Improve AI logic

services/ai_insights/app/models/__init__.py

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+"""Database models for AI Insights Service."""
+
+from app.models.ai_insight import AIInsight
+from app.models.insight_feedback import InsightFeedback
+from app.models.insight_correlation import InsightCorrelation
+
+__all__ = [
+    "AIInsight",
+    "InsightFeedback",
+    "InsightCorrelation",
+]

services/ai_insights/app/models/ai_insight.py

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+"""AI Insight database model."""
+
+from sqlalchemy import Column, String, Integer, Boolean, DECIMAL, TIMESTAMP, Text, Index, CheckConstraint
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+from sqlalchemy.sql import func
+import uuid
+
+from app.core.database import Base
+
+
+class AIInsight(Base):
+    """AI Insight model for storing intelligent recommendations and predictions."""
+
+    __tablename__ = "ai_insights"
+
+    # Primary Key
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+
+    # Tenant Information
+    tenant_id = Column(UUID(as_uuid=True), nullable=False, index=True)
+
+    # Classification
+    type = Column(
+        String(50),
+        nullable=False,
+        index=True,
+        comment="optimization, alert, prediction, recommendation, insight, anomaly"
+    )
+    priority = Column(
+        String(20),
+        nullable=False,
+        index=True,
+        comment="low, medium, high, critical"
+    )
+    category = Column(
+        String(50),
+        nullable=False,
+        index=True,
+        comment="forecasting, inventory, production, procurement, customer, cost, quality, efficiency, demand, maintenance, energy, scheduling"
+    )
+
+    # Content
+    title = Column(String(255), nullable=False)
+    description = Column(Text, nullable=False)
+
+    # Impact Information
+    impact_type = Column(
+        String(50),
+        comment="cost_savings, revenue_increase, waste_reduction, efficiency_gain, quality_improvement, risk_mitigation"
+    )
+    impact_value = Column(DECIMAL(10, 2), comment="Numeric impact value")
+    impact_unit = Column(
+        String(20),
+        comment="euros, percentage, hours, units, euros/month, euros/year"
+    )
+
+    # Confidence and Metrics
+    confidence = Column(
+        Integer,
+        CheckConstraint('confidence >= 0 AND confidence <= 100'),
+        nullable=False,
+        index=True,
+        comment="Confidence score 0-100"
+    )
+    metrics_json = Column(
+        JSONB,
+        comment="Dynamic metrics specific to insight type"
+    )
+
+    # Actionability
+    actionable = Column(
+        Boolean,
+        default=True,
+        nullable=False,
+        index=True,
+        comment="Whether this insight can be acted upon"
+    )
+    recommendation_actions = Column(
+        JSONB,
+        comment="List of possible actions: [{label, action, endpoint}]"
+    )
+
+    # Status
+    status = Column(
+        String(20),
+        default='new',
+        nullable=False,
+        index=True,
+        comment="new, acknowledged, in_progress, applied, dismissed, expired"
+    )
+
+    # Source Information
+    source_service = Column(
+        String(50),
+        comment="Service that generated this insight"
+    )
+    source_data_id = Column(
+        String(100),
+        comment="Reference to source data (e.g., forecast_id, model_id)"
+    )
+
+    # Timestamps
+    created_at = Column(
+        TIMESTAMP(timezone=True),
+        server_default=func.now(),
+        nullable=False,
+        index=True
+    )
+    updated_at = Column(
+        TIMESTAMP(timezone=True),
+        server_default=func.now(),
+        onupdate=func.now(),
+        nullable=False
+    )
+    applied_at = Column(TIMESTAMP(timezone=True), comment="When insight was applied")
+    expired_at = Column(
+        TIMESTAMP(timezone=True),
+        comment="When insight expires (auto-calculated based on TTL)"
+    )
+
+    # Composite Indexes
+    __table_args__ = (
+        Index('idx_tenant_status_category', 'tenant_id', 'status', 'category'),
+        Index('idx_tenant_created_confidence', 'tenant_id', 'created_at', 'confidence'),
+        Index('idx_actionable_status', 'actionable', 'status'),
+    )
+
+    def __repr__(self):
+        return f"<AIInsight(id={self.id}, type={self.type}, title={self.title[:30]}, confidence={self.confidence})>"

services/ai_insights/app/models/insight_correlation.py

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+"""Insight Correlation database model for cross-service intelligence."""
+
+from sqlalchemy import Column, String, Integer, DECIMAL, TIMESTAMP, ForeignKey, Index
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+import uuid
+
+from app.core.database import Base
+
+
+class InsightCorrelation(Base):
+    """Track correlations between insights from different services."""
+
+    __tablename__ = "insight_correlations"
+
+    # Primary Key
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+
+    # Foreign Keys to AIInsights
+    parent_insight_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey('ai_insights.id', ondelete='CASCADE'),
+        nullable=False,
+        index=True,
+        comment="Primary insight that leads to correlation"
+    )
+    child_insight_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey('ai_insights.id', ondelete='CASCADE'),
+        nullable=False,
+        index=True,
+        comment="Related insight"
+    )
+
+    # Correlation Information
+    correlation_type = Column(
+        String(50),
+        nullable=False,
+        comment="forecast_inventory, production_procurement, weather_customer, demand_supplier, etc."
+    )
+    correlation_strength = Column(
+        DECIMAL(3, 2),
+        nullable=False,
+        comment="0.00 to 1.00 indicating strength of correlation"
+    )
+
+    # Combined Metrics
+    combined_confidence = Column(
+        Integer,
+        comment="Weighted combined confidence of both insights"
+    )
+
+    # Timestamp
+    created_at = Column(
+        TIMESTAMP(timezone=True),
+        server_default=func.now(),
+        nullable=False,
+        index=True
+    )
+
+    # Composite Indexes
+    __table_args__ = (
+        Index('idx_parent_child', 'parent_insight_id', 'child_insight_id'),
+        Index('idx_correlation_type', 'correlation_type'),
+    )
+
+    def __repr__(self):
+        return f"<InsightCorrelation(id={self.id}, type={self.correlation_type}, strength={self.correlation_strength})>"

services/ai_insights/app/models/insight_feedback.py

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+"""Insight Feedback database model for closed-loop learning."""
+
+from sqlalchemy import Column, String, Boolean, DECIMAL, TIMESTAMP, Text, ForeignKey, Index
+from sqlalchemy.dialects.postgresql import UUID, JSONB
+from sqlalchemy.sql import func
+from sqlalchemy.orm import relationship
+import uuid
+
+from app.core.database import Base
+
+
+class InsightFeedback(Base):
+    """Feedback tracking for AI Insights to enable learning."""
+
+    __tablename__ = "insight_feedback"
+
+    # Primary Key
+    id = Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
+
+    # Foreign Key to AIInsight
+    insight_id = Column(
+        UUID(as_uuid=True),
+        ForeignKey('ai_insights.id', ondelete='CASCADE'),
+        nullable=False,
+        index=True
+    )
+
+    # Action Information
+    action_taken = Column(
+        String(100),
+        comment="Specific action that was taken from recommendation_actions"
+    )
+
+    # Result Data
+    result_data = Column(
+        JSONB,
+        comment="Detailed result data from applying the insight"
+    )
+
+    # Success Tracking
+    success = Column(
+        Boolean,
+        nullable=False,
+        index=True,
+        comment="Whether the insight application was successful"
+    )
+    error_message = Column(
+        Text,
+        comment="Error message if success = false"
+    )
+
+    # Impact Comparison
+    expected_impact_value = Column(
+        DECIMAL(10, 2),
+        comment="Expected impact value from original insight"
+    )
+    actual_impact_value = Column(
+        DECIMAL(10, 2),
+        comment="Measured actual impact after application"
+    )
+    variance_percentage = Column(
+        DECIMAL(5, 2),
+        comment="(actual - expected) / expected * 100"
+    )
+
+    # User Information
+    applied_by = Column(
+        String(100),
+        comment="User or system that applied the insight"
+    )
+
+    # Timestamp
+    created_at = Column(
+        TIMESTAMP(timezone=True),
+        server_default=func.now(),
+        nullable=False,
+        index=True
+    )
+
+    # Composite Indexes
+    __table_args__ = (
+        Index('idx_insight_success', 'insight_id', 'success'),
+        Index('idx_created_success', 'created_at', 'success'),
+    )
+
+    def __repr__(self):
+        return f"<InsightFeedback(id={self.id}, insight_id={self.insight_id}, success={self.success})>"