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Improve AI logic

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This commit is contained in:
Urtzi Alfaro
2025-11-05 13:34:56 +01:00
parent 5c87fbcf48
commit 394ad3aea4
218 changed files with 30627 additions and 7658 deletions
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3
services/tenant/app/models/__init__.py
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@@ -14,6 +14,7 @@ AuditLog = create_audit_log_model(Base)
# Import all models to register them with the Base metadata
from .tenants import Tenant, TenantMember, Subscription
from .coupon import CouponModel, CouponRedemptionModel
from .events import Event, EventTemplate
# List all models for easier access
__all__ = [
@@ -23,4 +24,6 @@ __all__ = [
"AuditLog",
"CouponModel",
"CouponRedemptionModel",
"Event",
"EventTemplate",
]

136
services/tenant/app/models/events.py Normal file
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@@ -0,0 +1,136 @@
"""
Event Calendar Models
Database models for tracking local events, promotions, and special occasions
"""
from sqlalchemy import Column, Integer, String, DateTime, Text, Boolean, Float, Date
from sqlalchemy.dialects.postgresql import UUID
from shared.database.base import Base
from datetime import datetime, timezone
import uuid
class Event(Base):
"""
Table to track events that affect bakery demand.
Events include:
- Local events (festivals, markets, concerts)
- Promotions and sales
- Weather events (heat waves, storms)
- School holidays and breaks
- Special occasions
"""
__tablename__ = "events"
# Primary identification
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
# Event information
event_name = Column(String(500), nullable=False)
event_type = Column(String(100), nullable=False, index=True) # promotion, festival, holiday, weather, school_break, sport_event, etc.
description = Column(Text, nullable=True)
# Date and time
event_date = Column(Date, nullable=False, index=True)
start_time = Column(DateTime(timezone=True), nullable=True)
end_time = Column(DateTime(timezone=True), nullable=True)
is_all_day = Column(Boolean, default=True)
# Impact estimation
expected_impact = Column(String(50), nullable=True) # low, medium, high, very_high
impact_multiplier = Column(Float, nullable=True) # Expected demand multiplier (e.g., 1.5 = 50% increase)
affected_product_categories = Column(String(500), nullable=True) # Comma-separated categories
# Location
location = Column(String(500), nullable=True)
is_local = Column(Boolean, default=True) # True if event is near bakery
# Status
is_confirmed = Column(Boolean, default=False)
is_recurring = Column(Boolean, default=False)
recurrence_pattern = Column(String(200), nullable=True) # e.g., "weekly:monday", "monthly:first_saturday"
# Actual impact (filled after event)
actual_impact_multiplier = Column(Float, nullable=True)
actual_sales_increase_percent = Column(Float, nullable=True)
# Metadata
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
created_by = Column(String(255), nullable=True)
notes = Column(Text, nullable=True)
def to_dict(self):
return {
"id": str(self.id),
"tenant_id": str(self.tenant_id),
"event_name": self.event_name,
"event_type": self.event_type,
"description": self.description,
"event_date": self.event_date.isoformat() if self.event_date else None,
"start_time": self.start_time.isoformat() if self.start_time else None,
"end_time": self.end_time.isoformat() if self.end_time else None,
"is_all_day": self.is_all_day,
"expected_impact": self.expected_impact,
"impact_multiplier": self.impact_multiplier,
"affected_product_categories": self.affected_product_categories,
"location": self.location,
"is_local": self.is_local,
"is_confirmed": self.is_confirmed,
"is_recurring": self.is_recurring,
"recurrence_pattern": self.recurrence_pattern,
"actual_impact_multiplier": self.actual_impact_multiplier,
"actual_sales_increase_percent": self.actual_sales_increase_percent,
"created_at": self.created_at.isoformat() if self.created_at else None,
"updated_at": self.updated_at.isoformat() if self.updated_at else None,
"created_by": self.created_by,
"notes": self.notes
}
class EventTemplate(Base):
"""
Template for recurring events.
Allows easy creation of events based on patterns.
"""
__tablename__ = "event_templates"
# Primary identification
id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
# Template information
template_name = Column(String(500), nullable=False)
event_type = Column(String(100), nullable=False)
description = Column(Text, nullable=True)
# Default values
default_impact = Column(String(50), nullable=True)
default_impact_multiplier = Column(Float, nullable=True)
default_affected_categories = Column(String(500), nullable=True)
# Recurrence
recurrence_pattern = Column(String(200), nullable=False) # e.g., "weekly:saturday", "monthly:last_sunday"
is_active = Column(Boolean, default=True)
# Metadata
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc))
def to_dict(self):
return {
"id": str(self.id),
"tenant_id": str(self.tenant_id),
"template_name": self.template_name,
"event_type": self.event_type,
"description": self.description,
"default_impact": self.default_impact,
"default_impact_multiplier": self.default_impact_multiplier,
"default_affected_categories": self.default_affected_categories,
"recurrence_pattern": self.recurrence_pattern,
"is_active": self.is_active,
"created_at": self.created_at.isoformat() if self.created_at else None,
"updated_at": self.updated_at.isoformat() if self.updated_at else None
}

41
services/tenant/app/models/tenant_settings.py
View File

@@ -154,6 +154,32 @@ class TenantSettings(Base):
"enable_supplier_score_optimization": True
})
# ML Insights Settings (AI Insights Service)
ml_insights_settings = Column(JSON, nullable=False, default=lambda: {
# Inventory ML (Safety Stock Optimization)
"inventory_lookback_days": 90,
"inventory_min_history_days": 30,
# Production ML (Yield Prediction)
"production_lookback_days": 90,
"production_min_history_runs": 30,
# Procurement ML (Supplier Analysis & Price Forecasting)
"supplier_analysis_lookback_days": 180,
"supplier_analysis_min_orders": 10,
"price_forecast_lookback_days": 180,
"price_forecast_horizon_days": 30,
# Forecasting ML (Dynamic Rules)
"rules_generation_lookback_days": 90,
"rules_generation_min_samples": 10,
# Global ML Settings
"enable_ml_insights": True,
"ml_insights_auto_trigger": False,
"ml_confidence_threshold": 0.80
})
# Timestamps
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), nullable=False)
updated_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc), onupdate=lambda: datetime.now(timezone.utc), nullable=False)
@@ -280,5 +306,20 @@ class TenantSettings(Base):
"diversification_threshold": 1000,
"max_single_percentage": 0.70,
"enable_supplier_score_optimization": True
},
"ml_insights_settings": {
"inventory_lookback_days": 90,
"inventory_min_history_days": 30,
"production_lookback_days": 90,
"production_min_history_runs": 30,
"supplier_analysis_lookback_days": 180,
"supplier_analysis_min_orders": 10,
"price_forecast_lookback_days": 180,
"price_forecast_horizon_days": 30,
"rules_generation_lookback_days": 90,
"rules_generation_min_samples": 10,
"enable_ml_insights": True,
"ml_insights_auto_trigger": False,
"ml_confidence_threshold": 0.80
}
}