Claude 4c647f4182 Fix onboarding API dependencies after removing manual path
**Root Cause:**
Frontend removed data-source-choice step and manual path, but backend
still required data-source-choice as dependency for smart-inventory-setup.

This caused: "Cannot complete step smart-inventory-setup: dependencies not met"

**Changes:**

1. **Removed data-source-choice step** (line 48)
   - No longer in ONBOARDING_STEPS list
   - AI-assisted is now the only path

2. **Updated setup dependencies** (line 79)
   - Before: "setup": ["user_registered", "data-source-choice"]
   - After: "setup": ["user_registered", "bakery-type-selection"]
   - Removed dependency on non-existent step

3. **Removed manual path steps**
   - Removed "inventory-setup" from ONBOARDING_STEPS
   - Kept only AI-assisted path steps

4. **Updated suppliers dependencies** (line 87)
   - Now requires "smart-inventory-setup" completion
   - Makes logical sense: need inventory before suppliers

5. **Simplified ML training validation** (lines 278-299)
   - Removed manual path check (inventory-setup)
   - Only validates AI path (smart-inventory-setup)
   - Cleaner logic without dual-path complexity

**Step Order (Updated):**
```
1. user_registered
2. bakery-type-selection
3. setup (tenant creation)
4. smart-inventory-setup (AI inventory)
5. product-categorization
6. initial-stock-entry
7. suppliers-setup
8. recipes-setup (optional)
9. production-processes (optional)
10. quality-setup (optional)
11. team-setup (optional)
12. ml-training
13. setup-review
14. completion
```

**Dependency Chain (Fixed):**
```
smart-inventory-setup → setup → bakery-type-selection → user_registered
(was broken: smart-inventory-setup → setup → data-source-choice [MISSING!])
```

**Files Modified:**
- services/auth/app/api/onboarding_progress.py:42-101,278-299

**Impact:**
 Onboarding steps now work correctly from initial bakery registration
 No more "dependencies not met" errors
 Backend matches frontend architecture
2025-11-07 08:18:39 +00:00
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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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