bakery-ia/services/forecasting/app/ml/rules_orchestrator.py

"""
Rules Orchestrator
Coordinates dynamic rules learning, insight posting, and integration with forecasting service
"""

import pandas as pd
from typing import Dict, List, Any, Optional
import structlog
from datetime import datetime
from uuid import UUID

from app.ml.dynamic_rules_engine import DynamicRulesEngine
from app.clients.ai_insights_client import AIInsightsClient

logger = structlog.get_logger()


class RulesOrchestrator:
    """
    Orchestrates dynamic rules learning and insight generation workflow.

    Workflow:
    1. Learn dynamic rules from historical data
    2. Generate insights comparing learned vs hardcoded rules
    3. Post insights to AI Insights Service
    4. Provide learned rules for forecasting integration
    5. Track rule updates and performance
    """

    def __init__(
        self,
        ai_insights_base_url: str = "http://ai-insights-service:8000"
    ):
        self.rules_engine = DynamicRulesEngine()
        self.ai_insights_client = AIInsightsClient(ai_insights_base_url)

    async def learn_and_post_rules(
        self,
        tenant_id: str,
        inventory_product_id: str,
        sales_data: pd.DataFrame,
        external_data: Optional[pd.DataFrame] = None,
        min_samples: int = 10
    ) -> Dict[str, Any]:
        """
        Complete workflow: Learn rules and post insights.

        Args:
            tenant_id: Tenant identifier
            inventory_product_id: Product identifier
            sales_data: Historical sales data
            external_data: Optional weather/events/holidays data
            min_samples: Minimum samples for rule learning

        Returns:
            Workflow results with learned rules and posted insights
        """
        logger.info(
            "Starting dynamic rules learning workflow",
            tenant_id=tenant_id,
            inventory_product_id=inventory_product_id
        )

        # Step 1: Learn all rules from data
        rules_results = await self.rules_engine.learn_all_rules(
            tenant_id=tenant_id,
            inventory_product_id=inventory_product_id,
            sales_data=sales_data,
            external_data=external_data,
            min_samples=min_samples
        )

        logger.info(
            "Rules learning complete",
            insights_generated=len(rules_results['insights']),
            rules_learned=len(rules_results['rules'])
        )

        # Step 2: Enrich insights with tenant_id and product context
        enriched_insights = self._enrich_insights(
            rules_results['insights'],
            tenant_id,
            inventory_product_id
        )

        # Step 3: Post insights to AI Insights Service
        if enriched_insights:
            post_results = await self.ai_insights_client.create_insights_bulk(
                tenant_id=UUID(tenant_id),
                insights=enriched_insights
            )

            logger.info(
                "Insights posted to AI Insights Service",
                total=post_results['total'],
                successful=post_results['successful'],
                failed=post_results['failed']
            )
        else:
            post_results = {'total': 0, 'successful': 0, 'failed': 0}
            logger.info("No insights to post")

        # Step 4: Return comprehensive results
        return {
            'tenant_id': tenant_id,
            'inventory_product_id': inventory_product_id,
            'learned_at': rules_results['learned_at'],
            'rules': rules_results['rules'],
            'insights_generated': len(enriched_insights),
            'insights_posted': post_results['successful'],
            'insights_failed': post_results['failed'],
            'created_insights': post_results.get('created_insights', [])
        }

    def _enrich_insights(
        self,
        insights: List[Dict[str, Any]],
        tenant_id: str,
        inventory_product_id: str
    ) -> List[Dict[str, Any]]:
        """
        Enrich insights with required fields for AI Insights Service.

        Args:
            insights: Raw insights from rules engine
            tenant_id: Tenant identifier
            inventory_product_id: Product identifier

        Returns:
            Enriched insights ready for posting
        """
        enriched = []

        for insight in insights:
            # Add required tenant_id and product context
            enriched_insight = insight.copy()
            enriched_insight['tenant_id'] = tenant_id

            # Add product context to metrics
            if 'metrics_json' not in enriched_insight:
                enriched_insight['metrics_json'] = {}

            enriched_insight['metrics_json']['inventory_product_id'] = inventory_product_id

            # Add source metadata
            enriched_insight['source_service'] = 'forecasting'
            enriched_insight['source_model'] = 'dynamic_rules_engine'
            enriched_insight['detected_at'] = datetime.utcnow().isoformat()

            enriched.append(enriched_insight)

        return enriched

    async def get_learned_rules_for_forecasting(
        self,
        inventory_product_id: str
    ) -> Dict[str, Any]:
        """
        Get learned rules in format ready for forecasting integration.

        Args:
            inventory_product_id: Product identifier

        Returns:
            Dictionary with learned multipliers for all rule types
        """
        return self.rules_engine.export_rules_for_prophet(inventory_product_id)

    def get_rule_multiplier(
        self,
        inventory_product_id: str,
        rule_type: str,
        key: str,
        default: float = 1.0
    ) -> float:
        """
        Get learned rule multiplier with fallback to default.

        Args:
            inventory_product_id: Product identifier
            rule_type: 'weather', 'holiday', 'event', 'day_of_week', 'month'
            key: Condition key
            default: Default multiplier if rule not learned

        Returns:
            Learned multiplier or default
        """
        learned = self.rules_engine.get_rule(inventory_product_id, rule_type, key)
        return learned if learned is not None else default

    async def update_rules_periodically(
        self,
        tenant_id: str,
        inventory_product_id: str,
        sales_data: pd.DataFrame,
        external_data: Optional[pd.DataFrame] = None
    ) -> Dict[str, Any]:
        """
        Update learned rules with new data (for periodic refresh).

        Args:
            tenant_id: Tenant identifier
            inventory_product_id: Product identifier
            sales_data: Updated historical sales data
            external_data: Updated external data

        Returns:
            Update results
        """
        logger.info(
            "Updating learned rules with new data",
            tenant_id=tenant_id,
            inventory_product_id=inventory_product_id,
            new_data_points=len(sales_data)
        )

        # Re-learn rules with updated data
        results = await self.learn_and_post_rules(
            tenant_id=tenant_id,
            inventory_product_id=inventory_product_id,
            sales_data=sales_data,
            external_data=external_data
        )

        logger.info(
            "Rules update complete",
            insights_posted=results['insights_posted']
        )

        return results

    async def close(self):
        """Close HTTP client connections."""
        await self.ai_insights_client.close()