Add AI insights feature
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@@ -14,6 +14,7 @@ import os
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# Add shared clients to path
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sys.path.append(os.path.join(os.path.dirname(__file__), '../../../..'))
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from shared.clients.ai_insights_client import AIInsightsClient
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from shared.messaging import UnifiedEventPublisher
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from app.ml.price_forecaster import PriceForecaster
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@@ -33,10 +34,12 @@ class PriceInsightsOrchestrator:
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def __init__(
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self,
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ai_insights_base_url: str = "http://ai-insights-service:8000"
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ai_insights_base_url: str = "http://ai-insights-service:8000",
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event_publisher: Optional[UnifiedEventPublisher] = None
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):
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self.forecaster = PriceForecaster()
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self.ai_insights_client = AIInsightsClient(ai_insights_base_url)
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self.event_publisher = event_publisher
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async def forecast_and_post_insights(
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self,
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@@ -107,7 +110,17 @@ class PriceInsightsOrchestrator:
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post_results = {'total': 0, 'successful': 0, 'failed': 0}
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logger.info("No insights to post for ingredient", ingredient_id=ingredient_id)
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# Step 4: Return comprehensive results
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# Step 4: Publish insight events to RabbitMQ
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created_insights = post_results.get('created_insights', [])
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if created_insights:
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ingredient_context = {'ingredient_id': ingredient_id}
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await self._publish_insight_events(
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tenant_id=tenant_id,
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insights=created_insights,
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ingredient_context=ingredient_context
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)
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# Step 5: Return comprehensive results
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return {
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'tenant_id': tenant_id,
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'ingredient_id': ingredient_id,
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@@ -261,6 +274,71 @@ class PriceInsightsOrchestrator:
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'bulk_opportunity_count': bulk_opportunity_count
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}
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async def _publish_insight_events(self, tenant_id, insights, ingredient_context=None):
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"""
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Publish insight events to RabbitMQ for alert processing.
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Args:
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tenant_id: Tenant identifier
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insights: List of created insights
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ingredient_context: Additional context about the ingredient
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"""
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if not self.event_publisher:
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logger.warning("No event publisher available for price insights")
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return
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for insight in insights:
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# Determine severity based on confidence and priority
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confidence = insight.get('confidence', 0)
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priority = insight.get('priority', 'medium')
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# Map priority to severity, with confidence as tiebreaker
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if priority == 'critical' or (priority == 'high' and confidence >= 70):
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severity = 'high'
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elif priority == 'high' or (priority == 'medium' and confidence >= 80):
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severity = 'medium'
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else:
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severity = 'low'
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# Prepare the event data
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event_data = {
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'insight_id': insight.get('id'),
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'type': insight.get('type'),
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'title': insight.get('title'),
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'description': insight.get('description'),
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'category': insight.get('category'),
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'priority': insight.get('priority'),
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'confidence': confidence,
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'recommendation': insight.get('recommendation_actions', []),
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'impact_type': insight.get('impact_type'),
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'impact_value': insight.get('impact_value'),
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'ingredient_id': ingredient_context.get('ingredient_id') if ingredient_context else None,
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'timestamp': insight.get('detected_at', datetime.utcnow().isoformat()),
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'source_service': 'procurement',
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'source_model': 'price_forecaster'
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}
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try:
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await self.event_publisher.publish_recommendation(
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event_type='ai_price_forecast',
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tenant_id=tenant_id,
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severity=severity,
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data=event_data
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)
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logger.info(
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"Published price insight event",
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tenant_id=tenant_id,
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insight_id=insight.get('id'),
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severity=severity
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)
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except Exception as e:
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logger.error(
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"Failed to publish price insight event",
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tenant_id=tenant_id,
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insight_id=insight.get('id'),
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error=str(e)
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)
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def _generate_portfolio_summary_insight(
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self,
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tenant_id: str,
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