Demo seed script was failing with ImportError because the
create_batch_reasoning_regular_schedule function was referenced
but not implemented in reasoning_types.py.
Added the missing helper function to support REGULAR_SCHEDULE
batch reasoning type for regular scheduled production batches.
Fixes container restart loop in demo-seed-production-batches pod.
Convert pipe-separated reasoning codes to structured JSON format for:
- Safety stock calculator (statistical calculations, errors)
- Price forecaster (procurement recommendations, volatility)
- Order optimization (EOQ, tier pricing)
This enables i18n translation of internal calculation reasoning
and provides structured data for frontend AI insights display.
Benefits:
- Consistent with PO/Batch reasoning_data format
- Frontend can translate using same i18n infrastructure
- Structured parameters enable rich UI visualization
- No legacy string parsing needed
Changes:
- safety_stock_calculator.py: Replace reasoning str with reasoning_data dict
- price_forecaster.py: Convert recommendation reasoning to structured format
- optimization.py: Update EOQ and tier pricing to use reasoning_data
Part of complete i18n implementation for AI insights.
This commit removes the last hardcoded English text from reasoning fields
across all backend services, completing the i18n implementation.
Changes by service:
Safety Stock Calculator (safety_stock_calculator.py):
- CALC:STATISTICAL_Z_SCORE - Statistical calculation with Z-score
- CALC:ADVANCED_VARIABILITY - Advanced formula with demand and lead time variability
- CALC:FIXED_PERCENTAGE - Fixed percentage of lead time demand
- All calculation methods now use structured codes with pipe-separated parameters
Price Forecaster (price_forecaster.py):
- PRICE_FORECAST:DECREASE_EXPECTED - Price expected to decrease
- PRICE_FORECAST:INCREASE_EXPECTED - Price expected to increase
- PRICE_FORECAST:HIGH_VOLATILITY - High price volatility detected
- PRICE_FORECAST:BELOW_AVERAGE - Current price below average (buy opportunity)
- PRICE_FORECAST:STABLE - Price stable, normal schedule
- All forecasts include relevant parameters (change_pct, days, etc.)
Optimization Utils (shared/utils/optimization.py):
- EOQ:BASE - Economic Order Quantity base calculation
- EOQ:MOQ_APPLIED - Minimum order quantity constraint applied
- EOQ:MAX_APPLIED - Maximum order quantity constraint applied
- TIER_PRICING:CURRENT_TIER - Current tier pricing
- TIER_PRICING:UPGRADED - Upgraded to higher tier for savings
- All optimizations include calculation parameters
Format: All codes use pattern "CATEGORY:TYPE|param1=value|param2=value"
This allows frontend to parse and translate with parameters while maintaining
technical accuracy for logging and debugging.
Frontend can now translate ALL reasoning codes across the entire system.
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
