for purchase orders and
production batches to enable multilingual dashboard support.

Backend Strategy:
- Generate structured JSON with type codes and parameters
- Store only reasoning_data (JSONB), not hardcoded text
- Frontend will translate using i18n libraries

Changes:
1. Created shared/schemas/reasoning_types.py
   - Defined reasoning types for POs and batches
   - Created helper functions for common reasoning patterns
   - Supports multiple reasoning types (low_stock, forecast_demand, etc.)

2. Production Service (services/production/app/services/production_service.py)
   - Generate reasoning_data when creating batches from forecast
   - Include parameters: product_name, predicted_demand, current_stock, etc.
   - Structure supports frontend i18n interpolation

3. Procurement Service (services/procurement/app/services/procurement_service.py)
   - Implemented actual PO creation (was placeholder before!)
   - Groups requirements by supplier
   - Generates reasoning_data based on context (low_stock vs forecast)
   - Creates PO items automatically

Example reasoning_data:
{
  "type": "low_stock_detection",
  "parameters": {
    "supplier_name": "Harinas del Norte",
    "product_names": ["Flour Type 55", "Flour Type 45"],
    "days_until_stockout": 3,
    "current_stock": 45.5,
    "required_stock": 200
  },
  "consequence": {
    "type": "stockout_risk",
    "severity": "high",
    "impact_days": 3
  }
}

Frontend will translate:
- EN: "Low stock detected for Harinas del Norte. Stock runs out in 3 days."
- ES: "Stock bajo detectado para Harinas del Norte. Se agota en 3 días."
- CA: "Estoc baix detectat per Harinas del Norte. S'esgota en 3 dies."

Next steps:
- Remove TEXT fields (reasoning, consequence) from models
- Update dashboard service to use reasoning_data
- Create frontend i18n translation keys
- Update dashboard components to translate dynamically
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🍞 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

  1. Clone the repository:

    git clone <repository-url>
    cd bakery-ia
    
  2. Set up environment variables:

    cp .env.example .env
    # Edit .env with your specific configuration
    
  3. Run with Docker Compose:

    docker-compose up --build
    
  4. 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:

  1. Set up your clouding.io Kubernetes cluster
  2. Update image references to your container registry
  3. Configure production-specific values
  4. Deploy using the production kustomization:
    kubectl apply -k infrastructure/kubernetes/environments/production/
    

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Submit a pull request

📄 License

This project is licensed under the MIT License.

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