Add comprehensive documentation and final improvements
Documentation Added: - AI_INSIGHTS_DEMO_SETUP_GUIDE.md: Complete setup guide for demo sessions - AI_INSIGHTS_DATA_FLOW.md: Architecture and data flow diagrams - AI_INSIGHTS_QUICK_START.md: Quick reference guide - DEMO_SESSION_ANALYSIS_REPORT.md: Detailed analysis of demo session d67eaae4 - ROOT_CAUSE_ANALYSIS_AND_FIXES.md: Complete analysis of 8 issues (6 fixed, 2 analyzed) - COMPLETE_FIX_SUMMARY.md: Executive summary of all fixes - FIX_MISSING_INSIGHTS.md: Forecasting and procurement fix guide - FINAL_STATUS_SUMMARY.md: Status overview - verify_fixes.sh: Automated verification script - enhance_procurement_data.py: Procurement data enhancement script Service Improvements: - Demo session cleanup worker: Use proper settings for Redis configuration with TLS/auth - Procurement service: Add Redis initialization with proper error handling and cleanup - Production fixture: Remove duplicate worker assignments (cleaned 56 duplicates) - Orchestrator fixture: Add purchase order metadata for better tracking Impact: - Complete documentation for troubleshooting and setup - Improved Redis connection handling across services - Clean production data without duplicates - Better error handling and logging 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Urtzi Alfaro
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shared/demo/fixtures/professional/enhance_procurement_data.py
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shared/demo/fixtures/professional/enhance_procurement_data.py
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#!/usr/bin/env python3
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"""
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Enhance Procurement Data for AI Insights
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Adds purchase order items with price trends to enable procurement insights
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"""
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import json
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import random
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from pathlib import Path
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# Set seed for reproducibility
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random.seed(42)
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# Price trend data (realistic price movements over 90 days)
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INGREDIENTS_WITH_TRENDS = [
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{
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"id": "10000000-0000-0000-0000-000000000001",
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"name": "Harina de Trigo T55",
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"base_price": 0.85,
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"trend": 0.08, # 8% increase over 90 days
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"variability": 0.02,
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"unit": "kg"
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},
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{
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"id": "10000000-0000-0000-0000-000000000002",
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"name": "Harina de Trigo T65",
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"base_price": 0.95,
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"trend": 0.06, # 6% increase
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"variability": 0.02,
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"unit": "kg"
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},
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{
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"id": "10000000-0000-0000-0000-000000000011",
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"name": "Mantequilla sin Sal",
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"base_price": 6.50,
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"trend": 0.12, # 12% increase (highest)
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"variability": 0.05,
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"unit": "kg"
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},
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{
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"id": "10000000-0000-0000-0000-000000000012",
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"name": "Leche Entera Fresca",
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"base_price": 0.95,
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"trend": -0.03, # 3% decrease (seasonal surplus)
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"variability": 0.02,
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"unit": "L"
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},
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{
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"id": "10000000-0000-0000-0000-000000000021",
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"name": "Levadura Fresca",
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"base_price": 4.20,
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"trend": 0.04, # 4% increase
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"variability": 0.03,
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"unit": "kg"
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},
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{
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"id": "10000000-0000-0000-0000-000000000032",
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"name": "Azúcar Blanco",
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"base_price": 1.10,
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"trend": 0.02, # 2% increase (stable)
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"variability": 0.01,
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"unit": "kg"
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},
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]
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def calculate_price(ingredient, days_ago):
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"""
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Calculate price based on linear trend + random variability
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Args:
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ingredient: Dict with base_price, trend, variability
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days_ago: Number of days in the past
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Returns:
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Price at that point in time
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"""
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# Apply trend proportionally based on how far back in time
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# If trend is 8% over 90 days, price 45 days ago had 4% increase from base
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trend_factor = 1 + (ingredient["trend"] * (90 - days_ago) / 90)
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# Add random variability
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variability = random.uniform(-ingredient["variability"], ingredient["variability"])
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price = ingredient["base_price"] * trend_factor * (1 + variability)
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return round(price, 2)
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def parse_days_ago(order_date_str):
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"""Parse order_date to extract days ago"""
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if 'BASE_TS' in order_date_str:
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if '- ' in order_date_str:
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# Extract number from "BASE_TS - 1d" or "BASE_TS - 1h"
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parts = order_date_str.split('- ')[1]
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if 'd' in parts:
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try:
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return int(parts.split('d')[0])
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except:
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pass
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elif 'h' in parts:
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# Hours - treat as 0 days
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return 0
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elif '+ ' in order_date_str:
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# Future date - treat as 0 days ago (current price)
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return 0
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return 30 # Default fallback
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def add_items_to_pos():
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"""Add items arrays to purchase orders with realistic price trends"""
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fixture_path = Path(__file__).parent / "07-procurement.json"
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print("🔧 Enhancing Procurement Data for AI Insights...")
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print()
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# Load existing data
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with open(fixture_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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pos = data.get('purchase_orders', [])
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print(f"📦 Found {len(pos)} purchase orders")
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print()
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items_added = 0
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for i, po in enumerate(pos):
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# Parse order date to get days ago
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order_date_str = po.get('order_date', 'BASE_TS - 1d')
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days_ago = parse_days_ago(order_date_str)
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# Select 2-4 random ingredients for this PO
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num_items = random.randint(2, 4)
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selected_ingredients = random.sample(INGREDIENTS_WITH_TRENDS, k=num_items)
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items = []
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po_subtotal = 0.0
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for ingredient in selected_ingredients:
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# Calculate price at this point in time
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unit_price = calculate_price(ingredient, days_ago)
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# Order quantity (realistic for ingredient type)
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if ingredient["unit"] == "kg":
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quantity = random.randint(100, 500)
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else: # Liters
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quantity = random.randint(50, 200)
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total_price = round(quantity * unit_price, 2)
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po_subtotal += total_price
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items.append({
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"ingredient_id": ingredient["id"],
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"ingredient_name": ingredient["name"],
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"ordered_quantity": float(quantity),
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"unit": ingredient["unit"],
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"unit_price": unit_price,
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"total_price": total_price,
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"received_quantity": None,
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"status": "pending" if po.get('status') != 'delivered' else "received"
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})
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# Add items to PO
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po['items'] = items
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# Update PO totals to match items
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po['subtotal'] = round(po_subtotal, 2)
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tax_rate = 0.21 # 21% IVA in Spain
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po['tax_amount'] = round(po_subtotal * tax_rate, 2)
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po['shipping_cost'] = 15.0 if po_subtotal < 500 else 20.0
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po['total_amount'] = round(po['subtotal'] + po['tax_amount'] + po['shipping_cost'], 2)
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items_added += len(items)
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print(f" ✓ PO-{i+1} ({order_date_str}): {len(items)} items, €{po['total_amount']:.2f} total")
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# Save back
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with open(fixture_path, 'w', encoding='utf-8') as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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print()
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print("=" * 60)
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print("✅ PROCUREMENT DATA ENHANCEMENT COMPLETE")
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print("=" * 60)
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print()
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print(f"📊 SUMMARY:")
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print(f" • Purchase orders enhanced: {len(pos)}")
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print(f" • Total items added: {items_added}")
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print(f" • Average items per PO: {items_added / len(pos):.1f}")
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print()
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print("🎯 PRICE TRENDS ADDED:")
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for ing in INGREDIENTS_WITH_TRENDS:
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direction = "↑" if ing["trend"] > 0 else "↓"
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print(f" {direction} {ing['name']}: {ing['trend']*100:+.1f}% over 90 days")
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print()
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print("🚀 PROCUREMENT INSIGHTS READY:")
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print(" ✓ Price Forecaster: Can detect trends & recommend actions")
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print(" ✓ Supplier Performance: Can analyze delivery reliability")
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print(" ✓ Cost Optimizer: Can identify bulk buying opportunities")
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print()
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print("Next steps:")
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print(" 1. Create new demo session")
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print(" 2. Wait 60 seconds for AI models")
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print(" 3. Check for procurement insights (expect 1-2)")
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print()
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if __name__ == "__main__":
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add_items_to_pos()
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