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
2025-12-16 11:32:45 +01:00
parent 4418ff0876
commit 9f3b39bd28
14 changed files with 3982 additions and 60 deletions

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@@ -448,7 +448,6 @@
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@@ -510,7 +509,6 @@
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@@ -838,7 +831,6 @@
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@@ -892,7 +884,6 @@
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@@ -945,7 +936,6 @@
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@@ -998,7 +988,6 @@
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@@ -1051,7 +1040,6 @@
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@@ -1105,7 +1093,6 @@
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"station_id": "STATION-02",
@@ -1158,7 +1145,6 @@
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"station_id": "STATION-01",
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@@ -1789,7 +1774,6 @@
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@@ -1911,7 +1894,6 @@
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@@ -2094,7 +2074,6 @@
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@@ -2155,7 +2134,6 @@
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@@ -2216,7 +2194,6 @@
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@@ -2277,7 +2254,6 @@
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@@ -2338,7 +2314,6 @@
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@@ -2460,7 +2434,6 @@
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@@ -2764,7 +2737,6 @@
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@@ -2977,7 +2949,6 @@
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@@ -3099,7 +3070,6 @@
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@@ -3160,7 +3130,6 @@
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"station_id": "STATION-01",
@@ -3221,7 +3190,6 @@
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@@ -3282,7 +3250,6 @@
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@@ -3343,7 +3310,6 @@
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@@ -3465,7 +3431,6 @@
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"staff_assigned": [
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@@ -3526,7 +3491,6 @@
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@@ -3587,7 +3551,6 @@
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@@ -3648,7 +3611,6 @@
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@@ -3770,7 +3732,6 @@
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@@ -3892,7 +3853,6 @@
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@@ -3953,7 +3913,6 @@
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@@ -4136,7 +4095,6 @@
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@@ -4197,7 +4155,6 @@
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@@ -4258,7 +4215,6 @@
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@@ -4502,7 +4458,6 @@
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@@ -4563,7 +4518,6 @@
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@@ -4746,7 +4700,6 @@
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"station_id": "STATION-03",
@@ -4807,7 +4760,6 @@
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"station_id": "STATION-02",
@@ -4868,7 +4820,6 @@
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"station_id": "STATION-01",
@@ -4929,7 +4880,6 @@
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"staff_assigned": [
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"50000000-0000-0000-0000-000000000006"
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"station_id": "STATION-02",
@@ -5051,7 +5001,6 @@
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"station_id": "STATION-02",
@@ -5112,7 +5061,6 @@
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"staff_assigned": [
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"50000000-0000-0000-0000-000000000005"
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"station_id": "STATION-01",
@@ -5173,7 +5121,6 @@
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"staff_assigned": [
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"station_id": "STATION-02",
@@ -5356,7 +5303,6 @@
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"staff_assigned": [
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"station_id": "STATION-01",
@@ -5417,7 +5363,6 @@
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@@ -5478,7 +5423,6 @@
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],
"station_id": "STATION-03",

View File

@@ -31,6 +31,102 @@
"errors_encountered": 0,
"warnings_generated": 2
},
"run_metadata": {
"purchase_orders": [
{
"id": "50000000-0000-0000-0000-000000000001",
"status": "completed",
"delivery_date": "BASE_TS - 2d",
"items": [
{
"ingredient_id": "10000000-0000-0000-0000-000000000001",
"product_name": "Harina de Trigo T55",
"quantity": 500.0,
"unit": "kilograms"
},
{
"ingredient_id": "10000000-0000-0000-0000-000000000002",
"product_name": "Harina de Trigo T65",
"quantity": 200.0,
"unit": "kilograms"
},
{
"ingredient_id": "10000000-0000-0000-0000-000000000005",
"product_name": "Harina de Centeno",
"quantity": 100.0,
"unit": "kilograms"
},
{
"ingredient_id": "10000000-0000-0000-0000-000000000031",
"product_name": "Sal Marina Fina",
"quantity": 50.0,
"unit": "kilograms"
}
]
},
{
"id": "50000000-0000-0000-0000-000000000002",
"status": "completed",
"delivery_date": "BASE_TS - 1d",
"items": [
{
"ingredient_id": "10000000-0000-0000-0000-000000000011",
"product_name": "Mantequilla sin Sal 82% MG",
"quantity": 80.0,
"unit": "kilograms"
}
]
},
{
"id": "50000000-0000-0000-0000-000000000004",
"status": "confirmed",
"delivery_date": "BASE_TS + 1d",
"items": [
{
"ingredient_id": "10000000-0000-0000-0000-000000000001",
"product_name": "Harina de Trigo T55",
"quantity": 1000.0,
"unit": "kilograms"
},
{
"ingredient_id": "10000000-0000-0000-0000-000000000021",
"product_name": "Levadura Fresca de Panadería",
"quantity": 50.0,
"unit": "kilograms"
}
]
}
],
"production_batches": [
{
"id": "40000000-0000-0000-0000-000000000001",
"product_id": "20000000-0000-0000-0000-000000000001",
"product_name": "Baguette Francesa Tradicional",
"status": "COMPLETED",
"scheduled_date": "BASE_TS - 1d 16h",
"quantity": 98.0,
"unit": "units"
},
{
"id": "40000000-0000-0000-0000-000000000002",
"product_id": "20000000-0000-0000-0000-000000000002",
"product_name": "Croissant de Mantequilla Artesanal",
"status": "COMPLETED",
"scheduled_date": "BASE_TS - 1d 15h",
"quantity": 115.0,
"unit": "units"
},
{
"id": "40000000-0000-0000-0000-000000000003",
"product_id": "20000000-0000-0000-0000-000000000003",
"product_name": "Pan de Pueblo con Masa Madre",
"status": "COMPLETED",
"scheduled_date": "BASE_TS - 1d 14h",
"quantity": 80.0,
"unit": "units"
}
]
},
"production_coordination": {
"batches_synchronized": [
{

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@@ -0,0 +1,209 @@
#!/usr/bin/env python3
"""
Enhance Procurement Data for AI Insights
Adds purchase order items with price trends to enable procurement insights
"""
import json
import random
from pathlib import Path
# Set seed for reproducibility
random.seed(42)
# Price trend data (realistic price movements over 90 days)
INGREDIENTS_WITH_TRENDS = [
{
"id": "10000000-0000-0000-0000-000000000001",
"name": "Harina de Trigo T55",
"base_price": 0.85,
"trend": 0.08, # 8% increase over 90 days
"variability": 0.02,
"unit": "kg"
},
{
"id": "10000000-0000-0000-0000-000000000002",
"name": "Harina de Trigo T65",
"base_price": 0.95,
"trend": 0.06, # 6% increase
"variability": 0.02,
"unit": "kg"
},
{
"id": "10000000-0000-0000-0000-000000000011",
"name": "Mantequilla sin Sal",
"base_price": 6.50,
"trend": 0.12, # 12% increase (highest)
"variability": 0.05,
"unit": "kg"
},
{
"id": "10000000-0000-0000-0000-000000000012",
"name": "Leche Entera Fresca",
"base_price": 0.95,
"trend": -0.03, # 3% decrease (seasonal surplus)
"variability": 0.02,
"unit": "L"
},
{
"id": "10000000-0000-0000-0000-000000000021",
"name": "Levadura Fresca",
"base_price": 4.20,
"trend": 0.04, # 4% increase
"variability": 0.03,
"unit": "kg"
},
{
"id": "10000000-0000-0000-0000-000000000032",
"name": "Azúcar Blanco",
"base_price": 1.10,
"trend": 0.02, # 2% increase (stable)
"variability": 0.01,
"unit": "kg"
},
]
def calculate_price(ingredient, days_ago):
"""
Calculate price based on linear trend + random variability
Args:
ingredient: Dict with base_price, trend, variability
days_ago: Number of days in the past
Returns:
Price at that point in time
"""
# Apply trend proportionally based on how far back in time
# If trend is 8% over 90 days, price 45 days ago had 4% increase from base
trend_factor = 1 + (ingredient["trend"] * (90 - days_ago) / 90)
# Add random variability
variability = random.uniform(-ingredient["variability"], ingredient["variability"])
price = ingredient["base_price"] * trend_factor * (1 + variability)
return round(price, 2)
def parse_days_ago(order_date_str):
"""Parse order_date to extract days ago"""
if 'BASE_TS' in order_date_str:
if '- ' in order_date_str:
# Extract number from "BASE_TS - 1d" or "BASE_TS - 1h"
parts = order_date_str.split('- ')[1]
if 'd' in parts:
try:
return int(parts.split('d')[0])
except:
pass
elif 'h' in parts:
# Hours - treat as 0 days
return 0
elif '+ ' in order_date_str:
# Future date - treat as 0 days ago (current price)
return 0
return 30 # Default fallback
def add_items_to_pos():
"""Add items arrays to purchase orders with realistic price trends"""
fixture_path = Path(__file__).parent / "07-procurement.json"
print("🔧 Enhancing Procurement Data for AI Insights...")
print()
# Load existing data
with open(fixture_path, 'r', encoding='utf-8') as f:
data = json.load(f)
pos = data.get('purchase_orders', [])
print(f"📦 Found {len(pos)} purchase orders")
print()
items_added = 0
for i, po in enumerate(pos):
# Parse order date to get days ago
order_date_str = po.get('order_date', 'BASE_TS - 1d')
days_ago = parse_days_ago(order_date_str)
# Select 2-4 random ingredients for this PO
num_items = random.randint(2, 4)
selected_ingredients = random.sample(INGREDIENTS_WITH_TRENDS, k=num_items)
items = []
po_subtotal = 0.0
for ingredient in selected_ingredients:
# Calculate price at this point in time
unit_price = calculate_price(ingredient, days_ago)
# Order quantity (realistic for ingredient type)
if ingredient["unit"] == "kg":
quantity = random.randint(100, 500)
else: # Liters
quantity = random.randint(50, 200)
total_price = round(quantity * unit_price, 2)
po_subtotal += total_price
items.append({
"ingredient_id": ingredient["id"],
"ingredient_name": ingredient["name"],
"ordered_quantity": float(quantity),
"unit": ingredient["unit"],
"unit_price": unit_price,
"total_price": total_price,
"received_quantity": None,
"status": "pending" if po.get('status') != 'delivered' else "received"
})
# Add items to PO
po['items'] = items
# Update PO totals to match items
po['subtotal'] = round(po_subtotal, 2)
tax_rate = 0.21 # 21% IVA in Spain
po['tax_amount'] = round(po_subtotal * tax_rate, 2)
po['shipping_cost'] = 15.0 if po_subtotal < 500 else 20.0
po['total_amount'] = round(po['subtotal'] + po['tax_amount'] + po['shipping_cost'], 2)
items_added += len(items)
print(f" ✓ PO-{i+1} ({order_date_str}): {len(items)} items, €{po['total_amount']:.2f} total")
# Save back
with open(fixture_path, 'w', encoding='utf-8') as f:
json.dump(data, f, indent=2, ensure_ascii=False)
print()
print("=" * 60)
print("✅ PROCUREMENT DATA ENHANCEMENT COMPLETE")
print("=" * 60)
print()
print(f"📊 SUMMARY:")
print(f" • Purchase orders enhanced: {len(pos)}")
print(f" • Total items added: {items_added}")
print(f" • Average items per PO: {items_added / len(pos):.1f}")
print()
print("🎯 PRICE TRENDS ADDED:")
for ing in INGREDIENTS_WITH_TRENDS:
direction = "" if ing["trend"] > 0 else ""
print(f" {direction} {ing['name']}: {ing['trend']*100:+.1f}% over 90 days")
print()
print("🚀 PROCUREMENT INSIGHTS READY:")
print(" ✓ Price Forecaster: Can detect trends & recommend actions")
print(" ✓ Supplier Performance: Can analyze delivery reliability")
print(" ✓ Cost Optimizer: Can identify bulk buying opportunities")
print()
print("Next steps:")
print(" 1. Create new demo session")
print(" 2. Wait 60 seconds for AI models")
print(" 3. Check for procurement insights (expect 1-2)")
print()
if __name__ == "__main__":
add_items_to_pos()