ALwrity Version 0.5.0 (Fastapi + React )
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ajaysi
286
backend/services/ai_analysis_db_service.py
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286
backend/services/ai_analysis_db_service.py
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"""
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AI Analysis Database Service
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Handles database operations for AI analysis results including storage and retrieval.
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"""
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from typing import Dict, Any, List, Optional
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from sqlalchemy.orm import Session
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from sqlalchemy import and_, desc
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from datetime import datetime, timedelta
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from loguru import logger
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from models.content_planning import AIAnalysisResult, ContentStrategy
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from services.database import get_db_session
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class AIAnalysisDBService:
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"""Service for managing AI analysis results in the database."""
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def __init__(self, db_session: Session = None):
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self.db = db_session or get_db_session()
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async def store_ai_analysis_result(
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self,
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user_id: int,
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analysis_type: str,
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insights: List[Dict[str, Any]],
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recommendations: List[Dict[str, Any]],
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performance_metrics: Optional[Dict[str, Any]] = None,
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personalized_data: Optional[Dict[str, Any]] = None,
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processing_time: Optional[float] = None,
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strategy_id: Optional[int] = None,
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ai_service_status: str = "operational"
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) -> AIAnalysisResult:
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"""Store AI analysis result in the database."""
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try:
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logger.info(f"Storing AI analysis result for user {user_id}, type: {analysis_type}")
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# Create new AI analysis result
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ai_result = AIAnalysisResult(
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user_id=user_id,
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strategy_id=strategy_id,
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analysis_type=analysis_type,
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insights=insights,
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recommendations=recommendations,
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performance_metrics=performance_metrics,
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personalized_data_used=personalized_data,
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processing_time=processing_time,
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ai_service_status=ai_service_status,
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created_at=datetime.utcnow(),
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updated_at=datetime.utcnow()
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)
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self.db.add(ai_result)
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self.db.commit()
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self.db.refresh(ai_result)
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logger.info(f"✅ AI analysis result stored successfully: {ai_result.id}")
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return ai_result
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except Exception as e:
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logger.error(f"❌ Error storing AI analysis result: {str(e)}")
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self.db.rollback()
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raise
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async def get_latest_ai_analysis(
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self,
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user_id: int,
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analysis_type: str,
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strategy_id: Optional[int] = None,
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max_age_hours: int = 24
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) -> Optional[Dict[str, Any]]:
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"""
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Get the latest AI analysis result with detailed logging.
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"""
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try:
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logger.info(f"🔍 Retrieving latest AI analysis for user {user_id}, type: {analysis_type}")
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# Build query
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query = self.db.query(AIAnalysisResult).filter(
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AIAnalysisResult.user_id == user_id,
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AIAnalysisResult.analysis_type == analysis_type
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)
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if strategy_id:
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query = query.filter(AIAnalysisResult.strategy_id == strategy_id)
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# Get the most recent result
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latest_result = query.order_by(AIAnalysisResult.created_at.desc()).first()
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if latest_result:
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logger.info(f"✅ Found recent AI analysis result: {latest_result.id}")
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# Convert to dictionary and log details
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result_dict = {
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"id": latest_result.id,
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"user_id": latest_result.user_id,
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"strategy_id": latest_result.strategy_id,
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"analysis_type": latest_result.analysis_type,
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"analysis_date": latest_result.created_at.isoformat(),
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"results": latest_result.insights or {},
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"recommendations": latest_result.recommendations or [],
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"personalized_data_used": latest_result.personalized_data_used,
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"ai_service_status": latest_result.ai_service_status
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}
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# Log the detailed structure
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logger.info(f"📊 AI Analysis Result Details:")
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logger.info(f" - Result ID: {result_dict['id']}")
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logger.info(f" - User ID: {result_dict['user_id']}")
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logger.info(f" - Strategy ID: {result_dict['strategy_id']}")
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logger.info(f" - Analysis Type: {result_dict['analysis_type']}")
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logger.info(f" - Analysis Date: {result_dict['analysis_date']}")
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logger.info(f" - Personalized Data Used: {result_dict['personalized_data_used']}")
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logger.info(f" - AI Service Status: {result_dict['ai_service_status']}")
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# Log results structure
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results = result_dict.get("results", {})
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logger.info(f" - Results Keys: {list(results.keys())}")
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logger.info(f" - Results Type: {type(results)}")
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# Log recommendations
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recommendations = result_dict.get("recommendations", [])
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logger.info(f" - Recommendations Count: {len(recommendations)}")
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logger.info(f" - Recommendations Type: {type(recommendations)}")
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# Log specific data if available
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if results:
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logger.info("🔍 RESULTS DATA BREAKDOWN:")
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for key, value in results.items():
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if isinstance(value, list):
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logger.info(f" {key}: {len(value)} items")
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elif isinstance(value, dict):
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logger.info(f" {key}: {len(value)} keys")
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else:
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logger.info(f" {key}: {value}")
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if recommendations:
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logger.info("🔍 RECOMMENDATIONS DATA BREAKDOWN:")
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for i, rec in enumerate(recommendations[:3]): # Log first 3
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if isinstance(rec, dict):
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logger.info(f" Recommendation {i+1}: {rec.get('title', 'N/A')}")
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logger.info(f" Type: {rec.get('type', 'N/A')}")
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logger.info(f" Priority: {rec.get('priority', 'N/A')}")
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else:
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logger.info(f" Recommendation {i+1}: {rec}")
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return result_dict
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else:
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logger.warning(f"⚠️ No AI analysis result found for user {user_id}, type: {analysis_type}")
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return None
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except Exception as e:
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logger.error(f"❌ Error retrieving latest AI analysis: {str(e)}")
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logger.error(f"Exception type: {type(e)}")
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import traceback
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logger.error(f"Traceback: {traceback.format_exc()}")
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return None
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async def get_user_ai_analyses(
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self,
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user_id: int,
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analysis_types: Optional[List[str]] = None,
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limit: int = 10
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) -> List[AIAnalysisResult]:
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"""Get all AI analysis results for a user."""
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try:
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logger.info(f"Retrieving AI analyses for user {user_id}")
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query = self.db.query(AIAnalysisResult).filter(
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AIAnalysisResult.user_id == user_id
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)
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# Filter by analysis types if provided
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if analysis_types:
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query = query.filter(AIAnalysisResult.analysis_type.in_(analysis_types))
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results = query.order_by(desc(AIAnalysisResult.created_at)).limit(limit).all()
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logger.info(f"✅ Retrieved {len(results)} AI analysis results for user {user_id}")
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return results
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except Exception as e:
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logger.error(f"❌ Error retrieving user AI analyses: {str(e)}")
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return []
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async def update_ai_analysis_result(
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self,
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result_id: int,
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updates: Dict[str, Any]
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) -> Optional[AIAnalysisResult]:
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"""Update an existing AI analysis result."""
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try:
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logger.info(f"Updating AI analysis result: {result_id}")
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result = self.db.query(AIAnalysisResult).filter(
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AIAnalysisResult.id == result_id
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).first()
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if not result:
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logger.warning(f"AI analysis result not found: {result_id}")
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return None
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# Update fields
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for key, value in updates.items():
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if hasattr(result, key):
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setattr(result, key, value)
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result.updated_at = datetime.utcnow()
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self.db.commit()
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self.db.refresh(result)
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logger.info(f"✅ AI analysis result updated successfully: {result_id}")
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return result
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except Exception as e:
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logger.error(f"❌ Error updating AI analysis result: {str(e)}")
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self.db.rollback()
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return None
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async def delete_old_ai_analyses(
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self,
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days_old: int = 30
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) -> int:
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"""Delete AI analysis results older than specified days."""
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try:
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logger.info(f"Cleaning up AI analysis results older than {days_old} days")
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cutoff_date = datetime.utcnow() - timedelta(days=days_old)
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deleted_count = self.db.query(AIAnalysisResult).filter(
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AIAnalysisResult.created_at < cutoff_date
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).delete()
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self.db.commit()
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logger.info(f"✅ Deleted {deleted_count} old AI analysis results")
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return deleted_count
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except Exception as e:
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logger.error(f"❌ Error deleting old AI analyses: {str(e)}")
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self.db.rollback()
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return 0
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async def get_analysis_statistics(
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self,
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user_id: Optional[int] = None
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) -> Dict[str, Any]:
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"""Get statistics about AI analysis results."""
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try:
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logger.info("Retrieving AI analysis statistics")
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query = self.db.query(AIAnalysisResult)
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if user_id:
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query = query.filter(AIAnalysisResult.user_id == user_id)
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total_analyses = query.count()
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# Get counts by analysis type
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type_counts = {}
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for analysis_type in ['performance_trends', 'strategic_intelligence', 'content_evolution', 'gap_analysis']:
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count = query.filter(AIAnalysisResult.analysis_type == analysis_type).count()
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type_counts[analysis_type] = count
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# Get average processing time
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avg_processing_time = self.db.query(
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self.db.func.avg(AIAnalysisResult.processing_time)
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).scalar() or 0
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stats = {
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'total_analyses': total_analyses,
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'analysis_type_counts': type_counts,
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'average_processing_time': float(avg_processing_time),
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'user_id': user_id
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}
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logger.info(f"✅ Retrieved AI analysis statistics: {stats}")
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return stats
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except Exception as e:
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logger.error(f"❌ Error retrieving AI analysis statistics: {str(e)}")
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return {
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'total_analyses': 0,
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'analysis_type_counts': {},
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'average_processing_time': 0,
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'user_id': user_id
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}
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