Add AI insights feature
This commit is contained in:
Urtzi Alfaro
@@ -358,13 +358,66 @@ async def clone_demo_data(
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except KeyError:
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process_stage_value = None
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# Transform foreign key references (product_id, recipe_id, order_id, forecast_id)
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transformed_product_id = None
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if batch_data.get('product_id'):
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try:
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transformed_product_id = str(transform_id(batch_data['product_id'], virtual_uuid))
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except (ValueError, Exception) as e:
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logger.warning("Failed to transform product_id",
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product_id=batch_data.get('product_id'),
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error=str(e))
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transformed_recipe_id = None
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if batch_data.get('recipe_id'):
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try:
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transformed_recipe_id = str(transform_id(batch_data['recipe_id'], virtual_uuid))
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except (ValueError, Exception) as e:
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logger.warning("Failed to transform recipe_id",
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recipe_id=batch_data.get('recipe_id'),
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error=str(e))
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transformed_order_id = None
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if batch_data.get('order_id'):
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try:
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transformed_order_id = str(transform_id(batch_data['order_id'], virtual_uuid))
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except (ValueError, Exception) as e:
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logger.warning("Failed to transform order_id",
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order_id=batch_data.get('order_id'),
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error=str(e))
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transformed_forecast_id = None
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if batch_data.get('forecast_id'):
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try:
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transformed_forecast_id = str(transform_id(batch_data['forecast_id'], virtual_uuid))
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except (ValueError, Exception) as e:
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logger.warning("Failed to transform forecast_id",
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forecast_id=batch_data.get('forecast_id'),
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error=str(e))
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# Transform equipment_used array
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transformed_equipment = []
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if batch_data.get('equipment_used'):
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for equip_id in batch_data['equipment_used']:
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try:
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transformed_equipment.append(str(transform_id(equip_id, virtual_uuid)))
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except (ValueError, Exception) as e:
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logger.warning("Failed to transform equipment_id",
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equipment_id=equip_id,
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error=str(e))
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# staff_assigned contains user IDs - these should NOT be transformed
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# because they reference actual user accounts which are NOT cloned
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# The demo uses the same user accounts across all virtual tenants
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staff_assigned = batch_data.get('staff_assigned', [])
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new_batch = ProductionBatch(
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id=str(transformed_id),
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tenant_id=virtual_uuid,
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batch_number=f"{session_id[:8]}-{batch_data.get('batch_number', f'BATCH-{uuid.uuid4().hex[:8].upper()}')}",
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product_id=batch_data.get('product_id'),
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product_id=transformed_product_id,
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product_name=batch_data.get('product_name'),
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recipe_id=batch_data.get('recipe_id'),
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recipe_id=transformed_recipe_id,
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planned_start_time=adjusted_planned_start,
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planned_end_time=adjusted_planned_end,
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planned_quantity=batch_data.get('planned_quantity'),
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@@ -389,11 +442,11 @@ async def clone_demo_data(
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waste_quantity=batch_data.get('waste_quantity'),
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defect_quantity=batch_data.get('defect_quantity'),
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waste_defect_type=batch_data.get('waste_defect_type'),
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equipment_used=batch_data.get('equipment_used'),
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staff_assigned=batch_data.get('staff_assigned'),
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equipment_used=transformed_equipment,
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staff_assigned=staff_assigned,
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station_id=batch_data.get('station_id'),
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order_id=batch_data.get('order_id'),
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forecast_id=batch_data.get('forecast_id'),
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order_id=transformed_order_id,
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forecast_id=transformed_forecast_id,
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is_rush_order=batch_data.get('is_rush_order', False),
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is_special_recipe=batch_data.get('is_special_recipe', False),
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is_ai_assisted=batch_data.get('is_ai_assisted', False),
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@@ -7,7 +7,7 @@ Provides endpoints to trigger ML insight generation for:
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- Process efficiency analysis
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"""
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from fastapi import APIRouter, Depends, HTTPException
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from fastapi import APIRouter, Depends, HTTPException, Request
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from pydantic import BaseModel, Field
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from typing import Optional, List
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from uuid import UUID
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@@ -71,6 +71,7 @@ class YieldPredictionResponse(BaseModel):
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async def trigger_yield_prediction(
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tenant_id: str,
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request_data: YieldPredictionRequest,
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request: Request,
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db: AsyncSession = Depends(get_db)
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):
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"""
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@@ -81,10 +82,12 @@ async def trigger_yield_prediction(
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2. Runs the YieldInsightsOrchestrator to predict yields
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3. Generates insights about yield optimization opportunities
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4. Posts insights to AI Insights Service
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5. Publishes recommendation events to RabbitMQ
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Args:
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tenant_id: Tenant UUID
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request_data: Prediction parameters
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request: FastAPI request (for app state access)
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db: Database session
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Returns:
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@@ -103,8 +106,13 @@ async def trigger_yield_prediction(
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from shared.clients.recipes_client import RecipesServiceClient
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from app.core.config import settings
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# Get event publisher from app state (if available)
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event_publisher = getattr(request.app.state, 'event_publisher', None) if hasattr(request, 'app') else None
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# Initialize orchestrator and recipes client
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orchestrator = YieldInsightsOrchestrator()
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orchestrator = YieldInsightsOrchestrator(
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event_publisher=event_publisher
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)
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recipes_client = RecipesServiceClient(settings)
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# Get recipes to analyze from recipes service via API
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@@ -186,12 +194,18 @@ async def trigger_yield_prediction(
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continue # Skip batches without complete data
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production_data.append({
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'production_date': batch.actual_start_time,
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'production_run_id': str(batch.id), # Required: unique identifier for each production run
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'recipe_id': str(batch.recipe_id), # Required: recipe identifier
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'started_at': batch.actual_start_time,
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'completed_at': batch.actual_end_time, # Optional but useful for duration analysis
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'batch_size': float(batch.planned_quantity), # Use planned_quantity as batch_size
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'planned_quantity': float(batch.planned_quantity),
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'actual_quantity': float(batch.actual_quantity),
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'yield_percentage': yield_pct,
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'worker_id': batch.notes or 'unknown', # Use notes field or default
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'batch_number': batch.batch_number
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'staff_assigned': batch.staff_assigned if batch.staff_assigned else ['unknown'],
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'batch_number': batch.batch_number,
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'equipment_id': batch.equipment_used[0] if batch.equipment_used and len(batch.equipment_used) > 0 else None,
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'notes': batch.quality_notes # Optional quality notes
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})
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if not production_data:
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@@ -202,6 +216,14 @@ async def trigger_yield_prediction(
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production_history = pd.DataFrame(production_data)
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# Debug: Log DataFrame columns and sample data
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logger.debug(
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"Production history DataFrame created",
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recipe_id=recipe_id,
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columns=list(production_history.columns),
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sample_data=production_history.head(1).to_dict('records') if len(production_history) > 0 else None
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)
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# Run yield analysis
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results = await orchestrator.analyze_and_post_insights(
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tenant_id=tenant_id,
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@@ -291,8 +313,6 @@ async def ml_insights_health():
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# INTERNAL ENDPOINTS (for demo-session service)
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# ================================================================
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from fastapi import Request
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# Create a separate router for internal endpoints to avoid the tenant prefix
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internal_router = APIRouter(
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tags=["ML Insights - Internal"]
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@@ -347,6 +367,7 @@ async def generate_yield_insights_internal(
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result = await trigger_yield_prediction(
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tenant_id=tenant_id,
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request_data=request_data,
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request=request,
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db=db
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)
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