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# AI Insights Service
Intelligent insights and recommendations service for bakery operations optimization.
## Overview
The AI Insights Service is a microservice that aggregates, scores, and manages intelligent recommendations across the bakery-ia platform. It provides:
The **AI Insights Service** provides intelligent, actionable recommendations to bakery operators by analyzing patterns across inventory, production, procurement, and sales data. It acts as a virtual operations consultant, proactively identifying opportunities for cost savings, waste reduction, and operational improvements. This service transforms raw data into business intelligence that drives profitability.
- **Unified Insight Management**: Centralized storage and retrieval of AI-generated insights
- **Confidence Scoring**: Standardized confidence calculation across different insight types
- **Impact Estimation**: Business value quantification for recommendations
- **Feedback Loop**: Closed-loop learning from applied insights
- **Cross-Service Intelligence**: Correlation detection between insights from different services
## Key Features
### Intelligent Recommendations
- **Inventory Optimization** - Smart reorder point suggestions and stock level adjustments
- **Production Planning** - Optimal batch size and scheduling recommendations
- **Procurement Suggestions** - Best supplier selection and order timing advice
- **Sales Opportunities** - Identify trending products and underperforming items
- **Cost Reduction** - Find areas to reduce waste and lower operational costs
- **Quality Improvements** - Detect patterns affecting product quality
### Unified Insight Management
- **Centralized Storage** - All AI-generated insights in one place
- **Confidence Scoring** - Standardized 0-100% confidence calculation across insight types
- **Impact Estimation** - Business value quantification for recommendations
- **Feedback Loop** - Closed-loop learning from applied insights
- **Cross-Service Intelligence** - Correlation detection between insights from different services
## Features

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# Alert Processor Service
## Overview
The **Alert Processor Service** acts as the central alert hub for the entire Bakery-IA platform, consuming events from all microservices via RabbitMQ and intelligently routing them as notifications. It applies business logic to determine alert severity, filters noise, aggregates related alerts, and ensures critical issues reach stakeholders immediately while preventing alert fatigue. This service is the intelligent layer between raw system events and actionable user notifications.
## Key Features
### Central Event Hub
- **RabbitMQ Consumer** - Listens to all service exchanges
- **Multi-Exchange Subscription** - Forecasting, inventory, production, procurement, etc.
- **Event Classification** - Categorize events by type and importance
- **Event Deduplication** - Prevent duplicate alerts
- **Event Aggregation** - Combine related events into single alert
- **Event Filtering** - Apply business rules to reduce noise
### Intelligent Alert Routing
- **Severity Classification** - Critical, high, medium, low
- **Priority Assignment** - Urgent, normal, informational
- **Channel Selection** - Email vs. WhatsApp based on severity
- **Recipient Determination** - Route to appropriate team members
- **Escalation Rules** - Escalate unacknowledged critical alerts
- **Alert Suppression** - Prevent alert storms during incidents
### Alert Types & Sources
- **Stockout Alerts** - From inventory service (critical)
- **Quality Issues** - From production service (high)
- **Forecast Anomalies** - From forecasting service (medium)
- **Equipment Maintenance** - From production service (medium)
- **Low Stock Warnings** - From inventory service (medium)
- **Payment Overdue** - From orders service (high)
- **Price Changes** - From suppliers service (low)
- **API Health Issues** - From external service (critical)
### Business Logic Engine
- **Time-Based Rules** - Alert behavior based on time of day
- **Frequency Limits** - Max alerts per hour/day
- **Threshold Management** - Configurable alert thresholds
- **Context Enrichment** - Add helpful context to alerts
- **Impact Assessment** - Calculate business impact
- **Recommendation Engine** - Suggest corrective actions
### Alert Lifecycle Management
- **Active Alert Tracking** - Monitor open alerts
- **Acknowledgment Handling** - Track alert acknowledgments
- **Resolution Tracking** - Monitor when issues are resolved
- **Alert History** - Complete audit trail
- **Alert Metrics** - Response times, resolution times
- **SLA Monitoring** - Track alert SLA compliance
### Alert Fatigue Prevention
- **Smart Throttling** - Limit similar alerts
- **Quiet Period Management** - Respect quiet hours
- **Digest Mode** - Batch low-priority alerts
- **Alert Grouping** - Combine related alerts
- **Snooze Functionality** - Temporarily suppress alerts
- **Alert Unsubscribe** - Opt out of specific alert types
## Business Value
### For Bakery Owners
- **No Missed Issues** - Critical problems always reach you
- **Reduced Noise** - Only important alerts, no spam
- **Fast Response** - Know issues within seconds
- **Business Context** - Alerts include impact and recommendations
- **Audit Trail** - Complete alert history for review
- **Configurable** - Adjust alert thresholds to your needs
### Quantifiable Impact
- **Issue Detection**: 90% faster (minutes vs. hours/days)
- **Response Time**: 70-90% faster with immediate alerts
- **Downtime Prevention**: 50-80% reduction through early warning
- **Alert Relevance**: 90%+ alerts are actionable (vs. 30-50% without filtering)
- **Staff Productivity**: 2-4 hours/week saved (not chasing issues)
- **Cost Avoidance**: €500-2,000/month (prevented stockouts, quality issues)
### For Operations Staff
- **Clear Priorities** - Know what needs attention first
- **Actionable Alerts** - Each alert has next steps
- **Mobile Alerts** - WhatsApp for critical issues
- **Alert Context** - Understand problem without investigation
- **Quick Resolution** - Faster problem solving with guidance
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Alert history
- **Caching**: Redis 7.4 - Active alerts cache
- **Messaging**: RabbitMQ 4.1 - Event consumption
- **Consumer**: aio-pika - Async RabbitMQ client
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Alert metrics
## API Endpoints (Key Routes)
### Alert Management
- `GET /api/v1/alerts` - List alerts with filters
- `GET /api/v1/alerts/{alert_id}` - Get alert details
- `POST /api/v1/alerts/{alert_id}/acknowledge` - Acknowledge alert
- `POST /api/v1/alerts/{alert_id}/resolve` - Mark alert resolved
- `POST /api/v1/alerts/{alert_id}/snooze` - Snooze alert temporarily
- `GET /api/v1/alerts/active` - Get active (unresolved) alerts
### Alert Configuration
- `GET /api/v1/alerts/config` - Get alert configuration
- `PUT /api/v1/alerts/config` - Update alert configuration
- `GET /api/v1/alerts/rules` - List alert rules
- `POST /api/v1/alerts/rules` - Create alert rule
- `PUT /api/v1/alerts/rules/{rule_id}` - Update rule
- `DELETE /api/v1/alerts/rules/{rule_id}` - Delete rule
### Alert Analytics
- `GET /api/v1/alerts/analytics/dashboard` - Alert dashboard
- `GET /api/v1/alerts/analytics/by-type` - Alerts by type
- `GET /api/v1/alerts/analytics/by-severity` - Alerts by severity
- `GET /api/v1/alerts/analytics/response-times` - Alert response metrics
- `GET /api/v1/alerts/analytics/resolution-times` - Resolution metrics
### Health & Monitoring
- `GET /api/v1/alerts/health` - Service health
- `GET /api/v1/alerts/consumer/status` - RabbitMQ consumer status
- `GET /api/v1/alerts/queue/stats` - Queue statistics
## Database Schema
### Main Tables
**alerts**
```sql
CREATE TABLE alerts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
alert_type VARCHAR(100) NOT NULL, -- stockout, quality_issue, forecast_anomaly, etc.
alert_category VARCHAR(100) NOT NULL, -- inventory, production, forecasting, procurement, etc.
severity VARCHAR(50) NOT NULL, -- critical, high, medium, low
priority VARCHAR(50) NOT NULL, -- urgent, normal, informational
status VARCHAR(50) DEFAULT 'active', -- active, acknowledged, resolved, snoozed
-- Alert content
title VARCHAR(500) NOT NULL,
description TEXT NOT NULL,
recommended_action TEXT,
business_impact TEXT,
-- Context
source_service VARCHAR(100) NOT NULL,
source_event_id VARCHAR(255),
source_event_type VARCHAR(100),
source_event_data JSONB,
-- Related entities
related_product_id UUID,
related_ingredient_id UUID,
related_batch_id UUID,
related_order_id UUID,
related_supplier_id UUID,
-- Lifecycle
created_at TIMESTAMP DEFAULT NOW(),
acknowledged_at TIMESTAMP,
acknowledged_by UUID,
resolved_at TIMESTAMP,
resolved_by UUID,
resolution_notes TEXT,
snoozed_until TIMESTAMP,
-- Notifications
notification_sent BOOLEAN DEFAULT FALSE,
notification_channel VARCHAR(50),
notification_id UUID,
-- Metrics
response_time_seconds INTEGER, -- Time to acknowledgment
resolution_time_seconds INTEGER, -- Time to resolution
INDEX idx_alerts_tenant_status (tenant_id, status),
INDEX idx_alerts_severity (tenant_id, severity, created_at DESC),
INDEX idx_alerts_type (tenant_id, alert_type)
);
```
**alert_rules**
```sql
CREATE TABLE alert_rules (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
rule_name VARCHAR(255) NOT NULL,
rule_type VARCHAR(100) NOT NULL, -- threshold, pattern, anomaly
is_active BOOLEAN DEFAULT TRUE,
-- Source
source_service VARCHAR(100),
source_event_type VARCHAR(100),
-- Conditions
condition_json JSONB NOT NULL, -- Rule logic in JSON
threshold_value DECIMAL(10, 2),
threshold_operator VARCHAR(10), -- >, <, =, >=, <=
-- Alert configuration
alert_type VARCHAR(100) NOT NULL,
severity VARCHAR(50) NOT NULL,
priority VARCHAR(50) NOT NULL,
title_template TEXT NOT NULL,
description_template TEXT NOT NULL,
action_template TEXT,
-- Notification
notify BOOLEAN DEFAULT TRUE,
notification_channels JSONB, -- ["email", "whatsapp"]
notify_roles JSONB, -- ["owner", "manager"]
-- Throttling
throttle_minutes INTEGER DEFAULT 0, -- Min time between same alerts
max_alerts_per_hour INTEGER,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, rule_name)
);
```
**alert_aggregations**
```sql
CREATE TABLE alert_aggregations (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
aggregation_key VARCHAR(255) NOT NULL, -- Unique key for grouping
alert_type VARCHAR(100) NOT NULL,
count INTEGER DEFAULT 1,
first_occurrence TIMESTAMP NOT NULL,
last_occurrence TIMESTAMP NOT NULL,
aggregated_alert_id UUID, -- Final alert created
individual_alert_ids JSONB, -- Array of aggregated alert IDs
is_active BOOLEAN DEFAULT TRUE,
UNIQUE(tenant_id, aggregation_key)
);
```
**alert_history**
```sql
CREATE TABLE alert_history (
id UUID PRIMARY KEY,
alert_id UUID REFERENCES alerts(id) ON DELETE CASCADE,
action VARCHAR(100) NOT NULL, -- created, acknowledged, resolved, snoozed
action_by UUID,
action_at TIMESTAMP DEFAULT NOW(),
notes TEXT,
previous_status VARCHAR(50),
new_status VARCHAR(50)
);
```
**alert_suppressions**
```sql
CREATE TABLE alert_suppressions (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
suppression_type VARCHAR(100) NOT NULL, -- maintenance_window, incident, manual
alert_types JSONB, -- Array of alert types to suppress
start_time TIMESTAMP NOT NULL,
end_time TIMESTAMP NOT NULL,
reason TEXT NOT NULL,
is_active BOOLEAN DEFAULT TRUE,
created_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW()
);
```
**alert_metrics**
```sql
CREATE TABLE alert_metrics (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
metric_date DATE NOT NULL,
alert_type VARCHAR(100),
severity VARCHAR(50),
-- Volume metrics
total_alerts INTEGER DEFAULT 0,
critical_alerts INTEGER DEFAULT 0,
high_alerts INTEGER DEFAULT 0,
acknowledged_alerts INTEGER DEFAULT 0,
resolved_alerts INTEGER DEFAULT 0,
-- Time metrics
avg_response_time_seconds INTEGER,
avg_resolution_time_seconds INTEGER,
max_response_time_seconds INTEGER,
max_resolution_time_seconds INTEGER,
-- SLA metrics
sla_met_count INTEGER DEFAULT 0,
sla_violated_count INTEGER DEFAULT 0,
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, metric_date, alert_type, severity)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_alerts_active ON alerts(tenant_id, status) WHERE status IN ('active', 'acknowledged');
CREATE INDEX idx_alerts_created ON alerts(tenant_id, created_at DESC);
CREATE INDEX idx_alert_rules_active ON alert_rules(tenant_id, is_active) WHERE is_active = TRUE;
CREATE INDEX idx_aggregations_active ON alert_aggregations(tenant_id, is_active) WHERE is_active = TRUE;
CREATE INDEX idx_suppressions_active ON alert_suppressions(tenant_id, is_active, start_time, end_time) WHERE is_active = TRUE;
```
## Business Logic Examples
### RabbitMQ Event Consumer
```python
async def start_alert_processor():
"""
Start consuming events from all service exchanges.
"""
connection = await aio_pika.connect_robust(os.getenv('RABBITMQ_URL'))
channel = await connection.channel()
# Set QoS (prefetch)
await channel.set_qos(prefetch_count=10)
# Define exchanges and routing keys to consume
subscriptions = [
('inventory', ['inventory.stockout', 'inventory.low_stock', 'inventory.expiring']),
('production', ['production.quality.issue', 'production.equipment.maintenance']),
('forecasting', ['forecasting.anomaly', 'forecasting.low_demand', 'forecasting.high_demand']),
('procurement', ['procurement.stockout_risk', 'procurement.po_failed']),
('orders', ['orders.overdue', 'orders.large_order']),
('suppliers', ['suppliers.performance_alert', 'suppliers.price_change']),
('external', ['external.api_health', 'external.holiday_alert']),
('pos', ['pos.sync_failed', 'pos.mapping_needed'])
]
for exchange_name, routing_keys in subscriptions:
# Declare exchange
exchange = await channel.declare_exchange(
exchange_name,
aio_pika.ExchangeType.TOPIC,
durable=True
)
# Create queue for this service
queue_name = f'alert_processor.{exchange_name}'
queue = await channel.declare_queue(queue_name, durable=True)
# Bind queue to routing keys
for routing_key in routing_keys:
await queue.bind(exchange, routing_key=routing_key)
# Start consuming
await queue.consume(process_event)
logger.info("Subscribed to exchange",
exchange=exchange_name,
routing_keys=routing_keys)
logger.info("Alert processor started, consuming events")
async def process_event(message: aio_pika.IncomingMessage):
"""
Process incoming event from RabbitMQ.
"""
async with message.process():
try:
# Parse message
event_data = json.loads(message.body.decode())
tenant_id = event_data.get('tenant_id')
event_type = event_data.get('event_type')
logger.info("Processing event",
exchange=message.exchange,
routing_key=message.routing_key,
event_type=event_type)
# Check for active suppressions
if await is_alert_suppressed(tenant_id, event_type):
logger.info("Alert suppressed",
tenant_id=tenant_id,
event_type=event_type)
return
# Apply alert rules
alert_rules = await get_matching_alert_rules(tenant_id, event_type)
for rule in alert_rules:
# Evaluate rule conditions
if await evaluate_rule_conditions(rule, event_data):
# Check throttling
if await is_throttled(tenant_id, rule.alert_type):
logger.info("Alert throttled",
alert_type=rule.alert_type)
continue
# Create or aggregate alert
alert = await create_or_aggregate_alert(
tenant_id,
rule,
event_data,
message.exchange,
message.routing_key
)
if alert:
# Send notification if required
if rule.notify:
await send_alert_notification(alert, rule)
except Exception as e:
logger.error("Event processing failed",
error=str(e),
exchange=message.exchange,
routing_key=message.routing_key)
```
### Alert Creation with Aggregation
```python
async def create_or_aggregate_alert(
tenant_id: UUID,
rule: AlertRule,
event_data: dict,
source_service: str,
source_event_type: str
) -> Alert:
"""
Create alert or aggregate with existing similar alerts.
"""
# Generate aggregation key
aggregation_key = generate_aggregation_key(rule.alert_type, event_data)
# Check for existing aggregation
aggregation = await db.query(AlertAggregation).filter(
AlertAggregation.tenant_id == tenant_id,
AlertAggregation.aggregation_key == aggregation_key,
AlertAggregation.is_active == True
).first()
if aggregation:
# Aggregate with existing
if (datetime.utcnow() - aggregation.last_occurrence).total_seconds() < 3600: # Within 1 hour
aggregation.count += 1
aggregation.last_occurrence = datetime.utcnow()
await db.commit()
logger.info("Alert aggregated",
aggregation_key=aggregation_key,
count=aggregation.count)
# Only create notification for first alert and every 10th
if aggregation.count % 10 == 1:
return await get_alert(aggregation.aggregated_alert_id)
else:
return None
# Render alert title and description from templates
from jinja2 import Template
title = Template(rule.title_template).render(**event_data)
description = Template(rule.description_template).render(**event_data)
action = Template(rule.action_template).render(**event_data) if rule.action_template else None
# Calculate business impact
business_impact = await calculate_business_impact(rule.alert_type, event_data)
# Create alert
alert = Alert(
tenant_id=tenant_id,
alert_type=rule.alert_type,
alert_category=source_service,
severity=rule.severity,
priority=rule.priority,
status='active',
title=title,
description=description,
recommended_action=action,
business_impact=business_impact,
source_service=source_service,
source_event_type=source_event_type,
source_event_data=event_data,
related_product_id=event_data.get('product_id'),
related_ingredient_id=event_data.get('ingredient_id'),
related_batch_id=event_data.get('batch_id')
)
db.add(alert)
# Create aggregation record
if aggregation_key:
aggregation = AlertAggregation(
tenant_id=tenant_id,
aggregation_key=aggregation_key,
alert_type=rule.alert_type,
count=1,
first_occurrence=datetime.utcnow(),
last_occurrence=datetime.utcnow(),
aggregated_alert_id=alert.id,
individual_alert_ids=[str(alert.id)]
)
db.add(aggregation)
# Log history
history = AlertHistory(
alert_id=alert.id,
action='created',
action_at=datetime.utcnow(),
new_status='active'
)
db.add(history)
await db.commit()
# Cache active alert in Redis
await cache_active_alert(alert)
logger.info("Alert created",
alert_id=str(alert.id),
alert_type=alert.alert_type,
severity=alert.severity)
return alert
def generate_aggregation_key(alert_type: str, event_data: dict) -> str:
"""
Generate unique key for alert aggregation.
"""
# Different keys for different alert types
if alert_type == 'stockout':
return f"stockout:{event_data.get('ingredient_id')}"
elif alert_type == 'quality_issue':
return f"quality:{event_data.get('supplier_id')}:{event_data.get('ingredient_id')}"
elif alert_type == 'low_stock':
return f"low_stock:{event_data.get('ingredient_id')}"
elif alert_type == 'forecast_anomaly':
return f"forecast:{event_data.get('product_id')}"
else:
return f"{alert_type}:general"
```
### Smart Alert Notification
```python
async def send_alert_notification(alert: Alert, rule: AlertRule):
"""
Send notification for alert based on severity and rules.
"""
# Determine recipients
recipients = await determine_alert_recipients(alert.tenant_id, rule.notify_roles)
# Determine notification channels based on severity
if alert.severity == 'critical':
channels = ['whatsapp', 'email']
elif alert.severity == 'high':
channels = rule.notification_channels or ['email']
else:
channels = ['email']
for recipient in recipients:
for channel in channels:
try:
# Create notification via Notification Service
from services.notification import send_notification
notification = await send_notification(
tenant_id=alert.tenant_id,
user_id=recipient.id,
notification_type='alert',
priority=alert.priority,
channel=channel,
subject=f"[{alert.severity.upper()}] {alert.title}",
message=format_alert_message(alert),
template_id=await get_alert_template_id(alert.alert_type, channel)
)
# Update alert with notification info
alert.notification_sent = True
alert.notification_channel = channel
alert.notification_id = notification.id
await db.commit()
logger.info("Alert notification sent",
alert_id=str(alert.id),
recipient=recipient.name,
channel=channel)
except Exception as e:
logger.error("Alert notification failed",
alert_id=str(alert.id),
recipient=recipient.name,
channel=channel,
error=str(e))
def format_alert_message(alert: Alert) -> str:
"""
Format alert message for notification.
"""
message = f"{alert.description}\n\n"
if alert.business_impact:
message += f"**Business Impact:**\n{alert.business_impact}\n\n"
if alert.recommended_action:
message += f"**Recommended Action:**\n{alert.recommended_action}\n\n"
message += f"Severity: {alert.severity.upper()}\n"
message += f"Time: {alert.created_at.strftime('%Y-%m-%d %H:%M')}"
return message
async def determine_alert_recipients(tenant_id: UUID, roles: list[str]) -> list:
"""
Determine who should receive alert based on roles.
"""
from services.tenant import get_tenant_members
members = await get_tenant_members(tenant_id)
recipients = []
for member in members:
if member.role in roles:
recipients.append(member)
# Ensure at least owner is notified for critical alerts
if not recipients:
owner = [m for m in members if m.role == 'owner']
recipients = owner if owner else members[:1]
return recipients
```
### Alert Acknowledgment
```python
async def acknowledge_alert(alert_id: UUID, user_id: UUID, notes: str = None) -> Alert:
"""
Acknowledge alert and track response time.
"""
alert = await db.get(Alert, alert_id)
if not alert:
raise ValueError("Alert not found")
if alert.status != 'active':
raise ValueError("Alert is not active")
# Update alert
alert.status = 'acknowledged'
alert.acknowledged_at = datetime.utcnow()
alert.acknowledged_by = user_id
# Calculate response time
response_time = (alert.acknowledged_at - alert.created_at).total_seconds()
alert.response_time_seconds = int(response_time)
# Log history
history = AlertHistory(
alert_id=alert.id,
action='acknowledged',
action_by=user_id,
action_at=datetime.utcnow(),
notes=notes,
previous_status='active',
new_status='acknowledged'
)
db.add(history)
await db.commit()
# Remove from active alerts cache
await remove_from_active_cache(alert.id)
logger.info("Alert acknowledged",
alert_id=str(alert.id),
user_id=str(user_id),
response_time_seconds=response_time)
return alert
```
## Events & Messaging
### Consumed Events (RabbitMQ)
The Alert Processor consumes events from all service exchanges. Key routing keys include:
**Inventory Service:**
- `inventory.stockout` - Critical stockout
- `inventory.low_stock` - Low stock warning
- `inventory.expiring` - Expiring items
**Production Service:**
- `production.quality.issue` - Quality problem
- `production.equipment.maintenance` - Maintenance due
**Forecasting Service:**
- `forecasting.anomaly` - Forecast anomaly detected
- `forecasting.low_demand` - Unusually low demand
- `forecasting.high_demand` - Unusually high demand
**Procurement Service:**
- `procurement.stockout_risk` - Risk of stockout
- `procurement.po_failed` - Purchase order failed
**Orders Service:**
- `orders.overdue` - Overdue payment
**Suppliers Service:**
- `suppliers.performance_alert` - Poor performance
- `suppliers.price_change` - Significant price change
**External Service:**
- `external.api_health` - External API down
### Published Events (RabbitMQ)
**Exchange**: `alerts`
**Routing Keys**: `alerts.created`, `alerts.escalated`
**Alert Created Event**
```json
{
"event_type": "alert_created",
"tenant_id": "uuid",
"alert_id": "uuid",
"alert_type": "stockout",
"severity": "critical",
"title": "Critical Stockout: Harina de Trigo",
"notification_sent": true,
"timestamp": "2025-11-06T09:00:00Z"
}
```
## Custom Metrics (Prometheus)
```python
# Alert metrics
alerts_created_total = Counter(
'alerts_created_total',
'Total alerts created',
['tenant_id', 'alert_type', 'severity']
)
alerts_active = Gauge(
'alerts_active',
'Current active alerts',
['tenant_id', 'severity']
)
alert_response_time_seconds = Histogram(
'alert_response_time_seconds',
'Time to acknowledge alert',
['tenant_id', 'severity'],
buckets=[60, 300, 600, 1800, 3600, 7200]
)
alert_resolution_time_seconds = Histogram(
'alert_resolution_time_seconds',
'Time to resolve alert',
['tenant_id', 'alert_type'],
buckets=[300, 1800, 3600, 7200, 14400, 28800, 86400]
)
rabbitmq_events_processed_total = Counter(
'rabbitmq_events_processed_total',
'Total RabbitMQ events processed',
['exchange', 'routing_key', 'status']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8016)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Alert Configuration:**
- `ENABLE_ALERT_AGGREGATION` - Aggregate similar alerts (default: true)
- `AGGREGATION_WINDOW_MINUTES` - Time window for aggregation (default: 60)
- `ENABLE_ALERT_THROTTLING` - Throttle repeated alerts (default: true)
- `DEFAULT_THROTTLE_MINUTES` - Default throttle period (default: 30)
**Notification Configuration:**
- `AUTO_NOTIFY` - Automatically send notifications (default: true)
- `CRITICAL_ALERT_CHANNELS` - Channels for critical (default: ["whatsapp", "email"])
- `HIGH_ALERT_CHANNELS` - Channels for high (default: ["email"])
**SLA Configuration:**
- `CRITICAL_RESPONSE_SLA_MINUTES` - SLA for critical alerts (default: 15)
- `HIGH_RESPONSE_SLA_MINUTES` - SLA for high alerts (default: 60)
- `ENABLE_ESCALATION` - Escalate unacknowledged alerts (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/alert_processor
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/alert_processor
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **All Services** - Consumes events from all microservices
- **Notification Service** - Sends alert notifications
- **Tenant Service** - User and role information
- **Auth Service** - User authentication
- **PostgreSQL** - Alert history
- **Redis** - Active alerts cache
- **RabbitMQ** - Event consumption
### Dependents
- **Frontend Dashboard** - Displays alerts UI
- **Notification Service** - Receives alert notifications
- **Analytics** - Alert metrics and trends
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Critical issues discovered too late (stockouts, quality problems)
- Information overload from multiple systems
- No prioritization of issues
- Alert fatigue from too many notifications
- No structured response process
- Missed issues buried in noise
### Solution
Bakery-IA Alert Processor provides:
- **Intelligent Filtering**: Only actionable alerts reach you
- **Smart Routing**: Critical = WhatsApp, Reports = Email
- **Context-Rich**: Alerts include impact and next steps
- **Noise Reduction**: Aggregation prevents alert storms
- **Fast Response**: 90% faster issue detection
- **Audit Trail**: Complete alert history
### Quantifiable Impact
**Issue Detection:**
- 90% faster detection (minutes vs. hours/days)
- 50-80% downtime reduction through early warning
- €500-2,000/month cost avoidance (prevented issues)
**Operational Efficiency:**
- 70-90% faster response time
- 90%+ alerts are actionable (vs. 30-50% without filtering)
- 2-4 hours/week saved (not chasing false alarms)
**Alert Quality:**
- 80% reduction in alert volume (through aggregation)
- 95%+ critical alerts acknowledged within SLA
- 100% audit trail for compliance
### Target Market Fit (Spanish Bakeries)
- **Mobile Culture**: WhatsApp for critical alerts matches Spanish habits
- **Owner-Operated**: Small teams need intelligent prioritization
- **Quality Focus**: Spanish consumers demand quality, alerts prevent issues
- **Regulatory**: Food safety alerts support HACCP compliance
### ROI Calculation
**Investment**: €0 additional (included in subscription)
**Cost Avoidance**: €500-2,000/month (prevented issues)
**Time Savings**: 2-4 hours/week × €15/hour = €120-240/month
**Monthly Value**: €620-2,240
**Annual ROI**: €7,440-26,880 value per bakery
**Payback**: Immediate (included in subscription)
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Auth Service
## Overview
The **Auth Service** is the security foundation of Bakery-IA, providing robust JWT-based authentication, user management, and GDPR-compliant data handling. It implements industry-standard security practices including refresh token rotation, role-based access control (RBAC), and comprehensive audit logging. This service ensures secure multi-tenant access while maintaining full compliance with EU data protection regulations.
## Key Features
### Authentication & Authorization
- **JWT Token Authentication** - Secure token-based auth with access + refresh tokens
- **User Registration** - Account creation with email validation
- **Login/Logout** - Secure authentication flow with token management
- **Token Refresh** - Automatic token refresh with rotation
- **Password Management** - Secure password hashing (bcrypt) and reset flow
- **Role-Based Access Control (RBAC)** - User roles and permissions
- **Multi-Factor Authentication** (planned) - Enhanced security option
### User Management
- **User Profiles** - Complete user information management
- **User Onboarding** - Multi-step onboarding progress tracking
- **Profile Updates** - Self-service profile editing
- **Account Deletion** - GDPR-compliant account removal
- **Login Attempts Tracking** - Brute force protection
- **Session Management** - Track and revoke user sessions
### GDPR Compliance
- **User Consent Management** - Track user consents for data processing
- **Consent History** - Complete audit trail of consent changes
- **Data Export** - User can export all their data (JSON/CSV)
- **Right to Erasure** - Complete data deletion on request
- **Data Minimization** - Only collect essential data
- **Privacy by Design** - Built-in privacy features
### Security Features
- **Brute Force Protection** - Login attempt limiting
- **Password Strength Requirements** - Enforce strong passwords
- **Token Expiry** - Short-lived access tokens (15 min)
- **Refresh Token Rotation** - Security best practice
- **Audit Logging** - Complete authentication audit trail
- **IP Tracking** - Monitor login locations
- **Suspicious Activity Detection** - Alert on unusual patterns
### Event Publishing
- **RabbitMQ Integration** - Publish user events for other services
- **User Created** - New user registration events
- **User Updated** - Profile change events
- **Login Events** - Authentication success/failure events
- **Consent Changes** - GDPR consent update events
## Business Value
### For Bakery Owners
- **Secure Access** - Enterprise-grade security protects business data
- **GDPR Compliance** - Avoid €20M fines for data violations
- **User Management** - Control team access and permissions
- **Audit Trail** - Complete history for security audits
- **Peace of Mind** - Industry-standard security practices
### For Platform Operations
- **Multi-Tenant Security** - Isolated access per tenant
- **Scalable Auth** - Handle thousands of users
- **Compliance Ready** - Built-in GDPR features
- **Audit Capability** - Complete security audit trails
### Quantifiable Impact
- **Security**: 99.9% protection against common attacks
- **Compliance**: 100% GDPR compliant, avoid €20M+ fines
- **Uptime**: 99.9% authentication availability
- **Performance**: <50ms token validation (cached)
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - User and auth data
- **Password Hashing**: bcrypt - Industry-standard password security
- **JWT**: python-jose - JSON Web Token generation and validation
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Messaging**: RabbitMQ 4.1 - Event publishing
- **Caching**: Redis 7.4 - Token validation cache (gateway)
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Custom metrics
## API Endpoints (Key Routes)
### Authentication
- `POST /api/v1/auth/register` - User registration
- `POST /api/v1/auth/login` - User login (returns access + refresh tokens)
- `POST /api/v1/auth/refresh` - Refresh access token
- `POST /api/v1/auth/logout` - User logout (invalidate tokens)
- `POST /api/v1/auth/verify-token` - Verify JWT token validity
### Password Management
- `POST /api/v1/auth/password/reset-request` - Request password reset email
- `POST /api/v1/auth/password/reset` - Reset password with token
- `PUT /api/v1/auth/password/change` - Change password (authenticated)
### User Profile
- `GET /api/v1/auth/me` - Get current user profile
- `PUT /api/v1/auth/profile` - Update user profile
- `DELETE /api/v1/auth/account` - Delete account (GDPR)
### User Onboarding
- `GET /api/v1/auth/onboarding/progress` - Get onboarding status
- `PUT /api/v1/auth/onboarding/step/{step}` - Complete onboarding step
- `POST /api/v1/auth/onboarding/complete` - Mark onboarding complete
### GDPR Compliance
- `GET /api/v1/auth/gdpr/consents` - Get user consents
- `POST /api/v1/auth/gdpr/consent` - Update consent
- `GET /api/v1/auth/gdpr/export` - Export user data (JSON)
- `POST /api/v1/auth/gdpr/delete-request` - Request account deletion
### Admin (Tenant Management)
- `GET /api/v1/auth/users` - List users (admin only)
- `GET /api/v1/auth/users/{user_id}` - Get user details (admin)
- `PUT /api/v1/auth/users/{user_id}/role` - Update user role (admin)
- `DELETE /api/v1/auth/users/{user_id}` - Delete user (admin)
## Database Schema
### Main Tables
**users**
```sql
CREATE TABLE users (
id UUID PRIMARY KEY,
email VARCHAR(255) UNIQUE NOT NULL,
password_hash VARCHAR(255) NOT NULL,
first_name VARCHAR(100),
last_name VARCHAR(100),
phone VARCHAR(50),
role VARCHAR(50) DEFAULT 'user', -- admin, owner, manager, user
is_active BOOLEAN DEFAULT TRUE,
is_verified BOOLEAN DEFAULT FALSE,
email_verified_at TIMESTAMP,
last_login_at TIMESTAMP,
last_login_ip VARCHAR(45),
failed_login_attempts INTEGER DEFAULT 0,
locked_until TIMESTAMP,
password_changed_at TIMESTAMP,
must_change_password BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
INDEX idx_email (email),
INDEX idx_active (is_active)
);
```
**refresh_tokens**
```sql
CREATE TABLE refresh_tokens (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
token_hash VARCHAR(255) NOT NULL,
expires_at TIMESTAMP NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
revoked_at TIMESTAMP,
replaced_by_token_id UUID,
device_info JSONB,
ip_address VARCHAR(45),
is_revoked BOOLEAN DEFAULT FALSE,
INDEX idx_user (user_id),
INDEX idx_token (token_hash),
INDEX idx_expires (expires_at)
);
```
**user_onboarding_progress**
```sql
CREATE TABLE user_onboarding_progress (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
step_name VARCHAR(100) NOT NULL,
completed BOOLEAN DEFAULT FALSE,
completed_at TIMESTAMP,
data JSONB,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(user_id, step_name)
);
```
**user_onboarding_summary**
```sql
CREATE TABLE user_onboarding_summary (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
total_steps INTEGER NOT NULL,
completed_steps INTEGER DEFAULT 0,
is_complete BOOLEAN DEFAULT FALSE,
completed_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(user_id)
);
```
**login_attempts**
```sql
CREATE TABLE login_attempts (
id UUID PRIMARY KEY,
email VARCHAR(255) NOT NULL,
ip_address VARCHAR(45),
user_agent TEXT,
success BOOLEAN NOT NULL,
failure_reason VARCHAR(255),
attempted_at TIMESTAMP DEFAULT NOW(),
INDEX idx_email_time (email, attempted_at),
INDEX idx_ip_time (ip_address, attempted_at)
);
```
**user_consents**
```sql
CREATE TABLE user_consents (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
consent_type VARCHAR(100) NOT NULL, -- terms, privacy, marketing, cookies
consented BOOLEAN NOT NULL,
consented_at TIMESTAMP NOT NULL,
withdrawn_at TIMESTAMP,
ip_address VARCHAR(45),
user_agent TEXT,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_user_type (user_id, consent_type)
);
```
**consent_history**
```sql
CREATE TABLE consent_history (
id UUID PRIMARY KEY,
user_id UUID REFERENCES users(id) ON DELETE CASCADE,
consent_type VARCHAR(100) NOT NULL,
action VARCHAR(50) NOT NULL, -- granted, withdrawn, updated
consented BOOLEAN NOT NULL,
previous_value BOOLEAN,
ip_address VARCHAR(45),
created_at TIMESTAMP DEFAULT NOW()
);
```
**audit_logs**
```sql
CREATE TABLE audit_logs (
id UUID PRIMARY KEY,
user_id UUID,
action VARCHAR(100) NOT NULL, -- login, logout, password_change, etc.
resource_type VARCHAR(100),
resource_id UUID,
ip_address VARCHAR(45),
user_agent TEXT,
details JSONB,
success BOOLEAN,
error_message TEXT,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_user_time (user_id, created_at),
INDEX idx_action (action),
INDEX idx_time (created_at)
);
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `auth`
**Routing Keys**: `auth.user.created`, `auth.user.updated`, `auth.user.deleted`, `auth.login`
**User Registered Event**
```json
{
"event_type": "user_registered",
"user_id": "uuid",
"email": "user@example.com",
"first_name": "John",
"last_name": "Doe",
"role": "user",
"tenant_id": "uuid",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Login Success Event**
```json
{
"event_type": "login_success",
"user_id": "uuid",
"email": "user@example.com",
"ip_address": "192.168.1.100",
"user_agent": "Mozilla/5.0...",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Login Failed Event**
```json
{
"event_type": "login_failed",
"email": "user@example.com",
"ip_address": "192.168.1.100",
"failure_reason": "invalid_password",
"attempts_count": 3,
"timestamp": "2025-11-06T10:30:00Z"
}
```
**GDPR Consent Updated Event**
```json
{
"event_type": "consent_updated",
"user_id": "uuid",
"consent_type": "marketing",
"consented": false,
"previous_value": true,
"timestamp": "2025-11-06T10:30:00Z"
}
```
## Custom Metrics (Prometheus)
```python
# Registration metrics
registrations_total = Counter(
'auth_registrations_total',
'Total user registrations',
['status'] # success, failed
)
# Login metrics
login_attempts_total = Counter(
'auth_login_attempts_total',
'Total login attempts',
['status', 'reason'] # success / failed (invalid_password, locked, etc.)
)
active_users_total = Gauge(
'auth_active_users',
'Total active users'
)
# Token metrics
tokens_issued_total = Counter(
'auth_tokens_issued_total',
'Total tokens issued',
['token_type'] # access, refresh
)
token_refresh_total = Counter(
'auth_token_refresh_total',
'Total token refreshes',
['status'] # success, failed
)
# Security metrics
failed_login_attempts = Counter(
'auth_failed_login_attempts_total',
'Failed login attempts',
['reason'] # invalid_password, account_locked, invalid_email
)
account_lockouts_total = Counter(
'auth_account_lockouts_total',
'Total account lockouts due to failed attempts'
)
# GDPR metrics
data_exports_total = Counter(
'auth_data_exports_total',
'GDPR data export requests'
)
account_deletions_total = Counter(
'auth_account_deletions_total',
'GDPR account deletion requests'
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8001)
- `DATABASE_URL` - PostgreSQL connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**JWT Configuration:**
- `JWT_SECRET_KEY` - Secret for signing tokens (required)
- `JWT_ALGORITHM` - Algorithm (default: HS256)
- `JWT_ACCESS_TOKEN_EXPIRE_MINUTES` - Access token lifetime (default: 15)
- `JWT_REFRESH_TOKEN_EXPIRE_DAYS` - Refresh token lifetime (default: 30)
**Password Security:**
- `BCRYPT_ROUNDS` - bcrypt work factor (default: 12)
- `MIN_PASSWORD_LENGTH` - Minimum password length (default: 8)
- `REQUIRE_PASSWORD_UPPERCASE` - Require uppercase (default: true)
- `REQUIRE_PASSWORD_LOWERCASE` - Require lowercase (default: true)
- `REQUIRE_PASSWORD_DIGIT` - Require digit (default: true)
- `REQUIRE_PASSWORD_SPECIAL` - Require special char (default: true)
**Security Configuration:**
- `MAX_LOGIN_ATTEMPTS` - Before account lockout (default: 5)
- `ACCOUNT_LOCKOUT_MINUTES` - Lockout duration (default: 30)
- `ENABLE_EMAIL_VERIFICATION` - Require email verification (default: false)
- `SESSION_TIMEOUT_MINUTES` - Inactive session timeout (default: 480)
**GDPR Configuration:**
- `ENABLE_GDPR_FEATURES` - Enable GDPR compliance (default: true)
- `DATA_RETENTION_DAYS` - Days to keep deleted user data (default: 30)
- `REQUIRE_CONSENT_ON_REGISTER` - Require consent (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- RabbitMQ 4.1 (optional)
### Local Development
```bash
cd services/auth
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/auth
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export JWT_SECRET_KEY=your-secret-key-here
alembic upgrade head
python main.py
```
### Testing
```bash
# Unit tests
pytest tests/unit/ -v
# Integration tests
pytest tests/integration/ -v
# Security tests
pytest tests/security/ -v
# Test with coverage
pytest --cov=app tests/ --cov-report=html
```
## Integration Points
### Dependencies
- **PostgreSQL** - User and auth data storage
- **RabbitMQ** - Event publishing
- **Email Service** (planned) - Password reset emails
### Dependents
- **API Gateway** - Token validation for all requests
- **Tenant Service** - User-tenant relationships
- **All Services** - User identification from JWT
- **Frontend Dashboard** - User authentication
## Security Implementation
### Password Hashing
```python
import bcrypt
def hash_password(password: str) -> str:
"""Hash password using bcrypt"""
salt = bcrypt.gensalt(rounds=12)
return bcrypt.hashpw(password.encode(), salt).decode()
def verify_password(password: str, password_hash: str) -> bool:
"""Verify password against hash"""
return bcrypt.checkpw(password.encode(), password_hash.encode())
```
### JWT Token Generation
```python
from jose import jwt
from datetime import datetime, timedelta
def create_access_token(user_id: str, email: str) -> str:
"""Create JWT access token"""
expires = datetime.utcnow() + timedelta(minutes=15)
payload = {
"sub": user_id,
"email": email,
"type": "access",
"exp": expires
}
return jwt.encode(payload, JWT_SECRET_KEY, algorithm=JWT_ALGORITHM)
def create_refresh_token(user_id: str) -> str:
"""Create JWT refresh token"""
expires = datetime.utcnow() + timedelta(days=30)
payload = {
"sub": user_id,
"type": "refresh",
"exp": expires
}
return jwt.encode(payload, JWT_SECRET_KEY, algorithm=JWT_ALGORITHM)
```
### Brute Force Protection
```python
async def check_login_attempts(email: str) -> bool:
"""Check if account is locked due to failed attempts"""
recent_attempts = await db.query(LoginAttempt).filter(
LoginAttempt.email == email,
LoginAttempt.success == False,
LoginAttempt.attempted_at > datetime.utcnow() - timedelta(minutes=30)
).count()
if recent_attempts >= MAX_LOGIN_ATTEMPTS:
# Lock account
user = await get_user_by_email(email)
user.locked_until = datetime.utcnow() + timedelta(minutes=30)
await db.commit()
return False
return True
```
## GDPR Compliance Implementation
### Data Export
```python
async def export_user_data(user_id: str) -> dict:
"""Export all user data (GDPR Article 20)"""
user = await get_user(user_id)
consents = await get_user_consents(user_id)
login_history = await get_login_attempts(user.email)
audit_logs = await get_audit_logs(user_id)
return {
"user_profile": {
"email": user.email,
"name": f"{user.first_name} {user.last_name}",
"phone": user.phone,
"created_at": user.created_at.isoformat(),
},
"consents": [
{
"type": c.consent_type,
"consented": c.consented,
"date": c.consented_at.isoformat()
} for c in consents
],
"login_history": [
{
"date": attempt.attempted_at.isoformat(),
"ip": attempt.ip_address,
"success": attempt.success
} for attempt in login_history[-100:] # Last 100
],
"audit_trail": [
{
"action": log.action,
"date": log.created_at.isoformat(),
"ip": log.ip_address
} for log in audit_logs
]
}
```
### Account Deletion
```python
async def delete_user_account(user_id: str, reason: str) -> None:
"""Delete user account (GDPR Article 17 - Right to Erasure)"""
user = await get_user(user_id)
# Anonymize user data (soft delete)
user.email = f"deleted_{user_id}@deleted.local"
user.password_hash = "DELETED"
user.first_name = "Deleted"
user.last_name = "User"
user.phone = None
user.is_active = False
user.deleted_at = datetime.utcnow()
user.deletion_reason = reason
# Revoke all tokens
await revoke_all_user_tokens(user_id)
# Keep audit logs for legal retention (30 days)
# Actual deletion happens after retention period
await db.commit()
# Publish deletion event
await publish_event("user_deleted", {"user_id": user_id})
```
## Security Measures
### Token Security
- Short-lived access tokens (15 min)
- Refresh token rotation
- Token revocation on logout
- Secure token storage (httpOnly cookies recommended for web)
### Password Security
- bcrypt hashing (work factor 12)
- Password strength requirements
- Password history (prevent reuse)
- Secure password reset flow
### Attack Prevention
- Brute force protection (5 attempts → 30 min lockout)
- Rate limiting (via API Gateway)
- SQL injection prevention (parameterized queries)
- XSS prevention (input validation)
- CSRF protection (token-based)
## Troubleshooting
### Common Issues
**Issue**: Login fails with "Account locked"
- **Cause**: Too many failed login attempts
- **Solution**: Wait 30 minutes or contact admin to unlock
**Issue**: Token refresh fails
- **Cause**: Refresh token expired or revoked
- **Solution**: Re-login to get new tokens
**Issue**: Password reset email not received
- **Cause**: Email service not configured
- **Solution**: Check SMTP settings or use admin password reset
**Issue**: GDPR export takes too long
- **Cause**: Large amount of user data
- **Solution**: Implement background job processing
## Competitive Advantages
1. **GDPR Built-In** - Full compliance out-of-the-box
2. **Enterprise Security** - Industry-standard JWT + bcrypt
3. **Audit Trail** - Complete authentication history
4. **Multi-Tenant Ready** - Isolated user authentication
5. **Scalable** - Handle thousands of concurrent users
6. **Event-Driven** - Integration-ready with RabbitMQ
7. **EU Compliant** - Designed for Spanish/EU market
## Future Enhancements
- **Multi-Factor Authentication (MFA)** - TOTP, SMS, email
- **Social Login** - Google, Facebook, Apple authentication
- **Biometric Auth** - Fingerprint, Face ID
- **OAuth2/OpenID Connect** - Standards-based SSO
- **Passwordless Auth** - Magic links, WebAuthn
- **Session Management UI** - View and revoke active sessions
- **Advanced Audit** - ML-based anomaly detection
---
**For VUE Madrid Business Plan**: The Auth Service demonstrates enterprise-grade security and full GDPR compliance, critical for EU operations. The built-in audit logging and data protection features prevent costly fines (up to €20M for GDPR violations) and provide peace of mind for bakery owners. This is a key differentiator vs competitors who lack proper data protection.

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# Demo Session Service
## Overview
The **Demo Session Service** creates ephemeral, isolated demo environments for sales demonstrations and prospect trials. It provisions temporary tenants with pre-seeded realistic bakery data, allowing prospects to explore the full platform without affecting production data. Demo sessions automatically expire after a configurable period (default: 24 hours) and are completely isolated from real customer tenants, making it safe for prospects to experiment freely.
## Key Features
### Demo Environment Provisioning
- **One-Click Demo Creation** - Create demo tenant in seconds
- **Pre-Seeded Data** - Realistic sales, inventory, forecast data
- **Isolated Tenants** - Complete separation from production
- **Temporary Credentials** - Auto-generated demo user accounts
- **Configurable Duration** - 1 hour to 7 days (default: 24 hours)
- **Instant Access** - No email verification required
### Realistic Demo Data
- **90 Days Sales History** - Realistic transaction patterns
- **Product Catalog** - 20+ common bakery products
- **Inventory** - Current stock levels and movements
- **Forecasts** - Pre-generated 7-day forecasts
- **Production Schedules** - Sample production plans
- **Suppliers** - 5+ sample supplier profiles
- **Team Members** - Sample staff with different roles
### Demo Scenarios
- **Standard Bakery** - Small neighborhood bakery (1 location)
- **Multi-Location** - Bakery chain (3 locations)
- **High-Volume** - Large production bakery
- **Custom Scenario** - Configurable for specific prospects
- **Spanish Locale** - Madrid-based bakery examples
- **Feature Showcase** - Highlight specific capabilities
### Session Management
- **Auto-Expiration** - Automatic cleanup after expiry
- **Session Extension** - Extend active demos
- **Session Termination** - Manually end demo
- **Session Analytics** - Track demo engagement
- **Concurrent Limits** - Prevent resource abuse
- **IP-Based Tracking** - Monitor demo usage
### Sales Enablement
- **Demo Link Generation** - Shareable demo URLs
- **Sales Dashboard** - Track active demos
- **Usage Analytics** - Feature engagement metrics
- **Lead Tracking** - Connect demos to CRM
- **Conversion Tracking** - Demo to trial to paid
- **Performance Metrics** - Demo success rates
### Security & Isolation
- **Tenant Isolation** - Complete data separation
- **Resource Limits** - Prevent abuse
- **Auto-Cleanup** - Remove expired demos
- **No Production Access** - Isolated database/environment
- **Rate Limiting** - Prevent demo spam
- **Audit Logging** - Track all demo activities
## Business Value
### For Sales Team
- **Instant Demos** - No setup time, always ready
- **Realistic Experience** - Prospects see real functionality
- **Risk-Free** - Prospects can't break anything
- **Consistent** - Every demo shows same quality data
- **Scalable** - Handle 100+ concurrent demos
- **Self-Service** - Prospects can explore independently
### Quantifiable Impact
- **Sales Cycle**: 30-50% shorter with live demos
- **Conversion Rate**: 2-3× higher vs. screenshots/videos
- **Demo Setup Time**: 0 minutes vs. 15-30 minutes manual
- **Lead Quality**: Higher engagement indicates serious interest
- **Sales Efficiency**: 5-10× more demos per sales rep
- **Cost Savings**: €500-1,500/month (sales time saved)
### For Prospects
- **Try Before Buy**: Experience platform hands-on
- **No Commitment**: No credit card, no sign-up friction
- **Immediate Access**: Start exploring in 30 seconds
- **Realistic Data**: Understand real-world value
- **Self-Paced**: Explore at own speed
- **Safe Environment**: Can't break or affect anything
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Demo session tracking
- **Demo DB**: Separate PostgreSQL - Isolated demo data
- **Caching**: Redis 7.4 - Session cache, rate limiting
- **Messaging**: RabbitMQ 4.1 - Cleanup events
- **Data Seeding**: Faker, custom data generators
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Demo metrics
## API Endpoints (Key Routes)
### Demo Session Management
- `POST /api/v1/demo-sessions` - Create new demo session
- `GET /api/v1/demo-sessions/{session_id}` - Get session details
- `POST /api/v1/demo-sessions/{session_id}/extend` - Extend session
- `DELETE /api/v1/demo-sessions/{session_id}` - Terminate session
- `GET /api/v1/demo-sessions/{session_id}/credentials` - Get login credentials
- `GET /api/v1/demo-sessions/active` - List active sessions
### Demo Scenarios
- `GET /api/v1/demo-sessions/scenarios` - List available scenarios
- `GET /api/v1/demo-sessions/scenarios/{scenario_id}` - Get scenario details
- `POST /api/v1/demo-sessions/scenarios/{scenario_id}/create` - Create session from scenario
### Sales Dashboard (Internal)
- `GET /api/v1/demo-sessions/analytics/dashboard` - Demo analytics
- `GET /api/v1/demo-sessions/analytics/usage` - Usage patterns
- `GET /api/v1/demo-sessions/analytics/conversion` - Demo to signup conversion
### Health & Monitoring
- `GET /api/v1/demo-sessions/health` - Service health
- `GET /api/v1/demo-sessions/cleanup/status` - Cleanup job status
## Database Schema
### Main Tables
**demo_sessions**
```sql
CREATE TABLE demo_sessions (
id UUID PRIMARY KEY,
session_token VARCHAR(255) UNIQUE NOT NULL,
demo_tenant_id UUID NOT NULL, -- Demo tenant in separate DB
-- Configuration
scenario_name VARCHAR(100) NOT NULL, -- standard_bakery, multi_location, etc.
duration_hours INTEGER DEFAULT 24,
-- Status
status VARCHAR(50) DEFAULT 'active', -- active, extended, expired, terminated
created_at TIMESTAMP DEFAULT NOW(),
expires_at TIMESTAMP NOT NULL,
extended_count INTEGER DEFAULT 0,
terminated_at TIMESTAMP,
termination_reason VARCHAR(255),
-- Tracking
created_by_ip INET,
user_agent TEXT,
referrer VARCHAR(500),
utm_source VARCHAR(100),
utm_campaign VARCHAR(100),
utm_medium VARCHAR(100),
-- Usage analytics
login_count INTEGER DEFAULT 0,
last_activity_at TIMESTAMP,
page_views INTEGER DEFAULT 0,
features_used JSONB, -- Array of feature names
-- Lead info (if provided)
lead_email VARCHAR(255),
lead_name VARCHAR(255),
lead_phone VARCHAR(50),
lead_company VARCHAR(255),
INDEX idx_sessions_status ON (status, expires_at),
INDEX idx_sessions_token ON (session_token)
);
```
**demo_scenarios**
```sql
CREATE TABLE demo_scenarios (
id UUID PRIMARY KEY,
scenario_name VARCHAR(100) UNIQUE NOT NULL,
display_name VARCHAR(255) NOT NULL,
description TEXT,
-- Configuration
business_name VARCHAR(255),
location_count INTEGER DEFAULT 1,
product_count INTEGER DEFAULT 20,
days_of_history INTEGER DEFAULT 90,
-- Features to highlight
featured_capabilities JSONB,
-- Data generation settings
seed_data_config JSONB,
is_active BOOLEAN DEFAULT TRUE,
created_at TIMESTAMP DEFAULT NOW()
);
```
**demo_session_events**
```sql
CREATE TABLE demo_session_events (
id UUID PRIMARY KEY,
session_id UUID REFERENCES demo_sessions(id) ON DELETE CASCADE,
event_type VARCHAR(100) NOT NULL, -- login, page_view, feature_used, action_performed
event_data JSONB,
ip_address INET,
occurred_at TIMESTAMP DEFAULT NOW(),
INDEX idx_session_events_session (session_id, occurred_at)
);
```
**demo_session_metrics**
```sql
CREATE TABLE demo_session_metrics (
id UUID PRIMARY KEY,
metric_date DATE NOT NULL,
scenario_name VARCHAR(100),
-- Volume
sessions_created INTEGER DEFAULT 0,
sessions_completed INTEGER DEFAULT 0, -- Not terminated early
sessions_expired INTEGER DEFAULT 0,
sessions_terminated INTEGER DEFAULT 0,
-- Engagement
avg_duration_minutes INTEGER,
avg_login_count DECIMAL(5, 2),
avg_page_views DECIMAL(5, 2),
avg_features_used DECIMAL(5, 2),
-- Conversion
demo_to_signup_count INTEGER DEFAULT 0,
conversion_rate_percentage DECIMAL(5, 2),
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(metric_date, scenario_name)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_sessions_expires ON demo_sessions(expires_at) WHERE status = 'active';
CREATE INDEX idx_sessions_scenario ON demo_sessions(scenario_name, created_at DESC);
CREATE INDEX idx_events_session_type ON demo_session_events(session_id, event_type);
```
## Business Logic Examples
### Demo Session Creation
```python
async def create_demo_session(
scenario_name: str = 'standard_bakery',
duration_hours: int = 24,
lead_info: dict = None,
request_info: dict = None
) -> DemoSession:
"""
Create new demo session with pre-seeded data.
"""
# Get scenario configuration
scenario = await db.query(DemoScenario).filter(
DemoScenario.scenario_name == scenario_name,
DemoScenario.is_active == True
).first()
if not scenario:
raise ValueError("Invalid scenario")
# Check concurrent demo limit
active_demos = await db.query(DemoSession).filter(
DemoSession.status == 'active',
DemoSession.expires_at > datetime.utcnow()
).count()
if active_demos >= MAX_CONCURRENT_DEMOS:
raise Exception("Maximum concurrent demos reached")
try:
# Generate session token
session_token = secrets.token_urlsafe(32)
# Create demo tenant in separate database
demo_tenant = await create_demo_tenant(scenario)
# Seed demo data
await seed_demo_data(demo_tenant.id, scenario)
# Create session record
session = DemoSession(
session_token=session_token,
demo_tenant_id=demo_tenant.id,
scenario_name=scenario_name,
duration_hours=duration_hours,
expires_at=datetime.utcnow() + timedelta(hours=duration_hours),
created_by_ip=request_info.get('ip'),
user_agent=request_info.get('user_agent'),
referrer=request_info.get('referrer'),
utm_source=request_info.get('utm_source'),
utm_campaign=request_info.get('utm_campaign'),
lead_email=lead_info.get('email') if lead_info else None,
lead_name=lead_info.get('name') if lead_info else None
)
db.add(session)
# Log event
event = DemoSessionEvent(
session_id=session.id,
event_type='session_created',
event_data={'scenario': scenario_name},
ip_address=request_info.get('ip')
)
db.add(event)
await db.commit()
logger.info("Demo session created",
session_id=str(session.id),
scenario=scenario_name,
duration_hours=duration_hours)
# Publish event
await publish_event('demo_sessions', 'demo.session_created', {
'session_id': str(session.id),
'scenario': scenario_name
})
return session
except Exception as e:
logger.error("Demo session creation failed",
scenario=scenario_name,
error=str(e))
raise
async def create_demo_tenant(scenario: DemoScenario) -> DemoTenant:
"""
Create isolated demo tenant in demo database.
"""
# Use separate database connection for demo data
demo_db = get_demo_database_connection()
tenant = DemoTenant(
tenant_name=scenario.business_name or "Demo Bakery",
email=f"demo_{uuid.uuid4().hex[:8]}@bakery-ia.com",
status='demo',
subscription_tier='pro', # Always show Pro features in demo
is_demo=True
)
demo_db.add(tenant)
await demo_db.commit()
return tenant
async def seed_demo_data(tenant_id: UUID, scenario: DemoScenario):
"""
Seed demo tenant with realistic data.
"""
demo_db = get_demo_database_connection()
# Seed configuration
config = scenario.seed_data_config or {}
product_count = config.get('product_count', 20)
days_of_history = config.get('days_of_history', 90)
# 1. Seed product catalog
products = await seed_products(demo_db, tenant_id, product_count)
# 2. Seed suppliers
suppliers = await seed_suppliers(demo_db, tenant_id, 5)
# 3. Seed inventory
await seed_inventory(demo_db, tenant_id, products, suppliers)
# 4. Seed sales history (90 days)
await seed_sales_history(demo_db, tenant_id, products, days_of_history)
# 5. Generate forecasts
await seed_forecasts(demo_db, tenant_id, products)
# 6. Seed production schedules
await seed_production_schedules(demo_db, tenant_id, products)
# 7. Seed team members
await seed_team_members(demo_db, tenant_id)
logger.info("Demo data seeded",
tenant_id=str(tenant_id),
products=len(products),
suppliers=len(suppliers))
async def seed_sales_history(
demo_db,
tenant_id: UUID,
products: list,
days: int = 90
) -> list:
"""
Generate realistic sales history using patterns.
"""
from faker import Faker
fake = Faker('es_ES') # Spanish locale
sales_records = []
start_date = date.today() - timedelta(days=days)
for day_offset in range(days):
current_date = start_date + timedelta(days=day_offset)
is_weekend = current_date.weekday() >= 5
is_holiday = await is_spanish_holiday(current_date)
# Adjust volume based on day type
base_transactions = 50
if is_weekend:
base_transactions = int(base_transactions * 1.4) # 40% more on weekends
if is_holiday:
base_transactions = int(base_transactions * 0.7) # 30% less on holidays
# Add randomness
daily_transactions = int(base_transactions * random.uniform(0.8, 1.2))
for _ in range(daily_transactions):
# Random product
product = random.choice(products)
# Realistic quantity (most orders are 1-5 units)
quantity = random.choices([1, 2, 3, 4, 5, 6, 10], weights=[40, 25, 15, 10, 5, 3, 2])[0]
# Calculate price with small variance
unit_price = product.price * random.uniform(0.95, 1.05)
sale = DemoSale(
tenant_id=tenant_id,
sale_date=current_date,
sale_time=fake.time(),
product_id=product.id,
product_name=product.name,
quantity=quantity,
unit_price=unit_price,
total_amount=quantity * unit_price,
channel='pos'
)
sales_records.append(sale)
# Bulk insert
demo_db.bulk_save_objects(sales_records)
await demo_db.commit()
return sales_records
```
### Auto-Cleanup Job
```python
async def cleanup_expired_demos():
"""
Background job to cleanup expired demo sessions.
Runs every hour.
"""
# Find expired sessions
expired_sessions = await db.query(DemoSession).filter(
DemoSession.status == 'active',
DemoSession.expires_at <= datetime.utcnow()
).all()
for session in expired_sessions:
try:
# Mark session as expired
session.status = 'expired'
session.terminated_at = datetime.utcnow()
# Delete demo tenant and all data
await delete_demo_tenant(session.demo_tenant_id)
# Log event
event = DemoSessionEvent(
session_id=session.id,
event_type='session_expired',
occurred_at=datetime.utcnow()
)
db.add(event)
logger.info("Demo session cleaned up",
session_id=str(session.id),
duration_hours=(session.terminated_at - session.created_at).total_seconds() / 3600)
except Exception as e:
logger.error("Demo cleanup failed",
session_id=str(session.id),
error=str(e))
continue
await db.commit()
logger.info("Demo cleanup completed",
expired_count=len(expired_sessions))
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `demo_sessions`
**Routing Keys**: `demo.session_created`, `demo.session_converted`
**Demo Session Created Event**
```json
{
"event_type": "demo_session_created",
"session_id": "uuid",
"scenario": "standard_bakery",
"duration_hours": 24,
"lead_email": "prospect@example.com",
"utm_source": "google_ads",
"timestamp": "2025-11-06T10:00:00Z"
}
```
**Demo Converted to Signup**
```json
{
"event_type": "demo_session_converted",
"session_id": "uuid",
"tenant_id": "uuid",
"scenario": "standard_bakery",
"demo_duration_hours": 2.5,
"features_used": ["forecasting", "inventory", "production"],
"timestamp": "2025-11-06T12:30:00Z"
}
```
## Custom Metrics (Prometheus)
```python
# Demo session metrics
demo_sessions_created_total = Counter(
'demo_sessions_created_total',
'Total demo sessions created',
['scenario']
)
demo_sessions_active = Gauge(
'demo_sessions_active',
'Current active demo sessions',
[]
)
demo_session_duration_hours = Histogram(
'demo_session_duration_hours',
'Demo session duration',
['scenario'],
buckets=[0.5, 1, 2, 4, 8, 12, 24, 48]
)
demo_to_signup_conversions_total = Counter(
'demo_to_signup_conversions_total',
'Demo sessions that converted to signup',
['scenario']
)
demo_feature_usage_total = Counter(
'demo_feature_usage_total',
'Feature usage in demos',
['feature_name']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8019)
- `DATABASE_URL` - Main PostgreSQL connection
- `DEMO_DATABASE_URL` - Isolated demo database
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Demo Configuration:**
- `DEFAULT_DEMO_DURATION_HOURS` - Default duration (default: 24)
- `MAX_DEMO_DURATION_HOURS` - Maximum duration (default: 168/7 days)
- `MAX_CONCURRENT_DEMOS` - Concurrent limit (default: 100)
- `CLEANUP_INTERVAL_MINUTES` - Cleanup frequency (default: 60)
**Data Seeding:**
- `DEMO_SALES_HISTORY_DAYS` - Sales history length (default: 90)
- `DEMO_PRODUCT_COUNT` - Number of products (default: 20)
- `DEMO_SUPPLIER_COUNT` - Number of suppliers (default: 5)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17 (2 databases: main + demo)
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/demo_session
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/demo_session
export DEMO_DATABASE_URL=postgresql://user:pass@localhost:5432/demo_data
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Separate Demo Database** - Isolated demo tenant data
- **Auth Service** - Demo user credentials
- **Data Generators** - Realistic data seeding
- **PostgreSQL** - Session tracking
- **Redis** - Rate limiting, caching
- **RabbitMQ** - Event publishing
### Dependents
- **Sales Team** - Demo creation
- **Marketing** - Landing page demos
- **Frontend** - Demo UI access
- **Analytics** - Demo conversion tracking
## Business Value for VUE Madrid
### Problem Statement
Traditional sales demos are difficult:
- Time-consuming setup (15-30 minutes per demo)
- Risk of breaking things in front of prospects
- Inconsistent demo quality
- No self-service for prospects
- Hard to track engagement
- Limited by sales rep availability
### Solution
Bakery-IA Demo Session Service provides:
- **Instant Demos**: Ready in 30 seconds
- **Risk-Free**: Isolated environments
- **Self-Service**: Prospects explore independently
- **Consistent Quality**: Same data every time
- **Engagement Tracking**: Know what prospects care about
- **Scalable**: Unlimited concurrent demos
### Quantifiable Impact
**Sales Efficiency:**
- 30-50% shorter sales cycle with live demos
- 2-3× conversion rate vs. static presentations
- 5-10× more demos per sales rep
- 0 minutes setup time vs. 15-30 minutes
- €500-1,500/month sales time saved
**Lead Quality:**
- Higher engagement = more qualified leads
- Feature usage indicates specific needs
- Demo-to-trial conversion: 35-45%
- Trial-to-paid conversion: 25-35%
- Overall demo-to-paid: 12-16%
**Marketing Value:**
- Self-service demos on landing page
- 24/7 availability for global prospects
- Viral potential (shareable demo links)
- Lower customer acquisition cost
- Better understanding of product-market fit
### Target Market Fit (Spanish Bakeries)
- **Visual Learners**: Spanish business culture values demonstrations
- **Trust Building**: Try-before-buy reduces risk perception
- **Language**: Demo data in Spanish increases resonance
- **Realistic**: Spanish products, Madrid locations feel authentic
### ROI for Platform
**Investment**: €100-300/month (compute + storage for demos)
**Value Generated**:
- 50+ demos/month → 20 trials → 6 paid customers
- 6 customers × €66 avg MRR = €396/month
- **Payback**: 1-3 months
- **ROI**: 30-400% depending on conversion rates
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# External Service
## Overview
The **External Service** integrates real-world data from Spanish sources to enhance demand forecasting accuracy. It fetches weather data from AEMET (Agencia Estatal de Meteorología - Spain's official weather agency), Madrid traffic patterns from Open Data Madrid, and Spanish holiday calendars (national, regional, and local festivities). This Spanish-specific data integration is what makes Bakery-IA's forecasting superior to generic solutions, achieving 70-85% accuracy by accounting for local conditions that affect bakery demand.
## Key Features
### AEMET Weather Integration
- **Official Spanish Weather Data** - Direct integration with AEMET API
- **7-Day Forecasts** - Temperature, precipitation, wind, humidity
- **Hourly Granularity** - Detailed forecasts for precise planning
- **Multiple Locations** - Support for all Spanish cities and regions
- **Weather Alerts** - Official meteorological warnings
- **Historical Weather** - Past weather data for model training
- **Free Public API** - No cost for AEMET data access
### Madrid Traffic Data
- **Open Data Madrid** - Official city traffic API
- **Traffic Intensity** - Real-time and historical traffic patterns
- **Multiple Districts** - Coverage across all Madrid districts
- **Business Districts** - Focus on commercial areas affecting foot traffic
- **Weekend Patterns** - Tourist and leisure traffic analysis
- **Event Detection** - Identify high-traffic periods
- **Public Transport** - Metro and bus disruption tracking
### Spanish Holiday Calendar
- **National Holidays** - All official Spanish public holidays
- **Regional Holidays** - Autonomous community-specific holidays
- **Local Festivities** - Municipal celebrations (e.g., San Isidro in Madrid)
- **School Holidays** - Vacation periods affecting demand
- **Religious Holidays** - Semana Santa, Christmas, etc.
- **Historical Data** - Past holidays for ML model training
- **Future Holidays** - 12-month advance holiday calendar
### Data Quality & Reliability
- **Automatic Retries** - Handle API failures gracefully
- **Data Caching** - Redis cache with smart TTL
- **Fallback Mechanisms** - Default values if API unavailable
- **Data Validation** - Ensure data quality before storage
- **Health Monitoring** - Track API availability
- **Rate Limit Management** - Respect API usage limits
- **Error Logging** - Detailed error tracking and alerts
### Feature Engineering
- **Weather Impact Scores** - Calculate weather influence on demand
- **Traffic Influence** - Quantify traffic effect on foot traffic
- **Holiday Types** - Categorize holidays by demand impact
- **Season Detection** - Identify seasonal patterns
- **Weekend vs. Weekday** - Business day classification
- **Combined Features** - Multi-factor feature generation
## Business Value
### For Bakery Owners
- **Superior Forecast Accuracy** - 70-85% vs. 50-60% without external data
- **Local Market Understanding** - Spanish-specific conditions
- **No Manual Input** - Automatic data fetching
- **Free Data Sources** - No additional API costs (AEMET, Open Data Madrid)
- **Competitive Advantage** - Data integration competitors don't have
- **Regulatory Compliance** - Official Spanish government data
### Quantifiable Impact
- **Forecast Improvement**: 15-25% accuracy gain from external data
- **Waste Reduction**: Additional 10-15% from weather-aware planning
- **Revenue Protection**: Avoid stockouts on high-traffic/good weather days
- **Cost Savings**: €200-500/month from improved forecasting
- **Market Fit**: Spanish-specific solution, not generic adaptation
- **Trust**: Official government data sources
### For Forecasting Accuracy
- **Weather Impact**: Rainy days → -20 to -30% bakery foot traffic
- **Good Weather**: Sunny weekends → +30-50% terrace/outdoor sales
- **Traffic Correlation**: High traffic areas → +15-25% sales
- **Holiday Boost**: National holidays → +40-60% demand (preparation day before)
- **School Holidays**: +20-30% family purchases
- **Combined Effect**: Multiple factors → 70-85% accuracy
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Historical data storage
- **Caching**: Redis 7.4 - API response caching
- **HTTP Client**: HTTPx - Async API calls
- **Scheduling**: APScheduler - Periodic data fetching
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - API health metrics
## API Endpoints (Key Routes)
### Weather Data (AEMET)
- `GET /api/v1/external/weather/current` - Current weather for location
- `GET /api/v1/external/weather/forecast` - 7-day weather forecast
- `GET /api/v1/external/weather/historical` - Historical weather data
- `POST /api/v1/external/weather/fetch` - Manually trigger weather fetch
- `GET /api/v1/external/weather/locations` - Supported locations
### Traffic Data (Madrid)
- `GET /api/v1/external/traffic/current` - Current traffic intensity
- `GET /api/v1/external/traffic/forecast` - Traffic forecast (if available)
- `GET /api/v1/external/traffic/historical` - Historical traffic patterns
- `POST /api/v1/external/traffic/fetch` - Manually trigger traffic fetch
- `GET /api/v1/external/traffic/districts` - Madrid districts coverage
### Holiday Calendar
- `GET /api/v1/external/holidays` - Get holidays for date range
- `GET /api/v1/external/holidays/upcoming` - Next 30 days holidays
- `GET /api/v1/external/holidays/year/{year}` - All holidays for year
- `POST /api/v1/external/holidays/fetch` - Manually trigger holiday fetch
- `GET /api/v1/external/holidays/types` - Holiday type definitions
### Feature Engineering
- `GET /api/v1/external/features/{date}` - All engineered features for date
- `GET /api/v1/external/features/range` - Features for date range
- `POST /api/v1/external/features/calculate` - Recalculate features
### Health & Monitoring
- `GET /api/v1/external/health` - External API health status
- `GET /api/v1/external/health/aemet` - AEMET API status
- `GET /api/v1/external/health/traffic` - Traffic API status
- `GET /api/v1/external/metrics` - API usage metrics
## Database Schema
### Main Tables
**weather_data**
```sql
CREATE TABLE weather_data (
id UUID PRIMARY KEY,
location_code VARCHAR(50) NOT NULL, -- AEMET location code (e.g., "28079" for Madrid)
location_name VARCHAR(255) NOT NULL,
forecast_date DATE NOT NULL,
forecast_time TIME,
data_type VARCHAR(50) NOT NULL, -- forecast, current, historical
-- Weather parameters
temperature_celsius DECIMAL(5, 2),
temperature_max DECIMAL(5, 2),
temperature_min DECIMAL(5, 2),
feels_like_celsius DECIMAL(5, 2),
humidity_percentage INTEGER,
precipitation_mm DECIMAL(5, 2),
precipitation_probability INTEGER,
wind_speed_kmh DECIMAL(5, 2),
wind_direction VARCHAR(10), -- N, NE, E, SE, S, SW, W, NW
cloud_cover_percentage INTEGER,
uv_index INTEGER,
weather_condition VARCHAR(100), -- sunny, cloudy, rainy, stormy, etc.
weather_description TEXT,
-- Metadata
fetched_at TIMESTAMP DEFAULT NOW(),
source VARCHAR(50) DEFAULT 'aemet',
raw_data JSONB,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(location_code, forecast_date, forecast_time, data_type)
);
```
**traffic_data**
```sql
CREATE TABLE traffic_data (
id UUID PRIMARY KEY,
district_code VARCHAR(50) NOT NULL, -- Madrid district code
district_name VARCHAR(255) NOT NULL,
measurement_date DATE NOT NULL,
measurement_time TIME NOT NULL,
data_type VARCHAR(50) NOT NULL, -- current, historical
-- Traffic parameters
traffic_intensity INTEGER, -- 0-100 scale
traffic_level VARCHAR(50), -- low, moderate, high, very_high
average_speed_kmh DECIMAL(5, 2),
congestion_percentage INTEGER,
vehicle_count INTEGER,
-- Metadata
fetched_at TIMESTAMP DEFAULT NOW(),
source VARCHAR(50) DEFAULT 'madrid_open_data',
raw_data JSONB,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(district_code, measurement_date, measurement_time)
);
```
**holidays**
```sql
CREATE TABLE holidays (
id UUID PRIMARY KEY,
holiday_date DATE NOT NULL,
holiday_name VARCHAR(255) NOT NULL,
holiday_type VARCHAR(50) NOT NULL, -- national, regional, local
region VARCHAR(100), -- e.g., "Madrid", "Cataluña", null for national
is_public_holiday BOOLEAN DEFAULT TRUE,
is_school_holiday BOOLEAN DEFAULT FALSE,
is_bank_holiday BOOLEAN DEFAULT FALSE,
-- Holiday characteristics
holiday_category VARCHAR(100), -- religious, civic, regional_day, etc.
preparation_day BOOLEAN DEFAULT FALSE, -- Day before major holiday
demand_impact VARCHAR(50), -- high, medium, low, negative
-- Metadata
source VARCHAR(50) DEFAULT 'spanish_government',
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(holiday_date, holiday_name, region)
);
```
**external_features**
```sql
CREATE TABLE external_features (
id UUID PRIMARY KEY,
tenant_id UUID, -- NULL for global features
location_code VARCHAR(50) NOT NULL,
feature_date DATE NOT NULL,
-- Weather features
temp_celsius DECIMAL(5, 2),
temp_max DECIMAL(5, 2),
temp_min DECIMAL(5, 2),
is_rainy BOOLEAN DEFAULT FALSE,
precipitation_mm DECIMAL(5, 2),
is_good_weather BOOLEAN DEFAULT FALSE, -- Sunny, warm, low wind
weather_score DECIMAL(3, 2), -- 0-1 score for demand impact
-- Traffic features
traffic_intensity INTEGER,
is_high_traffic BOOLEAN DEFAULT FALSE,
traffic_score DECIMAL(3, 2), -- 0-1 score for demand impact
-- Holiday features
is_holiday BOOLEAN DEFAULT FALSE,
holiday_type VARCHAR(50),
is_preparation_day BOOLEAN DEFAULT FALSE,
days_to_next_holiday INTEGER,
days_from_prev_holiday INTEGER,
holiday_score DECIMAL(3, 2), -- 0-1 score for demand impact
-- Temporal features
is_weekend BOOLEAN DEFAULT FALSE,
day_of_week INTEGER, -- 0=Monday, 6=Sunday
week_of_month INTEGER,
is_month_start BOOLEAN DEFAULT FALSE,
is_month_end BOOLEAN DEFAULT FALSE,
-- Combined impact score
overall_demand_impact DECIMAL(3, 2), -- -1 to +1 (negative to positive impact)
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(location_code, feature_date)
);
```
**api_health_log**
```sql
CREATE TABLE api_health_log (
id UUID PRIMARY KEY,
api_name VARCHAR(50) NOT NULL, -- aemet, madrid_traffic, holidays
check_time TIMESTAMP NOT NULL DEFAULT NOW(),
status VARCHAR(50) NOT NULL, -- healthy, degraded, unavailable
response_time_ms INTEGER,
error_message TEXT,
consecutive_failures INTEGER DEFAULT 0
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_weather_location_date ON weather_data(location_code, forecast_date DESC);
CREATE INDEX idx_traffic_district_date ON traffic_data(district_code, measurement_date DESC);
CREATE INDEX idx_holidays_date ON holidays(holiday_date);
CREATE INDEX idx_holidays_region ON holidays(region, holiday_date);
CREATE INDEX idx_features_location_date ON external_features(location_code, feature_date DESC);
CREATE INDEX idx_api_health_api_time ON api_health_log(api_name, check_time DESC);
```
## Business Logic Examples
### AEMET Weather Fetching
```python
async def fetch_aemet_weather_forecast(location_code: str = "28079") -> list[WeatherData]:
"""
Fetch 7-day weather forecast from AEMET for given location.
Location code 28079 = Madrid
"""
AEMET_API_KEY = os.getenv('AEMET_API_KEY')
AEMET_BASE_URL = "https://opendata.aemet.es/opendata/api"
# Check cache first
cache_key = f"aemet:forecast:{location_code}"
cached = await redis.get(cache_key)
if cached:
return json.loads(cached)
try:
# Step 1: Request forecast data URL from AEMET
async with httpx.AsyncClient() as client:
response = await client.get(
f"{AEMET_BASE_URL}/prediccion/especifica/municipio/diaria/{location_code}",
headers={"api_key": AEMET_API_KEY},
timeout=10.0
)
if response.status_code != 200:
raise Exception(f"AEMET API error: {response.status_code}")
# AEMET returns a URL to download the actual data
data_url = response.json().get('datos')
# Step 2: Fetch actual forecast data
async with httpx.AsyncClient() as client:
forecast_response = await client.get(data_url, timeout=10.0)
if forecast_response.status_code != 200:
raise Exception(f"AEMET data fetch error: {forecast_response.status_code}")
forecast_json = forecast_response.json()
# Step 3: Parse and store forecast data
weather_records = []
prediccion = forecast_json[0].get('prediccion', {})
dias = prediccion.get('dia', [])
for dia in dias[:7]: # Next 7 days
fecha = datetime.strptime(dia['fecha'], '%Y-%m-%dT%H:%M:%S').date()
# Extract weather parameters
temp_max = dia.get('temperatura', {}).get('maxima')
temp_min = dia.get('temperatura', {}).get('minima')
precip_prob = dia.get('probPrecipitacion', [{}])[0].get('value')
weather_state = dia.get('estadoCielo', [{}])[0].get('descripcion', '')
# Create weather record
weather = WeatherData(
location_code=location_code,
location_name="Madrid",
forecast_date=fecha,
data_type='forecast',
temperature_max=Decimal(str(temp_max)) if temp_max else None,
temperature_min=Decimal(str(temp_min)) if temp_min else None,
precipitation_probability=int(precip_prob) if precip_prob else 0,
weather_condition=parse_weather_condition(weather_state),
weather_description=weather_state,
source='aemet',
raw_data=dia
)
db.add(weather)
weather_records.append(weather)
await db.commit()
# Cache for 6 hours
await redis.setex(cache_key, 21600, json.dumps([w.to_dict() for w in weather_records]))
# Log successful fetch
await log_api_health('aemet', 'healthy', response_time_ms=int(response.elapsed.total_seconds() * 1000))
logger.info("AEMET weather fetched successfully",
location_code=location_code,
days=len(weather_records))
return weather_records
except Exception as e:
# Log failure
await log_api_health('aemet', 'unavailable', error_message=str(e))
logger.error("AEMET fetch failed",
location_code=location_code,
error=str(e))
# Return cached data if available (even if expired)
fallback_cached = await redis.get(f"aemet:fallback:{location_code}")
if fallback_cached:
logger.info("Using fallback cached weather data")
return json.loads(fallback_cached)
raise
def parse_weather_condition(aemet_description: str) -> str:
"""
Parse AEMET weather description to simplified condition.
"""
description_lower = aemet_description.lower()
if 'despejado' in description_lower or 'soleado' in description_lower:
return 'sunny'
elif 'nuboso' in description_lower or 'nubes' in description_lower:
return 'cloudy'
elif 'lluvia' in description_lower or 'lluvioso' in description_lower:
return 'rainy'
elif 'tormenta' in description_lower:
return 'stormy'
elif 'nieve' in description_lower:
return 'snowy'
elif 'niebla' in description_lower:
return 'foggy'
else:
return 'unknown'
```
### Madrid Traffic Data Fetching
```python
async def fetch_madrid_traffic_data() -> list[TrafficData]:
"""
Fetch current traffic data from Madrid Open Data portal.
"""
MADRID_TRAFFIC_URL = "https://opendata.madrid.es/api/traffic/intensidad"
# Check cache (traffic data updates every 15 minutes)
cache_key = "madrid:traffic:current"
cached = await redis.get(cache_key)
if cached:
return json.loads(cached)
try:
async with httpx.AsyncClient() as client:
response = await client.get(MADRID_TRAFFIC_URL, timeout=10.0)
if response.status_code != 200:
raise Exception(f"Madrid Traffic API error: {response.status_code}")
traffic_json = response.json()
# Parse traffic data per district
traffic_records = []
now = datetime.now()
for district_data in traffic_json.get('districts', []):
district_code = district_data.get('code')
district_name = district_data.get('name')
intensity = district_data.get('intensity') # 0-100
# Classify traffic level
if intensity >= 80:
level = 'very_high'
elif intensity >= 60:
level = 'high'
elif intensity >= 30:
level = 'moderate'
else:
level = 'low'
traffic = TrafficData(
district_code=district_code,
district_name=district_name,
measurement_date=now.date(),
measurement_time=now.time().replace(second=0, microsecond=0),
data_type='current',
traffic_intensity=intensity,
traffic_level=level,
source='madrid_open_data',
raw_data=district_data
)
db.add(traffic)
traffic_records.append(traffic)
await db.commit()
# Cache for 15 minutes
await redis.setex(cache_key, 900, json.dumps([t.to_dict() for t in traffic_records]))
await log_api_health('madrid_traffic', 'healthy')
logger.info("Madrid traffic data fetched successfully",
districts=len(traffic_records))
return traffic_records
except Exception as e:
await log_api_health('madrid_traffic', 'unavailable', error_message=str(e))
logger.error("Madrid traffic fetch failed", error=str(e))
# Use fallback
fallback_cached = await redis.get("madrid:traffic:fallback")
if fallback_cached:
return json.loads(fallback_cached)
raise
```
### Spanish Holiday Calendar
```python
async def fetch_spanish_holidays(year: int = None) -> list[Holiday]:
"""
Fetch Spanish holidays for given year.
Includes national, regional (Madrid), and local holidays.
"""
if year is None:
year = datetime.now().year
# Check if already fetched
existing = await db.query(Holiday).filter(
Holiday.holiday_date >= date(year, 1, 1),
Holiday.holiday_date <= date(year, 12, 31)
).count()
if existing > 0:
logger.info("Holidays already fetched for year", year=year)
return await db.query(Holiday).filter(
Holiday.holiday_date >= date(year, 1, 1),
Holiday.holiday_date <= date(year, 12, 31)
).all()
holidays_list = []
# National holidays (fixed dates)
national_holidays = [
(1, 1, "Año Nuevo", "civic"),
(1, 6, "Reyes Magos", "religious"),
(5, 1, "Día del Trabajo", "civic"),
(8, 15, "Asunción de la Virgen", "religious"),
(10, 12, "Fiesta Nacional de España", "civic"),
(11, 1, "Todos los Santos", "religious"),
(12, 6, "Día de la Constitución", "civic"),
(12, 8, "Inmaculada Concepción", "religious"),
(12, 25, "Navidad", "religious"),
]
for month, day, name, category in national_holidays:
holiday = Holiday(
holiday_date=date(year, month, day),
holiday_name=name,
holiday_type='national',
holiday_category=category,
is_public_holiday=True,
is_bank_holiday=True,
demand_impact='high'
)
db.add(holiday)
holidays_list.append(holiday)
# Madrid regional holidays
madrid_holidays = [
(5, 2, "Día de la Comunidad de Madrid", "regional_day"),
(5, 15, "San Isidro (Patrón de Madrid)", "religious"),
(11, 9, "Nuestra Señora de la Almudena", "religious"),
]
for month, day, name, category in madrid_holidays:
holiday = Holiday(
holiday_date=date(year, month, day),
holiday_name=name,
holiday_type='regional',
region='Madrid',
holiday_category=category,
is_public_holiday=True,
demand_impact='high'
)
db.add(holiday)
holidays_list.append(holiday)
# Movable holidays (Easter-based)
easter_date = calculate_easter_date(year)
movable_holidays = [
(-3, "Jueves Santo", "religious", "high"),
(-2, "Viernes Santo", "religious", "high"),
(+1, "Lunes de Pascua", "religious", "medium"),
]
for days_offset, name, category, impact in movable_holidays:
holiday_date = easter_date + timedelta(days=days_offset)
holiday = Holiday(
holiday_date=holiday_date,
holiday_name=name,
holiday_type='national',
holiday_category=category,
is_public_holiday=True,
demand_impact=impact
)
db.add(holiday)
holidays_list.append(holiday)
# Add preparation days (day before major holidays)
major_holidays = [date(year, 12, 24), easter_date - timedelta(days=1)]
for prep_date in major_holidays:
prep_holiday = Holiday(
holiday_date=prep_date,
holiday_name=f"Preparación {prep_date.strftime('%d/%m')}",
holiday_type='national',
holiday_category='preparation',
preparation_day=True,
demand_impact='high'
)
db.add(prep_holiday)
holidays_list.append(prep_holiday)
await db.commit()
logger.info("Spanish holidays fetched successfully",
year=year,
count=len(holidays_list))
return holidays_list
def calculate_easter_date(year: int) -> date:
"""
Calculate Easter Sunday using Gauss's Easter algorithm.
"""
a = year % 19
b = year // 100
c = year % 100
d = b // 4
e = b % 4
f = (b + 8) // 25
g = (b - f + 1) // 3
h = (19 * a + b - d - g + 15) % 30
i = c // 4
k = c % 4
l = (32 + 2 * e + 2 * i - h - k) % 7
m = (a + 11 * h + 22 * l) // 451
month = (h + l - 7 * m + 114) // 31
day = ((h + l - 7 * m + 114) % 31) + 1
return date(year, month, day)
```
### Feature Engineering
```python
async def calculate_external_features(
location_code: str,
feature_date: date
) -> ExternalFeatures:
"""
Calculate all external features for given location and date.
"""
# Get weather data
weather = await db.query(WeatherData).filter(
WeatherData.location_code == location_code,
WeatherData.forecast_date == feature_date,
WeatherData.data_type == 'forecast'
).first()
# Get traffic data (if Madrid)
traffic = None
if location_code == "28079": # Madrid
traffic = await db.query(TrafficData).filter(
TrafficData.measurement_date == feature_date
).order_by(TrafficData.measurement_time.desc()).first()
# Get holiday info
holiday = await db.query(Holiday).filter(
Holiday.holiday_date == feature_date
).first()
# Calculate weather features
is_rainy = False
is_good_weather = False
weather_score = 0.5 # Neutral
if weather:
is_rainy = weather.precipitation_mm and weather.precipitation_mm > 2.0
is_good_weather = (
weather.temperature_max and weather.temperature_max > 15 and
weather.temperature_max < 28 and
weather.weather_condition == 'sunny' and
not is_rainy
)
# Weather score: -1 (very negative) to +1 (very positive)
if is_good_weather:
weather_score = 0.8
elif is_rainy:
weather_score = -0.5
elif weather.weather_condition == 'cloudy':
weather_score = 0.3
# Calculate traffic features
is_high_traffic = False
traffic_score = 0.5
if traffic:
is_high_traffic = traffic.traffic_intensity >= 70
traffic_score = traffic.traffic_intensity / 100.0 # 0-1 scale
# Calculate holiday features
is_holiday = holiday is not None and holiday.is_public_holiday
is_preparation_day = holiday is not None and holiday.preparation_day
holiday_score = 0.5
if is_preparation_day:
holiday_score = 1.0 # Very high demand day before holiday
elif is_holiday:
holiday_score = 0.3 # Lower demand on actual holiday (stores closed)
# Calculate days to/from holidays
next_holiday = await db.query(Holiday).filter(
Holiday.holiday_date > feature_date,
Holiday.is_public_holiday == True
).order_by(Holiday.holiday_date.asc()).first()
prev_holiday = await db.query(Holiday).filter(
Holiday.holiday_date < feature_date,
Holiday.is_public_holiday == True
).order_by(Holiday.holiday_date.desc()).first()
days_to_next_holiday = (next_holiday.holiday_date - feature_date).days if next_holiday else 365
days_from_prev_holiday = (feature_date - prev_holiday.holiday_date).days if prev_holiday else 365
# Temporal features
is_weekend = feature_date.weekday() >= 5
day_of_week = feature_date.weekday()
week_of_month = (feature_date.day - 1) // 7 + 1
is_month_start = feature_date.day <= 5
is_month_end = feature_date.day >= 25
# Calculate overall demand impact
# Weights: weather 30%, holiday 40%, traffic 20%, temporal 10%
overall_impact = (
weather_score * 0.30 +
holiday_score * 0.40 +
traffic_score * 0.20 +
(1.0 if is_weekend else 0.7) * 0.10
)
# Create features record
features = ExternalFeatures(
location_code=location_code,
feature_date=feature_date,
temp_celsius=weather.temperature_max if weather else None,
temp_max=weather.temperature_max if weather else None,
temp_min=weather.temperature_min if weather else None,
is_rainy=is_rainy,
precipitation_mm=weather.precipitation_mm if weather else None,
is_good_weather=is_good_weather,
weather_score=Decimal(str(round(weather_score, 2))),
traffic_intensity=traffic.traffic_intensity if traffic else None,
is_high_traffic=is_high_traffic,
traffic_score=Decimal(str(round(traffic_score, 2))),
is_holiday=is_holiday,
holiday_type=holiday.holiday_type if holiday else None,
is_preparation_day=is_preparation_day,
days_to_next_holiday=days_to_next_holiday,
days_from_prev_holiday=days_from_prev_holiday,
holiday_score=Decimal(str(round(holiday_score, 2))),
is_weekend=is_weekend,
day_of_week=day_of_week,
week_of_month=week_of_month,
is_month_start=is_month_start,
is_month_end=is_month_end,
overall_demand_impact=Decimal(str(round(overall_impact, 2)))
)
db.add(features)
await db.commit()
return features
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `external`
**Routing Keys**: `external.weather_updated`, `external.holiday_alert`, `external.api_health`
**Weather Updated Event**
```json
{
"event_type": "weather_updated",
"location_code": "28079",
"location_name": "Madrid",
"forecast_days": 7,
"significant_change": true,
"alert": "rain_expected_tomorrow",
"impact_assessment": "negative",
"timestamp": "2025-11-06T08:00:00Z"
}
```
**Holiday Alert Event**
```json
{
"event_type": "holiday_alert",
"holiday_date": "2025-12-24",
"holiday_name": "Nochebuena (Preparación)",
"holiday_type": "preparation",
"days_until": 3,
"demand_impact": "high",
"recommendation": "Increase production by 50-70%",
"timestamp": "2025-12-21T08:00:00Z"
}
```
**API Health Alert**
```json
{
"event_type": "api_health_alert",
"api_name": "aemet",
"status": "unavailable",
"consecutive_failures": 5,
"error_message": "Connection timeout",
"fallback_active": true,
"action_required": "Monitor situation, using cached data",
"timestamp": "2025-11-06T11:30:00Z"
}
```
### Consumed Events
- **From Orchestrator**: Daily scheduled fetch triggers
- **From Forecasting**: Request for specific date features
## Custom Metrics (Prometheus)
```python
# External API metrics
external_api_calls_total = Counter(
'external_api_calls_total',
'Total external API calls',
['api_name', 'status']
)
external_api_response_time_seconds = Histogram(
'external_api_response_time_seconds',
'External API response time',
['api_name'],
buckets=[0.1, 0.5, 1.0, 2.0, 5.0, 10.0]
)
external_api_health_status = Gauge(
'external_api_health_status',
'External API health (1=healthy, 0=unavailable)',
['api_name']
)
weather_forecast_data_points = Gauge(
'weather_forecast_data_points',
'Number of weather forecast data points',
['location_code']
)
holidays_calendar_size = Gauge(
'holidays_calendar_size',
'Number of holidays in calendar',
['year']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8014)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**AEMET Configuration:**
- `AEMET_API_KEY` - AEMET API key (free registration)
- `AEMET_DEFAULT_LOCATION` - Default location code (default: "28079" for Madrid)
- `AEMET_CACHE_TTL_HOURS` - Cache duration (default: 6)
- `AEMET_RETRY_ATTEMPTS` - Retry attempts on failure (default: 3)
**Madrid Traffic Configuration:**
- `MADRID_TRAFFIC_ENABLED` - Enable traffic data (default: true)
- `MADRID_TRAFFIC_CACHE_TTL_MINUTES` - Cache duration (default: 15)
- `MADRID_DEFAULT_DISTRICT` - Default district (default: "centro")
**Holiday Configuration:**
- `HOLIDAY_YEARS_AHEAD` - Years to fetch ahead (default: 2)
- `HOLIDAY_ALERT_DAYS` - Days before holiday to alert (default: 7)
**Feature Engineering:**
- `ENABLE_AUTO_FEATURE_CALCULATION` - Auto-calculate features (default: true)
- `FEATURE_CALCULATION_DAYS_AHEAD` - Days to calculate (default: 30)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
- AEMET API key (free at https://opendata.aemet.es)
### Local Development
```bash
cd services/external
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/external
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export AEMET_API_KEY=your_aemet_api_key
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **AEMET API** - Spanish weather data
- **Madrid Open Data** - Traffic data
- **Spanish Government** - Holiday calendar
- **Auth Service** - User authentication
- **PostgreSQL** - External data storage
- **Redis** - API response caching
- **RabbitMQ** - Event publishing
### Dependents
- **Forecasting Service** - Uses external features for ML models
- **AI Insights Service** - Weather/holiday-based recommendations
- **Production Service** - Weather-aware production planning
- **Notification Service** - Holiday and weather alerts
- **Frontend Dashboard** - Display weather and holidays
## Business Value for VUE Madrid
### Problem Statement
Generic forecasting solutions fail in local markets because they:
- Ignore local weather impact on foot traffic
- Don't account for regional holidays and celebrations
- Miss traffic patterns affecting customer flow
- Use generic features, not Spanish-specific data
- Achieve only 50-60% accuracy
### Solution
Bakery-IA External Service provides:
- **Spanish Official Data**: AEMET, Madrid Open Data, Spanish holidays
- **Local Market Understanding**: Weather, traffic, festivities
- **Superior Accuracy**: 70-85% vs. 50-60% generic solutions
- **Free Data Sources**: No additional API costs
- **Competitive Moat**: Integration competitors cannot easily replicate
### Quantifiable Impact
**Forecast Accuracy Improvement:**
- +15-25% accuracy gain from external data integration
- Weather impact: Rainy days = -20 to -30% foot traffic
- Holiday boost: Major holidays = +40-60% demand (preparation day)
- Traffic correlation: High traffic = +15-25% sales
**Cost Savings:**
- €200-500/month from improved forecast accuracy
- Additional 10-15% waste reduction from weather-aware planning
- Avoid stockouts on high-demand days (good weather + holidays)
**Market Differentiation:**
- Spanish-specific solution, not generic adaptation
- Official government data sources (trust & credibility)
- First-mover advantage in Spanish bakery market
- Data integration barrier to entry for competitors
### Target Market Fit (Spanish Bakeries)
- **Weather Sensitivity**: Spanish outdoor culture = weather-dependent sales
- **Holiday Culture**: Spain has 14+ public holidays/year affecting demand
- **Regional Specificity**: Each autonomous community has unique holidays
- **Trust**: Official government data sources (AEMET, Madrid city)
- **Regulatory**: Spanish authorities require Spanish-compliant solutions
### ROI Calculation
**Investment**: €0 additional (included in subscription)
**Forecast Improvement Value**: €200-500/month
**Waste Reduction**: Additional €150-300/month
**Total Monthly Value**: €350-800
**Annual ROI**: €4,200-9,600 value per bakery
**Payback**: Immediate (included in subscription)
### Competitive Advantage
- **Unique Data**: Competitors use generic weather APIs, not AEMET
- **Spanish Expertise**: Deep understanding of Spanish market
- **Free APIs**: AEMET and Madrid Open Data are free (no cost to scale)
- **Regulatory Alignment**: Spanish official data meets compliance needs
- **First-Mover**: Few competitors integrate Spanish-specific external data
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Inventory Service
## Overview
The **Inventory Service** is the operational backbone of Bakery-IA, managing ingredient tracking, stock levels, expiration dates, and food safety compliance. It implements FIFO (First-In-First-Out) consumption logic, automated low-stock alerts, and HACCP-compliant temperature monitoring. This service is critical for achieving zero food waste, maintaining food safety standards, and ensuring bakeries never run out of essential ingredients.
## Key Features
### Comprehensive Ingredient Management
- **Ingredient Catalog** - Complete database of all ingredients with categories
- **Stock Tracking** - Real-time stock levels with FIFO consumption
- **Batch Tracking** - Lot numbers and traceability for food safety
- **Expiration Management** - Automated expiry alerts and FIFO rotation
- **Low Stock Alerts** - Configurable threshold notifications
- **Barcode Support** - Barcode scanning for quick stock updates
- **Multi-Location** - Track inventory across multiple storage locations
### Stock Movement Tracking
- **In/Out Transactions** - Complete audit trail of stock movements
- **Product Transformations** - Track ingredient consumption in production
- **Adjustment Logging** - Record inventory adjustments with reasons
- **Historical Analysis** - Analyze consumption patterns over time
- **Waste Tracking** - Monitor and categorize waste (expired, damaged, etc.)
### Food Safety Compliance (HACCP)
- **Temperature Monitoring** - Critical control point temperature logs
- **Food Safety Alerts** - Automated safety notifications
- **Compliance Tracking** - HACCP compliance audit trail
- **Expiry Management** - Prevent use of expired ingredients
- **Lot Traceability** - Complete ingredient traceability
- **Safety Checklists** - Digital food safety inspection forms
### Sustainability & Reporting
- **Waste Reduction Tracking** - Monitor progress toward zero waste
- **Environmental Impact** - Carbon footprint and sustainability metrics
- **SDG Compliance** - Sustainable Development Goals reporting
- **Grant Reporting** - EU grant compliance reports
- **Business Model Detection** - Auto-detect B2B/B2C inventory patterns
### Dashboard & Analytics
- **Real-Time KPIs** - Current stock levels, expiring items, low stock warnings
- **Consumption Analytics** - Usage patterns and forecasting input
- **Valuation Reports** - Current inventory value and cost tracking
- **Reorder Recommendations** - Intelligent reorder point suggestions
- **Expiry Calendar** - Visual timeline of expiring products
## Business Value
### For Bakery Owners
- **Zero Food Waste Goal** - Reduce waste 20-40% through expiry management and FIFO
- **Food Safety Compliance** - HACCP compliance built-in, avoid health violations
- **Cost Control** - Track inventory value, prevent over-purchasing
- **Never Stock Out** - Automated low-stock alerts ensure continuous operations
- **Traceability** - Complete ingredient tracking for recalls and audits
### Quantifiable Impact
- **Waste Reduction**: 20-40% through FIFO and expiry management
- **Cost Savings**: €200-600/month from reduced waste and better purchasing
- **Time Savings**: 8-12 hours/week on manual inventory tracking
- **Compliance**: 100% HACCP compliance, avoid €5,000+ fines
- **Inventory Accuracy**: 95%+ vs. 70-80% with manual tracking
### For Operations Managers
- **Multi-Location Visibility** - See all inventory across locations
- **Automated Reordering** - System suggests what and when to order
- **Waste Analysis** - Identify patterns and reduce waste
- **Compliance Reporting** - Generate HACCP reports for inspections
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Ingredient and stock data
- **Caching**: Redis 7.4 - Dashboard KPI cache
- **Messaging**: RabbitMQ 4.1 - Alert publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Custom metrics
## API Endpoints (Key Routes)
### Ingredient Management
- `POST /api/v1/inventory/ingredients` - Create ingredient
- `GET /api/v1/inventory/ingredients` - List all ingredients
- `GET /api/v1/inventory/ingredients/{ingredient_id}` - Get ingredient details
- `PUT /api/v1/inventory/ingredients/{ingredient_id}` - Update ingredient
- `DELETE /api/v1/inventory/ingredients/{ingredient_id}` - Delete ingredient
### Stock Management
- `GET /api/v1/inventory/stock` - List current stock levels
- `GET /api/v1/inventory/stock/{stock_id}` - Get stock item details
- `POST /api/v1/inventory/stock/adjustment` - Adjust stock levels
- `POST /api/v1/inventory/stock/receive` - Receive new stock
- `POST /api/v1/inventory/stock/consume` - Consume stock (production use)
- `GET /api/v1/inventory/stock/movements` - Stock movement history
### Alerts & Monitoring
- `GET /api/v1/inventory/alerts` - Get active alerts
- `GET /api/v1/inventory/alerts/low-stock` - Low stock items
- `GET /api/v1/inventory/alerts/expiring` - Items expiring soon
- `POST /api/v1/inventory/alerts/configure` - Configure alert thresholds
### Food Safety
- `GET /api/v1/inventory/food-safety/compliance` - HACCP compliance status
- `POST /api/v1/inventory/food-safety/temperature-log` - Log temperature reading
- `GET /api/v1/inventory/food-safety/temperature-logs` - Temperature history
- `POST /api/v1/inventory/food-safety/alert` - Report food safety issue
### Analytics & Reporting
- `GET /api/v1/inventory/dashboard` - Dashboard KPIs
- `GET /api/v1/inventory/analytics/consumption` - Consumption patterns
- `GET /api/v1/inventory/analytics/waste` - Waste analysis
- `GET /api/v1/inventory/analytics/valuation` - Current inventory value
- `GET /api/v1/inventory/reports/haccp` - HACCP compliance report
- `GET /api/v1/inventory/reports/sustainability` - Sustainability report
## Database Schema
### Main Tables
**ingredients**
```sql
CREATE TABLE ingredients (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
name VARCHAR(255) NOT NULL,
category VARCHAR(100), -- flour, sugar, dairy, etc.
unit VARCHAR(50) NOT NULL, -- kg, liters, units
supplier_id UUID,
reorder_point DECIMAL(10, 2), -- Minimum stock level
reorder_quantity DECIMAL(10, 2), -- Standard order quantity
unit_cost DECIMAL(10, 2),
barcode VARCHAR(100),
storage_location VARCHAR(255),
storage_temperature_min DECIMAL(5, 2),
storage_temperature_max DECIMAL(5, 2),
shelf_life_days INTEGER,
is_active BOOLEAN DEFAULT TRUE,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, name)
);
```
**stock**
```sql
CREATE TABLE stock (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
ingredient_id UUID REFERENCES ingredients(id),
quantity DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
lot_number VARCHAR(100),
received_date DATE NOT NULL,
expiry_date DATE,
supplier_id UUID,
location VARCHAR(255),
unit_cost DECIMAL(10, 2),
status VARCHAR(50) DEFAULT 'available', -- available, reserved, expired, damaged
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
INDEX idx_tenant_ingredient (tenant_id, ingredient_id),
INDEX idx_expiry (expiry_date),
INDEX idx_status (status)
);
```
**stock_movements**
```sql
CREATE TABLE stock_movements (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
stock_id UUID REFERENCES stock(id),
ingredient_id UUID REFERENCES ingredients(id),
movement_type VARCHAR(50) NOT NULL, -- in, out, adjustment, waste, production
quantity DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
reference_id UUID, -- production_batch_id, order_id, etc.
reference_type VARCHAR(50), -- production, sale, adjustment, waste
reason TEXT,
performed_by UUID,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_tenant_date (tenant_id, created_at),
INDEX idx_ingredient (ingredient_id)
);
```
**stock_alerts**
```sql
CREATE TABLE stock_alerts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
ingredient_id UUID REFERENCES ingredients(id),
alert_type VARCHAR(50) NOT NULL, -- low_stock, expiring_soon, expired, temperature
severity VARCHAR(20) NOT NULL, -- low, medium, high, urgent
message TEXT NOT NULL,
current_quantity DECIMAL(10, 2),
threshold_quantity DECIMAL(10, 2),
expiry_date DATE,
days_until_expiry INTEGER,
is_acknowledged BOOLEAN DEFAULT FALSE,
acknowledged_at TIMESTAMP,
acknowledged_by UUID,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_tenant_active (tenant_id, is_acknowledged)
);
```
**product_transformations**
```sql
CREATE TABLE product_transformations (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
production_batch_id UUID,
ingredient_id UUID REFERENCES ingredients(id),
quantity_consumed DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
stock_consumed JSONB, -- Array of stock IDs with quantities (FIFO)
created_at TIMESTAMP DEFAULT NOW()
);
```
**food_safety_compliance**
```sql
CREATE TABLE food_safety_compliance (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
compliance_type VARCHAR(100), -- haccp, temperature, expiry, cleaning
check_date DATE NOT NULL,
status VARCHAR(50) NOT NULL, -- compliant, non_compliant, warning
details TEXT,
corrective_actions TEXT,
verified_by UUID,
created_at TIMESTAMP DEFAULT NOW()
);
```
**temperature_logs**
```sql
CREATE TABLE temperature_logs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
location VARCHAR(255) NOT NULL, -- freezer_1, fridge_2, storage_room
temperature DECIMAL(5, 2) NOT NULL,
unit VARCHAR(10) DEFAULT 'C',
is_within_range BOOLEAN,
min_acceptable DECIMAL(5, 2),
max_acceptable DECIMAL(5, 2),
recorded_by UUID,
recorded_at TIMESTAMP DEFAULT NOW(),
INDEX idx_tenant_location (tenant_id, location, recorded_at)
);
```
**food_safety_alerts**
```sql
CREATE TABLE food_safety_alerts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
alert_type VARCHAR(100), -- temperature_violation, expired_used, contamination
severity VARCHAR(20) NOT NULL,
location VARCHAR(255),
ingredient_id UUID,
description TEXT,
corrective_action_required TEXT,
status VARCHAR(50) DEFAULT 'open', -- open, investigating, resolved
resolved_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW()
);
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `inventory`
**Routing Keys**: `inventory.low_stock`, `inventory.expiring`, `inventory.food_safety`
**Low Stock Alert Event**
```json
{
"event_type": "low_stock_alert",
"tenant_id": "uuid",
"ingredient_id": "uuid",
"ingredient_name": "Harina integral",
"current_quantity": 15.5,
"unit": "kg",
"reorder_point": 50.0,
"recommended_order_quantity": 100.0,
"days_until_stockout": 3,
"severity": "high",
"message": "Stock bajo: Harina integral (15.5 kg). Punto de reorden: 50 kg.",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Expiring Soon Alert Event**
```json
{
"event_type": "expiring_soon_alert",
"tenant_id": "uuid",
"stock_id": "uuid",
"ingredient_id": "uuid",
"ingredient_name": "Leche fresca",
"quantity": 20.0,
"unit": "liters",
"lot_number": "LOT-2025-1105",
"expiry_date": "2025-11-09",
"days_until_expiry": 3,
"location": "Nevera 1",
"severity": "medium",
"message": "Leche fresca expira en 3 días (20 litros, LOT-2025-1105)",
"recommended_action": "Usar en producción antes del 09/11/2025",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Food Safety Alert Event**
```json
{
"event_type": "food_safety_alert",
"tenant_id": "uuid",
"alert_type": "temperature_violation",
"severity": "urgent",
"location": "Congelador 2",
"temperature": -12.5,
"acceptable_range": {
"min": -18.0,
"max": -15.0
},
"duration_minutes": 45,
"affected_items": ["uuid1", "uuid2"],
"message": "Violación de temperatura en Congelador 2: -12.5°C (rango: -18°C a -15°C)",
"corrective_action_required": "Revisar congelador inmediatamente y verificar integridad de productos",
"timestamp": "2025-11-06T10:30:00Z"
}
```
### Consumed Events
- **From Procurement**: Stock received from suppliers
- **From Production**: Ingredient consumption in production
- **From Sales**: Finished product sales (for inventory valuation)
## Custom Metrics (Prometheus)
```python
# Stock level metrics
stock_quantity_gauge = Gauge(
'inventory_stock_quantity',
'Current stock quantity',
['tenant_id', 'ingredient_id', 'ingredient_name']
)
low_stock_items_total = Gauge(
'inventory_low_stock_items',
'Number of items below reorder point',
['tenant_id']
)
expiring_items_total = Gauge(
'inventory_expiring_items',
'Number of items expiring within 7 days',
['tenant_id']
)
# Waste metrics
waste_quantity = Counter(
'inventory_waste_quantity_total',
'Total waste quantity',
['tenant_id', 'ingredient_category', 'reason'] # expired, damaged, etc.
)
waste_value_euros = Counter(
'inventory_waste_value_euros_total',
'Total waste value in euros',
['tenant_id']
)
# Inventory valuation
inventory_value_total = Gauge(
'inventory_value_euros',
'Total inventory value',
['tenant_id']
)
# Food safety metrics
temperature_violations = Counter(
'inventory_temperature_violations_total',
'Temperature violations detected',
['tenant_id', 'location']
)
food_safety_alerts_total = Counter(
'inventory_food_safety_alerts_total',
'Food safety alerts generated',
['tenant_id', 'alert_type', 'severity']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8005)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Alert Configuration:**
- `LOW_STOCK_CHECK_INTERVAL_HOURS` - How often to check (default: 6)
- `EXPIRY_WARNING_DAYS` - Days before expiry to alert (default: 7)
- `URGENT_EXPIRY_DAYS` - Days for urgent expiry alerts (default: 3)
- `ENABLE_AUTO_ALERTS` - Automatic alert generation (default: true)
**FIFO Configuration:**
- `ENABLE_FIFO_ENFORCEMENT` - Enforce FIFO consumption (default: true)
- `FIFO_VIOLATION_ALERT` - Alert on FIFO violations (default: true)
**Food Safety Configuration:**
- `TEMPERATURE_CHECK_INTERVAL_MINUTES` - Temp log frequency (default: 60)
- `TEMPERATURE_VIOLATION_THRESHOLD_MINUTES` - Time before alert (default: 30)
- `ENABLE_HACCP_COMPLIANCE` - Enable HACCP tracking (default: true)
**Sustainability Configuration:**
- `TRACK_CARBON_FOOTPRINT` - Enable carbon tracking (default: true)
- `TRACK_SDG_METRICS` - Enable SDG reporting (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1 (optional)
### Local Development
```bash
cd services/inventory
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/inventory
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
### Testing
```bash
# Unit tests
pytest tests/unit/ -v
# Integration tests
pytest tests/integration/ -v
# FIFO logic tests
pytest tests/test_fifo.py -v
# Test with coverage
pytest --cov=app tests/ --cov-report=html
```
## Integration Points
### Dependencies
- **Procurement Service** - Receive stock from purchase orders
- **Production Service** - Consume ingredients in production
- **Forecasting Service** - Provide consumption data for forecasts
- **Suppliers Service** - Supplier information for stock items
- **PostgreSQL** - Inventory data storage
- **Redis** - Dashboard KPI cache
- **RabbitMQ** - Alert publishing
### Dependents
- **Production Service** - Check ingredient availability
- **Procurement Service** - Get reorder recommendations
- **AI Insights Service** - Analyze inventory patterns
- **Frontend Dashboard** - Display inventory status
- **Notification Service** - Send inventory alerts
## FIFO Implementation
### FIFO Consumption Logic
```python
async def consume_ingredient_fifo(
tenant_id: str,
ingredient_id: str,
quantity_needed: float
) -> list[dict]:
"""Consume ingredients using FIFO (First-In-First-Out)"""
# Get available stock ordered by received_date (oldest first)
available_stock = await db.query(Stock).filter(
Stock.tenant_id == tenant_id,
Stock.ingredient_id == ingredient_id,
Stock.status == 'available',
Stock.quantity > 0
).order_by(Stock.received_date.asc()).all()
consumed_items = []
remaining_needed = quantity_needed
for stock_item in available_stock:
if remaining_needed <= 0:
break
quantity_to_consume = min(stock_item.quantity, remaining_needed)
# Update stock quantity
stock_item.quantity -= quantity_to_consume
if stock_item.quantity == 0:
stock_item.status = 'depleted'
# Record consumption
consumed_items.append({
'stock_id': stock_item.id,
'lot_number': stock_item.lot_number,
'quantity_consumed': quantity_to_consume,
'received_date': stock_item.received_date,
'expiry_date': stock_item.expiry_date
})
remaining_needed -= quantity_to_consume
if remaining_needed > 0:
raise InsufficientStockError(
f"Insufficient stock. Needed: {quantity_needed}, "
f"Available: {quantity_needed - remaining_needed}"
)
await db.commit()
return consumed_items
```
## Security Measures
### Data Protection
- **Tenant Isolation** - All inventory scoped to tenant_id
- **Input Validation** - Validate all quantities and dates
- **Audit Trail** - Complete history of stock movements
- **Access Control** - Role-based permissions
### Food Safety Security
- **Temperature Log Integrity** - Tamper-proof temperature records
- **Lot Traceability** - Complete ingredient tracking for recalls
- **Audit Compliance** - HACCP-compliant record keeping
- **Alert Escalation** - Critical food safety alerts escalate automatically
## Troubleshooting
### Common Issues
**Issue**: FIFO not working correctly
- **Cause**: Stock items missing received_date
- **Solution**: Ensure all stock has received_date set
**Issue**: Low stock alerts not firing
- **Cause**: Reorder points not configured
- **Solution**: Set reorder_point for each ingredient
**Issue**: Expiry alerts too frequent
- **Cause**: `EXPIRY_WARNING_DAYS` set too high
- **Solution**: Adjust to 3-5 days instead of 7
**Issue**: Temperature violations not detected
- **Cause**: Temperature logs not being recorded
- **Solution**: Check temperature monitoring device integration
## Competitive Advantages
1. **FIFO Enforcement** - Automatic expiry prevention
2. **Food Safety Built-In** - HACCP compliance out-of-the-box
3. **Sustainability Tracking** - SDG reporting for EU grants
4. **Barcode Support** - Quick stock updates
5. **Multi-Location** - Track inventory across sites
6. **Spanish Market** - HACCP compliant for Spanish regulations
7. **Zero Waste Focus** - Waste reduction analytics
## Future Enhancements
- **IoT Sensor Integration** - Automatic temperature monitoring
- **AI-Powered Reorder Points** - Dynamic reorder point calculation
- **Image Recognition** - Photo-based stock counting
- **Blockchain Traceability** - Immutable ingredient tracking
- **Mobile Barcode App** - Smartphone barcode scanning
- **Supplier Integration** - Direct supplier ordering
- **Predictive Expiry** - Predict expiry based on storage conditions
---
**For VUE Madrid Business Plan**: The Inventory Service demonstrates commitment to food safety (HACCP compliance), sustainability (20-40% waste reduction), and operational excellence. The FIFO enforcement and expiry management features directly address EU food waste regulations and support SDG goals, making this ideal for grant applications. The €200-600/month cost savings and compliance benefits provide clear ROI for bakery owners.

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# Notification Service
## Overview
The **Notification Service** handles multi-channel communication with bakery owners, managers, and staff through Email (SMTP) and WhatsApp (Twilio). It delivers critical operational alerts (stockouts, quality issues, equipment maintenance), business insights (daily summaries, forecast updates), and customer communications (order confirmations, delivery notifications). The service ensures that important information reaches the right people at the right time through their preferred communication channel.
## Key Features
### Multi-Channel Communication
- **Email (SMTP)** - Professional email notifications
- **WhatsApp (Twilio)** - Instant messaging for urgent alerts
- **SMS (Twilio)** - Fallback text messaging
- **Channel Prioritization** - Auto-select channel by urgency
- **Channel Preferences** - User-defined communication preferences
- **Multi-Recipient** - Send to individuals or groups
- **Delivery Tracking** - Monitor delivery status per channel
### Email Capabilities
- **HTML Templates** - Professional branded emails
- **Plain Text Fallback** - Ensure compatibility
- **Attachments** - PDF reports, invoices, documents
- **Inline Images** - Logo, charts embedded
- **Email Templates** - Pre-designed templates per alert type
- **Variable Substitution** - Dynamic content per recipient
- **Batch Sending** - Efficient bulk email delivery
### WhatsApp Integration
- **Twilio API** - Official WhatsApp Business API
- **Rich Messages** - Text, images, documents
- **Message Templates** - Pre-approved templates for compliance
- **Interactive Messages** - Quick reply buttons
- **Media Support** - Send images, PDFs
- **Delivery Receipts** - Read/delivered status
- **Opt-In Management** - GDPR-compliant consent tracking
### Notification Types
- **Critical Alerts** - Stockouts, equipment failure, quality issues
- **High Priority** - Low stock warnings, forecast anomalies
- **Medium Priority** - Daily summaries, scheduled reports
- **Low Priority** - Weekly digests, monthly reports
- **Informational** - System updates, tips, best practices
- **Customer Notifications** - Order confirmations, delivery updates
### Template Management
- **Template Library** - 20+ pre-built templates
- **Template Versioning** - Track template changes
- **Multi-Language** - Spanish, English, Catalan
- **Variable Placeholders** - Dynamic content insertion
- **Template Preview** - Test before sending
- **Custom Templates** - Create tenant-specific templates
- **Template Analytics** - Open rates, click rates
### Delivery Management
- **Queue System** - Prioritized delivery queue
- **Retry Logic** - Automatic retry on failure
- **Rate Limiting** - Respect API rate limits
- **Delivery Status** - Track sent, delivered, failed, read
- **Failure Handling** - Fallback to alternative channels
- **Bounce Management** - Handle invalid addresses
- **Unsubscribe Management** - Honor opt-out requests
### Analytics & Reporting
- **Delivery Metrics** - Success rates per channel
- **Open Rates** - Email open tracking
- **Click Rates** - Link click tracking
- **Response Times** - Time to read/acknowledge
- **Channel Effectiveness** - Compare channel performance
- **Cost Analysis** - Communication costs per channel
- **User Engagement** - Active users per channel
## Business Value
### For Bakery Owners
- **Real-Time Alerts** - Know critical issues immediately
- **Channel Flexibility** - Email for reports, WhatsApp for urgent
- **Cost Effective** - WhatsApp cheaper than SMS
- **Professional Communication** - Branded emails enhance reputation
- **Customer Engagement** - Order updates improve satisfaction
- **Remote Management** - Stay informed from anywhere
### Quantifiable Impact
- **Response Time**: 90% faster with WhatsApp alerts (minutes vs. hours)
- **Issue Resolution**: 50-70% faster with immediate notifications
- **Cost Savings**: €50-150/month (WhatsApp vs. SMS or phone calls)
- **Customer Satisfaction**: 20-30% improvement with order updates
- **Staff Efficiency**: 3-5 hours/week saved on manual communication
- **Alert Reliability**: 99%+ delivery rate
### For Operations Staff
- **Instant Alerts** - WhatsApp notifications on critical issues
- **Actionable Information** - Clear instructions in alerts
- **Mobile Access** - Receive alerts on phone
- **Alert History** - Review past notifications
- **Acknowledgment** - Confirm receipt of critical alerts
### For Customers
- **Order Confirmation** - Immediate order receipt
- **Delivery Updates** - Know when order is ready
- **Personalized Communication** - Address by name
- **Multiple Channels** - Choose email or WhatsApp
- **Professional Image** - Branded communication
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Notification history
- **Queue**: RabbitMQ 4.1 - Notification queue
- **Caching**: Redis 7.4 - Template cache
- **Email**: SMTP (SendGrid, Amazon SES, SMTP server)
- **WhatsApp**: Twilio API - WhatsApp Business
- **SMS**: Twilio API - SMS fallback
- **Templates**: Jinja2 - Template rendering
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Delivery metrics
## API Endpoints (Key Routes)
### Notification Sending
- `POST /api/v1/notifications/send` - Send notification
- `POST /api/v1/notifications/send-email` - Send email
- `POST /api/v1/notifications/send-whatsapp` - Send WhatsApp
- `POST /api/v1/notifications/send-sms` - Send SMS
- `POST /api/v1/notifications/send-batch` - Bulk send
### Notification Management
- `GET /api/v1/notifications` - List notifications
- `GET /api/v1/notifications/{notification_id}` - Get notification details
- `GET /api/v1/notifications/{notification_id}/status` - Check delivery status
- `POST /api/v1/notifications/{notification_id}/retry` - Retry failed notification
- `DELETE /api/v1/notifications/{notification_id}` - Cancel pending notification
### Template Management
- `GET /api/v1/notifications/templates` - List templates
- `GET /api/v1/notifications/templates/{template_id}` - Get template
- `POST /api/v1/notifications/templates` - Create template
- `PUT /api/v1/notifications/templates/{template_id}` - Update template
- `POST /api/v1/notifications/templates/{template_id}/preview` - Preview template
- `DELETE /api/v1/notifications/templates/{template_id}` - Delete template
### User Preferences
- `GET /api/v1/notifications/preferences` - Get user preferences
- `PUT /api/v1/notifications/preferences` - Update preferences
- `POST /api/v1/notifications/preferences/opt-out` - Opt out of notifications
- `POST /api/v1/notifications/preferences/opt-in` - Opt in to notifications
### Analytics
- `GET /api/v1/notifications/analytics/dashboard` - Notification dashboard
- `GET /api/v1/notifications/analytics/delivery-rates` - Delivery success rates
- `GET /api/v1/notifications/analytics/channel-performance` - Channel comparison
- `GET /api/v1/notifications/analytics/engagement` - User engagement metrics
### Webhooks
- `POST /api/v1/notifications/webhooks/twilio` - Twilio status webhook
- `POST /api/v1/notifications/webhooks/sendgrid` - SendGrid webhook
- `POST /api/v1/notifications/webhooks/ses` - Amazon SES webhook
## Database Schema
### Main Tables
**notifications**
```sql
CREATE TABLE notifications (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
notification_type VARCHAR(100) NOT NULL, -- alert, report, customer, system
priority VARCHAR(50) NOT NULL, -- critical, high, medium, low
channel VARCHAR(50) NOT NULL, -- email, whatsapp, sms
status VARCHAR(50) DEFAULT 'pending', -- pending, queued, sent, delivered, failed, cancelled
-- Recipient
recipient_user_id UUID,
recipient_name VARCHAR(255),
recipient_email VARCHAR(255),
recipient_phone VARCHAR(50),
-- Content
subject VARCHAR(500),
message_body TEXT NOT NULL,
template_id UUID,
template_variables JSONB,
-- Attachments
attachments JSONB, -- Array of attachment URLs
-- Delivery
scheduled_at TIMESTAMP,
sent_at TIMESTAMP,
delivered_at TIMESTAMP,
read_at TIMESTAMP,
failed_at TIMESTAMP,
failure_reason TEXT,
retry_count INTEGER DEFAULT 0,
max_retries INTEGER DEFAULT 3,
-- External IDs
external_message_id VARCHAR(255), -- Twilio SID, SendGrid message ID
external_status VARCHAR(100),
-- Tracking
opened BOOLEAN DEFAULT FALSE,
clicked BOOLEAN DEFAULT FALSE,
open_count INTEGER DEFAULT 0,
click_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
INDEX idx_notifications_tenant_status (tenant_id, status),
INDEX idx_notifications_recipient (recipient_user_id),
INDEX idx_notifications_scheduled (scheduled_at) WHERE status = 'pending'
);
```
**notification_templates**
```sql
CREATE TABLE notification_templates (
id UUID PRIMARY KEY,
tenant_id UUID, -- NULL for global templates
template_name VARCHAR(255) NOT NULL,
template_code VARCHAR(100) NOT NULL, -- Unique code for reference
template_type VARCHAR(100) NOT NULL, -- alert, report, customer, system
channel VARCHAR(50) NOT NULL, -- email, whatsapp, sms, all
-- Content
subject_template TEXT,
body_template TEXT NOT NULL,
html_body_template TEXT, -- For email
-- Variables
required_variables JSONB, -- Array of required variable names
sample_variables JSONB, -- Sample data for preview
-- Configuration
language VARCHAR(10) DEFAULT 'es', -- es, en, ca
is_active BOOLEAN DEFAULT TRUE,
is_system_template BOOLEAN DEFAULT FALSE, -- Cannot be modified by users
version INTEGER DEFAULT 1,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, template_code, channel)
);
```
**notification_preferences**
```sql
CREATE TABLE notification_preferences (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
user_id UUID NOT NULL,
-- Channel preferences
email_enabled BOOLEAN DEFAULT TRUE,
whatsapp_enabled BOOLEAN DEFAULT TRUE,
sms_enabled BOOLEAN DEFAULT TRUE,
preferred_channel VARCHAR(50) DEFAULT 'email',
-- Notification type preferences
critical_alerts_enabled BOOLEAN DEFAULT TRUE,
high_priority_enabled BOOLEAN DEFAULT TRUE,
medium_priority_enabled BOOLEAN DEFAULT TRUE,
low_priority_enabled BOOLEAN DEFAULT TRUE,
-- Timing preferences
quiet_hours_start TIME, -- e.g., 22:00
quiet_hours_end TIME, -- e.g., 08:00
weekend_notifications BOOLEAN DEFAULT TRUE,
-- Specific notification types
stockout_alerts BOOLEAN DEFAULT TRUE,
quality_alerts BOOLEAN DEFAULT TRUE,
forecast_updates BOOLEAN DEFAULT TRUE,
daily_summary BOOLEAN DEFAULT TRUE,
weekly_report BOOLEAN DEFAULT TRUE,
-- Contact details
email_address VARCHAR(255),
phone_number VARCHAR(50),
whatsapp_number VARCHAR(50),
whatsapp_opt_in BOOLEAN DEFAULT FALSE,
whatsapp_opt_in_date TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, user_id)
);
```
**notification_delivery_log**
```sql
CREATE TABLE notification_delivery_log (
id UUID PRIMARY KEY,
notification_id UUID REFERENCES notifications(id) ON DELETE CASCADE,
attempt_number INTEGER NOT NULL,
channel VARCHAR(50) NOT NULL,
status VARCHAR(50) NOT NULL, -- success, failed, bounced
status_code VARCHAR(50),
status_message TEXT,
provider_response JSONB,
attempted_at TIMESTAMP DEFAULT NOW(),
INDEX idx_delivery_log_notification (notification_id)
);
```
**notification_events**
```sql
CREATE TABLE notification_events (
id UUID PRIMARY KEY,
notification_id UUID REFERENCES notifications(id) ON DELETE CASCADE,
event_type VARCHAR(50) NOT NULL, -- opened, clicked, bounced, complained
event_data JSONB,
user_agent TEXT,
ip_address INET,
occurred_at TIMESTAMP DEFAULT NOW(),
INDEX idx_events_notification (notification_id),
INDEX idx_events_type (notification_id, event_type)
);
```
**notification_costs**
```sql
CREATE TABLE notification_costs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
month DATE NOT NULL, -- First day of month
channel VARCHAR(50) NOT NULL,
-- Volume
notifications_sent INTEGER DEFAULT 0,
notifications_delivered INTEGER DEFAULT 0,
-- Costs (in euros)
estimated_cost DECIMAL(10, 4) DEFAULT 0.0000,
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, month, channel)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_notifications_tenant_priority ON notifications(tenant_id, priority, status);
CREATE INDEX idx_notifications_sent_at ON notifications(sent_at DESC);
CREATE INDEX idx_templates_tenant_active ON notification_templates(tenant_id, is_active);
CREATE INDEX idx_preferences_user ON notification_preferences(user_id);
CREATE INDEX idx_delivery_log_status ON notification_delivery_log(status, attempted_at DESC);
```
## Business Logic Examples
### Send Email Notification
```python
async def send_email_notification(
tenant_id: UUID,
recipient_email: str,
recipient_name: str,
subject: str,
body: str,
html_body: str = None,
priority: str = 'medium',
attachments: list = None
) -> Notification:
"""
Send email notification via SMTP.
"""
# Create notification record
notification = Notification(
tenant_id=tenant_id,
notification_type='email',
priority=priority,
channel='email',
status='queued',
recipient_name=recipient_name,
recipient_email=recipient_email,
subject=subject,
message_body=body,
attachments=attachments
)
db.add(notification)
await db.flush()
try:
# Configure SMTP
smtp_config = await get_smtp_config(tenant_id)
# Create email message
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
msg = MIMEMultipart('alternative')
msg['From'] = smtp_config.from_address
msg['To'] = recipient_email
msg['Subject'] = subject
# Add plain text body
part1 = MIMEText(body, 'plain', 'utf-8')
msg.attach(part1)
# Add HTML body if provided
if html_body:
part2 = MIMEText(html_body, 'html', 'utf-8')
msg.attach(part2)
# Add attachments
if attachments:
for attachment in attachments:
part = MIMEBase('application', 'octet-stream')
with open(attachment['path'], 'rb') as file:
part.set_payload(file.read())
encoders.encode_base64(part)
part.add_header(
'Content-Disposition',
f'attachment; filename= {attachment["filename"]}'
)
msg.attach(part)
# Send email
import smtplib
with smtplib.SMTP(smtp_config.host, smtp_config.port) as server:
if smtp_config.use_tls:
server.starttls()
if smtp_config.username and smtp_config.password:
server.login(smtp_config.username, smtp_config.password)
server.send_message(msg)
# Update notification status
notification.status = 'sent'
notification.sent_at = datetime.utcnow()
# Log delivery
log = NotificationDeliveryLog(
notification_id=notification.id,
attempt_number=1,
channel='email',
status='success'
)
db.add(log)
await db.commit()
logger.info("Email sent successfully",
notification_id=str(notification.id),
recipient=recipient_email)
return notification
except Exception as e:
notification.status = 'failed'
notification.failed_at = datetime.utcnow()
notification.failure_reason = str(e)
notification.retry_count += 1
# Log failure
log = NotificationDeliveryLog(
notification_id=notification.id,
attempt_number=notification.retry_count,
channel='email',
status='failed',
status_message=str(e)
)
db.add(log)
await db.commit()
logger.error("Email send failed",
notification_id=str(notification.id),
error=str(e))
# Retry if within limits
if notification.retry_count < notification.max_retries:
await schedule_retry(notification.id, delay_minutes=5)
raise
```
### Send WhatsApp Notification
```python
async def send_whatsapp_notification(
tenant_id: UUID,
recipient_phone: str,
recipient_name: str,
message: str,
priority: str = 'high',
template_name: str = None,
template_variables: dict = None
) -> Notification:
"""
Send WhatsApp notification via Twilio.
"""
# Check user opt-in
preferences = await get_user_preferences_by_phone(tenant_id, recipient_phone)
if not preferences or not preferences.whatsapp_opt_in:
raise ValueError("User has not opted in to WhatsApp notifications")
# Create notification record
notification = Notification(
tenant_id=tenant_id,
notification_type='alert',
priority=priority,
channel='whatsapp',
status='queued',
recipient_name=recipient_name,
recipient_phone=recipient_phone,
message_body=message
)
db.add(notification)
await db.flush()
try:
# Configure Twilio
from twilio.rest import Client
twilio_config = await get_twilio_config(tenant_id)
client = Client(twilio_config.account_sid, twilio_config.auth_token)
# Format phone number (E.164 format)
formatted_phone = format_phone_e164(recipient_phone)
# Send WhatsApp message
if template_name:
# Use WhatsApp template (pre-approved)
twilio_message = client.messages.create(
from_=f'whatsapp:{twilio_config.whatsapp_number}',
to=f'whatsapp:{formatted_phone}',
content_sid=template_name,
content_variables=json.dumps(template_variables) if template_variables else None
)
else:
# Send freeform message
twilio_message = client.messages.create(
from_=f'whatsapp:{twilio_config.whatsapp_number}',
to=f'whatsapp:{formatted_phone}',
body=message
)
# Update notification status
notification.status = 'sent'
notification.sent_at = datetime.utcnow()
notification.external_message_id = twilio_message.sid
notification.external_status = twilio_message.status
# Log delivery
log = NotificationDeliveryLog(
notification_id=notification.id,
attempt_number=1,
channel='whatsapp',
status='success',
status_code=twilio_message.status,
provider_response={'sid': twilio_message.sid}
)
db.add(log)
await db.commit()
logger.info("WhatsApp sent successfully",
notification_id=str(notification.id),
recipient=recipient_phone,
twilio_sid=twilio_message.sid)
return notification
except Exception as e:
notification.status = 'failed'
notification.failed_at = datetime.utcnow()
notification.failure_reason = str(e)
notification.retry_count += 1
log = NotificationDeliveryLog(
notification_id=notification.id,
attempt_number=notification.retry_count,
channel='whatsapp',
status='failed',
status_message=str(e)
)
db.add(log)
await db.commit()
logger.error("WhatsApp send failed",
notification_id=str(notification.id),
error=str(e))
# Fallback to SMS if critical
if priority == 'critical' and notification.retry_count >= notification.max_retries:
await send_sms_notification(
tenant_id, recipient_phone, recipient_name, message, priority
)
raise
def format_phone_e164(phone: str) -> str:
"""
Format phone number to E.164 standard (e.g., +34612345678).
"""
import phonenumbers
# Parse phone number (assume Spain +34 if no country code)
try:
parsed = phonenumbers.parse(phone, 'ES')
return phonenumbers.format_number(parsed, phonenumbers.PhoneNumberFormat.E164)
except:
# If parsing fails, return as-is
return phone
```
### Template Rendering
```python
async def render_notification_template(
template_id: UUID,
variables: dict
) -> dict:
"""
Render notification template with variables.
"""
# Get template
template = await db.get(NotificationTemplate, template_id)
if not template:
raise ValueError("Template not found")
# Validate required variables
required_vars = template.required_variables or []
missing_vars = [v for v in required_vars if v not in variables]
if missing_vars:
raise ValueError(f"Missing required variables: {', '.join(missing_vars)}")
# Render subject
from jinja2 import Template
subject = None
if template.subject_template:
subject_template = Template(template.subject_template)
subject = subject_template.render(**variables)
# Render body
body_template = Template(template.body_template)
body = body_template.render(**variables)
# Render HTML body if available
html_body = None
if template.html_body_template:
html_template = Template(template.html_body_template)
html_body = html_template.render(**variables)
return {
'subject': subject,
'body': body,
'html_body': html_body,
'template_name': template.template_name,
'channel': template.channel
}
```
### Smart Channel Selection
```python
async def send_smart_notification(
tenant_id: UUID,
user_id: UUID,
notification_type: str,
priority: str,
subject: str,
message: str,
template_id: UUID = None,
template_variables: dict = None
) -> Notification:
"""
Send notification via optimal channel based on priority and user preferences.
"""
# Get user preferences
preferences = await get_user_preferences(tenant_id, user_id)
user = await get_user(user_id)
# Determine channel based on priority and preferences
channel = None
if priority == 'critical':
# Critical: WhatsApp if enabled, else SMS, else email
if preferences.whatsapp_enabled and preferences.whatsapp_opt_in:
channel = 'whatsapp'
elif preferences.sms_enabled:
channel = 'sms'
else:
channel = 'email'
elif priority == 'high':
# High: Preferred channel if enabled
if preferences.preferred_channel == 'whatsapp' and preferences.whatsapp_enabled:
channel = 'whatsapp'
elif preferences.preferred_channel == 'sms' and preferences.sms_enabled:
channel = 'sms'
else:
channel = 'email'
else:
# Medium/Low: Email default
channel = 'email'
# Check quiet hours
if not priority == 'critical':
if await is_quiet_hours(preferences):
# Delay notification until quiet hours end
send_at = await calculate_quiet_hours_end(preferences)
logger.info("Delaying notification due to quiet hours",
user_id=str(user_id),
send_at=send_at)
return await schedule_notification(
tenant_id, user_id, channel, subject, message,
scheduled_at=send_at
)
# Send via selected channel
if channel == 'whatsapp':
return await send_whatsapp_notification(
tenant_id,
preferences.whatsapp_number or user.phone,
user.name,
message,
priority,
template_variables=template_variables
)
elif channel == 'sms':
return await send_sms_notification(
tenant_id,
user.phone,
user.name,
message,
priority
)
else: # email
# Render template if provided
if template_id:
rendered = await render_notification_template(template_id, template_variables)
subject = rendered['subject']
message = rendered['body']
html_body = rendered['html_body']
else:
html_body = None
return await send_email_notification(
tenant_id,
preferences.email_address or user.email,
user.name,
subject,
message,
html_body=html_body,
priority=priority
)
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `notifications`
**Routing Keys**: `notifications.sent`, `notifications.failed`, `notifications.delivered`
**Notification Sent Event**
```json
{
"event_type": "notification_sent",
"tenant_id": "uuid",
"notification_id": "uuid",
"channel": "whatsapp",
"priority": "critical",
"recipient_name": "Juan García",
"notification_type": "stockout_alert",
"timestamp": "2025-11-06T09:00:00Z"
}
```
**Notification Failed Event**
```json
{
"event_type": "notification_failed",
"tenant_id": "uuid",
"notification_id": "uuid",
"channel": "email",
"priority": "high",
"failure_reason": "SMTP connection timeout",
"retry_count": 2,
"max_retries": 3,
"will_retry": true,
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Notification Delivered Event**
```json
{
"event_type": "notification_delivered",
"tenant_id": "uuid",
"notification_id": "uuid",
"channel": "whatsapp",
"delivered_at": "2025-11-06T09:01:00Z",
"read_at": "2025-11-06T09:02:00Z",
"delivery_time_seconds": 60,
"timestamp": "2025-11-06T09:02:00Z"
}
```
### Consumed Events
- **From Alert Processor**: Alert events trigger notifications
- **From Orchestrator**: Daily summaries, scheduled reports
- **From Orders**: Order confirmations, delivery updates
- **From Production**: Quality issue alerts, batch completion
- **From Procurement**: Stockout warnings, purchase order confirmations
## Custom Metrics (Prometheus)
```python
# Notification metrics
notifications_sent_total = Counter(
'notifications_sent_total',
'Total notifications sent',
['tenant_id', 'channel', 'priority', 'status']
)
notification_delivery_time_seconds = Histogram(
'notification_delivery_time_seconds',
'Time from creation to delivery',
['tenant_id', 'channel'],
buckets=[1, 5, 10, 30, 60, 300, 600]
)
notification_delivery_rate = Gauge(
'notification_delivery_rate_percentage',
'Notification delivery success rate',
['tenant_id', 'channel']
)
notification_costs_euros = Counter(
'notification_costs_euros_total',
'Total notification costs',
['tenant_id', 'channel']
)
email_open_rate = Gauge(
'email_open_rate_percentage',
'Email open rate',
['tenant_id', 'template_type']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8015)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Email (SMTP) Configuration:**
- `SMTP_HOST` - SMTP server host
- `SMTP_PORT` - SMTP server port (default: 587)
- `SMTP_USERNAME` - SMTP username
- `SMTP_PASSWORD` - SMTP password
- `SMTP_FROM_ADDRESS` - From email address
- `SMTP_USE_TLS` - Enable TLS (default: true)
**Twilio Configuration:**
- `TWILIO_ACCOUNT_SID` - Twilio account SID
- `TWILIO_AUTH_TOKEN` - Twilio auth token
- `TWILIO_WHATSAPP_NUMBER` - WhatsApp sender number (format: +1234567890)
- `TWILIO_SMS_NUMBER` - SMS sender number
**Delivery Configuration:**
- `MAX_RETRY_ATTEMPTS` - Maximum retry attempts (default: 3)
- `RETRY_DELAY_MINUTES` - Delay between retries (default: 5)
- `ENABLE_QUIET_HOURS` - Respect user quiet hours (default: true)
- `BATCH_SIZE` - Bulk sending batch size (default: 100)
**Cost Configuration:**
- `WHATSAPP_COST_PER_MESSAGE` - Cost per WhatsApp (default: 0.005 EUR)
- `SMS_COST_PER_MESSAGE` - Cost per SMS (default: 0.08 EUR)
- `EMAIL_COST_PER_MESSAGE` - Cost per email (default: 0.001 EUR)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
- SMTP server or SendGrid account
- Twilio account (for WhatsApp/SMS)
### Local Development
```bash
cd services/notification
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/notification
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export SMTP_HOST=smtp.sendgrid.net
export SMTP_USERNAME=apikey
export SMTP_PASSWORD=your_sendgrid_api_key
export TWILIO_ACCOUNT_SID=your_twilio_sid
export TWILIO_AUTH_TOKEN=your_twilio_token
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **SMTP Server** - Email delivery (SendGrid, SES, SMTP)
- **Twilio API** - WhatsApp and SMS delivery
- **Auth Service** - User information
- **PostgreSQL** - Notification history
- **Redis** - Template caching
- **RabbitMQ** - Alert consumption
### Dependents
- **Alert Processor** - Sends alerts via notifications
- **Orders Service** - Customer order notifications
- **Orchestrator** - Daily summaries and reports
- **All Services** - Critical alerts routing
- **Frontend Dashboard** - Notification preferences UI
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Delayed awareness of critical issues (stockouts discovered too late)
- Manual phone calls and texts consuming staff time
- No systematic customer communication (order confirmations)
- Expensive SMS costs (€0.08/message in Spain)
- No record of communications sent
- Staff missing important alerts
### Solution
Bakery-IA Notification Service provides:
- **Real-Time Alerts**: WhatsApp notifications within seconds
- **Multi-Channel**: Email for reports, WhatsApp for urgent
- **Cost Effective**: WhatsApp 90% cheaper than SMS
- **Customer Communication**: Professional order updates
- **Communication History**: Complete audit trail
- **Smart Routing**: Right message, right channel, right time
### Quantifiable Impact
**Cost Savings:**
- €50-150/month using WhatsApp vs. SMS (90% cost reduction)
- 3-5 hours/week saved on manual phone calls/texts (€180-300/month)
- **Total: €230-450/month savings**
**Operational Efficiency:**
- 90% faster response to critical issues (minutes vs. hours)
- 50-70% faster issue resolution with immediate awareness
- 99%+ alert delivery reliability
- 24/7 notification delivery (no manual intervention)
**Customer Satisfaction:**
- 20-30% improvement with order confirmation/updates
- Professional brand image with branded emails
- Customer choice of email or WhatsApp
- Personalized communication
### Target Market Fit (Spanish Bakeries)
- **WhatsApp Culture**: Spain has 91% WhatsApp penetration rate
- **Mobile First**: Bakery owners/managers always on mobile
- **Cost Sensitive**: SMS costs high in Spain (€0.08 vs. €0.005 WhatsApp)
- **Communication Style**: Spanish business culture values personal touch
- **GDPR Compliance**: Opt-in management meets EU regulations
### ROI Calculation
**Investment**: €0 additional (included in subscription) + Twilio costs
**Cost Savings**: €230-450/month (vs. SMS + manual communication)
**Operational Value**: 50-70% faster issue resolution
**Monthly Value**: €230-450 savings + operational efficiency
**Annual ROI**: €2,760-5,400 value per bakery
**Payback**: Immediate (cost savings from day one)
### Competitive Advantage
- **WhatsApp Integration**: Few Spanish bakery platforms offer WhatsApp
- **Multi-Channel**: Flexibility competitors don't provide
- **Smart Routing**: Auto-select channel by urgency/preference
- **Cost Effective**: 90% cheaper than SMS-only solutions
- **GDPR Compliant**: Built-in opt-in management
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Orchestrator Service
## Overview
The **Orchestrator Service** automates daily operational workflows by coordinating tasks across multiple microservices. It schedules and executes recurring jobs like daily forecasting, production planning, procurement needs calculation, and report generation. Operating on a configurable schedule (default: daily at 8:00 AM Madrid time), it ensures that bakery owners start each day with fresh forecasts, optimized production plans, and actionable insights - all without manual intervention.
## Key Features
### Workflow Automation
- **Daily Forecasting** - Generate 7-day demand forecasts every morning
- **Production Planning** - Calculate production schedules from forecasts
- **Procurement Planning** - Identify purchasing needs automatically
- **Inventory Projections** - Project stock levels for next 14 days
- **Report Generation** - Daily summaries, weekly digests
- **Model Retraining** - Weekly ML model updates
- **Alert Cleanup** - Archive resolved alerts
### Scheduling System
- **Cron-Based Scheduling** - Flexible schedule configuration
- **Timezone-Aware** - Respects tenant timezone (Madrid default)
- **Configurable Frequency** - Daily, weekly, monthly workflows
- **Time-Based Execution** - Run at optimal times (early morning)
- **Holiday Awareness** - Skip or adjust on public holidays
- **Weekend Handling** - Different schedules for weekends
### Workflow Execution
- **Sequential Workflows** - Execute steps in correct order
- **Parallel Execution** - Run independent tasks concurrently
- **Error Handling** - Retry failed tasks with exponential backoff
- **Timeout Management** - Cancel long-running tasks
- **Progress Tracking** - Monitor workflow execution status
- **Result Caching** - Cache workflow results in Redis
### Multi-Tenant Management
- **Per-Tenant Workflows** - Execute for all active tenants
- **Tenant Priority** - Prioritize by subscription tier
- **Tenant Filtering** - Skip suspended or cancelled tenants
- **Load Balancing** - Distribute tenant workflows evenly
- **Resource Limits** - Prevent resource exhaustion
### Monitoring & Observability
- **Workflow Metrics** - Execution time, success rate
- **Health Checks** - Service and job health monitoring
- **Failure Alerts** - Notify on workflow failures
- **Audit Logging** - Complete execution history
- **Performance Tracking** - Identify slow workflows
- **Cost Tracking** - Monitor computational costs
### Leader Election
- **Distributed Coordination** - Redis-based leader election
- **High Availability** - Multiple orchestrator instances
- **Automatic Failover** - New leader elected on failure
- **Split-Brain Prevention** - Ensure only one leader
- **Leader Health** - Continuous health monitoring
## Business Value
### For Bakery Owners
- **Zero Manual Work** - Forecasts and plans generated automatically
- **Consistent Execution** - Never forget to plan production
- **Early Morning Ready** - Start day with fresh data (8:00 AM)
- **Weekend Coverage** - Works 7 days/week, 365 days/year
- **Reliable** - Automatic retries on failures
- **Transparent** - Clear audit trail of all automation
### Quantifiable Impact
- **Time Savings**: 15-20 hours/week on manual planning (€900-1,200/month)
- **Consistency**: 100% vs. 70-80% manual execution rate
- **Early Detection**: Issues identified before business hours
- **Error Reduction**: 95%+ accuracy vs. 80-90% manual
- **Staff Freedom**: Staff focus on operations, not planning
- **Scalability**: Handles 10,000+ tenants automatically
### For Platform Operations
- **Automation**: 95%+ of platform operations automated
- **Scalability**: Linear cost scaling with tenants
- **Reliability**: 99.9%+ workflow success rate
- **Predictability**: Consistent execution times
- **Resource Efficiency**: Optimal resource utilization
- **Cost Control**: Prevent runaway computational costs
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Scheduler**: APScheduler - Job scheduling
- **Database**: PostgreSQL 17 - Workflow history
- **Caching**: Redis 7.4 - Leader election, results cache
- **Messaging**: RabbitMQ 4.1 - Event publishing
- **HTTP Client**: HTTPx - Async service calls
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Workflow metrics
## API Endpoints (Key Routes)
### Workflow Management
- `GET /api/v1/orchestrator/workflows` - List workflows
- `GET /api/v1/orchestrator/workflows/{workflow_id}` - Get workflow details
- `POST /api/v1/orchestrator/workflows/{workflow_id}/execute` - Manually trigger workflow
- `PUT /api/v1/orchestrator/workflows/{workflow_id}` - Update workflow configuration
- `POST /api/v1/orchestrator/workflows/{workflow_id}/enable` - Enable workflow
- `POST /api/v1/orchestrator/workflows/{workflow_id}/disable` - Disable workflow
### Execution History
- `GET /api/v1/orchestrator/executions` - List workflow executions
- `GET /api/v1/orchestrator/executions/{execution_id}` - Get execution details
- `GET /api/v1/orchestrator/executions/{execution_id}/logs` - Get execution logs
- `GET /api/v1/orchestrator/executions/failed` - List failed executions
- `POST /api/v1/orchestrator/executions/{execution_id}/retry` - Retry failed execution
### Scheduling
- `GET /api/v1/orchestrator/schedule` - Get current schedule
- `PUT /api/v1/orchestrator/schedule` - Update schedule
- `GET /api/v1/orchestrator/schedule/next-run` - Get next execution time
### Health & Monitoring
- `GET /api/v1/orchestrator/health` - Service health
- `GET /api/v1/orchestrator/leader` - Current leader instance
- `GET /api/v1/orchestrator/metrics` - Workflow metrics
- `GET /api/v1/orchestrator/statistics` - Execution statistics
## Database Schema
### Main Tables
**orchestrator_workflows**
```sql
CREATE TABLE orchestrator_workflows (
id UUID PRIMARY KEY,
workflow_name VARCHAR(255) NOT NULL UNIQUE,
workflow_type VARCHAR(100) NOT NULL, -- daily, weekly, monthly, on_demand
description TEXT,
-- Schedule
cron_expression VARCHAR(100), -- e.g., "0 8 * * *" for 8 AM daily
timezone VARCHAR(50) DEFAULT 'Europe/Madrid',
is_enabled BOOLEAN DEFAULT TRUE,
-- Execution
max_execution_time_seconds INTEGER DEFAULT 3600,
max_retries INTEGER DEFAULT 3,
retry_delay_seconds INTEGER DEFAULT 300,
-- Workflow steps
steps JSONB NOT NULL, -- Array of workflow steps
-- Status
last_execution_at TIMESTAMP,
last_success_at TIMESTAMP,
last_failure_at TIMESTAMP,
next_execution_at TIMESTAMP,
consecutive_failures INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**orchestrator_executions**
```sql
CREATE TABLE orchestrator_executions (
id UUID PRIMARY KEY,
workflow_id UUID REFERENCES orchestrator_workflows(id),
workflow_name VARCHAR(255) NOT NULL,
execution_type VARCHAR(50) NOT NULL, -- scheduled, manual
triggered_by UUID, -- User ID if manual
-- Tenant
tenant_id UUID, -- NULL for global workflows
-- Status
status VARCHAR(50) DEFAULT 'pending', -- pending, running, completed, failed, cancelled
started_at TIMESTAMP,
completed_at TIMESTAMP,
duration_seconds INTEGER,
-- Results
steps_completed INTEGER DEFAULT 0,
steps_total INTEGER DEFAULT 0,
steps_failed INTEGER DEFAULT 0,
error_message TEXT,
result_summary JSONB,
-- Leader info
executed_by_instance VARCHAR(255), -- Instance ID that ran this
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_executions_workflow_date (workflow_id, created_at DESC),
INDEX idx_executions_tenant_date (tenant_id, created_at DESC)
);
```
**orchestrator_execution_logs**
```sql
CREATE TABLE orchestrator_execution_logs (
id UUID PRIMARY KEY,
execution_id UUID REFERENCES orchestrator_executions(id) ON DELETE CASCADE,
step_name VARCHAR(255) NOT NULL,
step_index INTEGER NOT NULL,
log_level VARCHAR(50) NOT NULL, -- info, warning, error
log_message TEXT NOT NULL,
log_data JSONB,
logged_at TIMESTAMP DEFAULT NOW(),
INDEX idx_execution_logs_execution (execution_id, step_index)
);
```
**orchestrator_leader**
```sql
CREATE TABLE orchestrator_leader (
id INTEGER PRIMARY KEY DEFAULT 1, -- Always 1 (singleton)
instance_id VARCHAR(255) NOT NULL,
instance_hostname VARCHAR(255),
became_leader_at TIMESTAMP NOT NULL,
last_heartbeat_at TIMESTAMP NOT NULL,
heartbeat_interval_seconds INTEGER DEFAULT 30,
CONSTRAINT single_leader CHECK (id = 1)
);
```
**orchestrator_metrics**
```sql
CREATE TABLE orchestrator_metrics (
id UUID PRIMARY KEY,
metric_date DATE NOT NULL,
workflow_name VARCHAR(255),
-- Volume
total_executions INTEGER DEFAULT 0,
successful_executions INTEGER DEFAULT 0,
failed_executions INTEGER DEFAULT 0,
-- Performance
avg_duration_seconds INTEGER,
min_duration_seconds INTEGER,
max_duration_seconds INTEGER,
-- Reliability
success_rate_percentage DECIMAL(5, 2),
avg_retry_count DECIMAL(5, 2),
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(metric_date, workflow_name)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_workflows_enabled ON orchestrator_workflows(is_enabled, next_execution_at);
CREATE INDEX idx_executions_status ON orchestrator_executions(status, started_at);
CREATE INDEX idx_executions_workflow_status ON orchestrator_executions(workflow_id, status);
CREATE INDEX idx_metrics_date ON orchestrator_metrics(metric_date DESC);
```
## Business Logic Examples
### Daily Workflow Orchestration
```python
async def execute_daily_workflow():
"""
Main daily workflow executed at 8:00 AM Madrid time.
Coordinates forecasting, production, and procurement.
"""
workflow_name = "daily_operations"
execution_id = uuid.uuid4()
logger.info("Starting daily workflow", execution_id=str(execution_id))
# Create execution record
execution = OrchestratorExecution(
id=execution_id,
workflow_name=workflow_name,
execution_type='scheduled',
status='running',
started_at=datetime.utcnow()
)
db.add(execution)
await db.flush()
try:
# Get all active tenants
tenants = await db.query(Tenant).filter(
Tenant.status == 'active'
).all()
execution.steps_total = len(tenants) * 5 # 5 steps per tenant
for tenant in tenants:
try:
# Step 1: Generate forecasts
await log_step(execution_id, "generate_forecasts", tenant.id, "Starting forecast generation")
forecast_result = await trigger_forecasting(tenant.id)
await log_step(execution_id, "generate_forecasts", tenant.id, f"Generated {forecast_result['count']} forecasts")
execution.steps_completed += 1
# Step 2: Calculate production needs
await log_step(execution_id, "calculate_production", tenant.id, "Calculating production needs")
production_result = await trigger_production_planning(tenant.id)
await log_step(execution_id, "calculate_production", tenant.id, f"Planned {production_result['batches']} batches")
execution.steps_completed += 1
# Step 3: Calculate procurement needs
await log_step(execution_id, "calculate_procurement", tenant.id, "Calculating procurement needs")
procurement_result = await trigger_procurement_planning(tenant.id)
await log_step(execution_id, "calculate_procurement", tenant.id, f"Identified {procurement_result['needs_count']} procurement needs")
execution.steps_completed += 1
# Step 4: Generate inventory projections
await log_step(execution_id, "project_inventory", tenant.id, "Projecting inventory")
inventory_result = await trigger_inventory_projection(tenant.id)
await log_step(execution_id, "project_inventory", tenant.id, "Inventory projections completed")
execution.steps_completed += 1
# Step 5: Send daily summary
await log_step(execution_id, "send_summary", tenant.id, "Sending daily summary")
await send_daily_summary(tenant.id, {
'forecasts': forecast_result,
'production': production_result,
'procurement': procurement_result
})
await log_step(execution_id, "send_summary", tenant.id, "Daily summary sent")
execution.steps_completed += 1
except Exception as e:
execution.steps_failed += 1
await log_step(execution_id, "tenant_workflow", tenant.id, f"Failed: {str(e)}", level='error')
logger.error("Tenant workflow failed",
tenant_id=str(tenant.id),
error=str(e))
continue
# Mark execution complete
execution.status = 'completed'
execution.completed_at = datetime.utcnow()
execution.duration_seconds = int((execution.completed_at - execution.started_at).total_seconds())
await db.commit()
logger.info("Daily workflow completed",
execution_id=str(execution_id),
tenants_processed=len(tenants),
duration_seconds=execution.duration_seconds)
# Publish event
await publish_event('orchestrator', 'orchestrator.workflow_completed', {
'workflow_name': workflow_name,
'execution_id': str(execution_id),
'tenants_processed': len(tenants),
'steps_completed': execution.steps_completed,
'steps_failed': execution.steps_failed
})
except Exception as e:
execution.status = 'failed'
execution.error_message = str(e)
execution.completed_at = datetime.utcnow()
execution.duration_seconds = int((execution.completed_at - execution.started_at).total_seconds())
await db.commit()
logger.error("Daily workflow failed",
execution_id=str(execution_id),
error=str(e))
# Send alert
await send_workflow_failure_alert(workflow_name, str(e))
raise
async def trigger_forecasting(tenant_id: UUID) -> dict:
"""
Call forecasting service to generate forecasts.
"""
async with httpx.AsyncClient() as client:
response = await client.post(
f"{FORECASTING_SERVICE_URL}/api/v1/forecasting/generate",
json={'tenant_id': str(tenant_id), 'days_ahead': 7},
timeout=300.0
)
if response.status_code != 200:
raise Exception(f"Forecasting failed: {response.text}")
return response.json()
async def trigger_production_planning(tenant_id: UUID) -> dict:
"""
Call production service to generate production schedules.
"""
async with httpx.AsyncClient() as client:
response = await client.post(
f"{PRODUCTION_SERVICE_URL}/api/v1/production/schedules/generate",
json={'tenant_id': str(tenant_id)},
timeout=180.0
)
if response.status_code != 200:
raise Exception(f"Production planning failed: {response.text}")
return response.json()
async def trigger_procurement_planning(tenant_id: UUID) -> dict:
"""
Call procurement service to calculate needs.
"""
async with httpx.AsyncClient() as client:
response = await client.post(
f"{PROCUREMENT_SERVICE_URL}/api/v1/procurement/needs/calculate",
json={'tenant_id': str(tenant_id), 'days_ahead': 14},
timeout=180.0
)
if response.status_code != 200:
raise Exception(f"Procurement planning failed: {response.text}")
return response.json()
```
### Leader Election
```python
async def start_leader_election():
"""
Participate in leader election using Redis.
Only the leader executes workflows.
"""
instance_id = f"{socket.gethostname()}_{uuid.uuid4().hex[:8]}"
while True:
try:
# Try to become leader
is_leader = await try_become_leader(instance_id)
if is_leader:
logger.info("This instance is the leader", instance_id=instance_id)
# Start workflow scheduler
await start_workflow_scheduler()
# Maintain leadership with heartbeats
while True:
await asyncio.sleep(30) # Heartbeat every 30 seconds
if not await maintain_leadership(instance_id):
logger.warning("Lost leadership", instance_id=instance_id)
break
else:
# Not leader, check again in 60 seconds
logger.info("This instance is a follower", instance_id=instance_id)
await asyncio.sleep(60)
except Exception as e:
logger.error("Leader election error",
instance_id=instance_id,
error=str(e))
await asyncio.sleep(60)
async def try_become_leader(instance_id: str) -> bool:
"""
Try to acquire leadership using Redis lock.
"""
# Try to set leader lock in Redis
lock_key = "orchestrator:leader:lock"
lock_acquired = await redis.set(
lock_key,
instance_id,
ex=90, # Expire in 90 seconds
nx=True # Only set if not exists
)
if lock_acquired:
# Record in database
leader = await db.query(OrchestratorLeader).filter(
OrchestratorLeader.id == 1
).first()
if not leader:
leader = OrchestratorLeader(
id=1,
instance_id=instance_id,
instance_hostname=socket.gethostname(),
became_leader_at=datetime.utcnow(),
last_heartbeat_at=datetime.utcnow()
)
db.add(leader)
else:
leader.instance_id = instance_id
leader.instance_hostname = socket.gethostname()
leader.became_leader_at = datetime.utcnow()
leader.last_heartbeat_at = datetime.utcnow()
await db.commit()
return True
return False
async def maintain_leadership(instance_id: str) -> bool:
"""
Maintain leadership by refreshing Redis lock.
"""
lock_key = "orchestrator:leader:lock"
# Check if we still hold the lock
current_leader = await redis.get(lock_key)
if current_leader != instance_id:
return False
# Refresh lock
await redis.expire(lock_key, 90)
# Update heartbeat
leader = await db.query(OrchestratorLeader).filter(
OrchestratorLeader.id == 1
).first()
if leader and leader.instance_id == instance_id:
leader.last_heartbeat_at = datetime.utcnow()
await db.commit()
return True
return False
```
### Workflow Scheduler
```python
async def start_workflow_scheduler():
"""
Start APScheduler to execute workflows on schedule.
"""
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from apscheduler.triggers.cron import CronTrigger
scheduler = AsyncIOScheduler(timezone='Europe/Madrid')
# Get workflow configurations
workflows = await db.query(OrchestratorWorkflow).filter(
OrchestratorWorkflow.is_enabled == True
).all()
for workflow in workflows:
# Parse cron expression
trigger = CronTrigger.from_crontab(workflow.cron_expression, timezone=workflow.timezone)
# Add job to scheduler
scheduler.add_job(
execute_workflow,
trigger=trigger,
args=[workflow.id],
id=str(workflow.id),
name=workflow.workflow_name,
max_instances=1, # Prevent concurrent executions
replace_existing=True
)
logger.info("Scheduled workflow",
workflow_name=workflow.workflow_name,
cron=workflow.cron_expression)
# Start scheduler
scheduler.start()
logger.info("Workflow scheduler started")
# Keep scheduler running
while True:
await asyncio.sleep(3600) # Check every hour
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `orchestrator`
**Routing Keys**: `orchestrator.workflow_completed`, `orchestrator.workflow_failed`
**Workflow Completed Event**
```json
{
"event_type": "orchestrator_workflow_completed",
"workflow_name": "daily_operations",
"execution_id": "uuid",
"tenants_processed": 125,
"steps_completed": 625,
"steps_failed": 3,
"duration_seconds": 1820,
"timestamp": "2025-11-06T08:30:20Z"
}
```
**Workflow Failed Event**
```json
{
"event_type": "orchestrator_workflow_failed",
"workflow_name": "daily_operations",
"execution_id": "uuid",
"error_message": "Database connection timeout",
"tenants_affected": 45,
"timestamp": "2025-11-06T08:15:30Z"
}
```
### Consumed Events
None - Orchestrator initiates workflows but doesn't consume events
## Custom Metrics (Prometheus)
```python
# Workflow metrics
workflow_executions_total = Counter(
'orchestrator_workflow_executions_total',
'Total workflow executions',
['workflow_name', 'status']
)
workflow_duration_seconds = Histogram(
'orchestrator_workflow_duration_seconds',
'Workflow execution duration',
['workflow_name'],
buckets=[60, 300, 600, 1200, 1800, 3600]
)
workflow_success_rate = Gauge(
'orchestrator_workflow_success_rate_percentage',
'Workflow success rate',
['workflow_name']
)
tenants_processed_total = Counter(
'orchestrator_tenants_processed_total',
'Total tenants processed',
['workflow_name', 'status']
)
leader_instance = Gauge(
'orchestrator_leader_instance',
'Current leader instance (1=leader, 0=follower)',
['instance_id']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8018)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Workflow Configuration:**
- `DAILY_WORKFLOW_CRON` - Daily workflow schedule (default: "0 8 * * *")
- `WEEKLY_WORKFLOW_CRON` - Weekly workflow schedule (default: "0 9 * * 1")
- `DEFAULT_TIMEZONE` - Default timezone (default: "Europe/Madrid")
- `MAX_WORKFLOW_DURATION_SECONDS` - Max execution time (default: 3600)
**Leader Election:**
- `ENABLE_LEADER_ELECTION` - Enable HA mode (default: true)
- `LEADER_HEARTBEAT_SECONDS` - Heartbeat interval (default: 30)
- `LEADER_LOCK_TTL_SECONDS` - Lock expiration (default: 90)
**Service URLs:**
- `FORECASTING_SERVICE_URL` - Forecasting service URL
- `PRODUCTION_SERVICE_URL` - Production service URL
- `PROCUREMENT_SERVICE_URL` - Procurement service URL
- `INVENTORY_SERVICE_URL` - Inventory service URL
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/orchestrator
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/orchestrator
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export FORECASTING_SERVICE_URL=http://localhost:8003
export PRODUCTION_SERVICE_URL=http://localhost:8007
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **All Services** - Calls service APIs to execute workflows
- **Redis** - Leader election and caching
- **PostgreSQL** - Workflow history
- **RabbitMQ** - Event publishing
### Dependents
- **All Services** - Benefit from automated workflows
- **Monitoring** - Tracks workflow execution
## Business Value for VUE Madrid
### Problem Statement
Manual daily operations don't scale:
- Staff forget to generate forecasts daily
- Production planning done inconsistently
- Procurement needs identified too late
- Reports generated manually
- No weekend/holiday coverage
- Human error in execution
### Solution
Bakery-IA Orchestrator provides:
- **Fully Automated**: 95%+ operations automated
- **Consistent Execution**: 100% vs. 70-80% manual
- **Early Morning Ready**: Data ready before business opens
- **365-Day Coverage**: Works weekends and holidays
- **Error Recovery**: Automatic retries
- **Scalable**: Handles 10,000+ tenants
### Quantifiable Impact
**Time Savings:**
- 15-20 hours/week per bakery on manual planning
- €900-1,200/month labor cost savings per bakery
- 100% consistency vs. 70-80% manual execution
**Operational Excellence:**
- 99.9%+ workflow success rate
- Issues identified before business hours
- Zero forgotten forecasts or plans
- Predictable daily operations
**Platform Scalability:**
- Linear cost scaling with tenants
- 10,000+ tenant capacity with one orchestrator
- €0.01-0.05 per tenant per day computational cost
- High availability with leader election
### ROI for Platform
**Investment**: €50-200/month (compute + infrastructure)
**Value Delivered**: €900-1,200/month per tenant
**Platform Scale**: €90,000-120,000/month at 100 tenants
**Cost Ratio**: <1% of value delivered
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Orders Service
## Overview
The **Orders Service** manages the complete customer order lifecycle from creation to fulfillment, tracking custom orders, wholesale orders, and direct sales. It maintains a comprehensive customer database with purchase history, enables order scheduling for pickup/delivery, and provides analytics on customer behavior and order patterns. This service is essential for B2B relationships with restaurants and hotels, as well as managing special orders for events and celebrations.
## Key Features
### Order Management
- **Multi-Channel Orders** - In-store, phone, online, wholesale
- **Order Lifecycle Tracking** - From pending to completed/cancelled
- **Custom Orders** - Special requests for events and celebrations
- **Recurring Orders** - Automated weekly/monthly orders for B2B
- **Order Scheduling** - Pickup/delivery date and time management
- **Order Priority** - Rush orders vs. standard processing
- **Order Status Updates** - Real-time status with customer notifications
### Customer Database
- **Customer Profiles** - Complete contact and preference information
- **Purchase History** - Track all orders per customer
- **Customer Segmentation** - B2B vs. B2C, loyalty tiers
- **Customer Preferences** - Favorite products, allergen notes
- **Credit Terms** - Payment terms for wholesale customers
- **Customer Analytics** - RFM analysis (Recency, Frequency, Monetary)
- **Customer Lifetime Value** - Total value per customer
### B2B Wholesale Management
- **Wholesale Pricing** - Custom pricing per B2B customer
- **Volume Discounts** - Automatic tier-based discounts
- **Delivery Routes** - Optimize delivery scheduling
- **Invoice Generation** - Automated invoicing with payment terms
- **Standing Orders** - Repeat orders without manual entry
- **Account Management** - Credit limits and payment tracking
### Order Fulfillment
- **Production Integration** - Orders trigger production planning
- **Inventory Reservation** - Reserve stock for confirmed orders
- **Fulfillment Status** - Track preparation and delivery
- **Delivery Management** - Route planning and tracking
- **Order Picking Lists** - Generate lists for warehouse staff
- **Quality Control** - Pre-delivery quality checks
### Payment Tracking
- **Payment Methods** - Cash, card, transfer, credit terms
- **Payment Status** - Paid, pending, overdue
- **Partial Payments** - Split payments over time
- **Invoice History** - Complete payment records
- **Overdue Alerts** - Automatic reminders for B2B accounts
- **Revenue Recognition** - Track revenue per order
### Analytics & Reporting
- **Order Dashboard** - Real-time order metrics
- **Customer Analytics** - Top customers, retention rates
- **Product Analytics** - Most ordered products
- **Revenue Analytics** - Daily/weekly/monthly revenue
- **Order Source Analysis** - Channel performance
- **Delivery Performance** - On-time delivery rates
## Business Value
### For Bakery Owners
- **Revenue Growth** - Better customer relationships drive repeat business
- **B2B Efficiency** - Automate wholesale order management
- **Cash Flow** - Track outstanding payments and credit terms
- **Customer Retention** - Purchase history enables personalized service
- **Order Accuracy** - Digital orders reduce errors vs. phone/paper
- **Analytics** - Understand customer behavior for marketing
### Quantifiable Impact
- **Revenue Growth**: 10-20% through improved B2B relationships
- **Time Savings**: 5-8 hours/week on order management
- **Order Accuracy**: 99%+ vs. 85-90% manual (phone/paper)
- **Payment Collection**: 30% faster with automated reminders
- **Customer Retention**: 15-25% improvement with history tracking
- **B2B Efficiency**: 50-70% time reduction on wholesale orders
### For Sales Staff
- **Quick Order Entry** - Fast order creation with customer lookup
- **Customer History** - See previous orders for upselling
- **Pricing Accuracy** - Automatic wholesale pricing application
- **Order Tracking** - Know exactly when orders will be ready
- **Customer Notes** - Allergen info and preferences visible
### For Customers
- **Order Confirmation** - Immediate confirmation with details
- **Order Tracking** - Real-time status updates
- **Order History** - View and repeat previous orders
- **Flexible Scheduling** - Choose pickup/delivery times
- **Payment Options** - Multiple payment methods
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Order and customer data
- **Caching**: Redis 7.4 - Customer and order cache
- **Messaging**: RabbitMQ 4.1 - Order event publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Validation**: Pydantic 2.0 - Schema validation
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Order metrics
## API Endpoints (Key Routes)
### Order Management
- `GET /api/v1/orders` - List orders with filters
- `POST /api/v1/orders` - Create new order
- `GET /api/v1/orders/{order_id}` - Get order details
- `PUT /api/v1/orders/{order_id}` - Update order
- `DELETE /api/v1/orders/{order_id}` - Cancel order
- `PUT /api/v1/orders/{order_id}/status` - Update order status
- `POST /api/v1/orders/{order_id}/complete` - Mark order complete
### Order Items
- `GET /api/v1/orders/{order_id}/items` - List order items
- `POST /api/v1/orders/{order_id}/items` - Add item to order
- `PUT /api/v1/orders/{order_id}/items/{item_id}` - Update order item
- `DELETE /api/v1/orders/{order_id}/items/{item_id}` - Remove item
### Customer Management
- `GET /api/v1/customers` - List customers with filters
- `POST /api/v1/customers` - Create new customer
- `GET /api/v1/customers/{customer_id}` - Get customer details
- `PUT /api/v1/customers/{customer_id}` - Update customer
- `GET /api/v1/customers/{customer_id}/orders` - Get customer order history
- `GET /api/v1/customers/{customer_id}/analytics` - Customer analytics
### Wholesale Management
- `GET /api/v1/orders/wholesale` - List wholesale orders
- `POST /api/v1/orders/wholesale/recurring` - Create recurring order
- `GET /api/v1/orders/wholesale/invoices` - List invoices
- `POST /api/v1/orders/wholesale/invoices/{invoice_id}/send` - Send invoice
- `GET /api/v1/orders/wholesale/overdue` - List overdue payments
### Fulfillment
- `GET /api/v1/orders/fulfillment/pending` - Orders pending fulfillment
- `POST /api/v1/orders/{order_id}/prepare` - Start order preparation
- `POST /api/v1/orders/{order_id}/ready` - Mark order ready
- `POST /api/v1/orders/{order_id}/deliver` - Mark order delivered
- `GET /api/v1/orders/fulfillment/picking-list` - Generate picking list
### Analytics
- `GET /api/v1/orders/analytics/dashboard` - Order dashboard KPIs
- `GET /api/v1/orders/analytics/revenue` - Revenue analytics
- `GET /api/v1/orders/analytics/customers/top` - Top customers
- `GET /api/v1/orders/analytics/products/popular` - Most ordered products
- `GET /api/v1/orders/analytics/channels` - Order channel breakdown
## Database Schema
### Main Tables
**customers**
```sql
CREATE TABLE customers (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
customer_type VARCHAR(50) NOT NULL, -- retail, wholesale, restaurant, hotel
business_name VARCHAR(255), -- For B2B customers
contact_name VARCHAR(255) NOT NULL,
email VARCHAR(255),
phone VARCHAR(50) NOT NULL,
secondary_phone VARCHAR(50),
address_line1 VARCHAR(255),
address_line2 VARCHAR(255),
city VARCHAR(100),
postal_code VARCHAR(20),
country VARCHAR(100) DEFAULT 'España',
tax_id VARCHAR(50), -- CIF/NIF for businesses
credit_limit DECIMAL(10, 2), -- For B2B customers
credit_term_days INTEGER DEFAULT 0, -- Payment terms (e.g., Net 30)
payment_status VARCHAR(50) DEFAULT 'good_standing', -- good_standing, overdue, suspended
customer_notes TEXT,
allergen_notes TEXT,
preferred_contact_method VARCHAR(50), -- email, phone, whatsapp
loyalty_tier VARCHAR(50) DEFAULT 'standard', -- standard, silver, gold, platinum
total_lifetime_value DECIMAL(12, 2) DEFAULT 0.00,
total_orders INTEGER DEFAULT 0,
last_order_date DATE,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, email),
UNIQUE(tenant_id, phone)
);
```
**orders**
```sql
CREATE TABLE orders (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
order_number VARCHAR(100) NOT NULL, -- Human-readable order number
customer_id UUID REFERENCES customers(id),
order_type VARCHAR(50) NOT NULL, -- retail, wholesale, custom, standing
order_source VARCHAR(50), -- in_store, phone, online, email
status VARCHAR(50) DEFAULT 'pending', -- pending, confirmed, preparing, ready, completed, cancelled
priority VARCHAR(50) DEFAULT 'standard', -- rush, standard, scheduled
order_date DATE NOT NULL DEFAULT CURRENT_DATE,
requested_date DATE, -- Pickup/delivery date
requested_time TIME, -- Pickup/delivery time
fulfilled_date DATE,
subtotal DECIMAL(10, 2) NOT NULL DEFAULT 0.00,
discount_amount DECIMAL(10, 2) DEFAULT 0.00,
tax_amount DECIMAL(10, 2) DEFAULT 0.00,
total_amount DECIMAL(10, 2) NOT NULL DEFAULT 0.00,
payment_method VARCHAR(50), -- cash, card, transfer, credit
payment_status VARCHAR(50) DEFAULT 'unpaid', -- unpaid, paid, partial, overdue
payment_due_date DATE,
delivery_method VARCHAR(50), -- pickup, delivery, shipping
delivery_address TEXT,
delivery_notes TEXT,
internal_notes TEXT,
created_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, order_number)
);
```
**order_items**
```sql
CREATE TABLE order_items (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
order_id UUID REFERENCES orders(id) ON DELETE CASCADE,
product_id UUID NOT NULL,
product_name VARCHAR(255) NOT NULL, -- Cached for performance
quantity DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
unit_price DECIMAL(10, 2) NOT NULL,
discount_percentage DECIMAL(5, 2) DEFAULT 0.00,
line_total DECIMAL(10, 2) NOT NULL,
custom_instructions TEXT,
recipe_id UUID, -- Link to recipe if applicable
production_batch_id UUID, -- Link to production batch
fulfilled_quantity DECIMAL(10, 2) DEFAULT 0.00,
fulfillment_status VARCHAR(50) DEFAULT 'pending', -- pending, reserved, prepared, fulfilled
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**customer_pricing**
```sql
CREATE TABLE customer_pricing (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
customer_id UUID REFERENCES customers(id) ON DELETE CASCADE,
product_id UUID NOT NULL,
custom_price DECIMAL(10, 2) NOT NULL,
discount_percentage DECIMAL(5, 2),
min_quantity DECIMAL(10, 2), -- Minimum order quantity for price
valid_from DATE DEFAULT CURRENT_DATE,
valid_until DATE,
is_active BOOLEAN DEFAULT TRUE,
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, customer_id, product_id)
);
```
**recurring_orders**
```sql
CREATE TABLE recurring_orders (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
customer_id UUID REFERENCES customers(id) ON DELETE CASCADE,
recurring_name VARCHAR(255) NOT NULL,
frequency VARCHAR(50) NOT NULL, -- daily, weekly, biweekly, monthly
delivery_day VARCHAR(50), -- Monday, Tuesday, etc.
delivery_time TIME,
order_items JSONB NOT NULL, -- Array of {product_id, quantity, unit}
is_active BOOLEAN DEFAULT TRUE,
next_order_date DATE,
last_generated_order_id UUID,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**order_status_history**
```sql
CREATE TABLE order_status_history (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
order_id UUID REFERENCES orders(id) ON DELETE CASCADE,
from_status VARCHAR(50),
to_status VARCHAR(50) NOT NULL,
changed_by UUID NOT NULL,
notes TEXT,
changed_at TIMESTAMP DEFAULT NOW()
);
```
**invoices**
```sql
CREATE TABLE invoices (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
invoice_number VARCHAR(100) NOT NULL,
order_id UUID REFERENCES orders(id),
customer_id UUID REFERENCES customers(id),
invoice_date DATE NOT NULL DEFAULT CURRENT_DATE,
due_date DATE NOT NULL,
subtotal DECIMAL(10, 2) NOT NULL,
tax_amount DECIMAL(10, 2) NOT NULL,
total_amount DECIMAL(10, 2) NOT NULL,
amount_paid DECIMAL(10, 2) DEFAULT 0.00,
amount_due DECIMAL(10, 2) NOT NULL,
status VARCHAR(50) DEFAULT 'sent', -- draft, sent, paid, overdue, cancelled
payment_terms VARCHAR(255),
notes TEXT,
sent_at TIMESTAMP,
paid_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, invoice_number)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_orders_tenant_status ON orders(tenant_id, status);
CREATE INDEX idx_orders_customer ON orders(customer_id);
CREATE INDEX idx_orders_date ON orders(tenant_id, order_date DESC);
CREATE INDEX idx_orders_requested_date ON orders(tenant_id, requested_date);
CREATE INDEX idx_customers_tenant_type ON customers(tenant_id, customer_type);
CREATE INDEX idx_order_items_order ON order_items(order_id);
CREATE INDEX idx_order_items_product ON order_items(tenant_id, product_id);
CREATE INDEX idx_invoices_status ON invoices(tenant_id, status);
CREATE INDEX idx_invoices_due_date ON invoices(tenant_id, due_date) WHERE status != 'paid';
```
## Business Logic Examples
### Order Creation with Pricing
```python
async def create_order(order_data: OrderCreate, current_user: User) -> Order:
"""
Create new order with automatic pricing and customer detection.
"""
# Get or create customer
customer = await get_or_create_customer(
order_data.customer_phone,
order_data.customer_name,
order_data.customer_email
)
# Generate order number
order_number = await generate_order_number(current_user.tenant_id)
# Create order
order = Order(
tenant_id=current_user.tenant_id,
order_number=order_number,
customer_id=customer.id,
order_type=order_data.order_type,
order_source=order_data.order_source,
status='pending',
order_date=date.today(),
requested_date=order_data.requested_date,
created_by=current_user.id
)
db.add(order)
await db.flush() # Get order.id
# Add order items with pricing
subtotal = Decimal('0.00')
for item_data in order_data.items:
# Get product price
base_price = await get_product_price(item_data.product_id)
# Check for customer-specific pricing
custom_price = await get_customer_price(
customer.id,
item_data.product_id,
item_data.quantity
)
unit_price = custom_price if custom_price else base_price
# Apply wholesale discount if applicable
if customer.customer_type == 'wholesale':
discount_pct = await calculate_volume_discount(
item_data.product_id,
item_data.quantity
)
else:
discount_pct = Decimal('0.00')
# Calculate line total
line_total = (unit_price * item_data.quantity) * (1 - discount_pct / 100)
# Create order item
order_item = OrderItem(
tenant_id=current_user.tenant_id,
order_id=order.id,
product_id=item_data.product_id,
product_name=item_data.product_name,
quantity=item_data.quantity,
unit=item_data.unit,
unit_price=unit_price,
discount_percentage=discount_pct,
line_total=line_total
)
db.add(order_item)
subtotal += line_total
# Calculate tax (e.g., Spanish IVA 10% for food)
tax_rate = Decimal('0.10')
tax_amount = subtotal * tax_rate
total_amount = subtotal + tax_amount
# Update order totals
order.subtotal = subtotal
order.tax_amount = tax_amount
order.total_amount = total_amount
# Set payment terms for B2B
if customer.customer_type == 'wholesale':
order.payment_due_date = date.today() + timedelta(days=customer.credit_term_days)
order.payment_status = 'unpaid'
else:
order.payment_status = 'paid' # Retail assumes immediate payment
await db.commit()
await db.refresh(order)
# Publish order created event
await publish_event('orders', 'order.created', {
'order_id': str(order.id),
'customer_id': str(customer.id),
'total_amount': float(order.total_amount),
'requested_date': order.requested_date.isoformat() if order.requested_date else None
})
return order
```
### Recurring Order Generation
```python
async def generate_recurring_orders(tenant_id: UUID):
"""
Generate orders from recurring order templates.
Run daily via orchestrator.
"""
# Get active recurring orders due today
today = date.today()
recurring_orders = await db.query(RecurringOrder).filter(
RecurringOrder.tenant_id == tenant_id,
RecurringOrder.is_active == True,
RecurringOrder.next_order_date <= today
).all()
generated_count = 0
for recurring in recurring_orders:
try:
# Create order from template
order = Order(
tenant_id=tenant_id,
order_number=await generate_order_number(tenant_id),
customer_id=recurring.customer_id,
order_type='standing',
order_source='auto_recurring',
status='confirmed',
order_date=today,
requested_date=recurring.next_order_date,
requested_time=recurring.delivery_time
)
db.add(order)
await db.flush()
# Add items from template
subtotal = Decimal('0.00')
for item_template in recurring.order_items:
product_price = await get_product_price(item_template['product_id'])
line_total = product_price * Decimal(str(item_template['quantity']))
order_item = OrderItem(
tenant_id=tenant_id,
order_id=order.id,
product_id=UUID(item_template['product_id']),
product_name=item_template['product_name'],
quantity=Decimal(str(item_template['quantity'])),
unit=item_template['unit'],
unit_price=product_price,
line_total=line_total
)
db.add(order_item)
subtotal += line_total
# Calculate totals
tax_amount = subtotal * Decimal('0.10')
order.subtotal = subtotal
order.tax_amount = tax_amount
order.total_amount = subtotal + tax_amount
# Update recurring order
recurring.last_generated_order_id = order.id
recurring.next_order_date = calculate_next_order_date(
recurring.next_order_date,
recurring.frequency
)
await db.commit()
generated_count += 1
# Publish event
await publish_event('orders', 'recurring_order.generated', {
'order_id': str(order.id),
'recurring_order_id': str(recurring.id),
'customer_id': str(recurring.customer_id)
})
except Exception as e:
logger.error("Failed to generate recurring order",
recurring_id=str(recurring.id),
error=str(e))
continue
logger.info("Generated recurring orders",
tenant_id=str(tenant_id),
count=generated_count)
return generated_count
```
### Customer RFM Analysis
```python
async def calculate_customer_rfm(customer_id: UUID) -> dict:
"""
Calculate RFM (Recency, Frequency, Monetary) metrics for customer.
"""
# Get customer orders
orders = await db.query(Order).filter(
Order.customer_id == customer_id,
Order.status.in_(['completed'])
).order_by(Order.order_date.desc()).all()
if not orders:
return {"rfm_score": 0, "segment": "inactive"}
# Recency: Days since last order
last_order_date = orders[0].order_date
recency_days = (date.today() - last_order_date).days
# Frequency: Number of orders in last 365 days
one_year_ago = date.today() - timedelta(days=365)
recent_orders = [o for o in orders if o.order_date >= one_year_ago]
frequency = len(recent_orders)
# Monetary: Total spend in last 365 days
monetary = sum(o.total_amount for o in recent_orders)
# Score each dimension (1-5 scale)
recency_score = 5 if recency_days <= 30 else \
4 if recency_days <= 60 else \
3 if recency_days <= 90 else \
2 if recency_days <= 180 else 1
frequency_score = 5 if frequency >= 12 else \
4 if frequency >= 6 else \
3 if frequency >= 3 else \
2 if frequency >= 1 else 1
monetary_score = 5 if monetary >= 5000 else \
4 if monetary >= 2000 else \
3 if monetary >= 500 else \
2 if monetary >= 100 else 1
# Overall RFM score
rfm_score = (recency_score + frequency_score + monetary_score) / 3
# Customer segment
if rfm_score >= 4.5:
segment = "champion"
elif rfm_score >= 3.5:
segment = "loyal"
elif rfm_score >= 2.5:
segment = "potential"
elif rfm_score >= 1.5:
segment = "at_risk"
else:
segment = "inactive"
return {
"rfm_score": round(rfm_score, 2),
"recency_days": recency_days,
"recency_score": recency_score,
"frequency": frequency,
"frequency_score": frequency_score,
"monetary": float(monetary),
"monetary_score": monetary_score,
"segment": segment
}
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `orders`
**Routing Keys**: `orders.created`, `orders.completed`, `orders.cancelled`, `orders.overdue`
**Order Created Event**
```json
{
"event_type": "order_created",
"tenant_id": "uuid",
"order_id": "uuid",
"order_number": "ORD-2025-1106-001",
"customer_id": "uuid",
"customer_name": "Restaurante El Prado",
"order_type": "wholesale",
"total_amount": 450.00,
"requested_date": "2025-11-07",
"requested_time": "06:00:00",
"item_count": 12,
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Order Completed Event**
```json
{
"event_type": "order_completed",
"tenant_id": "uuid",
"order_id": "uuid",
"order_number": "ORD-2025-1106-001",
"customer_id": "uuid",
"total_amount": 450.00,
"payment_status": "paid",
"completed_at": "2025-11-07T06:15:00Z",
"timestamp": "2025-11-07T06:15:00Z"
}
```
**Payment Overdue Alert**
```json
{
"event_type": "payment_overdue",
"tenant_id": "uuid",
"invoice_id": "uuid",
"invoice_number": "INV-2025-1106-001",
"customer_id": "uuid",
"customer_name": "Hotel Gran Vía",
"amount_due": 850.00,
"days_overdue": 15,
"due_date": "2025-10-22",
"timestamp": "2025-11-06T09:00:00Z"
}
```
### Consumed Events
- **From Production**: Batch completion updates order fulfillment status
- **From Inventory**: Stock availability affects order confirmation
- **From Forecasting**: Demand forecasts inform production for pending orders
## Custom Metrics (Prometheus)
```python
# Order metrics
orders_total = Counter(
'orders_total',
'Total orders created',
['tenant_id', 'order_type', 'order_source', 'status']
)
order_value_euros = Histogram(
'order_value_euros',
'Order value distribution',
['tenant_id', 'order_type'],
buckets=[10, 25, 50, 100, 200, 500, 1000, 2000, 5000]
)
# Customer metrics
customers_total = Gauge(
'customers_total',
'Total customers',
['tenant_id', 'customer_type']
)
customer_lifetime_value_euros = Histogram(
'customer_lifetime_value_euros',
'Customer lifetime value distribution',
['tenant_id', 'customer_type'],
buckets=[100, 500, 1000, 2000, 5000, 10000, 20000, 50000]
)
# Fulfillment metrics
order_fulfillment_time_hours = Histogram(
'order_fulfillment_time_hours',
'Time from order to fulfillment',
['tenant_id', 'order_type'],
buckets=[1, 6, 12, 24, 48, 72]
)
# Payment metrics
invoice_payment_time_days = Histogram(
'invoice_payment_time_days',
'Days from invoice to payment',
['tenant_id'],
buckets=[0, 7, 14, 21, 30, 45, 60, 90]
)
overdue_invoices_total = Gauge(
'overdue_invoices_total',
'Total overdue invoices',
['tenant_id']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8010)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Order Configuration:**
- `AUTO_CONFIRM_RETAIL_ORDERS` - Auto-confirm retail orders (default: true)
- `ORDER_NUMBER_PREFIX` - Order number prefix (default: "ORD")
- `DEFAULT_TAX_RATE` - Default tax rate (default: 0.10 for Spain's 10% IVA)
- `ENABLE_RECURRING_ORDERS` - Enable recurring order generation (default: true)
**Payment Configuration:**
- `DEFAULT_CREDIT_TERMS_DAYS` - Default payment terms (default: 30)
- `OVERDUE_ALERT_THRESHOLD_DAYS` - Days before overdue alert (default: 7)
- `MAX_CREDIT_LIMIT` - Maximum credit limit per customer (default: 10000.00)
**Notification:**
- `SEND_ORDER_CONFIRMATION` - Send order confirmation to customer (default: true)
- `SEND_READY_NOTIFICATION` - Notify when order ready (default: true)
- `SEND_OVERDUE_REMINDERS` - Send overdue payment reminders (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/orders
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/orders
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Customers Service** - Customer data (if separate)
- **Products Service** - Product catalog and pricing
- **Inventory Service** - Stock availability checks
- **Production Service** - Production planning for orders
- **Auth Service** - User authentication
- **PostgreSQL** - Order and customer data
- **Redis** - Caching
- **RabbitMQ** - Event publishing
### Dependents
- **Production Service** - Orders trigger production planning
- **Inventory Service** - Orders reserve stock
- **Invoicing/Accounting** - Financial reporting
- **Notification Service** - Order confirmations and alerts
- **AI Insights Service** - Customer behavior analysis
- **Frontend Dashboard** - Order management UI
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Manual order tracking on paper or spreadsheets
- Lost orders and miscommunication (especially phone orders)
- No customer purchase history for relationship management
- Complex wholesale order management with multiple B2B clients
- Overdue payment tracking for credit accounts
- No analytics on customer behavior or product popularity
### Solution
Bakery-IA Orders Service provides:
- **Digital Order Management**: Capture all orders across channels
- **Customer Database**: Complete purchase history and preferences
- **B2B Automation**: Recurring orders and automated invoicing
- **Payment Tracking**: Monitor outstanding payments with alerts
- **Analytics**: Customer segmentation and product performance
### Quantifiable Impact
**Revenue Growth:**
- 10-20% revenue increase through improved B2B relationships
- 5-10% from reduced lost orders (99% order accuracy)
- 15-25% customer retention improvement with history tracking
- **Total: €300-600/month additional revenue per bakery**
**Time Savings:**
- 5-8 hours/week on order management and tracking
- 2-3 hours/week on invoicing and payment follow-up
- 1-2 hours/week on customer lookup and history
- **Total: 8-13 hours/week saved**
**Financial Performance:**
- 30% faster payment collection (overdue alerts)
- 50-70% time reduction on wholesale order processing
- 99%+ order accuracy vs. 85-90% manual
### Target Market Fit (Spanish Bakeries)
- **B2B Focus**: Many Spanish bakeries supply restaurants, hotels, cafés
- **Payment Terms**: Spanish B2B typically uses Net 30-60 payment terms
- **Relationship-Driven**: Customer history critical for Spanish business culture
- **Regulatory**: Spanish tax law requires proper invoicing and records
### ROI Calculation
**Investment**: €0 additional (included in platform subscription)
**Monthly Value**: €300-600 additional revenue + cost savings
**Annual ROI**: €3,600-7,200 value per bakery
**Payback**: Immediate (included in subscription)
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# POS Service
## Overview
The **POS (Point of Sale) Service** integrates with popular POS systems like Square, Toast, and Lightspeed to automatically sync sales transactions into Bakery-IA. It eliminates manual sales data entry, ensures real-time sales tracking, and provides the foundation for accurate demand forecasting. This service bridges the gap between retail operations and business intelligence, making the platform immediately valuable for bakeries already using modern POS systems.
## Key Features
### Multi-POS Integration
- **Square Integration** - Full API integration with Square POS
- **Toast Integration** - Restaurant POS system integration
- **Lightspeed Integration** - Retail POS system integration
- **Webhook Support** - Real-time transaction sync via webhooks
- **OAuth Authentication** - Secure POS account linking
- **Multi-Location Support** - Handle multiple store locations
- **Automatic Reconnection** - Handle API token expiration gracefully
### Sales Data Synchronization
- **Real-Time Sync** - Transactions sync within seconds
- **Historical Import** - Import past sales data on initial setup
- **Product Mapping** - Map POS products to Bakery-IA products
- **Transaction Deduplication** - Prevent duplicate entries
- **Data Validation** - Ensure data quality and accuracy
- **Sync Status Tracking** - Monitor sync health and errors
- **Manual Sync Trigger** - Force sync on demand
### Transaction Processing
- **Line Item Details** - Product, quantity, price per transaction
- **Payment Methods** - Cash, card, contactless tracking
- **Customer Data** - Customer name, email if available
- **Discounts & Taxes** - Full transaction details preserved
- **Refunds & Voids** - Handle transaction cancellations
- **Tips & Gratuities** - Track additional revenue
- **Transaction Metadata** - Store name, cashier, timestamp
### Product Catalog Sync
- **Product Import** - Sync product catalog from POS
- **Category Mapping** - Map POS categories to Bakery-IA
- **Price Sync** - Keep prices updated
- **Product Updates** - Detect new products automatically
- **SKU Matching** - Match by SKU, name, or manual mapping
- **Inventory Integration** - Link POS products to inventory items
### Analytics & Monitoring
- **Sync Dashboard** - Monitor sync status across POS systems
- **Error Tracking** - Log and alert on sync failures
- **Data Quality Metrics** - Track unmapped products, errors
- **Sync Performance** - Monitor sync speed and latency
- **Transaction Volume** - Daily/hourly transaction counts
- **API Health Monitoring** - Track POS API availability
### Configuration Management
- **POS Account Linking** - Connect POS accounts via OAuth
- **Mapping Configuration** - Product and category mappings
- **Sync Schedule** - Configure sync frequency
- **Webhook Management** - Register/update webhook endpoints
- **API Credentials** - Secure storage of API keys
- **Multi-Tenant Isolation** - Separate POS accounts per tenant
## Business Value
### For Bakery Owners
- **Zero Manual Entry** - Sales automatically sync to Bakery-IA
- **Real-Time Visibility** - Know sales performance instantly
- **Accurate Forecasting** - ML models use actual sales data
- **Time Savings** - Eliminate daily sales data entry
- **Data Accuracy** - 99.9%+ vs. manual entry errors
- **Immediate ROI** - Value from day one of POS connection
### Quantifiable Impact
- **Time Savings**: 5-8 hours/week eliminating manual entry
- **Data Accuracy**: 99.9%+ vs. 85-95% manual entry
- **Forecast Improvement**: 10-20% better accuracy with real data
- **Revenue Tracking**: Real-time vs. end-of-day manual reconciliation
- **Setup Time**: 15 minutes to connect vs. hours of manual entry
- **Error Elimination**: Zero transcription errors
### For Sales Staff
- **No Extra Work** - POS integration is invisible to staff
- **Focus on Customers** - No post-sale data entry
- **Instant Reporting** - Managers see sales in real-time
### For Managers
- **Real-Time Dashboards** - Sales performance updates live
- **Product Performance** - Know what's selling instantly
- **Multi-Store Visibility** - All locations in one view
- **Trend Detection** - Spot patterns as they emerge
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Transaction and mapping data
- **Caching**: Redis 7.4 - Transaction deduplication cache
- **Messaging**: RabbitMQ 4.1 - Transaction event publishing
- **HTTP Client**: HTTPx - Async API calls to POS systems
- **OAuth**: Authlib - OAuth 2.0 flows for POS authentication
- **Webhooks**: FastAPI webhook receivers
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Sync metrics
## API Endpoints (Key Routes)
### POS Account Management
- `GET /api/v1/pos/accounts` - List connected POS accounts
- `POST /api/v1/pos/accounts` - Connect new POS account
- `GET /api/v1/pos/accounts/{account_id}` - Get account details
- `PUT /api/v1/pos/accounts/{account_id}` - Update account
- `DELETE /api/v1/pos/accounts/{account_id}` - Disconnect account
- `POST /api/v1/pos/accounts/{account_id}/reconnect` - Refresh OAuth tokens
### OAuth & Authentication
- `GET /api/v1/pos/oauth/square/authorize` - Start Square OAuth flow
- `GET /api/v1/pos/oauth/square/callback` - Square OAuth callback
- `GET /api/v1/pos/oauth/toast/authorize` - Start Toast OAuth flow
- `GET /api/v1/pos/oauth/toast/callback` - Toast OAuth callback
- `GET /api/v1/pos/oauth/lightspeed/authorize` - Start Lightspeed OAuth
- `GET /api/v1/pos/oauth/lightspeed/callback` - Lightspeed callback
### Synchronization
- `POST /api/v1/pos/sync/{account_id}` - Trigger manual sync
- `POST /api/v1/pos/sync/{account_id}/historical` - Import historical data
- `GET /api/v1/pos/sync/{account_id}/status` - Get sync status
- `GET /api/v1/pos/sync/{account_id}/history` - Sync history log
### Product Mapping
- `GET /api/v1/pos/mappings` - List product mappings
- `POST /api/v1/pos/mappings` - Create product mapping
- `PUT /api/v1/pos/mappings/{mapping_id}` - Update mapping
- `DELETE /api/v1/pos/mappings/{mapping_id}` - Delete mapping
- `GET /api/v1/pos/mappings/unmapped` - List unmapped POS products
- `POST /api/v1/pos/mappings/auto-map` - Auto-map by name/SKU
### Webhooks
- `POST /api/v1/pos/webhooks/square` - Square webhook receiver
- `POST /api/v1/pos/webhooks/toast` - Toast webhook receiver
- `POST /api/v1/pos/webhooks/lightspeed` - Lightspeed webhook receiver
- `POST /api/v1/pos/accounts/{account_id}/webhooks/register` - Register webhooks
### Analytics
- `GET /api/v1/pos/analytics/dashboard` - POS sync dashboard
- `GET /api/v1/pos/analytics/sync-health` - Sync health metrics
- `GET /api/v1/pos/analytics/unmapped-revenue` - Revenue from unmapped products
## Database Schema
### Main Tables
**pos_accounts**
```sql
CREATE TABLE pos_accounts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_provider VARCHAR(50) NOT NULL, -- square, toast, lightspeed
account_name VARCHAR(255),
location_id VARCHAR(255), -- POS location identifier
location_name VARCHAR(255),
-- OAuth credentials (encrypted)
access_token TEXT,
refresh_token TEXT,
token_expires_at TIMESTAMP,
merchant_id VARCHAR(255),
-- Sync configuration
sync_enabled BOOLEAN DEFAULT TRUE,
sync_frequency_minutes INTEGER DEFAULT 15,
last_sync_at TIMESTAMP,
last_successful_sync_at TIMESTAMP,
next_sync_at TIMESTAMP,
-- Webhook configuration
webhook_id VARCHAR(255),
webhook_url VARCHAR(500),
webhook_signature_key TEXT,
-- Status
status VARCHAR(50) DEFAULT 'active', -- active, disconnected, error
error_message TEXT,
error_count INTEGER DEFAULT 0,
last_error_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, pos_provider, location_id)
);
```
**pos_transactions**
```sql
CREATE TABLE pos_transactions (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_account_id UUID REFERENCES pos_accounts(id) ON DELETE CASCADE,
pos_transaction_id VARCHAR(255) NOT NULL, -- Original POS transaction ID
pos_provider VARCHAR(50) NOT NULL,
-- Transaction details
transaction_date TIMESTAMP NOT NULL,
transaction_type VARCHAR(50) DEFAULT 'sale', -- sale, refund, void
status VARCHAR(50), -- completed, pending, failed
-- Financial
subtotal DECIMAL(10, 2) NOT NULL,
tax_amount DECIMAL(10, 2) DEFAULT 0.00,
discount_amount DECIMAL(10, 2) DEFAULT 0.00,
tip_amount DECIMAL(10, 2) DEFAULT 0.00,
total_amount DECIMAL(10, 2) NOT NULL,
currency VARCHAR(10) DEFAULT 'EUR',
-- Payment
payment_method VARCHAR(50), -- cash, card, contactless, mobile
card_last_four VARCHAR(4),
card_brand VARCHAR(50),
-- Customer (if available)
customer_name VARCHAR(255),
customer_email VARCHAR(255),
customer_phone VARCHAR(50),
-- Metadata
cashier_name VARCHAR(255),
device_name VARCHAR(255),
receipt_number VARCHAR(100),
-- Processing
synced_to_sales BOOLEAN DEFAULT FALSE,
sales_record_id UUID,
sync_error TEXT,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, pos_provider, pos_transaction_id)
);
```
**pos_transaction_items**
```sql
CREATE TABLE pos_transaction_items (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_transaction_id UUID REFERENCES pos_transactions(id) ON DELETE CASCADE,
pos_item_id VARCHAR(255), -- POS product ID
-- Product details
product_name VARCHAR(255) NOT NULL,
product_sku VARCHAR(100),
category VARCHAR(100),
quantity DECIMAL(10, 2) NOT NULL,
unit_price DECIMAL(10, 2) NOT NULL,
discount_amount DECIMAL(10, 2) DEFAULT 0.00,
line_total DECIMAL(10, 2) NOT NULL,
-- Mapping
mapped_product_id UUID, -- Bakery-IA product ID
is_mapped BOOLEAN DEFAULT FALSE,
-- Modifiers (e.g., "Extra frosting")
modifiers JSONB,
created_at TIMESTAMP DEFAULT NOW()
);
```
**pos_product_mappings**
```sql
CREATE TABLE pos_product_mappings (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_account_id UUID REFERENCES pos_accounts(id) ON DELETE CASCADE,
pos_product_id VARCHAR(255) NOT NULL,
pos_product_name VARCHAR(255) NOT NULL,
pos_product_sku VARCHAR(100),
pos_category VARCHAR(100),
-- Mapping
bakery_product_id UUID NOT NULL, -- Link to products catalog
bakery_product_name VARCHAR(255) NOT NULL,
-- Configuration
mapping_type VARCHAR(50) DEFAULT 'manual', -- manual, auto, sku
confidence_score DECIMAL(3, 2), -- For auto-mapping
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, pos_account_id, pos_product_id)
);
```
**pos_sync_logs**
```sql
CREATE TABLE pos_sync_logs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_account_id UUID REFERENCES pos_accounts(id) ON DELETE CASCADE,
sync_started_at TIMESTAMP NOT NULL,
sync_completed_at TIMESTAMP,
sync_duration_seconds INTEGER,
-- Status
status VARCHAR(50) NOT NULL, -- success, partial, failed
error_message TEXT,
-- Metrics
transactions_fetched INTEGER DEFAULT 0,
transactions_processed INTEGER DEFAULT 0,
transactions_failed INTEGER DEFAULT 0,
new_products_discovered INTEGER DEFAULT 0,
unmapped_products_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW()
);
```
**pos_webhooks**
```sql
CREATE TABLE pos_webhooks (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
pos_account_id UUID REFERENCES pos_accounts(id) ON DELETE CASCADE,
webhook_event_id VARCHAR(255), -- POS webhook event ID
event_type VARCHAR(100) NOT NULL, -- payment.created, order.updated, etc.
event_data JSONB NOT NULL,
received_at TIMESTAMP DEFAULT NOW(),
processed_at TIMESTAMP,
processing_status VARCHAR(50) DEFAULT 'pending', -- pending, processed, failed
error_message TEXT,
retry_count INTEGER DEFAULT 0
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_pos_accounts_tenant ON pos_accounts(tenant_id, status);
CREATE INDEX idx_pos_transactions_tenant_date ON pos_transactions(tenant_id, transaction_date DESC);
CREATE INDEX idx_pos_transactions_account ON pos_transactions(pos_account_id);
CREATE INDEX idx_pos_transactions_synced ON pos_transactions(tenant_id, synced_to_sales) WHERE synced_to_sales = FALSE;
CREATE INDEX idx_pos_transaction_items_transaction ON pos_transaction_items(pos_transaction_id);
CREATE INDEX idx_pos_transaction_items_unmapped ON pos_transaction_items(tenant_id, is_mapped) WHERE is_mapped = FALSE;
CREATE INDEX idx_pos_mappings_account ON pos_product_mappings(pos_account_id);
CREATE INDEX idx_pos_sync_logs_account_date ON pos_sync_logs(pos_account_id, sync_started_at DESC);
```
## Business Logic Examples
### Square Transaction Sync
```python
async def sync_square_transactions(pos_account_id: UUID, start_date: datetime = None) -> dict:
"""
Sync transactions from Square POS.
"""
# Get POS account
pos_account = await get_pos_account(pos_account_id)
if pos_account.pos_provider != 'square':
raise ValueError("Not a Square account")
# Check token expiration
if pos_account.token_expires_at and pos_account.token_expires_at < datetime.utcnow():
await refresh_square_oauth_token(pos_account)
# Create sync log
sync_log = POSSyncLog(
tenant_id=pos_account.tenant_id,
pos_account_id=pos_account.id,
sync_started_at=datetime.utcnow(),
status='in_progress'
)
db.add(sync_log)
await db.flush()
try:
# Default to last sync time or 24 hours ago
if not start_date:
start_date = pos_account.last_successful_sync_at or (datetime.utcnow() - timedelta(days=1))
# Call Square API
async with httpx.AsyncClient() as client:
response = await client.post(
f"https://connect.squareup.com/v2/payments/list",
headers={
"Authorization": f"Bearer {pos_account.access_token}",
"Content-Type": "application/json"
},
json={
"location_id": pos_account.location_id,
"begin_time": start_date.isoformat(),
"end_time": datetime.utcnow().isoformat(),
"limit": 100
}
)
if response.status_code != 200:
raise Exception(f"Square API error: {response.text}")
data = response.json()
payments = data.get('payments', [])
transactions_processed = 0
transactions_failed = 0
for payment in payments:
try:
# Check for duplicate
existing = await db.query(POSTransaction).filter(
POSTransaction.tenant_id == pos_account.tenant_id,
POSTransaction.pos_transaction_id == payment['id']
).first()
if existing:
continue # Skip duplicates
# Create transaction
transaction = POSTransaction(
tenant_id=pos_account.tenant_id,
pos_account_id=pos_account.id,
pos_transaction_id=payment['id'],
pos_provider='square',
transaction_date=datetime.fromisoformat(payment['created_at'].replace('Z', '+00:00')),
transaction_type='sale' if payment['status'] == 'COMPLETED' else 'pending',
status=payment['status'].lower(),
total_amount=Decimal(payment['amount_money']['amount']) / 100,
currency=payment['amount_money']['currency'],
payment_method=payment.get('card_details', {}).get('card', {}).get('card_brand', 'unknown').lower(),
card_last_four=payment.get('card_details', {}).get('card', {}).get('last_4'),
receipt_number=payment.get('receipt_number')
)
db.add(transaction)
await db.flush()
# Get line items from order
if 'order_id' in payment:
order_response = await client.get(
f"https://connect.squareup.com/v2/orders/{payment['order_id']}",
headers={"Authorization": f"Bearer {pos_account.access_token}"}
)
if order_response.status_code == 200:
order = order_response.json().get('order', {})
line_items = order.get('line_items', [])
for item in line_items:
# Create transaction item
pos_item = POSTransactionItem(
tenant_id=pos_account.tenant_id,
pos_transaction_id=transaction.id,
pos_item_id=item.get('catalog_object_id'),
product_name=item['name'],
quantity=Decimal(item['quantity']),
unit_price=Decimal(item['base_price_money']['amount']) / 100,
line_total=Decimal(item['total_money']['amount']) / 100
)
# Check for mapping
mapping = await get_product_mapping(
pos_account.id,
item.get('catalog_object_id')
)
if mapping:
pos_item.mapped_product_id = mapping.bakery_product_id
pos_item.is_mapped = True
db.add(pos_item)
# Sync to sales service
await sync_transaction_to_sales(transaction)
transactions_processed += 1
except Exception as e:
logger.error("Failed to process Square payment",
payment_id=payment.get('id'),
error=str(e))
transactions_failed += 1
continue
# Update sync log
sync_log.sync_completed_at = datetime.utcnow()
sync_log.sync_duration_seconds = int((sync_log.sync_completed_at - sync_log.sync_started_at).total_seconds())
sync_log.status = 'success' if transactions_failed == 0 else 'partial'
sync_log.transactions_fetched = len(payments)
sync_log.transactions_processed = transactions_processed
sync_log.transactions_failed = transactions_failed
# Update pos account
pos_account.last_sync_at = datetime.utcnow()
pos_account.last_successful_sync_at = datetime.utcnow()
pos_account.error_count = 0
await db.commit()
# Publish sync completed event
await publish_event('pos', 'pos.sync_completed', {
'tenant_id': str(pos_account.tenant_id),
'pos_account_id': str(pos_account.id),
'transactions_processed': transactions_processed,
'transactions_failed': transactions_failed
})
return {
'status': 'success',
'transactions_processed': transactions_processed,
'transactions_failed': transactions_failed
}
except Exception as e:
sync_log.status = 'failed'
sync_log.error_message = str(e)
sync_log.sync_completed_at = datetime.utcnow()
pos_account.error_count += 1
pos_account.last_error_at = datetime.utcnow()
pos_account.error_message = str(e)
await db.commit()
logger.error("Square sync failed",
pos_account_id=str(pos_account_id),
error=str(e))
raise
```
### Auto Product Mapping
```python
async def auto_map_products(pos_account_id: UUID) -> dict:
"""
Automatically map POS products to Bakery-IA products using name/SKU matching.
"""
# Get unmapped transaction items
unmapped_items = await db.query(POSTransactionItem).filter(
POSTransactionItem.pos_account_id == pos_account_id,
POSTransactionItem.is_mapped == False
).all()
# Get unique products
unique_products = {}
for item in unmapped_items:
key = (item.pos_item_id, item.product_name, item.product_sku)
if key not in unique_products:
unique_products[key] = item
# Get all Bakery-IA products
bakery_products = await get_all_products(pos_account.tenant_id)
mapped_count = 0
high_confidence_count = 0
for (pos_id, pos_name, pos_sku), item in unique_products.items():
best_match = None
confidence = 0.0
# Try SKU match first (highest confidence)
if pos_sku:
for product in bakery_products:
if product.sku and product.sku.upper() == pos_sku.upper():
best_match = product
confidence = 1.0
break
# Try name match (fuzzy matching)
if not best_match:
from difflib import SequenceMatcher
for product in bakery_products:
# Calculate similarity ratio
ratio = SequenceMatcher(None, pos_name.lower(), product.name.lower()).ratio()
if ratio > confidence and ratio > 0.80: # 80% similarity threshold
best_match = product
confidence = ratio
# Create mapping if confidence is high enough
if best_match and confidence >= 0.80:
mapping = POSProductMapping(
tenant_id=pos_account.tenant_id,
pos_account_id=pos_account_id,
pos_product_id=pos_id,
pos_product_name=pos_name,
pos_product_sku=pos_sku,
bakery_product_id=best_match.id,
bakery_product_name=best_match.name,
mapping_type='auto',
confidence_score=Decimal(str(round(confidence, 2)))
)
db.add(mapping)
# Update all unmapped items with this product
await db.query(POSTransactionItem).filter(
POSTransactionItem.pos_account_id == pos_account_id,
POSTransactionItem.pos_item_id == pos_id,
POSTransactionItem.is_mapped == False
).update({
'mapped_product_id': best_match.id,
'is_mapped': True
})
mapped_count += 1
if confidence >= 0.95:
high_confidence_count += 1
await db.commit()
return {
'total_unmapped_products': len(unique_products),
'products_mapped': mapped_count,
'high_confidence_mappings': high_confidence_count,
'remaining_unmapped': len(unique_products) - mapped_count
}
```
### Webhook Handler
```python
async def handle_square_webhook(request: Request) -> dict:
"""
Handle incoming webhook from Square.
"""
# Verify webhook signature
signature = request.headers.get('X-Square-Signature')
body = await request.body()
# Signature verification (simplified)
# In production, use proper HMAC verification with webhook signature key
# Parse webhook payload
payload = await request.json()
event_type = payload.get('type')
merchant_id = payload.get('merchant_id')
# Find POS account
pos_account = await db.query(POSAccount).filter(
POSAccount.pos_provider == 'square',
POSAccount.merchant_id == merchant_id,
POSAccount.status == 'active'
).first()
if not pos_account:
logger.warning("Webhook received for unknown merchant", merchant_id=merchant_id)
return {'status': 'ignored', 'reason': 'unknown_merchant'}
# Store webhook for processing
webhook = POSWebhook(
tenant_id=pos_account.tenant_id,
pos_account_id=pos_account.id,
webhook_event_id=payload.get('event_id'),
event_type=event_type,
event_data=payload,
processing_status='pending'
)
db.add(webhook)
await db.commit()
# Process webhook asynchronously
# (In production, use background task queue)
try:
if event_type == 'payment.created':
# Sync this specific payment
payment_id = payload.get('data', {}).get('id')
await sync_specific_square_payment(pos_account, payment_id)
webhook.processing_status = 'processed'
webhook.processed_at = datetime.utcnow()
except Exception as e:
webhook.processing_status = 'failed'
webhook.error_message = str(e)
logger.error("Webhook processing failed",
webhook_id=str(webhook.id),
error=str(e))
await db.commit()
return {'status': 'received'}
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `pos`
**Routing Keys**: `pos.sync_completed`, `pos.mapping_needed`, `pos.error`
**POS Sync Completed Event**
```json
{
"event_type": "pos_sync_completed",
"tenant_id": "uuid",
"pos_account_id": "uuid",
"pos_provider": "square",
"location_name": "VUE Madrid - Centro",
"transactions_processed": 45,
"transactions_failed": 0,
"new_products_discovered": 3,
"sync_duration_seconds": 12,
"timestamp": "2025-11-06T10:30:00Z"
}
```
**POS Mapping Needed Alert**
```json
{
"event_type": "pos_mapping_needed",
"tenant_id": "uuid",
"pos_account_id": "uuid",
"unmapped_products_count": 5,
"unmapped_revenue_euros": 125.50,
"sample_unmapped_products": [
{"pos_product_name": "Croissant Especial", "transaction_count": 12},
{"pos_product_name": "Pan Integral Grande", "transaction_count": 8}
],
"timestamp": "2025-11-06T14:00:00Z"
}
```
**POS Error Alert**
```json
{
"event_type": "pos_error",
"tenant_id": "uuid",
"pos_account_id": "uuid",
"pos_provider": "square",
"error_type": "authentication_failed",
"error_message": "OAuth token expired",
"consecutive_failures": 3,
"action_required": "Reconnect POS account",
"timestamp": "2025-11-06T11:30:00Z"
}
```
### Consumed Events
- **From Sales**: Sales data validation triggers re-sync if discrepancies found
- **From Orchestrator**: Daily sync triggers for all active POS accounts
## Custom Metrics (Prometheus)
```python
# POS metrics
pos_accounts_total = Gauge(
'pos_accounts_total',
'Total connected POS accounts',
['tenant_id', 'pos_provider', 'status']
)
pos_transactions_synced_total = Counter(
'pos_transactions_synced_total',
'Total transactions synced from POS',
['tenant_id', 'pos_provider']
)
pos_sync_duration_seconds = Histogram(
'pos_sync_duration_seconds',
'POS sync duration',
['tenant_id', 'pos_provider'],
buckets=[5, 10, 30, 60, 120, 300]
)
pos_sync_errors_total = Counter(
'pos_sync_errors_total',
'Total POS sync errors',
['tenant_id', 'pos_provider', 'error_type']
)
pos_unmapped_products_total = Gauge(
'pos_unmapped_products_total',
'Products without mapping',
['tenant_id', 'pos_account_id']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8013)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**POS Provider Configuration:**
- `SQUARE_APP_ID` - Square application ID
- `SQUARE_APP_SECRET` - Square application secret
- `TOAST_CLIENT_ID` - Toast client ID
- `TOAST_CLIENT_SECRET` - Toast client secret
- `LIGHTSPEED_CLIENT_ID` - Lightspeed client ID
- `LIGHTSPEED_CLIENT_SECRET` - Lightspeed client secret
**Sync Configuration:**
- `DEFAULT_SYNC_FREQUENCY_MINUTES` - Default sync interval (default: 15)
- `ENABLE_WEBHOOKS` - Use webhooks for real-time sync (default: true)
- `MAX_SYNC_RETRIES` - Max retry attempts (default: 3)
- `HISTORICAL_IMPORT_DAYS` - Days to import on initial setup (default: 90)
**Mapping Configuration:**
- `AUTO_MAPPING_ENABLED` - Enable automatic product mapping (default: true)
- `AUTO_MAPPING_CONFIDENCE_THRESHOLD` - Minimum confidence (default: 0.80)
- `ALERT_ON_UNMAPPED_PRODUCTS` - Alert for unmapped products (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
- POS system developer accounts (Square, Toast, Lightspeed)
### Local Development
```bash
cd services/pos
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/pos
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export SQUARE_APP_ID=your_square_app_id
export SQUARE_APP_SECRET=your_square_app_secret
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **POS Providers** - Square, Toast, Lightspeed APIs
- **Auth Service** - User authentication
- **PostgreSQL** - Transaction and mapping data
- **Redis** - Deduplication cache
- **RabbitMQ** - Event publishing
### Dependents
- **Sales Service** - Receives synced transaction data
- **Forecasting Service** - Uses sales data for ML models
- **Inventory Service** - Stock deduction from sales
- **Notification Service** - Sync error alerts
- **Frontend Dashboard** - POS connection and mapping UI
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Hours of daily manual sales data entry
- Transcription errors reducing forecast accuracy
- Delayed visibility into sales performance
- No integration between POS and business intelligence
- Double data entry (POS + spreadsheets/accounting)
### Solution
Bakery-IA POS Service provides:
- **Zero Manual Entry**: Automatic transaction sync from POS
- **Real-Time Data**: Sales data available within seconds
- **Higher Accuracy**: 99.9%+ vs. 85-95% manual entry
- **Immediate Value**: Works from day one, no setup needed
- **Universal Compatibility**: Works with popular POS systems
### Quantifiable Impact
**Time Savings:**
- 5-8 hours/week eliminating manual data entry
- 1-2 hours/week on sales reconciliation
- **Total: 6-10 hours/week saved**
**Data Quality:**
- 99.9%+ accuracy vs. 85-95% manual entry
- Zero transcription errors
- Real-time vs. end-of-day data availability
- 10-20% forecast accuracy improvement
**Operational Efficiency:**
- 15-minute setup vs. hours of daily manual entry
- Automatic sync every 15 minutes
- Multi-location support in single dashboard
- Instant error detection and alerts
### Target Market Fit (Spanish Bakeries)
- **POS Adoption**: Growing use of Square, Toast, Lightspeed in Spain
- **Labor Costs**: Spanish minimum wage makes manual entry expensive
- **Modernization**: New generation of bakery owners embrace technology
- **Market Trend**: Digital transformation in retail/food service
### ROI Calculation
**Investment**: €0 additional (included in platform subscription)
**Time Savings Value**: 6-10 hours/week × €15/hour = €360-600/month
**Forecast Improvement Value**: 10-20% better accuracy = €100-400/month
**Total Monthly Value**: €460-1,000
**Annual ROI**: €5,520-12,000 value per bakery
**Payback**: Immediate (included in subscription)
### Competitive Advantage
- **First-Mover**: Few Spanish bakery platforms offer POS integration
- **Multi-POS Support**: Flexibility for customers to choose POS
- **Plug-and-Play**: 15-minute setup vs. competitors requiring IT setup
- **Real-Time**: Webhook support for instant sync vs. batch processing
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Procurement Service
## Overview
The **Procurement Service** automates ingredient purchasing by analyzing production schedules, inventory levels, and demand forecasts to generate optimized purchase orders. It prevents stockouts while minimizing excess inventory, manages supplier relationships with automated purchase order generation, and tracks delivery performance. This service is critical for maintaining optimal stock levels and ensuring continuous production operations.
## Key Features
### Automated Procurement Planning
- **Intelligent Replenishment** - Auto-calculate purchasing needs from production plans
- **Forecast-Driven Planning** - Use demand forecasts to anticipate ingredient needs
- **Inventory Projection** - Project stock levels 7-30 days ahead
- **Lead Time Management** - Account for supplier delivery times
- **Safety Stock Calculation** - Maintain buffers for critical ingredients
- **Multi-Scenario Planning** - Plan for normal, peak, and low demand periods
### Purchase Order Management
- **Automated PO Generation** - One-click purchase order creation
- **Supplier Allocation** - Smart supplier selection based on price, quality, delivery
- **PO Templates** - Standard orders for recurring purchases
- **Batch Ordering** - Combine multiple ingredients per supplier
- **Order Tracking** - Monitor PO status from creation to delivery
- **Order History** - Complete purchase order archive
### Supplier Integration
- **Multi-Supplier Management** - Handle 10+ suppliers per ingredient
- **Price Comparison** - Automatic best price selection
- **Delivery Schedule** - Track expected delivery dates
- **Order Confirmation** - Automated email/API confirmation to suppliers
- **Performance Tracking** - Monitor on-time delivery and quality
- **Supplier Scorecards** - Data-driven supplier evaluation
### Stock Optimization
- **Reorder Point Calculation** - When to order based on consumption rate
- **Economic Order Quantity (EOQ)** - Optimal order size calculation
- **ABC Analysis** - Prioritize critical ingredients
- **Stockout Prevention** - 85-95% stockout prevention rate
- **Overstock Alerts** - Warn against excessive inventory
- **Seasonal Adjustment** - Adjust for seasonal demand patterns
### Cost Management
- **Price Tracking** - Monitor ingredient price trends over time
- **Budget Management** - Track spending against procurement budgets
- **Cost Variance Analysis** - Compare planned vs. actual costs
- **Volume Discounts** - Automatic discount application
- **Contract Pricing** - Manage fixed-price contracts with suppliers
- **Cost Savings Reports** - Quantify procurement optimization savings
### Analytics & Reporting
- **Procurement Dashboard** - Real-time procurement KPIs
- **Spend Analysis** - Category and supplier spending breakdown
- **Lead Time Analytics** - Average delivery times per supplier
- **Stockout Reports** - Track missed orders due to stockouts
- **Supplier Performance** - On-time delivery and quality metrics
- **ROI Tracking** - Measure procurement efficiency gains
## Business Value
### For Bakery Owners
- **Stockout Prevention** - Never miss production due to missing ingredients
- **Cost Optimization** - 5-15% procurement cost savings through automation
- **Cash Flow Management** - Optimize inventory investment
- **Supplier Leverage** - Data-driven supplier negotiations
- **Time Savings** - Automated ordering vs. manual tracking
- **Compliance** - Proper purchase order documentation for accounting
### Quantifiable Impact
- **Stockout Prevention**: 85-95% reduction in production delays
- **Cost Savings**: 5-15% through optimized ordering and price comparison
- **Time Savings**: 8-12 hours/week on manual ordering and tracking
- **Inventory Reduction**: 20-30% lower inventory levels with same service
- **Supplier Performance**: 15-25% improvement in on-time delivery
- **Waste Reduction**: 10-20% less spoilage from excess inventory
### For Procurement Staff
- **Automated Calculations** - System calculates what and when to order
- **Supplier Insights** - Best supplier recommendations with data
- **Order Tracking** - Visibility into all pending orders
- **Exception Management** - Focus on issues, not routine orders
- **Performance Metrics** - Clear KPIs for procurement efficiency
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Procurement data
- **Caching**: Redis 7.4 - Calculation results cache
- **Messaging**: RabbitMQ 4.1 - Event publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Validation**: Pydantic 2.0 - Schema validation
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Procurement metrics
## API Endpoints (Key Routes)
### Procurement Planning
- `GET /api/v1/procurement/needs` - Calculate current procurement needs
- `POST /api/v1/procurement/needs/calculate` - Trigger needs calculation
- `GET /api/v1/procurement/needs/{need_id}` - Get procurement need details
- `GET /api/v1/procurement/projections` - Get inventory projections
### Purchase Orders
- `GET /api/v1/procurement/purchase-orders` - List purchase orders
- `POST /api/v1/procurement/purchase-orders` - Create purchase order
- `GET /api/v1/procurement/purchase-orders/{po_id}` - Get PO details
- `PUT /api/v1/procurement/purchase-orders/{po_id}` - Update PO
- `POST /api/v1/procurement/purchase-orders/{po_id}/send` - Send PO to supplier
- `POST /api/v1/procurement/purchase-orders/{po_id}/receive` - Mark PO received
- `POST /api/v1/procurement/purchase-orders/{po_id}/cancel` - Cancel PO
### Purchase Order Items
- `GET /api/v1/procurement/purchase-orders/{po_id}/items` - List PO items
- `POST /api/v1/procurement/purchase-orders/{po_id}/items` - Add item to PO
- `PUT /api/v1/procurement/purchase-orders/{po_id}/items/{item_id}` - Update item
- `DELETE /api/v1/procurement/purchase-orders/{po_id}/items/{item_id}` - Remove item
### Supplier Management
- `GET /api/v1/procurement/suppliers/{supplier_id}/products` - Supplier product catalog
- `GET /api/v1/procurement/suppliers/{supplier_id}/pricing` - Get supplier pricing
- `POST /api/v1/procurement/suppliers/{supplier_id}/pricing` - Update pricing
- `GET /api/v1/procurement/suppliers/recommend` - Get supplier recommendations
### Analytics
- `GET /api/v1/procurement/analytics/dashboard` - Procurement dashboard
- `GET /api/v1/procurement/analytics/spend` - Spending analysis
- `GET /api/v1/procurement/analytics/supplier-performance` - Supplier metrics
- `GET /api/v1/procurement/analytics/stockouts` - Stockout analysis
- `GET /api/v1/procurement/analytics/lead-times` - Lead time analysis
## Database Schema
### Main Tables
**procurement_needs**
```sql
CREATE TABLE procurement_needs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
ingredient_id UUID NOT NULL,
ingredient_name VARCHAR(255) NOT NULL,
calculation_date DATE NOT NULL DEFAULT CURRENT_DATE,
current_stock DECIMAL(10, 2) NOT NULL,
projected_consumption DECIMAL(10, 2) NOT NULL, -- Next 7-30 days
safety_stock DECIMAL(10, 2) NOT NULL,
reorder_point DECIMAL(10, 2) NOT NULL,
recommended_order_quantity DECIMAL(10, 2) NOT NULL,
recommended_order_unit VARCHAR(50) NOT NULL,
urgency VARCHAR(50) NOT NULL, -- critical, high, medium, low
estimated_stockout_date DATE,
recommended_supplier_id UUID,
estimated_cost DECIMAL(10, 2),
status VARCHAR(50) DEFAULT 'pending', -- pending, ordered, cancelled
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_needs_tenant_status (tenant_id, status),
INDEX idx_needs_urgency (tenant_id, urgency)
);
```
**purchase_orders**
```sql
CREATE TABLE purchase_orders (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
po_number VARCHAR(100) NOT NULL, -- Human-readable PO number
supplier_id UUID NOT NULL,
supplier_name VARCHAR(255) NOT NULL, -- Cached for performance
order_date DATE NOT NULL DEFAULT CURRENT_DATE,
expected_delivery_date DATE,
actual_delivery_date DATE,
status VARCHAR(50) DEFAULT 'draft', -- draft, sent, confirmed, in_transit, received, cancelled
payment_terms VARCHAR(100), -- Net 30, Net 60, COD, etc.
payment_status VARCHAR(50) DEFAULT 'unpaid', -- unpaid, paid, overdue
subtotal DECIMAL(10, 2) DEFAULT 0.00,
tax_amount DECIMAL(10, 2) DEFAULT 0.00,
total_amount DECIMAL(10, 2) DEFAULT 0.00,
delivery_address TEXT,
contact_person VARCHAR(255),
contact_phone VARCHAR(50),
contact_email VARCHAR(255),
internal_notes TEXT,
supplier_notes TEXT,
sent_at TIMESTAMP,
confirmed_at TIMESTAMP,
received_at TIMESTAMP,
created_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, po_number)
);
```
**purchase_order_items**
```sql
CREATE TABLE purchase_order_items (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
purchase_order_id UUID REFERENCES purchase_orders(id) ON DELETE CASCADE,
ingredient_id UUID NOT NULL,
ingredient_name VARCHAR(255) NOT NULL,
quantity_ordered DECIMAL(10, 2) NOT NULL,
quantity_received DECIMAL(10, 2) DEFAULT 0.00,
unit VARCHAR(50) NOT NULL,
unit_price DECIMAL(10, 2) NOT NULL,
discount_percentage DECIMAL(5, 2) DEFAULT 0.00,
line_total DECIMAL(10, 2) NOT NULL,
tax_rate DECIMAL(5, 2) DEFAULT 0.00,
expected_quality_grade VARCHAR(50),
actual_quality_grade VARCHAR(50),
quality_notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**supplier_products**
```sql
CREATE TABLE supplier_products (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID NOT NULL,
ingredient_id UUID NOT NULL,
supplier_product_code VARCHAR(100),
supplier_product_name VARCHAR(255),
unit_price DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
minimum_order_quantity DECIMAL(10, 2),
lead_time_days INTEGER DEFAULT 3,
is_preferred BOOLEAN DEFAULT FALSE,
quality_grade VARCHAR(50),
valid_from DATE DEFAULT CURRENT_DATE,
valid_until DATE,
is_active BOOLEAN DEFAULT TRUE,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, supplier_id, ingredient_id)
);
```
**inventory_projections**
```sql
CREATE TABLE inventory_projections (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
ingredient_id UUID NOT NULL,
ingredient_name VARCHAR(255) NOT NULL,
projection_date DATE NOT NULL,
projected_stock DECIMAL(10, 2) NOT NULL,
projected_consumption DECIMAL(10, 2) NOT NULL,
projected_receipts DECIMAL(10, 2) DEFAULT 0.00,
stockout_risk VARCHAR(50), -- none, low, medium, high, critical
calculated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, ingredient_id, projection_date)
);
```
**reorder_points**
```sql
CREATE TABLE reorder_points (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
ingredient_id UUID NOT NULL,
ingredient_name VARCHAR(255) NOT NULL,
reorder_point DECIMAL(10, 2) NOT NULL,
safety_stock DECIMAL(10, 2) NOT NULL,
economic_order_quantity DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
average_daily_consumption DECIMAL(10, 2) NOT NULL,
lead_time_days INTEGER NOT NULL,
calculation_method VARCHAR(50), -- manual, auto_basic, auto_advanced
last_calculated_at TIMESTAMP DEFAULT NOW(),
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, ingredient_id)
);
```
**procurement_budgets**
```sql
CREATE TABLE procurement_budgets (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
budget_period VARCHAR(50) NOT NULL, -- monthly, quarterly, annual
period_start DATE NOT NULL,
period_end DATE NOT NULL,
category VARCHAR(100), -- flour, dairy, packaging, etc.
budgeted_amount DECIMAL(10, 2) NOT NULL,
actual_spent DECIMAL(10, 2) DEFAULT 0.00,
variance DECIMAL(10, 2) DEFAULT 0.00,
variance_percentage DECIMAL(5, 2) DEFAULT 0.00,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, period_start, category)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_po_tenant_status ON purchase_orders(tenant_id, status);
CREATE INDEX idx_po_supplier ON purchase_orders(supplier_id);
CREATE INDEX idx_po_expected_delivery ON purchase_orders(tenant_id, expected_delivery_date);
CREATE INDEX idx_po_items_po ON purchase_order_items(purchase_order_id);
CREATE INDEX idx_supplier_products_supplier ON supplier_products(supplier_id);
CREATE INDEX idx_supplier_products_ingredient ON supplier_products(tenant_id, ingredient_id);
CREATE INDEX idx_projections_date ON inventory_projections(tenant_id, projection_date);
```
## Business Logic Examples
### Automated Procurement Needs Calculation
```python
async def calculate_procurement_needs(tenant_id: UUID, days_ahead: int = 14) -> list[ProcurementNeed]:
"""
Calculate ingredient procurement needs for next N days.
Accounts for: current stock, production plans, forecasts, lead times, safety stock.
"""
needs = []
# Get all ingredients
ingredients = await get_all_ingredients(tenant_id)
for ingredient in ingredients:
# Get current stock
current_stock = await get_current_stock_level(tenant_id, ingredient.id)
# Get planned consumption from production schedules
planned_consumption = await get_planned_ingredient_consumption(
tenant_id,
ingredient.id,
days_ahead=days_ahead
)
# Get forecasted consumption (if no production plans)
forecast_consumption = await get_forecasted_consumption(
tenant_id,
ingredient.id,
days_ahead=days_ahead
)
# Total projected consumption
projected_consumption = max(planned_consumption, forecast_consumption)
# Get reorder point and safety stock
reorder_config = await get_reorder_point(tenant_id, ingredient.id)
reorder_point = reorder_config.reorder_point
safety_stock = reorder_config.safety_stock
eoq = reorder_config.economic_order_quantity
# Get supplier lead time
supplier = await get_preferred_supplier(tenant_id, ingredient.id)
lead_time_days = supplier.lead_time_days if supplier else 3
# Calculate projected stock at end of period
projected_stock_end = current_stock - projected_consumption
# Determine if order needed
if projected_stock_end < reorder_point:
# Calculate order quantity
shortage = reorder_point - projected_stock_end
order_quantity = max(shortage + safety_stock, eoq)
# Estimate stockout date
daily_consumption = projected_consumption / days_ahead
days_until_stockout = current_stock / daily_consumption if daily_consumption > 0 else 999
stockout_date = date.today() + timedelta(days=int(days_until_stockout))
# Determine urgency
if days_until_stockout <= lead_time_days:
urgency = 'critical'
elif days_until_stockout <= lead_time_days * 1.5:
urgency = 'high'
elif days_until_stockout <= lead_time_days * 2:
urgency = 'medium'
else:
urgency = 'low'
# Get estimated cost
unit_price = await get_ingredient_unit_price(tenant_id, ingredient.id)
estimated_cost = order_quantity * unit_price
# Create procurement need
need = ProcurementNeed(
tenant_id=tenant_id,
ingredient_id=ingredient.id,
ingredient_name=ingredient.name,
current_stock=current_stock,
projected_consumption=projected_consumption,
safety_stock=safety_stock,
reorder_point=reorder_point,
recommended_order_quantity=order_quantity,
recommended_order_unit=ingredient.unit,
urgency=urgency,
estimated_stockout_date=stockout_date,
recommended_supplier_id=supplier.id if supplier else None,
estimated_cost=estimated_cost,
status='pending'
)
db.add(need)
needs.append(need)
await db.commit()
# Publish procurement needs event
if needs:
await publish_event('procurement', 'procurement.needs_calculated', {
'tenant_id': str(tenant_id),
'needs_count': len(needs),
'critical_count': sum(1 for n in needs if n.urgency == 'critical'),
'total_estimated_cost': sum(n.estimated_cost for n in needs)
})
logger.info("Procurement needs calculated",
tenant_id=str(tenant_id),
needs_count=len(needs),
days_ahead=days_ahead)
return needs
```
### Automated Purchase Order Generation
```python
async def generate_purchase_orders(tenant_id: UUID) -> list[PurchaseOrder]:
"""
Generate purchase orders from pending procurement needs.
Groups items by supplier for efficiency.
"""
# Get pending procurement needs
needs = await db.query(ProcurementNeed).filter(
ProcurementNeed.tenant_id == tenant_id,
ProcurementNeed.status == 'pending',
ProcurementNeed.urgency.in_(['critical', 'high'])
).all()
if not needs:
return []
# Group needs by supplier
supplier_groups = {}
for need in needs:
supplier_id = need.recommended_supplier_id
if supplier_id not in supplier_groups:
supplier_groups[supplier_id] = []
supplier_groups[supplier_id].append(need)
# Create purchase order per supplier
purchase_orders = []
for supplier_id, supplier_needs in supplier_groups.items():
# Get supplier details
supplier = await get_supplier(supplier_id)
# Generate PO number
po_number = await generate_po_number(tenant_id)
# Calculate expected delivery date
lead_time = supplier.lead_time_days or 3
expected_delivery = date.today() + timedelta(days=lead_time)
# Create purchase order
po = PurchaseOrder(
tenant_id=tenant_id,
po_number=po_number,
supplier_id=supplier.id,
supplier_name=supplier.name,
order_date=date.today(),
expected_delivery_date=expected_delivery,
status='draft',
payment_terms=supplier.payment_terms or 'Net 30',
delivery_address=await get_default_delivery_address(tenant_id),
contact_person=supplier.contact_name,
contact_phone=supplier.phone,
contact_email=supplier.email,
created_by=tenant_id # System-generated
)
db.add(po)
await db.flush() # Get po.id
# Add items to PO
subtotal = Decimal('0.00')
for need in supplier_needs:
# Get supplier pricing
supplier_product = await get_supplier_product(
supplier_id,
need.ingredient_id
)
unit_price = supplier_product.unit_price
quantity = need.recommended_order_quantity
line_total = unit_price * quantity
po_item = PurchaseOrderItem(
tenant_id=tenant_id,
purchase_order_id=po.id,
ingredient_id=need.ingredient_id,
ingredient_name=need.ingredient_name,
quantity_ordered=quantity,
unit=need.recommended_order_unit,
unit_price=unit_price,
line_total=line_total
)
db.add(po_item)
subtotal += line_total
# Mark need as ordered
need.status = 'ordered'
# Calculate totals (Spanish IVA 10% on food products)
tax_amount = subtotal * Decimal('0.10')
total_amount = subtotal + tax_amount
po.subtotal = subtotal
po.tax_amount = tax_amount
po.total_amount = total_amount
purchase_orders.append(po)
await db.commit()
# Publish event
await publish_event('procurement', 'purchase_orders.generated', {
'tenant_id': str(tenant_id),
'po_count': len(purchase_orders),
'total_value': sum(po.total_amount for po in purchase_orders)
})
logger.info("Purchase orders generated",
tenant_id=str(tenant_id),
count=len(purchase_orders))
return purchase_orders
```
### Economic Order Quantity (EOQ) Calculation
```python
def calculate_eoq(
annual_demand: float,
ordering_cost_per_order: float,
holding_cost_per_unit_per_year: float
) -> float:
"""
Calculate Economic Order Quantity using Wilson's formula.
EOQ = sqrt((2 * D * S) / H)
Where:
- D = Annual demand
- S = Ordering cost per order
- H = Holding cost per unit per year
"""
if holding_cost_per_unit_per_year == 0:
return 0
eoq = math.sqrt(
(2 * annual_demand * ordering_cost_per_order) / holding_cost_per_unit_per_year
)
return round(eoq, 2)
async def calculate_reorder_point(
tenant_id: UUID,
ingredient_id: UUID
) -> ReorderPoint:
"""
Calculate reorder point and safety stock for ingredient.
Reorder Point = (Average Daily Consumption × Lead Time) + Safety Stock
Safety Stock = Z-score × σ × sqrt(Lead Time)
"""
# Get historical consumption data (last 90 days)
consumption_history = await get_consumption_history(
tenant_id,
ingredient_id,
days=90
)
# Calculate average daily consumption
if len(consumption_history) > 0:
avg_daily_consumption = sum(consumption_history) / len(consumption_history)
std_dev_consumption = statistics.stdev(consumption_history) if len(consumption_history) > 1 else 0
else:
avg_daily_consumption = 0
std_dev_consumption = 0
# Get supplier lead time
supplier = await get_preferred_supplier(tenant_id, ingredient_id)
lead_time_days = supplier.lead_time_days if supplier else 3
# Calculate safety stock (95% service level, Z=1.65)
z_score = 1.65
safety_stock = z_score * std_dev_consumption * math.sqrt(lead_time_days)
# Calculate reorder point
reorder_point = (avg_daily_consumption * lead_time_days) + safety_stock
# Calculate EOQ
annual_demand = avg_daily_consumption * 365
ordering_cost = 25.0 # Estimated administrative cost per order
unit_price = await get_ingredient_unit_price(tenant_id, ingredient_id)
holding_cost_rate = 0.20 # 20% of unit cost per year
holding_cost = unit_price * holding_cost_rate
eoq = calculate_eoq(annual_demand, ordering_cost, holding_cost)
# Store reorder point configuration
reorder_config = await db.get(ReorderPoint, {'tenant_id': tenant_id, 'ingredient_id': ingredient_id})
if not reorder_config:
reorder_config = ReorderPoint(
tenant_id=tenant_id,
ingredient_id=ingredient_id,
ingredient_name=await get_ingredient_name(ingredient_id)
)
db.add(reorder_config)
reorder_config.reorder_point = round(reorder_point, 2)
reorder_config.safety_stock = round(safety_stock, 2)
reorder_config.economic_order_quantity = round(eoq, 2)
reorder_config.average_daily_consumption = round(avg_daily_consumption, 2)
reorder_config.lead_time_days = lead_time_days
reorder_config.calculation_method = 'auto_advanced'
reorder_config.last_calculated_at = datetime.utcnow()
await db.commit()
return reorder_config
```
### Supplier Recommendation Engine
```python
async def recommend_supplier(
tenant_id: UUID,
ingredient_id: UUID,
quantity: float
) -> UUID:
"""
Recommend best supplier based on price, quality, and delivery performance.
Scoring: 40% price, 30% quality, 30% delivery
"""
# Get all suppliers for ingredient
suppliers = await db.query(SupplierProduct).filter(
SupplierProduct.tenant_id == tenant_id,
SupplierProduct.ingredient_id == ingredient_id,
SupplierProduct.is_active == True
).all()
if not suppliers:
return None
supplier_scores = []
for supplier_product in suppliers:
# Get supplier performance metrics
supplier = await get_supplier(supplier_product.supplier_id)
performance = await get_supplier_performance(supplier.id)
# Price score (lower is better, normalized 0-100)
prices = [sp.unit_price for sp in suppliers]
min_price = min(prices)
max_price = max(prices)
if max_price > min_price:
price_score = 100 * (1 - (supplier_product.unit_price - min_price) / (max_price - min_price))
else:
price_score = 100
# Quality score (0-100, from supplier ratings)
quality_score = supplier.quality_rating or 75
# Delivery score (0-100, based on on-time delivery %)
delivery_score = performance.on_time_delivery_percentage if performance else 80
# Weighted total score
total_score = (
price_score * 0.40 +
quality_score * 0.30 +
delivery_score * 0.30
)
supplier_scores.append({
'supplier_id': supplier.id,
'supplier_name': supplier.name,
'total_score': total_score,
'price_score': price_score,
'quality_score': quality_score,
'delivery_score': delivery_score,
'unit_price': supplier_product.unit_price
})
# Sort by total score descending
supplier_scores.sort(key=lambda x: x['total_score'], reverse=True)
# Return best supplier
return supplier_scores[0]['supplier_id'] if supplier_scores else None
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `procurement`
**Routing Keys**: `procurement.needs_calculated`, `procurement.po_created`, `procurement.po_received`, `procurement.stockout_risk`
**Procurement Needs Calculated Event**
```json
{
"event_type": "procurement_needs_calculated",
"tenant_id": "uuid",
"needs_count": 12,
"critical_count": 3,
"high_count": 5,
"total_estimated_cost": 2450.00,
"critical_ingredients": [
{"ingredient_id": "uuid", "ingredient_name": "Harina de Trigo", "days_until_stockout": 2},
{"ingredient_id": "uuid", "ingredient_name": "Levadura", "days_until_stockout": 3}
],
"timestamp": "2025-11-06T08:00:00Z"
}
```
**Purchase Order Created Event**
```json
{
"event_type": "purchase_order_created",
"tenant_id": "uuid",
"po_id": "uuid",
"po_number": "PO-2025-1106-001",
"supplier_id": "uuid",
"supplier_name": "Harinas García",
"total_amount": 850.00,
"item_count": 5,
"expected_delivery_date": "2025-11-10",
"status": "sent",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Purchase Order Received Event**
```json
{
"event_type": "purchase_order_received",
"tenant_id": "uuid",
"po_id": "uuid",
"po_number": "PO-2025-1106-001",
"supplier_id": "uuid",
"expected_delivery_date": "2025-11-10",
"actual_delivery_date": "2025-11-09",
"on_time": true,
"quality_issues": false,
"timestamp": "2025-11-09T07:30:00Z"
}
```
**Stockout Risk Alert**
```json
{
"event_type": "stockout_risk",
"tenant_id": "uuid",
"ingredient_id": "uuid",
"ingredient_name": "Mantequilla",
"current_stock": 5.5,
"unit": "kg",
"projected_consumption_7days": 12.0,
"days_until_stockout": 3,
"risk_level": "high",
"recommended_action": "Place order immediately",
"timestamp": "2025-11-06T09:00:00Z"
}
```
### Consumed Events
- **From Production**: Production schedules trigger procurement needs calculation
- **From Forecasting**: Demand forecasts inform procurement planning
- **From Inventory**: Stock level changes update projections
- **From Orchestrator**: Daily procurement planning trigger
## Custom Metrics (Prometheus)
```python
# Procurement metrics
procurement_needs_total = Counter(
'procurement_needs_total',
'Total procurement needs identified',
['tenant_id', 'urgency']
)
purchase_orders_total = Counter(
'purchase_orders_total',
'Total purchase orders created',
['tenant_id', 'supplier_id', 'status']
)
purchase_order_value_euros = Histogram(
'purchase_order_value_euros',
'Purchase order value distribution',
['tenant_id'],
buckets=[100, 250, 500, 1000, 2000, 5000, 10000]
)
# Supplier performance metrics
supplier_delivery_time_days = Histogram(
'supplier_delivery_time_days',
'Supplier delivery time',
['tenant_id', 'supplier_id'],
buckets=[1, 2, 3, 5, 7, 10, 14, 21]
)
supplier_on_time_delivery = Gauge(
'supplier_on_time_delivery_percentage',
'Supplier on-time delivery rate',
['tenant_id', 'supplier_id']
)
# Stock optimization metrics
stockout_events_total = Counter(
'stockout_events_total',
'Total stockout events',
['tenant_id', 'ingredient_id']
)
inventory_turnover_ratio = Gauge(
'inventory_turnover_ratio',
'Inventory turnover ratio',
['tenant_id', 'category']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8011)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Procurement Configuration:**
- `DEFAULT_LEAD_TIME_DAYS` - Default supplier lead time (default: 3)
- `SAFETY_STOCK_SERVICE_LEVEL` - Z-score for safety stock (default: 1.65 for 95%)
- `PROJECTION_DAYS_AHEAD` - Days to project inventory (default: 14)
- `ENABLE_AUTO_PO_GENERATION` - Auto-create POs (default: false)
- `AUTO_PO_MIN_VALUE` - Minimum PO value for auto-creation (default: 100.00)
**Cost Configuration:**
- `DEFAULT_ORDERING_COST` - Administrative cost per order (default: 25.00)
- `DEFAULT_HOLDING_COST_RATE` - Annual holding cost rate (default: 0.20)
- `ENABLE_BUDGET_ALERTS` - Alert on budget variance (default: true)
- `BUDGET_VARIANCE_THRESHOLD` - Alert threshold percentage (default: 10.0)
**Supplier Configuration:**
- `PRICE_WEIGHT` - Supplier scoring weight for price (default: 0.40)
- `QUALITY_WEIGHT` - Supplier scoring weight for quality (default: 0.30)
- `DELIVERY_WEIGHT` - Supplier scoring weight for delivery (default: 0.30)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/procurement
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/procurement
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Production Service** - Production schedules for consumption projection
- **Inventory Service** - Current stock levels
- **Forecasting Service** - Demand forecasts for planning
- **Recipes Service** - Ingredient requirements per recipe
- **Suppliers Service** - Supplier data and pricing
- **Auth Service** - User authentication
- **PostgreSQL** - Procurement data
- **Redis** - Calculation caching
- **RabbitMQ** - Event publishing
### Dependents
- **Inventory Service** - Purchase orders create inventory receipts
- **Accounting Service** - Purchase orders for expense tracking
- **Notification Service** - Stockout and PO alerts
- **AI Insights Service** - Procurement optimization recommendations
- **Frontend Dashboard** - Procurement management UI
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Manual ordering leading to stockouts or overstock
- No visibility into future ingredient needs
- Reactive procurement (order when empty, too late)
- No systematic supplier performance tracking
- Manual price comparison across suppliers
- Excess inventory tying up cash
### Solution
Bakery-IA Procurement Service provides:
- **Automated Planning**: System calculates what and when to order
- **Stockout Prevention**: 85-95% reduction in production delays
- **Cost Optimization**: Supplier recommendations based on data
- **Inventory Optimization**: 20-30% less inventory with same service
- **Supplier Management**: Performance tracking and leverage
### Quantifiable Impact
**Cost Savings:**
- €200-400/month from optimized ordering (5-15% procurement savings)
- €100-300/month from reduced excess inventory
- €150-500/month from stockout prevention (lost production)
- **Total: €450-1,200/month savings**
**Time Savings:**
- 8-12 hours/week on manual ordering and tracking
- 2-3 hours/week on supplier communication
- 1-2 hours/week on inventory checks
- **Total: 11-17 hours/week saved**
**Operational Improvements:**
- 85-95% stockout prevention rate
- 20-30% inventory reduction
- 15-25% supplier delivery improvement
- 10-20% less spoilage from overstock
### Target Market Fit (Spanish Bakeries)
- **Cash Flow Sensitive**: Spanish SMBs need optimal inventory investment
- **Supplier Relationships**: Data enables better supplier negotiations
- **Regulatory**: Proper PO documentation for Spanish tax compliance
- **Growth**: Automation enables scaling without procurement staff
### ROI Calculation
**Investment**: €0 additional (included in platform subscription)
**Monthly Savings**: €450-1,200
**Annual ROI**: €5,400-14,400 value per bakery
**Payback**: Immediate (included in subscription)
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Production Service
## Overview
The **Production Service** orchestrates all bakery manufacturing operations, from automated production scheduling based on forecasts to quality control tracking and equipment management. It transforms demand predictions into actionable production plans, ensuring optimal efficiency, consistent quality, and minimal waste. This service is the bridge between forecasting intelligence and actual bakery operations.
## Key Features
### Automated Production Planning
- **Forecast-Driven Scheduling** - Automatic production schedules from demand forecasts
- **Batch Management** - Track all production batches from start to finish
- **Capacity Planning** - Optimize production capacity utilization
- **Multi-Day Scheduling** - Plan production up to 7 days ahead
- **Recipe Integration** - Automatic ingredient calculation from recipes
- **Equipment Scheduling** - Allocate ovens, mixers, and equipment efficiently
### Production Execution
- **Batch Tracking** - Real-time status of all active production batches
- **Production Logs** - Detailed execution records with timestamps
- **Ingredient Consumption** - Automatic FIFO stock deduction
- **Yield Tracking** - Actual vs. expected production yields
- **Waste Recording** - Track production waste and reasons
- **Real-Time Alerts** - Notifications for production issues
### Quality Control
- **Quality Check Templates** - Standardized quality control forms
- **Digital Checklists** - Paperless quality inspections
- **Quality Metrics** - Track quality scores over time
- **Non-Conformance Tracking** - Record and resolve quality issues
- **Batch Quality History** - Complete quality audit trail
### Equipment Management
- **Equipment Tracking** - All bakery equipment inventory
- **Maintenance Schedules** - Preventive maintenance tracking
- **Equipment Usage** - Monitor utilization and performance
- **Downtime Logging** - Track equipment failures
- **Maintenance Alerts** - Automatic maintenance reminders
### Analytics & Reporting
- **Production Dashboard** - Real-time production KPIs
- **Efficiency Metrics** - OEE (Overall Equipment Effectiveness)
- **Cost Analysis** - Production cost per batch
- **Trend Analysis** - Historical production patterns
- **Performance Reports** - Daily, weekly, monthly summaries
## Business Value
### For Bakery Owners
- **Automated Scheduling** - Save 10-15 hours/week on production planning
- **Waste Reduction** - 15-25% reduction through optimized batch sizes
- **Quality Consistency** - Standardized processes across all batches
- **Cost Control** - Track and reduce production costs
- **Compliance** - Complete production audit trail
### Quantifiable Impact
- **Time Savings**: 10-15 hours/week on planning
- **Waste Reduction**: 15-25% through optimization
- **Cost Savings**: €300-800/month from efficiency gains
- **Quality Improvement**: 20-30% fewer defects
- **Capacity Utilization**: 85%+ (vs 65-70% manual)
### For Production Staff
- **Clear Instructions** - Digital recipes and batch cards
- **Quality Guidance** - Step-by-step quality checks
- **Equipment Visibility** - Know what's available
- **Prioritization** - Know what to produce first
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Production data
- **Caching**: Redis 7.4 - Dashboard KPIs
- **Messaging**: RabbitMQ 4.1 - Alert publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Custom metrics
## API Endpoints (Key Routes)
### Production Scheduling
- `GET /api/v1/production/schedules` - List production schedules
- `POST /api/v1/production/schedules` - Create production schedule
- `GET /api/v1/production/schedules/{schedule_id}` - Get schedule details
- `PUT /api/v1/production/schedules/{schedule_id}` - Update schedule
- `POST /api/v1/production/schedules/generate` - Auto-generate from forecasts
### Batch Management
- `GET /api/v1/production/batches` - List production batches
- `POST /api/v1/production/batches` - Create production batch
- `GET /api/v1/production/batches/{batch_id}` - Get batch details
- `PUT /api/v1/production/batches/{batch_id}/status` - Update batch status
- `POST /api/v1/production/batches/{batch_id}/complete` - Complete batch
### Quality Control
- `GET /api/v1/production/quality/templates` - List QC templates
- `POST /api/v1/production/quality/checks` - Record quality check
- `GET /api/v1/production/quality/checks/{batch_id}` - Get batch quality
- `GET /api/v1/production/quality/metrics` - Quality metrics dashboard
### Equipment Management
- `GET /api/v1/production/equipment` - List all equipment
- `POST /api/v1/production/equipment` - Add equipment
- `PUT /api/v1/production/equipment/{equipment_id}` - Update equipment
- `POST /api/v1/production/equipment/{equipment_id}/maintenance` - Log maintenance
### Analytics
- `GET /api/v1/production/dashboard` - Production dashboard KPIs
- `GET /api/v1/production/analytics/efficiency` - Efficiency metrics
- `GET /api/v1/production/analytics/costs` - Cost analysis
- `GET /api/v1/production/analytics/waste` - Waste analysis
## Database Schema
### Main Tables
**production_schedules**
```sql
CREATE TABLE production_schedules (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
schedule_name VARCHAR(255),
schedule_date DATE NOT NULL,
status VARCHAR(50) DEFAULT 'pending', -- pending, in_progress, completed
total_batches INTEGER DEFAULT 0,
completed_batches INTEGER DEFAULT 0,
generated_from_forecast_id UUID,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
INDEX idx_tenant_date (tenant_id, schedule_date)
);
```
**production_batches**
```sql
CREATE TABLE production_batches (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
schedule_id UUID REFERENCES production_schedules(id),
batch_number VARCHAR(100) NOT NULL,
product_id UUID NOT NULL,
recipe_id UUID NOT NULL,
quantity_planned DECIMAL(10, 2) NOT NULL,
quantity_actual DECIMAL(10, 2),
unit VARCHAR(50) NOT NULL,
status VARCHAR(50) DEFAULT 'planned', -- planned, in_progress, quality_check, completed, failed
priority INTEGER DEFAULT 5,
start_time TIMESTAMP,
end_time TIMESTAMP,
assigned_to UUID,
equipment_used JSONB,
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, batch_number)
);
```
**quality_check_templates**
```sql
CREATE TABLE quality_check_templates (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
template_name VARCHAR(255) NOT NULL,
product_category VARCHAR(100),
check_items JSONB NOT NULL, -- Array of check items with criteria
passing_score INTEGER DEFAULT 80,
is_active BOOLEAN DEFAULT TRUE,
created_at TIMESTAMP DEFAULT NOW()
);
```
**quality_checks**
```sql
CREATE TABLE quality_checks (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
batch_id UUID REFERENCES production_batches(id),
template_id UUID REFERENCES quality_check_templates(id),
performed_by UUID NOT NULL,
check_results JSONB NOT NULL, -- Results for each check item
overall_score INTEGER,
passed BOOLEAN,
issues_found TEXT,
corrective_actions TEXT,
performed_at TIMESTAMP DEFAULT NOW()
);
```
**equipment**
```sql
CREATE TABLE equipment (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
equipment_name VARCHAR(255) NOT NULL,
equipment_type VARCHAR(100), -- oven, mixer, proofer, etc.
capacity VARCHAR(100),
location VARCHAR(255),
status VARCHAR(50) DEFAULT 'operational', -- operational, maintenance, broken
last_maintenance_date DATE,
next_maintenance_date DATE,
maintenance_interval_days INTEGER DEFAULT 90,
total_usage_hours INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, equipment_name)
);
```
**production_capacity**
```sql
CREATE TABLE production_capacity (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
date DATE NOT NULL,
shift VARCHAR(50), -- morning, afternoon, night
available_hours DECIMAL(5, 2),
used_hours DECIMAL(5, 2) DEFAULT 0,
utilization_percentage DECIMAL(5, 2),
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, date, shift)
);
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `production`
**Routing Keys**: `production.batch.completed`, `production.quality.issue`, `production.equipment.maintenance`
**Batch Completed Event**
```json
{
"event_type": "batch_completed",
"tenant_id": "uuid",
"batch_id": "uuid",
"batch_number": "BATCH-2025-1106-001",
"product_id": "uuid",
"product_name": "Baguette",
"quantity_planned": 100,
"quantity_actual": 98,
"yield_percentage": 98.0,
"quality_score": 92,
"quality_passed": true,
"duration_minutes": 240,
"completed_at": "2025-11-06T14:30:00Z",
"timestamp": "2025-11-06T14:30:00Z"
}
```
**Quality Issue Alert**
```json
{
"event_type": "quality_issue",
"tenant_id": "uuid",
"batch_id": "uuid",
"product_name": "Croissant",
"quality_score": 65,
"passing_score": 80,
"issues_found": "Color too dark, texture inconsistent",
"severity": "high",
"corrective_actions": "Adjust oven temperature, check proofing time",
"timestamp": "2025-11-06T10:30:00Z"
}
```
### Consumed Events
- **From Forecasting**: Daily forecasts for production planning
- **From Orchestrator**: Scheduled production triggers
- **From Inventory**: Stock availability checks
## Custom Metrics (Prometheus)
```python
# Production metrics
batches_produced_total = Counter(
'production_batches_total',
'Total production batches',
['tenant_id', 'product_category', 'status']
)
production_yield_percentage = Histogram(
'production_yield_percentage',
'Production yield percentage',
['tenant_id', 'product_id'],
buckets=[70, 80, 85, 90, 95, 98, 100]
)
# Quality metrics
quality_checks_total = Counter(
'production_quality_checks_total',
'Total quality checks performed',
['tenant_id', 'passed']
)
quality_score_distribution = Histogram(
'production_quality_score',
'Quality score distribution',
['tenant_id'],
buckets=[50, 60, 70, 80, 85, 90, 95, 100]
)
# Efficiency metrics
production_duration_minutes = Histogram(
'production_duration_minutes',
'Production batch duration',
['tenant_id', 'product_category'],
buckets=[30, 60, 120, 180, 240, 360, 480]
)
capacity_utilization = Gauge(
'production_capacity_utilization_percentage',
'Production capacity utilization',
['tenant_id', 'shift']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8007)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Production Configuration:**
- `DEFAULT_BATCH_SIZE` - Standard batch size (default: 100)
- `MAX_BATCHES_PER_DAY` - Maximum daily batches (default: 20)
- `ENABLE_AUTO_SCHEDULING` - Auto-generate schedules (default: true)
- `SCHEDULE_GENERATION_TIME` - Daily schedule time (default: "08:00")
**Quality Control:**
- `DEFAULT_PASSING_SCORE` - Minimum quality score (default: 80)
- `ENABLE_QUALITY_ALERTS` - Alert on quality issues (default: true)
- `QUALITY_CHECK_REQUIRED` - Require QC for all batches (default: true)
**Equipment:**
- `MAINTENANCE_REMINDER_DAYS` - Days before maintenance (default: 7)
- `ENABLE_EQUIPMENT_TRACKING` - Track equipment usage (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/production
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/production
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Forecasting Service** - Demand forecasts for scheduling
- **Recipes Service** - Recipe details for batches
- **Inventory Service** - Stock availability and consumption
- **Equipment data** - Equipment tracking
- **PostgreSQL** - Production data storage
- **Redis** - Dashboard caching
- **RabbitMQ** - Event publishing
### Dependents
- **Inventory Service** - Ingredient consumption updates
- **AI Insights Service** - Production efficiency insights
- **Orchestrator Service** - Triggers daily scheduling
- **Frontend Dashboard** - Display production status
## Business Value for VUE Madrid
- **Automation**: 10-15 hours/week saved on manual planning
- **Waste Reduction**: 15-25% through optimized scheduling
- **Quality Improvement**: Standardized processes, 20-30% fewer defects
- **Compliance**: Complete production audit trail
- **Efficiency**: 85%+ capacity utilization vs 65-70% manual
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Recipes Service
## Overview
The **Recipes Service** is the central knowledge base for all bakery products, managing detailed recipes with precise ingredient quantities, preparation instructions, and cost calculations. It enables standardized production, accurate batch scaling, nutritional tracking, and cost management across all bakery operations. This service ensures consistent product quality and provides the foundation for production planning and inventory management.
## Key Features
### Recipe Management
- **Complete Recipe Database** - Store all product recipes with full details
- **Ingredient Specifications** - Precise quantities, units, and preparation notes
- **Multi-Step Instructions** - Detailed preparation steps with timing
- **Recipe Versioning** - Track recipe changes over time
- **Recipe Categories** - Organize by product type (bread, pastries, cakes, etc.)
- **Recipe Status** - Active, draft, archived recipe states
### Batch Scaling
- **Automatic Scaling** - Calculate ingredients for any batch size
- **Unit Conversion** - Convert between kg, g, L, mL, units
- **Yield Calculation** - Expected output per recipe
- **Scaling Validation** - Ensure scaled quantities are practical
- **Multi-Batch Planning** - Scale for multiple simultaneous batches
- **Equipment Consideration** - Validate against equipment capacity
### Cost Calculation
- **Real-Time Costing** - Current ingredient prices from Inventory
- **Cost Per Unit** - Calculate cost per individual product
- **Profit Margin Analysis** - Compare cost vs. selling price
- **Cost Breakdown** - Ingredient-level cost contribution
- **Historical Cost Tracking** - Monitor cost changes over time
- **Target Price Alerts** - Notify when costs exceed thresholds
### Nutritional Information
- **Nutritional Facts** - Calories, protein, carbs, fats per serving
- **Allergen Tracking** - Common allergens (gluten, nuts, dairy, eggs)
- **Dietary Labels** - Vegan, vegetarian, gluten-free indicators
- **Regulatory Compliance** - EU food labeling requirements
- **Serving Size** - Standard serving definitions
- **Label Generation** - Auto-generate compliant food labels
### Recipe Intelligence
- **Popular Recipes** - Track most-produced recipes
- **Cost Optimization Suggestions** - Identify expensive recipes
- **Ingredient Substitutions** - Alternative ingredient recommendations
- **Seasonal Recipes** - Highlight seasonal products
- **Recipe Performance** - Track yield accuracy and quality
- **Cross-Service Integration** - Used by Production, Inventory, Procurement
## Business Value
### For Bakery Owners
- **Standardized Production** - Consistent product quality every time
- **Cost Control** - Know exact cost and profit margin per product
- **Pricing Optimization** - Data-driven pricing decisions
- **Regulatory Compliance** - Meet EU food labeling requirements
- **Waste Reduction** - Accurate scaling prevents over-production
- **Knowledge Preservation** - Recipes survive staff turnover
### Quantifiable Impact
- **Time Savings**: 3-5 hours/week on recipe calculations
- **Cost Accuracy**: 99%+ vs. manual estimation (±20-30%)
- **Waste Reduction**: 10-15% through accurate batch scaling
- **Quality Consistency**: 95%+ batch consistency vs. 70-80% manual
- **Compliance**: Avoid €500-5,000 fines for labeling violations
- **Pricing Optimization**: 5-10% profit margin improvement
### For Production Staff
- **Clear Instructions** - Step-by-step production guidance
- **Exact Quantities** - No guesswork on ingredient amounts
- **Scaling Confidence** - Reliably produce any batch size
- **Quality Standards** - Know expected yield and appearance
- **Allergen Awareness** - Critical safety information visible
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Recipe data storage
- **Caching**: Redis 7.4 - Recipe and cost cache
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Validation**: Pydantic 2.0 - Schema validation
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Custom metrics
## API Endpoints (Key Routes)
### Recipe Management
- `GET /api/v1/recipes` - List all recipes with filters
- `POST /api/v1/recipes` - Create new recipe
- `GET /api/v1/recipes/{recipe_id}` - Get recipe details
- `PUT /api/v1/recipes/{recipe_id}` - Update recipe
- `DELETE /api/v1/recipes/{recipe_id}` - Delete recipe (soft delete)
- `GET /api/v1/recipes/{recipe_id}/versions` - Get recipe version history
### Ingredient Management
- `GET /api/v1/recipes/{recipe_id}/ingredients` - List recipe ingredients
- `POST /api/v1/recipes/{recipe_id}/ingredients` - Add ingredient to recipe
- `PUT /api/v1/recipes/{recipe_id}/ingredients/{ingredient_id}` - Update ingredient quantity
- `DELETE /api/v1/recipes/{recipe_id}/ingredients/{ingredient_id}` - Remove ingredient
### Batch Scaling
- `POST /api/v1/recipes/{recipe_id}/scale` - Scale recipe to batch size
- `POST /api/v1/recipes/{recipe_id}/scale/multiple` - Scale for multiple batches
- `GET /api/v1/recipes/{recipe_id}/scale/validate` - Validate scaling parameters
### Cost Calculation
- `GET /api/v1/recipes/{recipe_id}/cost` - Get current recipe cost
- `GET /api/v1/recipes/{recipe_id}/cost/history` - Historical cost data
- `GET /api/v1/recipes/cost/analysis` - Cost analysis dashboard
- `POST /api/v1/recipes/{recipe_id}/cost/target` - Set target cost threshold
### Nutritional Information
- `GET /api/v1/recipes/{recipe_id}/nutrition` - Get nutritional facts
- `PUT /api/v1/recipes/{recipe_id}/nutrition` - Update nutritional data
- `GET /api/v1/recipes/{recipe_id}/allergens` - Get allergen information
- `GET /api/v1/recipes/{recipe_id}/label` - Generate food label
### Analytics
- `GET /api/v1/recipes/analytics/popular` - Most used recipes
- `GET /api/v1/recipes/analytics/costly` - Most expensive recipes
- `GET /api/v1/recipes/analytics/profitable` - Most profitable recipes
- `GET /api/v1/recipes/analytics/categories` - Recipe category breakdown
## Database Schema
### Main Tables
**recipes**
```sql
CREATE TABLE recipes (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_name VARCHAR(255) NOT NULL,
product_id UUID, -- Link to product catalog
category VARCHAR(100), -- bread, pastry, cake, etc.
description TEXT,
preparation_time_minutes INTEGER,
baking_time_minutes INTEGER,
total_time_minutes INTEGER,
difficulty VARCHAR(50), -- easy, medium, hard
servings INTEGER, -- Standard serving count
yield_quantity DECIMAL(10, 2), -- Expected output quantity
yield_unit VARCHAR(50), -- kg, units, etc.
status VARCHAR(50) DEFAULT 'active', -- active, draft, archived
version INTEGER DEFAULT 1,
parent_recipe_id UUID, -- For versioning
created_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, recipe_name, version)
);
```
**recipe_ingredients**
```sql
CREATE TABLE recipe_ingredients (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_id UUID REFERENCES recipes(id) ON DELETE CASCADE,
ingredient_id UUID NOT NULL, -- Link to inventory items
ingredient_name VARCHAR(255) NOT NULL, -- Cached for performance
quantity DECIMAL(10, 3) NOT NULL,
unit VARCHAR(50) NOT NULL, -- kg, g, L, mL, units
preparation_notes TEXT, -- e.g., "sifted", "room temperature"
is_optional BOOLEAN DEFAULT FALSE,
substitutes JSONB, -- Alternative ingredients
cost_per_unit DECIMAL(10, 2), -- Cached from inventory
display_order INTEGER DEFAULT 0, -- Order in recipe
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**recipe_instructions**
```sql
CREATE TABLE recipe_instructions (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_id UUID REFERENCES recipes(id) ON DELETE CASCADE,
step_number INTEGER NOT NULL,
instruction_text TEXT NOT NULL,
duration_minutes INTEGER, -- Time for this step
temperature_celsius INTEGER, -- Oven temperature if applicable
equipment_needed VARCHAR(255),
tips TEXT,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(recipe_id, step_number)
);
```
**recipe_nutrition**
```sql
CREATE TABLE recipe_nutrition (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_id UUID REFERENCES recipes(id) ON DELETE CASCADE,
serving_size DECIMAL(10, 2),
serving_unit VARCHAR(50),
calories DECIMAL(10, 2),
protein_g DECIMAL(10, 2),
carbohydrates_g DECIMAL(10, 2),
fat_g DECIMAL(10, 2),
fiber_g DECIMAL(10, 2),
sugar_g DECIMAL(10, 2),
sodium_mg DECIMAL(10, 2),
allergens JSONB, -- Array of allergen codes
dietary_labels JSONB, -- vegan, vegetarian, gluten-free, etc.
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(recipe_id)
);
```
**recipe_costs**
```sql
CREATE TABLE recipe_costs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_id UUID REFERENCES recipes(id),
calculated_at TIMESTAMP DEFAULT NOW(),
total_ingredient_cost DECIMAL(10, 2) NOT NULL,
cost_per_unit DECIMAL(10, 2) NOT NULL,
cost_breakdown JSONB, -- Per-ingredient costs
selling_price DECIMAL(10, 2),
profit_margin_percentage DECIMAL(5, 2),
is_current BOOLEAN DEFAULT TRUE -- Most recent calculation
);
```
**recipe_scaling_history**
```sql
CREATE TABLE recipe_scaling_history (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
recipe_id UUID REFERENCES recipes(id),
scaled_by UUID NOT NULL, -- User who scaled
original_yield DECIMAL(10, 2),
target_yield DECIMAL(10, 2),
scaling_factor DECIMAL(10, 4),
scaled_ingredients JSONB, -- Calculated quantities
used_in_batch_id UUID, -- Link to production batch
created_at TIMESTAMP DEFAULT NOW()
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_recipes_tenant_status ON recipes(tenant_id, status);
CREATE INDEX idx_recipes_category ON recipes(tenant_id, category);
CREATE INDEX idx_recipe_ingredients_recipe ON recipe_ingredients(recipe_id);
CREATE INDEX idx_recipe_ingredients_ingredient ON recipe_ingredients(tenant_id, ingredient_id);
CREATE INDEX idx_recipe_costs_current ON recipe_costs(recipe_id, is_current) WHERE is_current = TRUE;
```
## Business Logic Examples
### Batch Scaling Algorithm
```python
async def scale_recipe(recipe_id: UUID, target_yield: float, target_unit: str) -> ScaledRecipe:
"""
Scale recipe ingredients to produce target yield.
"""
# Get recipe and ingredients
recipe = await get_recipe(recipe_id)
ingredients = await get_recipe_ingredients(recipe_id)
# Calculate scaling factor
scaling_factor = target_yield / recipe.yield_quantity
# Scale each ingredient
scaled_ingredients = []
for ingredient in ingredients:
scaled_quantity = ingredient.quantity * scaling_factor
# Round to practical values (e.g., 0.5g increments)
scaled_quantity = round_to_practical_value(scaled_quantity, ingredient.unit)
scaled_ingredients.append({
"ingredient_id": ingredient.ingredient_id,
"ingredient_name": ingredient.ingredient_name,
"original_quantity": ingredient.quantity,
"scaled_quantity": scaled_quantity,
"unit": ingredient.unit,
"preparation_notes": ingredient.preparation_notes
})
# Store scaling history
await store_scaling_history(recipe_id, recipe.yield_quantity, target_yield, scaling_factor)
return ScaledRecipe(
recipe_id=recipe_id,
recipe_name=recipe.recipe_name,
original_yield=recipe.yield_quantity,
target_yield=target_yield,
scaling_factor=scaling_factor,
scaled_ingredients=scaled_ingredients
)
```
### Real-Time Cost Calculation
```python
async def calculate_recipe_cost(recipe_id: UUID) -> RecipeCost:
"""
Calculate current recipe cost based on live ingredient prices.
"""
# Get recipe ingredients
ingredients = await get_recipe_ingredients(recipe_id)
total_cost = 0.0
cost_breakdown = []
for ingredient in ingredients:
# Get current price from inventory service
current_price = await get_ingredient_current_price(
ingredient.ingredient_id
)
# Calculate cost for this ingredient
ingredient_cost = ingredient.quantity * current_price
total_cost += ingredient_cost
cost_breakdown.append({
"ingredient_name": ingredient.ingredient_name,
"quantity": ingredient.quantity,
"unit": ingredient.unit,
"price_per_unit": current_price,
"total_cost": ingredient_cost,
"percentage_of_total": 0 # Calculated after loop
})
# Calculate percentages
for item in cost_breakdown:
item["percentage_of_total"] = (item["total_cost"] / total_cost) * 100
# Get recipe yield
recipe = await get_recipe(recipe_id)
cost_per_unit = total_cost / recipe.yield_quantity
# Get selling price if available
selling_price = await get_product_selling_price(recipe.product_id)
profit_margin = None
if selling_price:
profit_margin = ((selling_price - cost_per_unit) / selling_price) * 100
# Store cost calculation
cost_record = await store_recipe_cost(
recipe_id=recipe_id,
total_cost=total_cost,
cost_per_unit=cost_per_unit,
cost_breakdown=cost_breakdown,
selling_price=selling_price,
profit_margin=profit_margin
)
return cost_record
```
### Unit Conversion System
```python
class UnitConverter:
"""
Convert between different measurement units.
"""
WEIGHT_CONVERSIONS = {
'kg': 1000, # Base unit: grams
'g': 1,
'mg': 0.001,
'lb': 453.592,
'oz': 28.3495
}
VOLUME_CONVERSIONS = {
'L': 1000, # Base unit: milliliters
'mL': 1,
'cup': 236.588,
'tbsp': 14.7868,
'tsp': 4.92892
}
@classmethod
def convert(cls, quantity: float, from_unit: str, to_unit: str) -> float:
"""
Convert quantity from one unit to another.
"""
# Check if units are in same category
if from_unit in cls.WEIGHT_CONVERSIONS and to_unit in cls.WEIGHT_CONVERSIONS:
# Convert to base unit (grams) then to target
base_quantity = quantity * cls.WEIGHT_CONVERSIONS[from_unit]
return base_quantity / cls.WEIGHT_CONVERSIONS[to_unit]
elif from_unit in cls.VOLUME_CONVERSIONS and to_unit in cls.VOLUME_CONVERSIONS:
# Convert to base unit (mL) then to target
base_quantity = quantity * cls.VOLUME_CONVERSIONS[from_unit]
return base_quantity / cls.VOLUME_CONVERSIONS[to_unit]
else:
raise ValueError(f"Cannot convert {from_unit} to {to_unit}")
@staticmethod
def round_to_practical_value(quantity: float, unit: str) -> float:
"""
Round to practical measurement values.
"""
if unit in ['kg']:
return round(quantity, 2) # 10g precision
elif unit in ['g', 'mL']:
return round(quantity, 1) # 0.1 precision
elif unit in ['mg']:
return round(quantity, 0) # 1mg precision
else:
return round(quantity, 2) # Default 2 decimals
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `recipes`
**Routing Keys**: `recipes.created`, `recipes.updated`, `recipes.cost_changed`
**Recipe Created Event**
```json
{
"event_type": "recipe_created",
"tenant_id": "uuid",
"recipe_id": "uuid",
"recipe_name": "Baguette Tradicional",
"category": "bread",
"yield_quantity": 100,
"yield_unit": "units",
"ingredient_count": 5,
"created_by": "uuid",
"timestamp": "2025-11-06T10:30:00Z"
}
```
**Recipe Cost Changed Event**
```json
{
"event_type": "recipe_cost_changed",
"tenant_id": "uuid",
"recipe_id": "uuid",
"recipe_name": "Croissant",
"old_cost_per_unit": 0.45,
"new_cost_per_unit": 0.52,
"change_percentage": 15.56,
"reason": "flour_price_increase",
"timestamp": "2025-11-06T14:00:00Z"
}
```
**Recipe Scaled Event**
```json
{
"event_type": "recipe_scaled",
"tenant_id": "uuid",
"recipe_id": "uuid",
"recipe_name": "Whole Wheat Bread",
"original_yield": 50,
"target_yield": 200,
"scaling_factor": 4.0,
"scaled_by": "uuid",
"used_in_batch_id": "uuid",
"timestamp": "2025-11-06T08:00:00Z"
}
```
### Consumed Events
- **From Inventory**: Ingredient price updates trigger cost recalculation
- **From Production**: Batch completion with actual yields updates recipe accuracy
- **From Procurement**: New ingredient purchases may affect costs
## Custom Metrics (Prometheus)
```python
# Recipe metrics
recipes_total = Counter(
'recipes_total',
'Total recipes in system',
['tenant_id', 'category', 'status']
)
recipe_cost_per_unit = Histogram(
'recipe_cost_per_unit_euros',
'Recipe cost per unit distribution',
['tenant_id', 'category'],
buckets=[0.10, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00, 3.00, 5.00]
)
# Scaling metrics
recipe_scaling_total = Counter(
'recipe_scaling_operations_total',
'Total recipe scaling operations',
['tenant_id', 'recipe_id']
)
scaling_factor_distribution = Histogram(
'recipe_scaling_factor',
'Recipe scaling factor distribution',
['tenant_id'],
buckets=[0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0]
)
# Cost calculation metrics
cost_calculations_total = Counter(
'recipe_cost_calculations_total',
'Total cost calculations performed',
['tenant_id']
)
profit_margin_percentage = Histogram(
'recipe_profit_margin_percentage',
'Recipe profit margin distribution',
['tenant_id', 'category'],
buckets=[0, 10, 20, 30, 40, 50, 60, 70, 80]
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8009)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Recipe Configuration:**
- `ENABLE_AUTO_COST_UPDATE` - Auto-recalculate costs on price changes (default: true)
- `COST_CACHE_TTL_SECONDS` - Cost cache duration (default: 3600)
- `MIN_PROFIT_MARGIN_PERCENTAGE` - Minimum acceptable margin (default: 30)
- `ALERT_ON_LOW_MARGIN` - Alert when margin drops below threshold (default: true)
**Scaling Configuration:**
- `MAX_SCALING_FACTOR` - Maximum scaling multiplier (default: 10.0)
- `MIN_SCALING_FACTOR` - Minimum scaling multiplier (default: 0.1)
- `ENABLE_PRACTICAL_ROUNDING` - Round to practical values (default: true)
**Validation:**
- `REQUIRE_NUTRITION_INFO` - Require nutritional data (default: false)
- `REQUIRE_ALLERGEN_INFO` - Require allergen declaration (default: true)
- `VALIDATE_INGREDIENT_AVAILABILITY` - Check inventory before saving (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/recipes
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/recipes
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
### Running Tests
```bash
pytest tests/ -v --cov=app
```
### API Documentation
Access Swagger UI: `http://localhost:8009/docs`
## Integration Points
### Dependencies
- **Inventory Service** - Real-time ingredient prices and availability
- **Production Service** - Recipe scaling for production batches
- **Procurement Service** - Ingredient specifications for ordering
- **Auth Service** - User authentication for recipe creation
- **PostgreSQL** - Recipe data storage
- **Redis** - Cost caching
- **RabbitMQ** - Event publishing
### Dependents
- **Production Service** - Uses recipes for batch planning
- **Inventory Service** - Knows required ingredients
- **Procurement Service** - Plans purchases based on recipes
- **Forecasting Service** - Recipe yield data for demand planning
- **AI Insights Service** - Cost optimization recommendations
- **Frontend Dashboard** - Recipe management UI
## Security Measures
### Authentication & Authorization
- JWT token validation on all endpoints
- Tenant isolation at database level
- Role-based access control (admin, manager, staff)
- Recipe ownership verification
### Data Protection
- Tenant-scoped queries (prevent data leaks)
- Input validation with Pydantic schemas
- SQL injection prevention (parameterized queries)
- XSS protection on recipe instructions
- Recipe versioning (prevent accidental overwrites)
### Audit Logging
```python
# Log all recipe modifications
logger.info(
"recipe_updated",
recipe_id=recipe.id,
tenant_id=recipe.tenant_id,
updated_by=current_user.id,
changes=changes_dict,
timestamp=datetime.utcnow()
)
```
## Competitive Advantages
### 1. Real-Time Cost Tracking
Unlike static recipe books, costs update automatically when ingredient prices change, enabling immediate pricing decisions.
### 2. Intelligent Scaling
Advanced scaling algorithm with practical rounding ensures recipes work in real-world production scenarios, not just mathematically.
### 3. Cross-Service Intelligence
Recipe data flows seamlessly to production, inventory, and procurement—no manual data entry or synchronization.
### 4. EU Compliance Built-In
Nutritional facts and allergen tracking meet EU food labeling regulations (EU FIC 1169/2011), avoiding costly fines.
### 5. Cost Breakdown Analysis
See exactly which ingredients drive costs, enabling targeted negotiations with suppliers or ingredient substitutions.
### 6. Recipe Versioning
Track recipe changes over time, enabling quality control and the ability to revert to previous versions.
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- Inconsistent product quality due to informal recipes
- Unknown production costs leading to poor pricing
- Manual batch scaling errors causing waste
- EU labeling compliance complexity
- Recipe knowledge lost when staff leave
### Solution
Bakery-IA Recipes Service provides:
- **Standardized Production**: Digital recipes ensure consistency
- **Cost Transparency**: Real-time cost calculation for informed pricing
- **Batch Scaling**: Automatic ingredient calculation for any volume
- **Compliance**: Built-in EU food labeling support
- **Knowledge Base**: Recipes preserved digitally forever
### Quantifiable Impact
**Cost Savings:**
- €50-150/month from improved pricing decisions
- €100-300/month from waste reduction (accurate scaling)
- €500-5,000 avoided fines (compliance)
- **Total: €150-450/month savings**
**Time Savings:**
- 3-5 hours/week on manual recipe calculations
- 2-3 hours/week on cost analysis
- 1-2 hours/week on batch planning
- **Total: 6-10 hours/week saved**
**Quality Improvements:**
- 95%+ batch consistency vs. 70-80% manual
- 99%+ cost accuracy vs. ±20-30% estimation
- 100% EU labeling compliance
- Zero recipe knowledge loss
### Target Market Fit (Spanish Bakeries)
- **Regulatory**: EU food labeling laws (FIC 1169/2011) require detailed allergen and nutritional information
- **Market Size**: 10,000+ bakeries in Spain need recipe management
- **Pain Point**: Most bakeries use paper recipes or personal knowledge
- **Differentiation**: First Spanish bakery platform with integrated recipe costing
### ROI Calculation
**Investment**: €0 additional (included in platform subscription)
**Monthly Savings**: €150-450
**Annual ROI**: €1,800-5,400 value per bakery
**Payback**: Immediate (included in subscription)
---
## Technical Innovation
### Intelligent Scaling Algorithm
Scales recipes while maintaining practical measurements (e.g., rounds to 0.5g increments for precision scales).
### Real-Time Cost Engine
Recalculates recipe costs in <100ms when ingredient prices change, using Redis caching for performance.
### EU Compliance Automation
Automatically generates EU-compliant food labels with nutritional facts and allergen declarations.
### Cross-Service Integration
Recipe data flows to 5+ other services (Production, Inventory, Procurement, Forecasting, AI Insights) enabling platform-wide intelligence.
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Sales Service
## Overview
The **Sales Service** is the foundational data layer of Bakery-IA, responsible for collecting, processing, and analyzing historical sales data. It provides the critical training data for AI forecasting models and delivers comprehensive sales analytics to help bakery owners understand their business performance. This service handles bulk data imports, real-time sales tracking, and generates actionable insights from sales patterns.
## Key Features
### Sales Data Management
- **Historical Sales Recording** - Complete sales transaction history with timestamps
- **Product Catalog Integration** - Link sales to products for detailed analytics
- **Multi-Channel Support** - Track sales from POS, online orders, and manual entries
- **Data Validation** - Ensure data quality and consistency
- **Bulk Import/Export** - CSV/Excel file processing for historical data migration
- **Real-Time Updates** - Live sales data ingestion from POS systems
### Sales Analytics
- **Revenue Tracking** - Daily, weekly, monthly, yearly revenue reports
- **Product Performance** - Best sellers, slow movers, profitability by product
- **Trend Analysis** - Identify growth patterns and seasonal variations
- **Customer Insights** - Purchase frequency, average transaction value
- **Comparative Analytics** - Period-over-period comparisons
- **Sales Forecasting Input** - Clean, structured data for ML training
### Data Import & Onboarding
- **CSV Upload** - Import historical sales from spreadsheets
- **Excel Support** - Process .xlsx files with multiple sheets
- **Column Mapping** - Flexible mapping of user data to system fields
- **Duplicate Detection** - Prevent duplicate sales entries
- **Error Handling** - Detailed error reporting for failed imports
- **Progress Tracking** - Real-time import job status updates
### Audit & Compliance
- **Complete Audit Trail** - Track all data modifications
- **Data Retention** - Configurable retention policies
- **GDPR Compliance** - Customer data anonymization and deletion
- **Export Capabilities** - Generate reports for accounting and tax compliance
## Business Value
### For Bakery Owners
- **Business Intelligence** - Understand which products drive revenue
- **Trend Identification** - Spot seasonal patterns and optimize inventory
- **Performance Tracking** - Monitor daily/weekly/monthly KPIs
- **Historical Analysis** - Learn from past performance to improve future decisions
- **Tax Compliance** - Export sales data for accounting and tax reporting
### Quantifiable Impact
- **Time Savings**: 5-8 hours/week on manual sales tracking and reporting
- **Accuracy**: 99%+ data accuracy vs. manual entry
- **Insights Speed**: Real-time analytics vs. weekly/monthly manual reports
- **Forecasting Foundation**: Clean sales data improves forecast accuracy by 15-25%
### For AI/ML Systems
- **Training Data Quality** - High-quality, structured data for Prophet models
- **Feature Engineering** - Pre-processed data with temporal features
- **Data Completeness** - Fill gaps and handle missing data
- **Real-Time Updates** - Continuous model improvement with new sales data
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Sales transaction storage
- **Data Processing**: Pandas, NumPy - CSV/Excel processing and analytics
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **File Processing**: openpyxl - Excel file handling
- **Validation**: Pydantic - Data validation and serialization
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Custom metrics
- **Caching**: Redis 7.4 - Analytics cache
## API Endpoints (Key Routes)
### Sales Data Management
- `POST /api/v1/sales` - Create single sales record
- `POST /api/v1/sales/batch` - Create multiple sales records
- `GET /api/v1/sales` - List sales with filtering and pagination
- `GET /api/v1/sales/{sale_id}` - Get specific sale details
- `PUT /api/v1/sales/{sale_id}` - Update sales record
- `DELETE /api/v1/sales/{sale_id}` - Delete sales record (soft delete)
### Bulk Operations
- `POST /api/v1/sales/import/csv` - Upload CSV file for bulk import
- `POST /api/v1/sales/import/excel` - Upload Excel file for bulk import
- `GET /api/v1/sales/import/jobs` - List import job history
- `GET /api/v1/sales/import/jobs/{job_id}` - Get import job status
- `GET /api/v1/sales/export/csv` - Export sales data to CSV
- `GET /api/v1/sales/export/excel` - Export sales data to Excel
### Analytics
- `GET /api/v1/sales/analytics/summary` - Overall sales summary
- `GET /api/v1/sales/analytics/revenue` - Revenue by period
- `GET /api/v1/sales/analytics/products` - Product performance metrics
- `GET /api/v1/sales/analytics/trends` - Trend analysis and patterns
- `GET /api/v1/sales/analytics/comparison` - Period comparison
- `GET /api/v1/sales/analytics/top-products` - Best selling products
### Data Quality
- `POST /api/v1/sales/validate` - Validate sales data before import
- `GET /api/v1/sales/duplicates` - Find potential duplicate records
- `POST /api/v1/sales/clean` - Clean and normalize data
- `GET /api/v1/sales/data-quality` - Data quality metrics
## Database Schema
### Main Tables
**sales_data**
```sql
CREATE TABLE sales_data (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
product_id UUID NOT NULL,
sale_date DATE NOT NULL,
sale_timestamp TIMESTAMP NOT NULL,
quantity DECIMAL(10, 2) NOT NULL,
unit_price DECIMAL(10, 2) NOT NULL,
total_amount DECIMAL(10, 2) NOT NULL,
currency VARCHAR(3) DEFAULT 'EUR',
channel VARCHAR(50), -- pos, online, manual
location VARCHAR(255),
customer_id UUID,
transaction_id VARCHAR(100),
payment_method VARCHAR(50),
discount_amount DECIMAL(10, 2) DEFAULT 0,
tax_amount DECIMAL(10, 2),
notes TEXT,
metadata JSONB,
is_deleted BOOLEAN DEFAULT FALSE,
deleted_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
created_by UUID,
INDEX idx_tenant_date (tenant_id, sale_date),
INDEX idx_product_date (product_id, sale_date),
INDEX idx_transaction (transaction_id)
);
```
**sales_import_jobs**
```sql
CREATE TABLE sales_import_jobs (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
job_name VARCHAR(255),
file_name VARCHAR(255),
file_size_bytes BIGINT,
file_type VARCHAR(50), -- csv, xlsx
total_rows INTEGER,
processed_rows INTEGER DEFAULT 0,
successful_rows INTEGER DEFAULT 0,
failed_rows INTEGER DEFAULT 0,
status VARCHAR(50), -- pending, processing, completed, failed
error_log JSONB,
column_mapping JSONB,
started_at TIMESTAMP,
completed_at TIMESTAMP,
created_by UUID,
created_at TIMESTAMP DEFAULT NOW()
);
```
**sales_products** (Cache)
```sql
CREATE TABLE sales_products (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
product_name VARCHAR(255) NOT NULL,
product_category VARCHAR(100),
unit VARCHAR(50),
last_sale_date DATE,
total_sales_count INTEGER DEFAULT 0,
total_revenue DECIMAL(12, 2) DEFAULT 0,
average_price DECIMAL(10, 2),
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, product_name)
);
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `sales`
**Routing Key**: `sales.data.imported`
**Sales Data Imported Event**
```json
{
"event_type": "sales_data_imported",
"tenant_id": "uuid",
"import_job_id": "uuid",
"file_name": "sales_history_2024.csv",
"total_records": 15000,
"successful_records": 14850,
"failed_records": 150,
"date_range": {
"start_date": "2024-01-01",
"end_date": "2024-12-31"
},
"products_affected": 45,
"total_revenue": 125000.50,
"trigger_retraining": true,
"timestamp": "2025-11-06T10:30:00Z"
}
```
### Consumed Events
- **From POS**: Real-time sales transactions
- **From Orders**: Completed order sales data
## Custom Metrics (Prometheus)
```python
sales_records_created_total = Counter(
'sales_records_created_total',
'Total sales records created',
['tenant_id', 'channel'] # pos, online, manual
)
sales_import_jobs_total = Counter(
'sales_import_jobs_total',
'Total import jobs',
['tenant_id', 'status', 'file_type']
)
sales_revenue_total = Counter(
'sales_revenue_euros_total',
'Total sales revenue in euros',
['tenant_id', 'product_category']
)
import_processing_duration = Histogram(
'sales_import_duration_seconds',
'Import job processing time',
['tenant_id', 'file_type'],
buckets=[1, 5, 10, 30, 60, 120, 300, 600]
)
data_quality_score = Gauge(
'sales_data_quality_score',
'Data quality score 0-100',
['tenant_id']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8002)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Import Configuration:**
- `MAX_IMPORT_FILE_SIZE_MB` - Maximum file size (default: 50)
- `MAX_IMPORT_ROWS` - Maximum rows per import (default: 100000)
- `IMPORT_BATCH_SIZE` - Rows per batch insert (default: 1000)
- `ENABLE_DUPLICATE_DETECTION` - Check for duplicates (default: true)
**Data Retention:**
- `SALES_DATA_RETENTION_YEARS` - Years to keep data (default: 10)
- `ENABLE_SOFT_DELETE` - Use soft deletes (default: true)
- `AUTO_CLEANUP_ENABLED` - Automatic old data cleanup (default: false)
**Analytics Cache:**
- `ANALYTICS_CACHE_TTL_MINUTES` - Cache lifetime (default: 60)
- `ENABLE_ANALYTICS_CACHE` - Enable caching (default: true)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1 (optional)
### Local Development
```bash
cd services/sales
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/sales
export REDIS_URL=redis://localhost:6379/0
alembic upgrade head
python main.py
```
### Testing
```bash
# Unit tests
pytest tests/unit/ -v
# Integration tests
pytest tests/integration/ -v
# Import tests
pytest tests/import/ -v
# Test with coverage
pytest --cov=app tests/ --cov-report=html
```
### Sample Data Import
```bash
# Create sample CSV
cat > sample_sales.csv << EOF
date,product,quantity,price
2024-01-01,Baguette,50,1.50
2024-01-01,Croissant,30,2.00
2024-01-02,Baguette,55,1.50
EOF
# Import via API
curl -X POST http://localhost:8002/api/v1/sales/import/csv \
-H "Content-Type: multipart/form-data" \
-F "file=@sample_sales.csv" \
-F "tenant_id=your-tenant-id"
```
## Integration Points
### Dependencies
- **PostgreSQL** - Sales data storage
- **Redis** - Analytics caching
- **RabbitMQ** - Event publishing
- **File System** - Temporary file storage for imports
### Dependents
- **Forecasting Service** - Fetch sales data for model training
- **Training Service** - Historical sales for ML training
- **Analytics Dashboard** - Display sales reports and charts
- **AI Insights Service** - Analyze sales patterns
- **Inventory Service** - Correlate sales with stock levels
- **Production Service** - Plan production based on sales history
## Data Quality Measures
### Validation Rules
```python
# Sales record validation
class SalesRecordValidator:
def validate(self, record: dict) -> tuple[bool, list[str]]:
errors = []
# Required fields
if not record.get('sale_date'):
errors.append("sale_date is required")
if not record.get('product_id'):
errors.append("product_id is required")
if not record.get('quantity') or record['quantity'] <= 0:
errors.append("quantity must be positive")
if not record.get('unit_price') or record['unit_price'] < 0:
errors.append("unit_price cannot be negative")
# Business logic validation
if record.get('discount_amount', 0) > record.get('total_amount', 0):
errors.append("discount cannot exceed total amount")
# Date validation
if record.get('sale_date'):
sale_date = parse_date(record['sale_date'])
if sale_date > datetime.now().date():
errors.append("sale_date cannot be in the future")
return len(errors) == 0, errors
```
### Duplicate Detection
```python
def detect_duplicates(tenant_id: str, records: list[dict]) -> list[dict]:
"""Find potential duplicate sales records"""
duplicates = []
for record in records:
existing = db.query(SalesData).filter(
SalesData.tenant_id == tenant_id,
SalesData.product_id == record['product_id'],
SalesData.sale_timestamp.between(
record['sale_timestamp'] - timedelta(minutes=5),
record['sale_timestamp'] + timedelta(minutes=5)
),
SalesData.quantity == record['quantity'],
SalesData.total_amount == record['total_amount']
).first()
if existing:
duplicates.append({
'new_record': record,
'existing_record_id': existing.id,
'match_confidence': calculate_match_confidence(record, existing)
})
return duplicates
```
## Security Measures
### Data Protection
- **Tenant Isolation** - All sales data scoped to tenant_id
- **Input Validation** - Pydantic schemas for all inputs
- **SQL Injection Prevention** - Parameterized queries
- **File Upload Security** - Virus scanning, size limits, type validation
- **Soft Deletes** - Preserve data for audit trail
### Access Control
- **Authentication Required** - JWT tokens for all endpoints
- **Role-Based Access** - Different permissions for owner/manager/staff
- **Audit Logging** - Track all data modifications
- **GDPR Compliance** - Customer data anonymization and export
## Performance Optimization
### Database Optimization
1. **Indexes** - Optimized indexes on tenant_id, sale_date, product_id
2. **Partitioning** - Table partitioning by year for large datasets
3. **Batch Inserts** - Insert 1000 rows per transaction during imports
4. **Connection Pooling** - Reuse database connections
5. **Query Optimization** - Materialized views for common analytics
### Import Performance
```python
# Batch import optimization
async def bulk_import_sales(records: list[dict], batch_size: int = 1000):
"""Optimized bulk import with batching"""
total_records = len(records)
for i in range(0, total_records, batch_size):
batch = records[i:i + batch_size]
# Prepare batch for bulk insert
sales_objects = [SalesData(**record) for record in batch]
# Bulk insert
db.bulk_save_objects(sales_objects)
await db.commit()
# Update progress
progress = (i + len(batch)) / total_records * 100
await update_import_progress(job_id, progress)
```
## Troubleshooting
### Common Issues
**Issue**: Import fails with "File too large" error
- **Cause**: File exceeds `MAX_IMPORT_FILE_SIZE_MB`
- **Solution**: Split file into smaller chunks or increase limit
**Issue**: Duplicate records detected
- **Cause**: Re-importing same data or POS sync issues
- **Solution**: Enable duplicate detection or manual review
**Issue**: Slow analytics queries
- **Cause**: Large dataset without proper indexes
- **Solution**: Add indexes, enable caching, or use materialized views
**Issue**: Missing sales data
- **Cause**: POS integration not working
- **Solution**: Check POS service logs and webhook configuration
## Competitive Advantages
1. **Bulk Import** - Easy migration from existing systems
2. **Multi-Channel Support** - Unified view across POS, online, manual
3. **Real-Time Analytics** - Instant insights vs. batch processing
4. **Data Quality** - Automated validation and cleaning
5. **ML-Ready Data** - Structured data perfect for forecasting
6. **Spanish Market** - Euro currency, Spanish date formats
7. **GDPR Compliant** - Built-in compliance features
## Future Enhancements
- **Real-Time Streaming** - Apache Kafka for high-volume sales
- **Advanced Analytics** - Customer segmentation, cohort analysis
- **Predictive Analytics** - Predict next purchase, customer lifetime value
- **Multi-Currency** - Support for international bakeries
- **Mobile POS** - Native mobile sales capture apps
- **Blockchain Audit** - Immutable sales records for compliance
- **AI-Powered Cleaning** - Automatic data quality improvements
---
**For VUE Madrid Business Plan**: The Sales Service provides the foundational data infrastructure that powers all AI/ML capabilities in Bakery-IA. The ability to easily import historical data (15,000+ records in minutes) and generate real-time analytics demonstrates technical sophistication and reduces customer onboarding time from days to hours. This is critical for rapid customer acquisition and SaaS scalability.

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# Suppliers Service
## Overview
The **Suppliers Service** manages the complete supplier database with performance tracking, quality ratings, and price comparison capabilities. It enables data-driven supplier selection, tracks delivery performance, manages supplier contracts, and provides scorecards for supplier evaluation. This service is essential for maintaining strong supplier relationships while optimizing costs and ensuring consistent ingredient quality.
## Key Features
### Supplier Management
- **Complete Supplier Database** - Contact details, payment terms, delivery schedules
- **Supplier Categories** - Flour mills, dairy, packaging, equipment, services
- **Multi-Contact Management** - Sales reps, delivery coordinators, accounts
- **Contract Management** - Track agreements, pricing contracts, terms
- **Supplier Status** - Active, inactive, preferred, blacklisted
- **Document Storage** - Contracts, certificates, insurance documents
- **Geographic Data** - Location, delivery zones, distance calculations
### Performance Tracking
- **Delivery Performance** - On-time delivery rate, lead time accuracy
- **Quality Metrics** - Product quality ratings, defect rates
- **Reliability Score** - Overall supplier reliability assessment
- **Order Fulfillment** - Order accuracy, complete shipment rate
- **Communication Score** - Responsiveness, issue resolution
- **Compliance Tracking** - Food safety certifications, insurance validity
### Price Management
- **Price Lists** - Current pricing per product
- **Price History** - Track price changes over time
- **Volume Discounts** - Tiered pricing based on order size
- **Contract Pricing** - Fixed prices for contract duration
- **Price Comparison** - Compare prices across suppliers
- **Price Alerts** - Notify on significant price changes
- **Cost Trend Analysis** - Identify price trends and seasonality
### Quality Assurance
- **Quality Ratings** - 1-5 star ratings per supplier
- **Quality Reviews** - Detailed quality assessments
- **Defect Tracking** - Record quality issues and resolution
- **Product Certifications** - Organic, fair trade, origin certifications
- **Lab Results** - Store test results and analysis
- **Corrective Actions** - Track quality improvement measures
- **Quality Trends** - Monitor quality over time
### Supplier Scorecards
- **Multi-Dimensional Scoring** - Price, quality, delivery, service
- **Weighted Metrics** - Customize scoring based on priorities
- **Trend Analysis** - Improve/decline over time
- **Ranking System** - Top suppliers by category
- **Performance Reports** - Monthly/quarterly scorecards
- **Benchmarking** - Compare against category averages
### Communication & Collaboration
- **Contact Log** - Track all supplier interactions
- **Email Integration** - Send POs and communications
- **Order History** - Complete purchase history per supplier
- **Issue Tracking** - Log and resolve supplier problems
- **Notes & Reminders** - Internal notes about suppliers
- **Calendar Integration** - Delivery schedules, contract renewals
## Business Value
### For Bakery Owners
- **Cost Optimization** - Data-driven supplier negotiations
- **Quality Assurance** - Track and improve supplier quality
- **Risk Management** - Identify unreliable suppliers early
- **Supplier Leverage** - Performance data strengthens negotiations
- **Compliance** - Track certifications and documentation
- **Strategic Relationships** - Focus on best-performing suppliers
### Quantifiable Impact
- **Cost Savings**: 5-10% through data-driven negotiations
- **Quality Improvement**: 15-25% fewer ingredient defects
- **Delivery Reliability**: 20-30% improvement in on-time delivery
- **Time Savings**: 3-5 hours/week on supplier management
- **Risk Reduction**: Avoid €500-5,000 in spoiled ingredients
- **Supplier Consolidation**: 20-30% fewer suppliers, better terms
### For Procurement Staff
- **Supplier Selection** - Clear data for choosing suppliers
- **Performance Visibility** - Know which suppliers excel
- **Price Comparison** - Quickly compare options
- **Issue Resolution** - Track problems to completion
- **Contract Management** - Never miss renewal dates
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Supplier data
- **Caching**: Redis 7.4 - Supplier data cache
- **Messaging**: RabbitMQ 4.1 - Event publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Validation**: Pydantic 2.0 - Schema validation
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Supplier metrics
## API Endpoints (Key Routes)
### Supplier Management
- `GET /api/v1/suppliers` - List suppliers with filters
- `POST /api/v1/suppliers` - Create new supplier
- `GET /api/v1/suppliers/{supplier_id}` - Get supplier details
- `PUT /api/v1/suppliers/{supplier_id}` - Update supplier
- `DELETE /api/v1/suppliers/{supplier_id}` - Delete supplier (soft delete)
- `GET /api/v1/suppliers/{supplier_id}/contacts` - Get supplier contacts
### Performance Tracking
- `GET /api/v1/suppliers/{supplier_id}/performance` - Get performance metrics
- `POST /api/v1/suppliers/{supplier_id}/performance/review` - Add performance review
- `GET /api/v1/suppliers/{supplier_id}/performance/history` - Performance history
- `GET /api/v1/suppliers/performance/rankings` - Supplier rankings
### Price Management
- `GET /api/v1/suppliers/{supplier_id}/pricing` - Get supplier price list
- `POST /api/v1/suppliers/{supplier_id}/pricing` - Add/update pricing
- `GET /api/v1/suppliers/{supplier_id}/pricing/history` - Price history
- `POST /api/v1/suppliers/pricing/compare` - Compare prices across suppliers
### Quality Management
- `GET /api/v1/suppliers/{supplier_id}/quality` - Get quality metrics
- `POST /api/v1/suppliers/{supplier_id}/quality/review` - Add quality review
- `POST /api/v1/suppliers/{supplier_id}/quality/issue` - Report quality issue
- `GET /api/v1/suppliers/{supplier_id}/quality/defects` - Defect history
### Scorecard & Analytics
- `GET /api/v1/suppliers/{supplier_id}/scorecard` - Generate supplier scorecard
- `GET /api/v1/suppliers/analytics/dashboard` - Supplier analytics dashboard
- `GET /api/v1/suppliers/analytics/top-performers` - Top performing suppliers
- `GET /api/v1/suppliers/analytics/cost-analysis` - Cost analysis by supplier
### Communication
- `GET /api/v1/suppliers/{supplier_id}/communications` - Communication log
- `POST /api/v1/suppliers/{supplier_id}/communications` - Log communication
- `POST /api/v1/suppliers/{supplier_id}/send-email` - Send email to supplier
- `GET /api/v1/suppliers/{supplier_id}/orders` - Order history
## Database Schema
### Main Tables
**suppliers**
```sql
CREATE TABLE suppliers (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_name VARCHAR(255) NOT NULL,
supplier_code VARCHAR(100), -- Internal supplier code
supplier_type VARCHAR(100), -- flour_mill, dairy, packaging, equipment, service
business_legal_name VARCHAR(255),
tax_id VARCHAR(50), -- CIF/NIF for Spanish suppliers
phone VARCHAR(50),
email VARCHAR(255),
website VARCHAR(255),
address_line1 VARCHAR(255),
address_line2 VARCHAR(255),
city VARCHAR(100),
state_province VARCHAR(100),
postal_code VARCHAR(20),
country VARCHAR(100) DEFAULT 'España',
payment_terms VARCHAR(100), -- Net 30, Net 60, COD, etc.
credit_limit DECIMAL(10, 2),
currency VARCHAR(10) DEFAULT 'EUR',
lead_time_days INTEGER DEFAULT 3,
minimum_order_value DECIMAL(10, 2),
delivery_days JSONB, -- ["Monday", "Wednesday", "Friday"]
status VARCHAR(50) DEFAULT 'active', -- active, inactive, preferred, blacklisted
is_preferred BOOLEAN DEFAULT FALSE,
notes TEXT,
-- Performance metrics (cached)
quality_rating DECIMAL(3, 2), -- 1.00 to 5.00
delivery_rating DECIMAL(3, 2),
price_competitiveness DECIMAL(3, 2),
overall_score DECIMAL(3, 2),
total_orders INTEGER DEFAULT 0,
on_time_deliveries INTEGER DEFAULT 0,
on_time_delivery_percentage DECIMAL(5, 2),
-- Compliance
food_safety_cert_valid BOOLEAN DEFAULT FALSE,
food_safety_cert_expiry DATE,
insurance_valid BOOLEAN DEFAULT FALSE,
insurance_expiry DATE,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, supplier_name)
);
```
**supplier_contacts**
```sql
CREATE TABLE supplier_contacts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
contact_name VARCHAR(255) NOT NULL,
job_title VARCHAR(255),
contact_type VARCHAR(50), -- sales, delivery, accounts, technical
phone VARCHAR(50),
mobile VARCHAR(50),
email VARCHAR(255),
is_primary BOOLEAN DEFAULT FALSE,
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**supplier_products**
```sql
CREATE TABLE supplier_products (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
ingredient_id UUID NOT NULL, -- Link to inventory ingredient
supplier_product_code VARCHAR(100),
supplier_product_name VARCHAR(255),
unit_price DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
minimum_order_quantity DECIMAL(10, 2),
packaging VARCHAR(100), -- "25kg bag", "1L bottle", etc.
lead_time_days INTEGER DEFAULT 3,
is_preferred BOOLEAN DEFAULT FALSE,
quality_grade VARCHAR(50), -- A, B, C or Premium, Standard, Economy
certifications JSONB, -- ["Organic", "Non-GMO", "Fair Trade"]
valid_from DATE DEFAULT CURRENT_DATE,
valid_until DATE,
is_active BOOLEAN DEFAULT TRUE,
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, supplier_id, ingredient_id)
);
```
**supplier_performance_reviews**
```sql
CREATE TABLE supplier_performance_reviews (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
review_date DATE NOT NULL DEFAULT CURRENT_DATE,
review_period_start DATE NOT NULL,
review_period_end DATE NOT NULL,
-- Performance scores (1-5)
quality_score DECIMAL(3, 2) NOT NULL,
delivery_score DECIMAL(3, 2) NOT NULL,
price_score DECIMAL(3, 2) NOT NULL,
service_score DECIMAL(3, 2) NOT NULL,
overall_score DECIMAL(3, 2) NOT NULL,
-- Metrics
total_orders INTEGER DEFAULT 0,
on_time_deliveries INTEGER DEFAULT 0,
on_time_percentage DECIMAL(5, 2),
quality_issues INTEGER DEFAULT 0,
defect_rate DECIMAL(5, 2),
average_delivery_time_days DECIMAL(5, 2),
total_spend DECIMAL(10, 2) DEFAULT 0.00,
-- Qualitative feedback
strengths TEXT,
weaknesses TEXT,
recommendations TEXT,
reviewed_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW()
);
```
**supplier_quality_issues**
```sql
CREATE TABLE supplier_quality_issues (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
purchase_order_id UUID, -- Link to specific PO if applicable
ingredient_id UUID,
issue_date DATE NOT NULL DEFAULT CURRENT_DATE,
issue_type VARCHAR(100) NOT NULL, -- defect, contamination, wrong_product, damaged, expired
severity VARCHAR(50) NOT NULL, -- critical, major, minor
description TEXT NOT NULL,
quantity_affected DECIMAL(10, 2),
unit VARCHAR(50),
financial_impact DECIMAL(10, 2),
-- Resolution
resolution_status VARCHAR(50) DEFAULT 'open', -- open, in_progress, resolved, closed
corrective_action TEXT,
supplier_response TEXT,
credit_issued DECIMAL(10, 2) DEFAULT 0.00,
resolved_date DATE,
resolved_by UUID,
reported_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**supplier_price_history**
```sql
CREATE TABLE supplier_price_history (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
ingredient_id UUID NOT NULL,
effective_date DATE NOT NULL,
unit_price DECIMAL(10, 2) NOT NULL,
unit VARCHAR(50) NOT NULL,
previous_price DECIMAL(10, 2),
price_change_percentage DECIMAL(5, 2),
reason VARCHAR(255), -- "market_increase", "contract_renewal", etc.
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_price_history_date (tenant_id, ingredient_id, effective_date DESC)
);
```
**supplier_communications**
```sql
CREATE TABLE supplier_communications (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
communication_date TIMESTAMP NOT NULL DEFAULT NOW(),
communication_type VARCHAR(50) NOT NULL, -- email, phone, meeting, visit
subject VARCHAR(255),
summary TEXT NOT NULL,
participants JSONB, -- Array of names
action_items TEXT,
follow_up_date DATE,
logged_by UUID NOT NULL,
created_at TIMESTAMP DEFAULT NOW()
);
```
**supplier_contracts**
```sql
CREATE TABLE supplier_contracts (
id UUID PRIMARY KEY,
tenant_id UUID NOT NULL,
supplier_id UUID REFERENCES suppliers(id) ON DELETE CASCADE,
contract_number VARCHAR(100),
contract_type VARCHAR(100), -- pricing, volume, exclusive, service
start_date DATE NOT NULL,
end_date DATE NOT NULL,
auto_renew BOOLEAN DEFAULT FALSE,
renewal_notice_days INTEGER DEFAULT 30,
contract_terms TEXT,
payment_terms VARCHAR(100),
minimum_volume DECIMAL(10, 2),
maximum_volume DECIMAL(10, 2),
fixed_pricing BOOLEAN DEFAULT FALSE,
contract_value DECIMAL(10, 2),
status VARCHAR(50) DEFAULT 'active', -- draft, active, expired, terminated
document_url VARCHAR(500),
notes TEXT,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_suppliers_tenant_status ON suppliers(tenant_id, status);
CREATE INDEX idx_suppliers_type ON suppliers(tenant_id, supplier_type);
CREATE INDEX idx_supplier_products_supplier ON supplier_products(supplier_id);
CREATE INDEX idx_supplier_products_ingredient ON supplier_products(tenant_id, ingredient_id);
CREATE INDEX idx_performance_reviews_supplier ON supplier_performance_reviews(supplier_id, review_date DESC);
CREATE INDEX idx_quality_issues_supplier ON supplier_quality_issues(supplier_id, issue_date DESC);
CREATE INDEX idx_quality_issues_status ON supplier_quality_issues(tenant_id, resolution_status);
```
## Business Logic Examples
### Supplier Scorecard Calculation
```python
async def calculate_supplier_scorecard(
supplier_id: UUID,
start_date: date,
end_date: date
) -> SupplierScorecard:
"""
Calculate comprehensive supplier scorecard for period.
Scoring: 30% Quality, 30% Delivery, 25% Price, 15% Service
"""
# Get all purchase orders for period
purchase_orders = await db.query(PurchaseOrder).filter(
PurchaseOrder.supplier_id == supplier_id,
PurchaseOrder.order_date >= start_date,
PurchaseOrder.order_date <= end_date,
PurchaseOrder.status == 'received'
).all()
if not purchase_orders:
return None
# QUALITY SCORE (1-5 scale)
quality_issues = await db.query(SupplierQualityIssue).filter(
SupplierQualityIssue.supplier_id == supplier_id,
SupplierQualityIssue.issue_date >= start_date,
SupplierQualityIssue.issue_date <= end_date
).all()
total_orders = len(purchase_orders)
critical_issues = len([i for i in quality_issues if i.severity == 'critical'])
major_issues = len([i for i in quality_issues if i.severity == 'major'])
minor_issues = len([i for i in quality_issues if i.severity == 'minor'])
# Defect rate
defect_rate = (critical_issues * 3 + major_issues * 2 + minor_issues) / total_orders if total_orders > 0 else 0
# Quality score: 5 stars minus penalties
quality_score = 5.0 - min(defect_rate, 4.0)
# DELIVERY SCORE (1-5 scale)
on_time_deliveries = len([
po for po in purchase_orders
if po.actual_delivery_date and po.expected_delivery_date
and po.actual_delivery_date <= po.expected_delivery_date
])
on_time_percentage = (on_time_deliveries / total_orders * 100) if total_orders > 0 else 0
# Delivery score based on on-time percentage
if on_time_percentage >= 95:
delivery_score = 5.0
elif on_time_percentage >= 90:
delivery_score = 4.5
elif on_time_percentage >= 85:
delivery_score = 4.0
elif on_time_percentage >= 75:
delivery_score = 3.0
elif on_time_percentage >= 60:
delivery_score = 2.0
else:
delivery_score = 1.0
# PRICE SCORE (1-5 scale)
# Compare supplier prices against market average
supplier_products = await get_supplier_products(supplier_id)
price_comparisons = []
for sp in supplier_products:
# Get all suppliers for this ingredient
all_suppliers = await db.query(SupplierProduct).filter(
SupplierProduct.tenant_id == sp.tenant_id,
SupplierProduct.ingredient_id == sp.ingredient_id,
SupplierProduct.is_active == True
).all()
if len(all_suppliers) > 1:
prices = [s.unit_price for s in all_suppliers]
avg_price = sum(prices) / len(prices)
price_ratio = sp.unit_price / avg_price if avg_price > 0 else 1.0
price_comparisons.append(price_ratio)
if price_comparisons:
avg_price_ratio = sum(price_comparisons) / len(price_comparisons)
# Lower ratio = better price = higher score
if avg_price_ratio <= 0.90:
price_score = 5.0 # 10%+ below market
elif avg_price_ratio <= 0.95:
price_score = 4.5 # 5-10% below market
elif avg_price_ratio <= 1.00:
price_score = 4.0 # At market
elif avg_price_ratio <= 1.05:
price_score = 3.0 # 5% above market
elif avg_price_ratio <= 1.10:
price_score = 2.0 # 10% above market
else:
price_score = 1.0 # 10%+ above market
else:
price_score = 3.0 # Default if no comparison available
# SERVICE SCORE (1-5 scale)
# Based on communication responsiveness and issue resolution
communications = await db.query(SupplierCommunication).filter(
SupplierCommunication.supplier_id == supplier_id,
SupplierCommunication.communication_date >= start_date,
SupplierCommunication.communication_date <= end_date
).all()
resolved_issues = len([
i for i in quality_issues
if i.resolution_status == 'resolved'
])
total_issues = len(quality_issues)
resolution_rate = (resolved_issues / total_issues * 100) if total_issues > 0 else 100
# Service score based on issue resolution
if resolution_rate >= 90 and len(communications) >= 2:
service_score = 5.0
elif resolution_rate >= 80:
service_score = 4.0
elif resolution_rate >= 70:
service_score = 3.0
elif resolution_rate >= 50:
service_score = 2.0
else:
service_score = 1.0
# WEIGHTED OVERALL SCORE
overall_score = (
quality_score * 0.30 +
delivery_score * 0.30 +
price_score * 0.25 +
service_score * 0.15
)
# Calculate total spend
total_spend = sum(po.total_amount for po in purchase_orders)
# Average lead time
lead_times = [
(po.actual_delivery_date - po.order_date).days
for po in purchase_orders
if po.actual_delivery_date and po.order_date
]
avg_lead_time = sum(lead_times) / len(lead_times) if lead_times else 0
# Create scorecard
scorecard = SupplierScorecard(
supplier_id=supplier_id,
period_start=start_date,
period_end=end_date,
quality_score=round(quality_score, 2),
delivery_score=round(delivery_score, 2),
price_score=round(price_score, 2),
service_score=round(service_score, 2),
overall_score=round(overall_score, 2),
total_orders=total_orders,
on_time_deliveries=on_time_deliveries,
on_time_percentage=round(on_time_percentage, 2),
quality_issues_count=len(quality_issues),
defect_rate=round(defect_rate, 4),
total_spend=total_spend,
average_lead_time_days=round(avg_lead_time, 1)
)
return scorecard
```
### Supplier Recommendation Engine
```python
async def recommend_supplier_for_ingredient(
tenant_id: UUID,
ingredient_id: UUID,
quantity: float,
urgency: str = 'normal'
) -> list[dict]:
"""
Recommend best suppliers for ingredient based on multiple criteria.
Returns ranked list with scores and reasoning.
"""
# Get all suppliers for ingredient
supplier_products = await db.query(SupplierProduct).filter(
SupplierProduct.tenant_id == tenant_id,
SupplierProduct.ingredient_id == ingredient_id,
SupplierProduct.is_active == True
).all()
if not supplier_products:
return []
recommendations = []
for sp in supplier_products:
supplier = await get_supplier(sp.supplier_id)
# Get supplier performance
scorecard = await get_latest_scorecard(sp.supplier_id)
# Calculate recommendation score
scores = {}
# Price score (40% weight for normal urgency, 20% for urgent)
price_weight = 0.20 if urgency == 'urgent' else 0.40
scores['price'] = scorecard.price_score if scorecard else 3.0
# Quality score (30% weight)
scores['quality'] = scorecard.quality_score if scorecard else 3.0
# Delivery score (30% for normal, 50% for urgent)
delivery_weight = 0.50 if urgency == 'urgent' else 0.30
scores['delivery'] = scorecard.delivery_score if scorecard else 3.0
# Service score (10% weight)
scores['service'] = scorecard.service_score if scorecard else 3.0
# Calculate weighted score
if urgency == 'urgent':
weighted_score = (
scores['price'] * 0.20 +
scores['quality'] * 0.30 +
scores['delivery'] * 0.50
)
else:
weighted_score = (
scores['price'] * 0.40 +
scores['quality'] * 0.30 +
scores['delivery'] * 0.20 +
scores['service'] * 0.10
)
# Check if minimum order quantity is met
meets_moq = quantity >= (sp.minimum_order_quantity or 0)
# Check lead time
lead_time_acceptable = sp.lead_time_days <= 3 if urgency == 'urgent' else True
# Calculate total cost
total_cost = sp.unit_price * quantity
recommendations.append({
'supplier_id': str(supplier.id),
'supplier_name': supplier.supplier_name,
'unit_price': float(sp.unit_price),
'total_cost': float(total_cost),
'lead_time_days': sp.lead_time_days,
'minimum_order_quantity': float(sp.minimum_order_quantity) if sp.minimum_order_quantity else None,
'meets_moq': meets_moq,
'lead_time_acceptable': lead_time_acceptable,
'quality_score': float(scores['quality']),
'delivery_score': float(scores['delivery']),
'price_score': float(scores['price']),
'service_score': float(scores['service']),
'weighted_score': float(weighted_score),
'recommendation_reason': generate_recommendation_reason(
scores, urgency, meets_moq, lead_time_acceptable
)
})
# Sort by weighted score descending
recommendations.sort(key=lambda x: x['weighted_score'], reverse=True)
return recommendations
def generate_recommendation_reason(
scores: dict,
urgency: str,
meets_moq: bool,
lead_time_acceptable: bool
) -> str:
"""Generate human-readable recommendation reason."""
reasons = []
if urgency == 'urgent' and lead_time_acceptable:
reasons.append("Fast delivery available")
if scores['quality'] >= 4.5:
reasons.append("Excellent quality rating")
elif scores['quality'] >= 4.0:
reasons.append("Good quality rating")
if scores['price'] >= 4.5:
reasons.append("Best price")
elif scores['price'] >= 4.0:
reasons.append("Competitive price")
if scores['delivery'] >= 4.5:
reasons.append("Excellent delivery record")
elif scores['delivery'] >= 4.0:
reasons.append("Reliable delivery")
if not meets_moq:
reasons.append("⚠️ Below minimum order quantity")
if not lead_time_acceptable:
reasons.append("⚠️ Lead time too long for urgent order")
return ", ".join(reasons) if reasons else "Standard supplier"
```
### Price Trend Analysis
```python
async def analyze_price_trends(
tenant_id: UUID,
ingredient_id: UUID,
months_back: int = 12
) -> dict:
"""
Analyze price trends for ingredient across all suppliers.
"""
start_date = date.today() - timedelta(days=months_back * 30)
# Get price history
price_history = await db.query(SupplierPriceHistory).filter(
SupplierPriceHistory.tenant_id == tenant_id,
SupplierPriceHistory.ingredient_id == ingredient_id,
SupplierPriceHistory.effective_date >= start_date
).order_by(SupplierPriceHistory.effective_date.asc()).all()
if not price_history:
return None
# Calculate statistics
prices = [p.unit_price for p in price_history]
current_price = prices[-1]
min_price = min(prices)
max_price = max(prices)
avg_price = sum(prices) / len(prices)
# Calculate trend (simple linear regression)
import statistics
if len(prices) > 2:
# Calculate slope
x = list(range(len(prices)))
x_mean = sum(x) / len(x)
y_mean = avg_price
numerator = sum((x[i] - x_mean) * (prices[i] - y_mean) for i in range(len(prices)))
denominator = sum((x[i] - x_mean) ** 2 for i in range(len(x)))
slope = numerator / denominator if denominator != 0 else 0
trend_direction = 'increasing' if slope > 0.01 else 'decreasing' if slope < -0.01 else 'stable'
else:
trend_direction = 'insufficient_data'
# Calculate volatility (coefficient of variation)
std_dev = statistics.stdev(prices) if len(prices) > 1 else 0
volatility = (std_dev / avg_price * 100) if avg_price > 0 else 0
# Identify best and worst suppliers
supplier_avg_prices = {}
for ph in price_history:
if ph.supplier_id not in supplier_avg_prices:
supplier_avg_prices[ph.supplier_id] = []
supplier_avg_prices[ph.supplier_id].append(ph.unit_price)
supplier_averages = {
sid: sum(prices) / len(prices)
for sid, prices in supplier_avg_prices.items()
}
best_supplier_id = min(supplier_averages, key=supplier_averages.get)
worst_supplier_id = max(supplier_averages, key=supplier_averages.get)
return {
'ingredient_id': str(ingredient_id),
'period_months': months_back,
'data_points': len(price_history),
'current_price': float(current_price),
'min_price': float(min_price),
'max_price': float(max_price),
'average_price': float(avg_price),
'price_range': float(max_price - min_price),
'trend_direction': trend_direction,
'volatility_percentage': round(volatility, 2),
'price_change_percentage': round((current_price - prices[0]) / prices[0] * 100, 2),
'best_supplier_id': str(best_supplier_id),
'best_supplier_avg_price': float(supplier_averages[best_supplier_id]),
'worst_supplier_id': str(worst_supplier_id),
'worst_supplier_avg_price': float(supplier_averages[worst_supplier_id]),
'price_difference_percentage': round(
(supplier_averages[worst_supplier_id] - supplier_averages[best_supplier_id]) /
supplier_averages[best_supplier_id] * 100, 2
)
}
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `suppliers`
**Routing Keys**: `suppliers.performance_alert`, `suppliers.price_change`, `suppliers.quality_issue`, `suppliers.contract_expiring`
**Supplier Performance Alert**
```json
{
"event_type": "supplier_performance_alert",
"tenant_id": "uuid",
"supplier_id": "uuid",
"supplier_name": "Harinas García",
"alert_type": "poor_delivery",
"on_time_delivery_percentage": 65.0,
"threshold": 80.0,
"period_days": 30,
"recommendation": "Consider alternative suppliers or renegotiate terms",
"timestamp": "2025-11-06T09:00:00Z"
}
```
**Supplier Price Change Event**
```json
{
"event_type": "supplier_price_change",
"tenant_id": "uuid",
"supplier_id": "uuid",
"supplier_name": "Lácteos del Norte",
"ingredient_id": "uuid",
"ingredient_name": "Mantequilla",
"old_price": 4.50,
"new_price": 5.20,
"change_percentage": 15.56,
"effective_date": "2025-11-15",
"reason": "market_increase",
"timestamp": "2025-11-06T14:00:00Z"
}
```
**Supplier Quality Issue Event**
```json
{
"event_type": "supplier_quality_issue",
"tenant_id": "uuid",
"supplier_id": "uuid",
"supplier_name": "Distribuidora Madrid",
"issue_id": "uuid",
"severity": "major",
"issue_type": "contamination",
"ingredient_name": "Harina Integral",
"description": "Foreign material found in bag",
"financial_impact": 125.00,
"timestamp": "2025-11-06T11:30:00Z"
}
```
**Contract Expiring Alert**
```json
{
"event_type": "supplier_contract_expiring",
"tenant_id": "uuid",
"supplier_id": "uuid",
"supplier_name": "Embalajes Premium",
"contract_id": "uuid",
"contract_type": "pricing",
"expiry_date": "2025-11-30",
"days_until_expiry": 24,
"auto_renew": false,
"action_required": "Review and renew contract",
"timestamp": "2025-11-06T08:00:00Z"
}
```
### Consumed Events
- **From Procurement**: Purchase orders update supplier performance
- **From Inventory**: Quality issues on received goods
- **From Accounting**: Payment history affects supplier relationships
## Custom Metrics (Prometheus)
```python
# Supplier metrics
suppliers_total = Gauge(
'suppliers_total',
'Total suppliers',
['tenant_id', 'supplier_type', 'status']
)
supplier_performance_score = Histogram(
'supplier_performance_score',
'Supplier overall performance score',
['tenant_id', 'supplier_id'],
buckets=[1.0, 2.0, 3.0, 3.5, 4.0, 4.5, 5.0]
)
supplier_quality_issues_total = Counter(
'supplier_quality_issues_total',
'Total quality issues',
['tenant_id', 'supplier_id', 'severity']
)
supplier_on_time_delivery_percentage = Histogram(
'supplier_on_time_delivery_percentage',
'Supplier on-time delivery rate',
['tenant_id', 'supplier_id'],
buckets=[50, 60, 70, 80, 85, 90, 95, 98, 100]
)
supplier_price_changes_total = Counter(
'supplier_price_changes_total',
'Total price changes',
['tenant_id', 'supplier_id', 'direction']
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8012)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Performance Configuration:**
- `QUALITY_SCORE_WEIGHT` - Quality scoring weight (default: 0.30)
- `DELIVERY_SCORE_WEIGHT` - Delivery scoring weight (default: 0.30)
- `PRICE_SCORE_WEIGHT` - Price scoring weight (default: 0.25)
- `SERVICE_SCORE_WEIGHT` - Service scoring weight (default: 0.15)
**Alert Configuration:**
- `MIN_ON_TIME_DELIVERY_PERCENTAGE` - Alert threshold (default: 80.0)
- `MAX_DEFECT_RATE_PERCENTAGE` - Alert threshold (default: 5.0)
- `PRICE_CHANGE_ALERT_PERCENTAGE` - Alert on price change (default: 10.0)
- `CONTRACT_EXPIRY_ALERT_DAYS` - Days before expiry (default: 30)
**Quality Configuration:**
- `REQUIRE_FOOD_SAFETY_CERT` - Require certifications (default: true)
- `CERT_EXPIRY_REMINDER_DAYS` - Remind before expiry (default: 60)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
### Local Development
```bash
cd services/suppliers
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/suppliers
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Auth Service** - User authentication
- **PostgreSQL** - Supplier data
- **Redis** - Caching
- **RabbitMQ** - Event publishing
### Dependents
- **Procurement Service** - Supplier selection for purchase orders
- **Inventory Service** - Quality tracking on receipts
- **AI Insights Service** - Supplier optimization recommendations
- **Notification Service** - Performance and contract alerts
- **Frontend Dashboard** - Supplier management UI
## Business Value for VUE Madrid
### Problem Statement
Spanish bakeries struggle with:
- No systematic supplier performance tracking
- Manual price comparison across suppliers
- No quality issue documentation
- Lost supplier history when staff changes
- No leverage in supplier negotiations
- Missed contract renewals and price increases
### Solution
Bakery-IA Suppliers Service provides:
- **Data-Driven Decisions**: Performance scorecards guide supplier selection
- **Cost Control**: Price tracking and comparison across suppliers
- **Quality Assurance**: Document and resolve quality issues systematically
- **Relationship Management**: Complete supplier history and communication log
- **Risk Management**: Track certifications, contracts, and performance
### Quantifiable Impact
**Cost Savings:**
- €100-300/month from data-driven negotiations (5-10% procurement savings)
- €150-500/month from reduced ingredient defects (15-25% quality improvement)
- €50-200/month from avoiding expired contracts with price increases
- **Total: €300-1,000/month savings**
**Time Savings:**
- 3-5 hours/week on supplier management and tracking
- 1-2 hours/week on price comparison
- 1-2 hours/week on quality issue documentation
- **Total: 5-9 hours/week saved**
**Operational Improvements:**
- 20-30% improvement in on-time delivery through supplier accountability
- 15-25% fewer ingredient defects through quality tracking
- 100% contract renewal visibility (avoid surprise price increases)
- 20-30% supplier consolidation (focus on best performers)
### Target Market Fit (Spanish Bakeries)
- **Supplier Relationships**: Spanish business culture values long-term relationships
- **Quality Focus**: Spanish consumers demand high-quality ingredients
- **Cost Pressure**: SMBs need every cost advantage in competitive market
- **Compliance**: Food safety certifications required by Spanish law
### ROI Calculation
**Investment**: €0 additional (included in platform subscription)
**Monthly Savings**: €300-1,000
**Annual ROI**: €3,600-12,000 value per bakery
**Payback**: Immediate (included in subscription)
---
**Copyright © 2025 Bakery-IA. All rights reserved.**

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# Tenant Service
## Overview
The **Tenant Service** manages the multi-tenant SaaS architecture, handling tenant (bakery) registration, subscription management via Stripe, team member administration, and billing. It provides tenant isolation, subscription tier enforcement, usage tracking, and automated billing workflows. This service is the foundation for scaling Bakery-IA to thousands of Spanish bakeries with a sustainable SaaS revenue model.
## Key Features
### Tenant Management
- **Tenant Registration** - New bakery signup and onboarding
- **Tenant Profiles** - Business information, settings, preferences
- **Multi-Location Support** - Multiple stores per tenant
- **Tenant Isolation** - Complete data separation between tenants
- **Tenant Settings** - Configurable features per tenant
- **Tenant Branding** - Custom logos, colors (Enterprise tier)
- **Tenant Status** - Active, trial, suspended, cancelled
### Subscription Management
- **Stripe Integration** - Full Stripe API integration
- **Subscription Tiers** - Free, Pro, Enterprise plans
- **Trial Management** - 14-day free trials
- **Upgrade/Downgrade** - Self-service plan changes
- **Proration** - Automatic prorated billing
- **Payment Methods** - Credit cards, SEPA Direct Debit (Europe)
- **Invoicing** - Automatic invoice generation
### Team Management
- **Multi-User Access** - Invite team members to tenant
- **Role-Based Access Control** - Owner, Admin, Manager, Staff
- **Permission Management** - Granular feature permissions
- **Team Member Invitations** - Email invites with expiry
- **Team Member Removal** - Revoke access
- **Activity Tracking** - Team member audit logs
### Billing & Usage
- **Usage Tracking** - API calls, storage, transactions
- **Usage Limits** - Enforce tier limits
- **Overage Handling** - Charge for overages or block access
- **Billing History** - Complete invoice history
- **Payment Status** - Track payment success/failure
- **Failed Payment Handling** - Retry logic, suspension
- **Revenue Analytics** - MRR, churn, LTV tracking
### Subscription Tiers
**Free Tier (€0/month):**
- 1 location
- 100 transactions/month
- 1 user
- Email support
- Basic features only
**Pro Tier (€49/month):**
- 3 locations
- Unlimited transactions
- 5 users
- Priority email support
- All features
- WhatsApp notifications
- Advanced analytics
**Enterprise Tier (€149/month):**
- Unlimited locations
- Unlimited transactions
- Unlimited users
- Phone + email support
- All features
- Custom branding
- Dedicated account manager
- SLA guarantee
### Compliance & Security
- **GDPR Compliance** - Data protection built-in
- **Data Residency** - EU data storage (Spain/Germany)
- **Tenant Data Export** - Complete data export capability
- **Tenant Deletion** - GDPR-compliant account deletion
- **Audit Logging** - Complete tenant activity logs
- **Security Settings** - 2FA, IP whitelist (Enterprise)
## Business Value
### For Bakery Owners
- **Predictable Pricing** - Clear monthly costs
- **Start Free** - Try before buying with 14-day trial
- **Scale as You Grow** - Upgrade when needed
- **Team Collaboration** - Invite staff with appropriate access
- **Professional Invoicing** - Automatic Spanish tax-compliant invoices
- **Easy Cancellation** - Cancel anytime, no long-term commitment
### Quantifiable Impact
- **MRR per Customer**: €0-149/month based on tier
- **Customer Acquisition Cost**: €200-300 (PPC + sales)
- **Customer Lifetime Value**: €1,200-3,600 (avg 24-month retention)
- **Churn Rate**: <10%/month target (industry: 5-15%)
- **Expansion Revenue**: 30-40% customers upgrade within 6 months
- **Payment Success Rate**: 95%+ with Stripe
### For Platform (Bakery-IA)
- **Scalable Revenue**: Subscription model scales with customers
- **Automated Billing**: No manual invoicing needed
- **European Payments**: SEPA support for Spanish/EU customers
- **Churn Prevention**: Usage tracking enables proactive retention
- **Expansion Opportunities**: Upsell based on usage
- **Financial Visibility**: Real-time revenue metrics
## Technology Stack
- **Framework**: FastAPI (Python 3.11+) - Async web framework
- **Database**: PostgreSQL 17 - Tenant and subscription data
- **Payments**: Stripe API - Payment processing
- **Caching**: Redis 7.4 - Subscription cache
- **Messaging**: RabbitMQ 4.1 - Event publishing
- **ORM**: SQLAlchemy 2.0 (async) - Database abstraction
- **Logging**: Structlog - Structured JSON logging
- **Metrics**: Prometheus Client - Subscription metrics
## API Endpoints (Key Routes)
### Tenant Management
- `POST /api/v1/tenants` - Create new tenant (signup)
- `GET /api/v1/tenants/{tenant_id}` - Get tenant details
- `PUT /api/v1/tenants/{tenant_id}` - Update tenant
- `DELETE /api/v1/tenants/{tenant_id}` - Delete tenant (GDPR)
- `GET /api/v1/tenants/{tenant_id}/settings` - Get settings
- `PUT /api/v1/tenants/{tenant_id}/settings` - Update settings
### Subscription Management
- `GET /api/v1/tenants/{tenant_id}/subscription` - Get subscription
- `POST /api/v1/tenants/{tenant_id}/subscription` - Create subscription
- `PUT /api/v1/tenants/{tenant_id}/subscription` - Update subscription (upgrade/downgrade)
- `DELETE /api/v1/tenants/{tenant_id}/subscription` - Cancel subscription
- `POST /api/v1/tenants/{tenant_id}/subscription/reactivate` - Reactivate cancelled subscription
### Payment Methods
- `GET /api/v1/tenants/{tenant_id}/payment-methods` - List payment methods
- `POST /api/v1/tenants/{tenant_id}/payment-methods` - Add payment method
- `PUT /api/v1/tenants/{tenant_id}/payment-methods/{pm_id}/default` - Set default
- `DELETE /api/v1/tenants/{tenant_id}/payment-methods/{pm_id}` - Remove payment method
### Team Management
- `GET /api/v1/tenants/{tenant_id}/members` - List team members
- `POST /api/v1/tenants/{tenant_id}/members/invite` - Invite team member
- `PUT /api/v1/tenants/{tenant_id}/members/{member_id}` - Update member role
- `DELETE /api/v1/tenants/{tenant_id}/members/{member_id}` - Remove member
- `GET /api/v1/tenants/invitations/{invitation_token}` - Get invitation details
- `POST /api/v1/tenants/invitations/{invitation_token}/accept` - Accept invitation
### Billing & Usage
- `GET /api/v1/tenants/{tenant_id}/invoices` - List invoices
- `GET /api/v1/tenants/{tenant_id}/invoices/{invoice_id}` - Get invoice
- `GET /api/v1/tenants/{tenant_id}/usage` - Current usage statistics
- `GET /api/v1/tenants/{tenant_id}/usage/history` - Historical usage
### Stripe Webhooks
- `POST /api/v1/stripe/webhooks` - Stripe webhook receiver
### Analytics (Internal)
- `GET /api/v1/tenants/analytics/mrr` - Monthly recurring revenue
- `GET /api/v1/tenants/analytics/churn` - Churn rate
- `GET /api/v1/tenants/analytics/ltv` - Customer lifetime value
## Database Schema
### Main Tables
**tenants**
```sql
CREATE TABLE tenants (
id UUID PRIMARY KEY,
tenant_name VARCHAR(255) NOT NULL,
business_legal_name VARCHAR(255),
tax_id VARCHAR(50), -- CIF/NIF for Spanish businesses
business_type VARCHAR(100), -- bakery, pastry_shop, cafe, franchise
-- Contact
email VARCHAR(255) NOT NULL,
phone VARCHAR(50),
address_line1 VARCHAR(255),
address_line2 VARCHAR(255),
city VARCHAR(100),
postal_code VARCHAR(20),
country VARCHAR(100) DEFAULT 'España',
-- Status
status VARCHAR(50) DEFAULT 'trial', -- trial, active, suspended, cancelled
trial_ends_at TIMESTAMP,
suspended_at TIMESTAMP,
suspended_reason TEXT,
cancelled_at TIMESTAMP,
cancellation_reason TEXT,
-- Subscription
subscription_tier VARCHAR(50) DEFAULT 'free', -- free, pro, enterprise
stripe_customer_id VARCHAR(255), -- Stripe customer ID
stripe_subscription_id VARCHAR(255), -- Stripe subscription ID
-- Settings
timezone VARCHAR(50) DEFAULT 'Europe/Madrid',
language VARCHAR(10) DEFAULT 'es',
currency VARCHAR(10) DEFAULT 'EUR',
settings JSONB, -- Custom settings
-- Usage limits
max_locations INTEGER DEFAULT 1,
max_users INTEGER DEFAULT 1,
max_transactions_per_month INTEGER DEFAULT 100,
-- Branding (Enterprise only)
logo_url VARCHAR(500),
primary_color VARCHAR(10),
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(email)
);
```
**tenant_subscriptions**
```sql
CREATE TABLE tenant_subscriptions (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
stripe_subscription_id VARCHAR(255) UNIQUE,
stripe_customer_id VARCHAR(255),
-- Plan details
plan_tier VARCHAR(50) NOT NULL, -- free, pro, enterprise
plan_interval VARCHAR(50) DEFAULT 'month', -- month, year
plan_amount DECIMAL(10, 2) NOT NULL, -- Monthly amount in euros
-- Status
status VARCHAR(50) NOT NULL, -- active, trialing, past_due, cancelled, unpaid
trial_start TIMESTAMP,
trial_end TIMESTAMP,
current_period_start TIMESTAMP,
current_period_end TIMESTAMP,
cancel_at_period_end BOOLEAN DEFAULT FALSE,
cancelled_at TIMESTAMP,
-- Payment
default_payment_method_id VARCHAR(255),
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
```
**tenant_members**
```sql
CREATE TABLE tenant_members (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
user_id UUID NOT NULL, -- Link to auth service user
role VARCHAR(50) NOT NULL, -- owner, admin, manager, staff
-- Permissions
permissions JSONB, -- Granular permissions
-- Status
status VARCHAR(50) DEFAULT 'active', -- active, inactive, invited
invited_by UUID,
invited_at TIMESTAMP,
accepted_at TIMESTAMP,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, user_id)
);
```
**tenant_invitations**
```sql
CREATE TABLE tenant_invitations (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
invitation_token VARCHAR(255) UNIQUE NOT NULL,
email VARCHAR(255) NOT NULL,
role VARCHAR(50) NOT NULL,
invited_by UUID NOT NULL,
status VARCHAR(50) DEFAULT 'pending', -- pending, accepted, expired, cancelled
expires_at TIMESTAMP NOT NULL,
accepted_at TIMESTAMP,
accepted_by UUID,
created_at TIMESTAMP DEFAULT NOW()
);
```
**tenant_usage**
```sql
CREATE TABLE tenant_usage (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
usage_date DATE NOT NULL,
-- Usage metrics
api_calls INTEGER DEFAULT 0,
transactions_count INTEGER DEFAULT 0,
storage_mb DECIMAL(10, 2) DEFAULT 0.00,
whatsapp_messages INTEGER DEFAULT 0,
sms_messages INTEGER DEFAULT 0,
emails_sent INTEGER DEFAULT 0,
-- Costs
estimated_cost DECIMAL(10, 4) DEFAULT 0.0000,
created_at TIMESTAMP DEFAULT NOW(),
UNIQUE(tenant_id, usage_date)
);
```
**tenant_invoices**
```sql
CREATE TABLE tenant_invoices (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
stripe_invoice_id VARCHAR(255) UNIQUE,
-- Invoice details
invoice_number VARCHAR(100),
invoice_date DATE NOT NULL,
due_date DATE,
period_start DATE,
period_end DATE,
-- Amounts
subtotal DECIMAL(10, 2) NOT NULL,
tax_amount DECIMAL(10, 2) DEFAULT 0.00,
total_amount DECIMAL(10, 2) NOT NULL,
amount_paid DECIMAL(10, 2) DEFAULT 0.00,
amount_due DECIMAL(10, 2) NOT NULL,
currency VARCHAR(10) DEFAULT 'EUR',
-- Status
status VARCHAR(50) NOT NULL, -- draft, open, paid, void, uncollectible
paid_at TIMESTAMP,
-- PDF
invoice_pdf_url VARCHAR(500),
created_at TIMESTAMP DEFAULT NOW()
);
```
**tenant_payment_methods**
```sql
CREATE TABLE tenant_payment_methods (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
stripe_payment_method_id VARCHAR(255) UNIQUE,
payment_method_type VARCHAR(50), -- card, sepa_debit
-- Card details (if card)
card_brand VARCHAR(50),
card_last_four VARCHAR(4),
card_exp_month INTEGER,
card_exp_year INTEGER,
-- SEPA details (if sepa_debit)
sepa_last_four VARCHAR(4),
sepa_bank_code VARCHAR(50),
sepa_country VARCHAR(10),
is_default BOOLEAN DEFAULT FALSE,
created_at TIMESTAMP DEFAULT NOW()
);
```
**tenant_audit_log**
```sql
CREATE TABLE tenant_audit_log (
id UUID PRIMARY KEY,
tenant_id UUID REFERENCES tenants(id) ON DELETE CASCADE,
user_id UUID,
action VARCHAR(100) NOT NULL, -- tenant_created, subscription_upgraded, member_invited, etc.
resource_type VARCHAR(100),
resource_id VARCHAR(255),
details JSONB,
ip_address INET,
user_agent TEXT,
created_at TIMESTAMP DEFAULT NOW(),
INDEX idx_audit_tenant_date (tenant_id, created_at DESC)
);
```
### Indexes for Performance
```sql
CREATE INDEX idx_tenants_status ON tenants(status);
CREATE INDEX idx_tenants_subscription_tier ON tenants(subscription_tier);
CREATE INDEX idx_subscriptions_stripe ON tenant_subscriptions(stripe_subscription_id);
CREATE INDEX idx_subscriptions_status ON tenant_subscriptions(tenant_id, status);
CREATE INDEX idx_members_tenant ON tenant_members(tenant_id);
CREATE INDEX idx_members_user ON tenant_members(user_id);
CREATE INDEX idx_invitations_token ON tenant_invitations(invitation_token);
CREATE INDEX idx_usage_tenant_date ON tenant_usage(tenant_id, usage_date DESC);
CREATE INDEX idx_invoices_tenant ON tenant_invoices(tenant_id, invoice_date DESC);
```
## Business Logic Examples
### Tenant Registration with Stripe
```python
async def create_tenant_with_subscription(
tenant_data: TenantCreate,
plan_tier: str = 'pro',
payment_method_id: str = None
) -> Tenant:
"""
Create new tenant and Stripe subscription.
"""
import stripe
stripe.api_key = os.getenv('STRIPE_SECRET_KEY')
# Create tenant
tenant = Tenant(
tenant_name=tenant_data.tenant_name,
business_legal_name=tenant_data.business_legal_name,
tax_id=tenant_data.tax_id,
email=tenant_data.email,
phone=tenant_data.phone,
country='España',
status='trial' if not payment_method_id else 'active',
subscription_tier=plan_tier,
trial_ends_at=datetime.utcnow() + timedelta(days=14) if not payment_method_id else None
)
# Set tier limits
if plan_tier == 'free':
tenant.max_locations = 1
tenant.max_users = 1
tenant.max_transactions_per_month = 100
elif plan_tier == 'pro':
tenant.max_locations = 3
tenant.max_users = 5
tenant.max_transactions_per_month = -1 # Unlimited
elif plan_tier == 'enterprise':
tenant.max_locations = -1 # Unlimited
tenant.max_users = -1 # Unlimited
tenant.max_transactions_per_month = -1 # Unlimited
db.add(tenant)
await db.flush()
try:
# Create Stripe customer
stripe_customer = stripe.Customer.create(
email=tenant.email,
name=tenant.business_legal_name or tenant.tenant_name,
metadata={
'tenant_id': str(tenant.id),
'tax_id': tenant.tax_id
},
tax_id_data=[{
'type': 'eu_vat', # Spanish NIF/CIF
'value': tenant.tax_id
}] if tenant.tax_id else None
)
tenant.stripe_customer_id = stripe_customer.id
# Attach payment method if provided
if payment_method_id:
stripe.PaymentMethod.attach(
payment_method_id,
customer=stripe_customer.id
)
# Set as default
stripe.Customer.modify(
stripe_customer.id,
invoice_settings={'default_payment_method': payment_method_id}
)
# Get price ID for plan
price_id = get_stripe_price_id(plan_tier, 'month')
# Create subscription
subscription_params = {
'customer': stripe_customer.id,
'items': [{'price': price_id}],
'metadata': {'tenant_id': str(tenant.id)},
'payment_behavior': 'default_incomplete' if not payment_method_id else 'allow_incomplete'
}
# Add trial if no payment method
if not payment_method_id:
subscription_params['trial_period_days'] = 14
stripe_subscription = stripe.Subscription.create(**subscription_params)
tenant.stripe_subscription_id = stripe_subscription.id
# Create subscription record
subscription = TenantSubscription(
tenant_id=tenant.id,
stripe_subscription_id=stripe_subscription.id,
stripe_customer_id=stripe_customer.id,
plan_tier=plan_tier,
plan_interval='month',
plan_amount=get_plan_amount(plan_tier),
status=stripe_subscription.status,
trial_start=datetime.fromtimestamp(stripe_subscription.trial_start) if stripe_subscription.trial_start else None,
trial_end=datetime.fromtimestamp(stripe_subscription.trial_end) if stripe_subscription.trial_end else None,
current_period_start=datetime.fromtimestamp(stripe_subscription.current_period_start),
current_period_end=datetime.fromtimestamp(stripe_subscription.current_period_end)
)
db.add(subscription)
# Create owner member record
owner_user = await create_user_from_tenant(tenant_data)
member = TenantMember(
tenant_id=tenant.id,
user_id=owner_user.id,
role='owner',
status='active'
)
db.add(member)
# Log audit
audit = TenantAuditLog(
tenant_id=tenant.id,
user_id=owner_user.id,
action='tenant_created',
details={
'plan_tier': plan_tier,
'trial': not bool(payment_method_id)
}
)
db.add(audit)
await db.commit()
# Publish event
await publish_event('tenants', 'tenant.created', {
'tenant_id': str(tenant.id),
'plan_tier': plan_tier,
'trial': not bool(payment_method_id)
})
logger.info("Tenant created with subscription",
tenant_id=str(tenant.id),
plan_tier=plan_tier)
return tenant
except stripe.error.StripeError as e:
# Rollback tenant creation
await db.rollback()
logger.error("Stripe subscription creation failed",
tenant_id=str(tenant.id) if tenant.id else None,
error=str(e))
raise Exception(f"Payment processing failed: {str(e)}")
def get_stripe_price_id(plan_tier: str, interval: str) -> str:
"""
Get Stripe price ID for plan tier and billing interval.
"""
# These would be created in Stripe dashboard
price_ids = {
('pro', 'month'): 'price_pro_monthly',
('pro', 'year'): 'price_pro_yearly',
('enterprise', 'month'): 'price_enterprise_monthly',
('enterprise', 'year'): 'price_enterprise_yearly'
}
return price_ids.get((plan_tier, interval))
def get_plan_amount(plan_tier: str) -> Decimal:
"""
Get plan amount in euros.
"""
amounts = {
'free': Decimal('0.00'),
'pro': Decimal('49.00'),
'enterprise': Decimal('149.00')
}
return amounts.get(plan_tier, Decimal('0.00'))
```
### Subscription Upgrade/Downgrade
```python
async def update_subscription(
tenant_id: UUID,
new_plan_tier: str,
user_id: UUID
) -> TenantSubscription:
"""
Upgrade or downgrade tenant subscription.
"""
import stripe
stripe.api_key = os.getenv('STRIPE_SECRET_KEY')
tenant = await db.get(Tenant, tenant_id)
subscription = await db.query(TenantSubscription).filter(
TenantSubscription.tenant_id == tenant_id,
TenantSubscription.status == 'active'
).first()
if not subscription:
raise ValueError("No active subscription found")
try:
# Get new price
new_price_id = get_stripe_price_id(new_plan_tier, subscription.plan_interval)
# Update Stripe subscription
stripe_subscription = stripe.Subscription.retrieve(subscription.stripe_subscription_id)
# Update subscription items (Stripe handles proration automatically)
stripe_subscription = stripe.Subscription.modify(
subscription.stripe_subscription_id,
items=[{
'id': stripe_subscription['items']['data'][0].id,
'price': new_price_id
}],
proration_behavior='always_invoice', # Create invoice for proration
metadata={'tenant_id': str(tenant_id)}
)
# Update tenant tier
old_tier = tenant.subscription_tier
tenant.subscription_tier = new_plan_tier
# Update limits
if new_plan_tier == 'free':
tenant.max_locations = 1
tenant.max_users = 1
tenant.max_transactions_per_month = 100
elif new_plan_tier == 'pro':
tenant.max_locations = 3
tenant.max_users = 5
tenant.max_transactions_per_month = -1
elif new_plan_tier == 'enterprise':
tenant.max_locations = -1
tenant.max_users = -1
tenant.max_transactions_per_month = -1
# Update subscription record
subscription.plan_tier = new_plan_tier
subscription.plan_amount = get_plan_amount(new_plan_tier)
subscription.status = stripe_subscription.status
# Log audit
audit = TenantAuditLog(
tenant_id=tenant_id,
user_id=user_id,
action='subscription_changed',
details={
'old_tier': old_tier,
'new_tier': new_plan_tier,
'change_type': 'upgrade' if get_plan_amount(new_plan_tier) > get_plan_amount(old_tier) else 'downgrade'
}
)
db.add(audit)
await db.commit()
# Publish event
await publish_event('tenants', 'tenant.subscription_changed', {
'tenant_id': str(tenant_id),
'old_tier': old_tier,
'new_tier': new_plan_tier
})
logger.info("Subscription updated",
tenant_id=str(tenant_id),
old_tier=old_tier,
new_tier=new_plan_tier)
return subscription
except stripe.error.StripeError as e:
logger.error("Subscription update failed",
tenant_id=str(tenant_id),
error=str(e))
raise Exception(f"Subscription update failed: {str(e)}")
```
### Stripe Webhook Handler
```python
async def handle_stripe_webhook(payload: bytes, sig_header: str):
"""
Handle Stripe webhook events.
"""
import stripe
stripe.api_key = os.getenv('STRIPE_SECRET_KEY')
webhook_secret = os.getenv('STRIPE_WEBHOOK_SECRET')
try:
event = stripe.Webhook.construct_event(
payload, sig_header, webhook_secret
)
except ValueError:
raise Exception("Invalid payload")
except stripe.error.SignatureVerificationError:
raise Exception("Invalid signature")
# Handle event types
if event['type'] == 'customer.subscription.updated':
subscription = event['data']['object']
await handle_subscription_updated(subscription)
elif event['type'] == 'customer.subscription.deleted':
subscription = event['data']['object']
await handle_subscription_cancelled(subscription)
elif event['type'] == 'invoice.paid':
invoice = event['data']['object']
await handle_invoice_paid(invoice)
elif event['type'] == 'invoice.payment_failed':
invoice = event['data']['object']
await handle_payment_failed(invoice)
elif event['type'] == 'customer.subscription.trial_will_end':
subscription = event['data']['object']
await handle_trial_ending(subscription)
logger.info("Stripe webhook processed",
event_type=event['type'],
event_id=event['id'])
async def handle_subscription_updated(stripe_subscription: dict):
"""
Handle subscription update from Stripe.
"""
tenant_id = UUID(stripe_subscription['metadata'].get('tenant_id'))
subscription = await db.query(TenantSubscription).filter(
TenantSubscription.stripe_subscription_id == stripe_subscription['id']
).first()
if subscription:
subscription.status = stripe_subscription['status']
subscription.current_period_start = datetime.fromtimestamp(stripe_subscription['current_period_start'])
subscription.current_period_end = datetime.fromtimestamp(stripe_subscription['current_period_end'])
subscription.cancel_at_period_end = stripe_subscription['cancel_at_period_end']
await db.commit()
async def handle_payment_failed(stripe_invoice: dict):
"""
Handle failed payment from Stripe.
"""
customer_id = stripe_invoice['customer']
tenant = await db.query(Tenant).filter(
Tenant.stripe_customer_id == customer_id
).first()
if tenant:
# Send notification
await send_payment_failed_notification(tenant.id)
# If 3rd failed attempt, suspend account
failed_attempts = await get_failed_payment_count(tenant.id)
if failed_attempts >= 3:
tenant.status = 'suspended'
tenant.suspended_at = datetime.utcnow()
tenant.suspended_reason = 'payment_failed'
await db.commit()
await send_account_suspended_notification(tenant.id)
```
## Events & Messaging
### Published Events (RabbitMQ)
**Exchange**: `tenants`
**Routing Keys**: `tenant.created`, `tenant.subscription_changed`, `tenant.cancelled`
**Tenant Created Event**
```json
{
"event_type": "tenant_created",
"tenant_id": "uuid",
"tenant_name": "Panadería García",
"plan_tier": "pro",
"trial": false,
"timestamp": "2025-11-06T10:00:00Z"
}
```
**Subscription Changed Event**
```json
{
"event_type": "tenant_subscription_changed",
"tenant_id": "uuid",
"old_tier": "pro",
"new_tier": "enterprise",
"change_type": "upgrade",
"timestamp": "2025-11-06T14:00:00Z"
}
```
## Custom Metrics (Prometheus)
```python
# Tenant metrics
tenants_total = Gauge(
'tenants_total',
'Total tenants',
['status', 'subscription_tier']
)
monthly_recurring_revenue_euros = Gauge(
'monthly_recurring_revenue_euros',
'Total MRR',
[]
)
churn_rate_percentage = Gauge(
'churn_rate_percentage_monthly',
'Monthly churn rate',
[]
)
trial_conversion_rate = Gauge(
'trial_conversion_rate_percentage',
'Trial to paid conversion rate',
[]
)
```
## Configuration
### Environment Variables
**Service Configuration:**
- `PORT` - Service port (default: 8017)
- `DATABASE_URL` - PostgreSQL connection string
- `REDIS_URL` - Redis connection string
- `RABBITMQ_URL` - RabbitMQ connection string
**Stripe Configuration:**
- `STRIPE_SECRET_KEY` - Stripe secret key
- `STRIPE_PUBLISHABLE_KEY` - Stripe publishable key
- `STRIPE_WEBHOOK_SECRET` - Stripe webhook signing secret
- `STRIPE_PRICE_PRO_MONTHLY` - Pro plan monthly price ID
- `STRIPE_PRICE_ENTERPRISE_MONTHLY` - Enterprise plan monthly price ID
**Trial Configuration:**
- `DEFAULT_TRIAL_DAYS` - Free trial length (default: 14)
- `TRIAL_REMINDER_DAYS` - Days before trial ends to remind (default: 3)
## Development Setup
### Prerequisites
- Python 3.11+
- PostgreSQL 17
- Redis 7.4
- RabbitMQ 4.1
- Stripe account
### Local Development
```bash
cd services/tenant
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
export DATABASE_URL=postgresql://user:pass@localhost:5432/tenant
export REDIS_URL=redis://localhost:6379/0
export RABBITMQ_URL=amqp://guest:guest@localhost:5672/
export STRIPE_SECRET_KEY=sk_test_your_key
export STRIPE_WEBHOOK_SECRET=whsec_your_secret
alembic upgrade head
python main.py
```
## Integration Points
### Dependencies
- **Stripe API** - Payment processing
- **Auth Service** - User management
- **PostgreSQL** - Tenant data
- **Redis** - Subscription caching
- **RabbitMQ** - Event publishing
### Dependents
- **All Services** - Tenant authentication and limits
- **Frontend Dashboard** - Subscription management UI
- **Billing** - Invoice generation
## Business Value for VUE Madrid
### Problem Statement
Manual billing and customer management doesn't scale:
- Manual invoicing time-consuming and error-prone
- No automated subscription management
- Difficult to track MRR and churn
- Complex European payment regulations (SEPA, VAT)
- No self-service tier changes
### Solution
Bakery-IA Tenant Service provides:
- **Automated Billing**: Stripe handles everything
- **European Payments**: SEPA Direct Debit for Spanish/EU
- **Self-Service**: Customers manage subscriptions
- **Revenue Visibility**: Real-time MRR, churn, LTV
- **Scalable**: Handle thousands of customers
### Quantifiable Impact
**Revenue Model:**
- €0-149/month per customer (€66 average)
- €60K/year at 100 customers
- €360K/year at 500 customers
- €1.8M/year at 2,000 customers
**Business Metrics:**
- 30-40% customers upgrade within 6 months
- 14-day trial → 35-45% conversion rate (industry standard)
- <10% monthly churn (target)
- €1,200-3,600 customer LTV (24-month retention)
**Operational Efficiency:**
- 100% automated billing (zero manual invoicing)
- 95%+ payment success rate (Stripe)
- Self-service reduces support by 70%
### Target Market Fit (Spanish Bakeries)
- **SEPA Support**: Direct debit popular in Spain/EU
- **Spanish Invoicing**: Tax-compliant invoices automatic
- **Euro Currency**: Native euro pricing, no conversion
- **Affordable**: €49/month accessible for SMBs
- **Transparent**: Clear pricing, no hidden fees
### ROI for Platform
**Investment**: Stripe fees 1.4% + €0.25/transaction (EU cards)
**Customer Acquisition Cost**: €200-300
**Payback Period**: 3-5 months (at €66 average MRR)
**Annual Value**: €66 × 12 = €792/customer/year
**3-Year LTV**: €2,376/customer (assuming retention)
---
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