280 lines
9.7 KiB
Python
280 lines
9.7 KiB
Python
"""
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ML Insights API Endpoints for Forecasting Service
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Provides endpoints to trigger ML insight generation for:
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- Dynamic business rules learning
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- Demand pattern analysis
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- Seasonal trend detection
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"""
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from fastapi import APIRouter, Depends, HTTPException, BackgroundTasks
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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}/forecasting/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 RulesGenerationRequest(BaseModel):
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"""Request schema for rules generation"""
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product_ids: Optional[List[str]] = Field(
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None,
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description="Specific product IDs to analyze. If None, analyzes 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 data to analyze",
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ge=30,
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le=365
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)
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min_samples: int = Field(
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10,
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description="Minimum samples required for rule learning",
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ge=5,
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le=100
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)
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class RulesGenerationResponse(BaseModel):
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"""Response schema for rules generation"""
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success: bool
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message: str
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tenant_id: str
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products_analyzed: int
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total_insights_generated: int
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total_insights_posted: int
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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("/generate-rules", response_model=RulesGenerationResponse)
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async def trigger_rules_generation(
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tenant_id: str,
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request_data: RulesGenerationRequest,
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db: AsyncSession = Depends(get_db)
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):
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"""
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Trigger dynamic business rules learning from historical sales data.
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This endpoint:
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1. Fetches historical sales data for specified products
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2. Runs the RulesOrchestrator to learn patterns
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3. Generates insights about optimal business rules
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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: Rules generation parameters
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db: Database session
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Returns:
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RulesGenerationResponse with generation results
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"""
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logger.info(
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"ML insights rules generation 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 and clients
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from app.ml.rules_orchestrator import RulesOrchestrator
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from shared.clients.sales_client import SalesServiceClient
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from shared.clients.inventory_client import InventoryServiceClient
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from app.core.config import settings
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# Initialize orchestrator and clients
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orchestrator = RulesOrchestrator()
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inventory_client = InventoryServiceClient(settings)
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# Get products to analyze from inventory service via API
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if request_data.product_ids:
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# Fetch specific products
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products = []
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for product_id in request_data.product_ids:
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product = await inventory_client.get_ingredient_by_id(
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ingredient_id=UUID(product_id),
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tenant_id=tenant_id
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)
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if product:
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products.append(product)
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else:
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# Fetch all products for tenant (limit to 10)
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all_products = await inventory_client.get_all_ingredients(tenant_id=tenant_id)
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products = all_products[:10] # Limit to prevent timeout
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if not products:
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return RulesGenerationResponse(
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success=False,
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message="No products found for analysis",
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tenant_id=tenant_id,
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products_analyzed=0,
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total_insights_generated=0,
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total_insights_posted=0,
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insights_by_product={},
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errors=["No products found"]
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)
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# Initialize sales client to fetch historical data
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sales_client = SalesServiceClient(config=settings, calling_service_name="forecasting")
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# Calculate date range
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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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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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product_name = product.get('name', 'Unknown')
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logger.info(f"Analyzing product {product_name} ({product_id})")
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# Fetch sales data for product
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sales_data = await sales_client.get_sales_data(
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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_data:
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logger.warning(f"No sales data for product {product_id}")
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continue
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# Convert to DataFrame
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sales_df = pd.DataFrame(sales_data)
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if len(sales_df) < request_data.min_samples:
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logger.warning(
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f"Insufficient data for product {product_id}: "
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f"{len(sales_df)} samples < {request_data.min_samples} required"
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)
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continue
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# Check what columns are available and map to expected format
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logger.debug(f"Sales data columns for product {product_id}: {sales_df.columns.tolist()}")
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# Map common field names to 'quantity' and 'date'
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if 'quantity' not in sales_df.columns:
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if 'total_quantity' in sales_df.columns:
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sales_df['quantity'] = sales_df['total_quantity']
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elif 'amount' in sales_df.columns:
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sales_df['quantity'] = sales_df['amount']
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else:
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logger.warning(f"No quantity field found for product {product_id}, skipping")
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continue
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if 'date' not in sales_df.columns:
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if 'sale_date' in sales_df.columns:
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sales_df['date'] = sales_df['sale_date']
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else:
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logger.warning(f"No date field found for product {product_id}, skipping")
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continue
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# Prepare sales data with required columns
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sales_df['date'] = pd.to_datetime(sales_df['date'])
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sales_df['quantity'] = sales_df['quantity'].astype(float)
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sales_df['day_of_week'] = sales_df['date'].dt.dayofweek
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sales_df['is_holiday'] = False # TODO: Add holiday detection
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sales_df['weather'] = 'unknown' # TODO: Add weather data
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# Run rules learning
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results = await orchestrator.learn_and_post_rules(
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tenant_id=tenant_id,
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inventory_product_id=product_id,
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sales_data=sales_df,
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external_data=None,
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min_samples=request_data.min_samples
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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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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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'rules_learned': len(results['rules'])
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}
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logger.info(
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f"Product {product_id} analysis complete",
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insights_posted=results['insights_posted']
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)
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except Exception as e:
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error_msg = f"Error analyzing 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 = RulesGenerationResponse(
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success=total_insights_posted > 0,
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message=f"Successfully generated {total_insights_posted} insights from {len(products)} products",
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tenant_id=tenant_id,
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products_analyzed=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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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 rules generation complete",
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tenant_id=tenant_id,
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total_insights=total_insights_posted
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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 rules generation 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"Rules generation 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": "forecasting-ml-insights",
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"endpoints": [
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"POST /ml/insights/generate-rules"
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]
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}
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