refactor: Convert internal services to structured JSON reasoning
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.
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Claude
@@ -502,32 +502,69 @@ class PriceForecaster:
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if expected_change_pct < -5:
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# Price expected to drop >5%
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action = 'wait'
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reasoning = f'PRICE_FORECAST:DECREASE_EXPECTED|change_pct={abs(expected_change_pct):.1f}|days=30'
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reasoning_data = {
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'type': 'decrease_expected',
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'parameters': {
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'change_pct': round(abs(expected_change_pct), 1),
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'forecast_days': 30,
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'current_price': round(current_price, 2),
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'forecast_mean': round(forecast_mean, 2)
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}
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}
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urgency = 'low'
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elif expected_change_pct > 5:
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# Price expected to increase >5%
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action = 'buy_now'
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reasoning = f'PRICE_FORECAST:INCREASE_EXPECTED|change_pct={expected_change_pct:.1f}|days=30'
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reasoning_data = {
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'type': 'increase_expected',
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'parameters': {
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'change_pct': round(expected_change_pct, 1),
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'forecast_days': 30,
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'current_price': round(current_price, 2),
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'forecast_mean': round(forecast_mean, 2)
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}
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}
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urgency = 'high'
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elif volatility['volatility_level'] == 'high':
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# High volatility - wait for dip
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action = 'wait_for_dip'
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reasoning = f'PRICE_FORECAST:HIGH_VOLATILITY|coefficient={volatility["coefficient_of_variation"]:.2f}'
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reasoning_data = {
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'type': 'high_volatility',
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'parameters': {
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'coefficient': round(volatility['coefficient_of_variation'], 2),
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'volatility_level': volatility['volatility_level'],
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'avg_daily_change_pct': round(volatility['avg_daily_change_pct'], 2)
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}
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}
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urgency = 'medium'
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elif current_price < price_stats['mean_price'] * 0.95:
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# Currently below average
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below_avg_pct = ((price_stats["mean_price"] - current_price) / price_stats["mean_price"] * 100)
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action = 'buy_now'
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reasoning = f'PRICE_FORECAST:BELOW_AVERAGE|current_price={current_price:.2f}|below_avg_pct={below_avg_pct:.1f}'
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reasoning_data = {
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'type': 'below_average',
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'parameters': {
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'current_price': round(current_price, 2),
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'mean_price': round(price_stats['mean_price'], 2),
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'below_avg_pct': round(below_avg_pct, 1)
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}
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}
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urgency = 'medium'
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else:
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# Neutral
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action = 'normal_purchase'
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reasoning = 'PRICE_FORECAST:STABLE'
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reasoning_data = {
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'type': 'stable',
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'parameters': {
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'current_price': round(current_price, 2),
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'forecast_mean': round(forecast_mean, 2),
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'expected_change_pct': round(expected_change_pct, 2)
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}
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}
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urgency = 'low'
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# Optimal purchase timing
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@@ -536,7 +573,7 @@ class PriceForecaster:
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return {
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'action': action,
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'reasoning': reasoning,
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'reasoning_data': reasoning_data,
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'urgency': urgency,
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'expected_price_change_pct': round(expected_change_pct, 2),
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'current_price': current_price,
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@@ -617,7 +654,7 @@ class PriceForecaster:
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'priority': recommendations['urgency'],
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'category': 'procurement',
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'title': f'Buy Now: Price Increasing {recommendations["expected_price_change_pct"]:.1f}%',
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'description': recommendations['reasoning'],
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'reasoning_data': recommendations['reasoning_data'],
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'impact_type': 'cost_avoidance',
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'impact_value': abs(recommendations['expected_price_change_pct']),
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'impact_unit': 'percentage',
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@@ -651,7 +688,11 @@ class PriceForecaster:
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'priority': 'medium',
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'category': 'procurement',
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'title': f'Wait to Buy: Price Decreasing {abs(recommendations["expected_price_change_pct"]):.1f}%',
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'description': recommendations['reasoning'] + f' Optimal purchase date: {recommendations["optimal_purchase_date"]}.',
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'reasoning_data': {
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**recommendations['reasoning_data'],
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'optimal_purchase_date': recommendations['optimal_purchase_date'],
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'days_until_optimal': recommendations['days_until_optimal']
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},
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'impact_type': 'cost_savings',
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'impact_value': abs(recommendations['expected_price_change_pct']),
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'impact_unit': 'percentage',
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@@ -788,7 +829,13 @@ class PriceForecaster:
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'volatility': {},
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'recommendations': {
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'action': 'insufficient_data',
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'reasoning': 'Not enough price history for reliable forecast. Need at least 180 days.',
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'reasoning_data': {
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'type': 'insufficient_data',
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'parameters': {
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'history_days': len(price_history),
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'min_required_days': 180
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}
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},
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'urgency': 'low'
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},
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'bulk_opportunities': {'has_bulk_opportunity': False},
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