Improve AI logic
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Urtzi Alfaro
391
shared/clients/ai_insights_client.py
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391
shared/clients/ai_insights_client.py
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"""
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AI Insights Service HTTP Client
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Shared client for all services to post and retrieve AI insights
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"""
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import httpx
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from typing import Dict, List, Any, Optional
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from uuid import UUID
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import structlog
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from datetime import datetime
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logger = structlog.get_logger()
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class AIInsightsClient:
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"""
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HTTP client for AI Insights Service.
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Allows services to post insights, retrieve orchestration-ready insights, and record feedback.
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"""
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def __init__(self, base_url: str, timeout: int = 30):
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"""
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Initialize AI Insights client.
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Args:
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base_url: Base URL of AI Insights Service (e.g., http://ai-insights-service:8000)
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timeout: Request timeout in seconds
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"""
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self.base_url = base_url.rstrip('/')
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self.timeout = timeout
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self.client = httpx.AsyncClient(timeout=self.timeout)
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async def close(self):
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"""Close the HTTP client."""
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await self.client.aclose()
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async def create_insight(
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self,
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tenant_id: UUID,
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insight_data: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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Create a new insight in AI Insights Service.
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Args:
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tenant_id: Tenant UUID
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insight_data: Insight data dictionary with fields:
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- type: str (optimization, alert, prediction, recommendation, insight, anomaly)
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- priority: str (low, medium, high, critical)
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- category: str (forecasting, procurement, production, inventory, etc.)
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- title: str
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- description: str
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- impact_type: str
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- impact_value: float
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- impact_unit: str
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- confidence: int (0-100)
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- metrics_json: dict
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- actionable: bool
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- recommendation_actions: list (optional)
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- source_service: str
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- source_model: str (optional)
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Returns:
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Created insight dict or None if failed
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"""
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url = f"{self.base_url}/api/v1/tenants/{tenant_id}/insights"
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try:
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# Ensure tenant_id is in the data
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insight_data['tenant_id'] = str(tenant_id)
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response = await self.client.post(url, json=insight_data)
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if response.status_code == 201:
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logger.info(
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"Insight created successfully",
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tenant_id=str(tenant_id),
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insight_title=insight_data.get('title')
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)
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return response.json()
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else:
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logger.error(
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"Failed to create insight",
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status_code=response.status_code,
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response=response.text,
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insight_title=insight_data.get('title')
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)
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return None
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except Exception as e:
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logger.error(
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"Error creating insight",
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error=str(e),
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tenant_id=str(tenant_id)
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)
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return None
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async def create_insights_bulk(
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self,
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tenant_id: UUID,
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insights: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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"""
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Create multiple insights in bulk.
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Args:
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tenant_id: Tenant UUID
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insights: List of insight data dictionaries
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Returns:
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Dictionary with success/failure counts
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"""
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results = {
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'total': len(insights),
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'successful': 0,
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'failed': 0,
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'created_insights': []
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}
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for insight_data in insights:
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result = await self.create_insight(tenant_id, insight_data)
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if result:
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results['successful'] += 1
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results['created_insights'].append(result)
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else:
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results['failed'] += 1
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logger.info(
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"Bulk insight creation complete",
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total=results['total'],
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successful=results['successful'],
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failed=results['failed']
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)
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return results
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async def get_insights(
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self,
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tenant_id: UUID,
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filters: Optional[Dict[str, Any]] = None
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) -> Optional[Dict[str, Any]]:
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"""
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Get insights for a tenant.
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Args:
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tenant_id: Tenant UUID
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filters: Optional filters:
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- category: str
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- priority: str
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- actionable_only: bool
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- min_confidence: int
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- page: int
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- page_size: int
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Returns:
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Paginated insights response or None if failed
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"""
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url = f"{self.base_url}/api/v1/tenants/{tenant_id}/insights"
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try:
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response = await self.client.get(url, params=filters or {})
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if response.status_code == 200:
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return response.json()
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else:
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logger.error(
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"Failed to get insights",
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status_code=response.status_code
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)
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return None
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except Exception as e:
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logger.error("Error getting insights", error=str(e))
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return None
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async def get_orchestration_ready_insights(
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self,
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tenant_id: UUID,
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target_date: datetime,
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min_confidence: int = 70
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) -> Optional[Dict[str, List[Dict[str, Any]]]]:
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"""
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Get insights ready for orchestration workflow.
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Args:
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tenant_id: Tenant UUID
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target_date: Target date for orchestration
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min_confidence: Minimum confidence threshold
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Returns:
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Categorized insights or None if failed:
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{
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"forecast_adjustments": [...],
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"procurement_recommendations": [...],
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"production_adjustments": [...],
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"inventory_optimization": [...],
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"risk_alerts": [...]
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}
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"""
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url = f"{self.base_url}/api/v1/tenants/{tenant_id}/insights/orchestration-ready"
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params = {
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'target_date': target_date.isoformat(),
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'min_confidence': min_confidence
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}
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try:
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response = await self.client.get(url, params=params)
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if response.status_code == 200:
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return response.json()
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else:
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logger.error(
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"Failed to get orchestration insights",
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status_code=response.status_code
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)
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return None
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except Exception as e:
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logger.error("Error getting orchestration insights", error=str(e))
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return None
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async def record_feedback(
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self,
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tenant_id: UUID,
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insight_id: UUID,
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feedback_data: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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Record feedback for an applied insight.
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Args:
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tenant_id: Tenant UUID
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insight_id: Insight UUID
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feedback_data: Feedback data with fields:
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- success: bool
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- applied_at: datetime (optional)
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- actual_impact_value: float (optional)
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- actual_impact_unit: str (optional)
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- notes: str (optional)
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Returns:
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Feedback response or None if failed
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"""
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url = f"{self.base_url}/api/v1/tenants/{tenant_id}/insights/{insight_id}/feedback"
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try:
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feedback_data['insight_id'] = str(insight_id)
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response = await self.client.post(url, json=feedback_data)
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if response.status_code in [200, 201]:
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logger.info(
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"Feedback recorded",
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insight_id=str(insight_id),
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success=feedback_data.get('success')
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)
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return response.json()
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else:
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logger.error(
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"Failed to record feedback",
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status_code=response.status_code
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)
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return None
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except Exception as e:
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logger.error("Error recording feedback", error=str(e))
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return None
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async def get_insights_summary(
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self,
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tenant_id: UUID,
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time_period_days: int = 30
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) -> Optional[Dict[str, Any]]:
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"""
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Get aggregate metrics summary for insights.
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Args:
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tenant_id: Tenant UUID
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time_period_days: Time period for metrics (default 30 days)
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Returns:
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Summary metrics or None if failed
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"""
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url = f"{self.base_url}/api/v1/tenants/{tenant_id}/insights/metrics/summary"
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params = {'time_period_days': time_period_days}
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try:
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response = await self.client.get(url, params=params)
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if response.status_code == 200:
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return response.json()
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else:
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logger.error(
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"Failed to get insights summary",
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status_code=response.status_code
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)
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return None
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except Exception as e:
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logger.error("Error getting insights summary", error=str(e))
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return None
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async def post_accuracy_metrics(
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self,
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tenant_id: UUID,
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validation_date: datetime,
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metrics: Dict[str, Any]
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) -> Optional[Dict[str, Any]]:
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"""
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Post forecast accuracy metrics to AI Insights Service.
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Creates an insight with accuracy validation results.
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Args:
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tenant_id: Tenant UUID
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validation_date: Date the forecasts were validated for
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metrics: Dictionary with accuracy metrics:
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- overall_mape: Mean Absolute Percentage Error
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- overall_rmse: Root Mean Squared Error
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- overall_mae: Mean Absolute Error
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- products_validated: Number of products validated
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- poor_accuracy_products: List of products with MAPE > 30%
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Returns:
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Created insight or None if failed
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"""
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mape = metrics.get('overall_mape', 0)
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products_validated = metrics.get('products_validated', 0)
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poor_count = len(metrics.get('poor_accuracy_products', []))
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# Determine priority based on MAPE
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if mape > 40:
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priority = 'critical'
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elif mape > 30:
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priority = 'high'
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elif mape > 20:
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priority = 'medium'
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else:
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priority = 'low'
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# Create insight
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insight_data = {
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'type': 'insight',
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'priority': priority,
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'category': 'forecasting',
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'title': f'Forecast Accuracy Validation - {validation_date.strftime("%Y-%m-%d")}',
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'description': (
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f'Validated {products_validated} product forecasts against actual sales. '
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f'Overall MAPE: {mape:.2f}%. '
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f'{poor_count} products require retraining (MAPE > 30%).'
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),
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'impact_type': 'accuracy',
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'impact_value': mape,
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'impact_unit': 'mape_percentage',
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'confidence': 100, # Validation is based on actual data
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'metrics_json': {
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'validation_date': validation_date.isoformat() if hasattr(validation_date, 'isoformat') else str(validation_date),
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'overall_mape': mape,
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'overall_rmse': metrics.get('overall_rmse', 0),
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'overall_mae': metrics.get('overall_mae', 0),
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'products_validated': products_validated,
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'poor_accuracy_count': poor_count,
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'poor_accuracy_products': metrics.get('poor_accuracy_products', [])
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},
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'actionable': poor_count > 0,
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'recommendation_actions': [
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f'Retrain models for {poor_count} products with poor accuracy'
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] if poor_count > 0 else [],
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'source_service': 'forecasting',
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'source_model': 'forecast_validation'
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}
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return await self.create_insight(tenant_id, insight_data)
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async def health_check(self) -> bool:
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"""
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Check if AI Insights Service is healthy.
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Returns:
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True if healthy, False otherwise
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"""
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url = f"{self.base_url}/health"
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try:
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response = await self.client.get(url)
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return response.status_code == 200
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except Exception as e:
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logger.error("AI Insights Service health check failed", error=str(e))
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return False
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