Improve AI logic
This commit is contained in:
Urtzi Alfaro
@@ -388,7 +388,8 @@ async def resolve_or_create_products_batch(
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request: BatchProductResolutionRequest,
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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db: AsyncSession = Depends(get_db),
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classifier: ProductClassifierService = Depends(get_product_classifier)
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):
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"""Resolve or create multiple products in a single optimized operation for sales import"""
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try:
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@@ -415,11 +416,14 @@ async def resolve_or_create_products_batch(
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resolved_count += 1
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logger.debug("Resolved existing product", product=product_name, tenant_id=tenant_id)
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else:
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category = product_data.get('category', 'general')
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# Use the product classifier to determine the appropriate type
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suggestion = classifier.classify_product(product_name)
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category = product_data.get('category', suggestion.category if hasattr(suggestion, 'category') else 'general')
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ingredient_data = {
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'name': product_name,
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'type': 'finished_product',
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'unit': 'unit',
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'type': suggestion.product_type.value if hasattr(suggestion, 'product_type') else 'finished_product',
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'unit': suggestion.unit_of_measure.value if hasattr(suggestion, 'unit_of_measure') else 'unit',
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'current_stock': 0,
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'reorder_point': 0,
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'cost_per_unit': 0,
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@@ -429,7 +433,8 @@ async def resolve_or_create_products_batch(
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created = await service.create_ingredient_fast(ingredient_data, tenant_id, db)
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product_mappings[product_name] = str(created.id)
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created_count += 1
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logger.debug("Created new product", product=product_name, tenant_id=tenant_id)
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logger.debug("Created new product", product=product_name,
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product_type=ingredient_data['type'], tenant_id=tenant_id)
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except Exception as e:
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logger.warning("Failed to resolve/create product",
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297
services/inventory/app/api/ml_insights.py
Normal file
297
services/inventory/app/api/ml_insights.py
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@@ -0,0 +1,297 @@
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"""
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ML Insights API Endpoints for Inventory Service
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Provides endpoints to trigger ML insight generation for:
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- Safety stock optimization
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- Inventory level recommendations
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- Demand pattern analysis
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"""
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from fastapi import APIRouter, Depends, HTTPException
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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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from datetime import datetime, timedelta
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import structlog
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import pandas as pd
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from app.core.database import get_db
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from sqlalchemy.ext.asyncio import AsyncSession
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logger = structlog.get_logger()
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router = APIRouter(
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prefix="/api/v1/tenants/{tenant_id}/inventory/ml/insights",
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tags=["ML Insights"]
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)
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# ================================================================
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# REQUEST/RESPONSE SCHEMAS
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# ================================================================
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class SafetyStockOptimizationRequest(BaseModel):
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"""Request schema for safety stock optimization"""
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product_ids: Optional[List[str]] = Field(
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None,
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description="Specific product IDs to optimize. If None, optimizes all products"
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)
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lookback_days: int = Field(
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90,
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description="Days of historical demand to analyze",
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ge=30,
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le=365
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)
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min_history_days: int = Field(
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30,
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description="Minimum days of history required",
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ge=7,
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le=180
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)
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class SafetyStockOptimizationResponse(BaseModel):
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"""Response schema for safety stock optimization"""
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success: bool
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message: str
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tenant_id: str
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products_optimized: int
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total_insights_generated: int
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total_insights_posted: int
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total_cost_savings: float
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insights_by_product: dict
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errors: List[str] = []
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# ================================================================
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# API ENDPOINTS
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# ================================================================
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@router.post("/optimize-safety-stock", response_model=SafetyStockOptimizationResponse)
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async def trigger_safety_stock_optimization(
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tenant_id: str,
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request_data: SafetyStockOptimizationRequest,
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db: AsyncSession = Depends(get_db)
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):
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"""
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Trigger safety stock optimization for inventory products.
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This endpoint:
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1. Fetches historical demand data for specified products
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2. Runs the SafetyStockInsightsOrchestrator to optimize levels
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3. Generates insights about safety stock recommendations
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4. Posts insights to AI Insights Service
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Args:
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tenant_id: Tenant UUID
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request_data: Optimization parameters
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db: Database session
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Returns:
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SafetyStockOptimizationResponse with optimization results
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"""
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logger.info(
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"ML insights safety stock optimization requested",
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tenant_id=tenant_id,
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product_ids=request_data.product_ids,
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lookback_days=request_data.lookback_days
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)
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try:
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# Import ML orchestrator
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from app.ml.safety_stock_insights_orchestrator import SafetyStockInsightsOrchestrator
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from app.models.inventory import Ingredient
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from sqlalchemy import select
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# Initialize orchestrator
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orchestrator = SafetyStockInsightsOrchestrator()
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# Get products to optimize
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if request_data.product_ids:
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query = select(Ingredient).where(
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Ingredient.tenant_id == UUID(tenant_id),
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Ingredient.id.in_([UUID(pid) for pid in request_data.product_ids])
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)
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else:
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query = select(Ingredient).where(
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Ingredient.tenant_id == UUID(tenant_id)
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).limit(10) # Limit to prevent timeout
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result = await db.execute(query)
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products = result.scalars().all()
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if not products:
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return SafetyStockOptimizationResponse(
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success=False,
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message="No products found for optimization",
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tenant_id=tenant_id,
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products_optimized=0,
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total_insights_generated=0,
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total_insights_posted=0,
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total_cost_savings=0.0,
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insights_by_product={},
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errors=["No products found"]
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)
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# Calculate date range for demand history
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end_date = datetime.utcnow()
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start_date = end_date - timedelta(days=request_data.lookback_days)
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# Process each product
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total_insights_generated = 0
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total_insights_posted = 0
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total_cost_savings = 0.0
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insights_by_product = {}
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errors = []
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for product in products:
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try:
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product_id = str(product.id)
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logger.info(f"Optimizing safety stock for {product.name} ({product_id})")
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# Fetch real sales/demand history from sales service
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from shared.clients.sales_client import SalesServiceClient
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from app.core.config import settings
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sales_client = SalesServiceClient(settings)
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try:
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# Fetch sales data for this product
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sales_response = await sales_client.get_sales_by_product(
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tenant_id=tenant_id,
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product_id=product_id,
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start_date=start_date.strftime('%Y-%m-%d'),
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end_date=end_date.strftime('%Y-%m-%d')
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)
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if not sales_response or not sales_response.get('sales'):
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logger.warning(
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f"No sales history for product {product_id}, skipping"
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)
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continue
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# Convert sales data to daily demand
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sales_data = sales_response.get('sales', [])
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demand_data = []
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for sale in sales_data:
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demand_data.append({
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'date': pd.to_datetime(sale.get('date') or sale.get('sale_date')),
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'quantity': float(sale.get('quantity', 0))
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})
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if not demand_data:
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logger.warning(
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f"No valid demand data for product {product_id}, skipping"
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)
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continue
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demand_history = pd.DataFrame(demand_data)
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# Aggregate by date if there are multiple sales per day
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demand_history = demand_history.groupby('date').agg({
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'quantity': 'sum'
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}).reset_index()
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if len(demand_history) < request_data.min_history_days:
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logger.warning(
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f"Insufficient demand history for product {product_id}: "
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f"{len(demand_history)} days < {request_data.min_history_days} required"
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)
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continue
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except Exception as e:
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logger.error(
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f"Error fetching sales data for product {product_id}: {e}",
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exc_info=True
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)
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continue
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# Product characteristics
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product_characteristics = {
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'lead_time_days': 7, # TODO: Get from supplier data
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'shelf_life_days': 30 if product.is_perishable else 365,
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'perishable': product.is_perishable
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}
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# Run optimization
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results = await orchestrator.optimize_and_post_insights(
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tenant_id=tenant_id,
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inventory_product_id=product_id,
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demand_history=demand_history,
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product_characteristics=product_characteristics,
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min_history_days=request_data.min_history_days
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)
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# Track results
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total_insights_generated += results['insights_generated']
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total_insights_posted += results['insights_posted']
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if results.get('cost_savings'):
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total_cost_savings += results['cost_savings']
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insights_by_product[product_id] = {
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'product_name': product.name,
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'insights_posted': results['insights_posted'],
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'optimal_safety_stock': results.get('optimal_safety_stock'),
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'cost_savings': results.get('cost_savings', 0.0)
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}
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logger.info(
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f"Product {product_id} optimization complete",
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insights_posted=results['insights_posted'],
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cost_savings=results.get('cost_savings', 0)
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)
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except Exception as e:
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error_msg = f"Error optimizing product {product_id}: {str(e)}"
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logger.error(error_msg, exc_info=True)
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errors.append(error_msg)
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# Close orchestrator
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await orchestrator.close()
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# Build response
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response = SafetyStockOptimizationResponse(
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success=total_insights_posted > 0,
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message=f"Successfully optimized {len(products)} products, generated {total_insights_posted} insights",
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tenant_id=tenant_id,
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products_optimized=len(products),
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total_insights_generated=total_insights_generated,
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total_insights_posted=total_insights_posted,
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total_cost_savings=round(total_cost_savings, 2),
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insights_by_product=insights_by_product,
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errors=errors
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)
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logger.info(
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"ML insights safety stock optimization complete",
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tenant_id=tenant_id,
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total_insights=total_insights_posted,
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total_savings=total_cost_savings
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)
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return response
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except Exception as e:
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logger.error(
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"ML insights safety stock optimization failed",
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tenant_id=tenant_id,
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error=str(e),
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exc_info=True
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)
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raise HTTPException(
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status_code=500,
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detail=f"Safety stock optimization failed: {str(e)}"
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)
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@router.get("/health")
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async def ml_insights_health():
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"""Health check for ML insights endpoints"""
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return {
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"status": "healthy",
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"service": "inventory-ml-insights",
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"endpoints": [
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"POST /ml/insights/optimize-safety-stock"
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]
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}
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