884 lines
31 KiB
Python
884 lines
31 KiB
Python
"""
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Data Source Implementations for Calendar Generation Framework
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Concrete implementations of data sources for content strategy, gap analysis,
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keywords, content pillars, performance data, and AI analysis.
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"""
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import logging
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from .interfaces import (
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DataSourceInterface,
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DataSourceType,
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DataSourcePriority,
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DataSourceValidationResult
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)
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logger = logging.getLogger(__name__)
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class ContentStrategyDataSource(DataSourceInterface):
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"""
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Enhanced content strategy data source with 30+ fields.
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Provides comprehensive content strategy data including business objectives,
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target audience, content pillars, brand voice, and editorial guidelines.
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"""
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def __init__(self):
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super().__init__(
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source_id="content_strategy",
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source_type=DataSourceType.STRATEGY,
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priority=DataSourcePriority.CRITICAL
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)
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self.version = "2.0.0"
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async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""
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Get comprehensive content strategy data.
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Args:
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user_id: User identifier
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strategy_id: Strategy identifier
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Returns:
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Dictionary containing comprehensive strategy data
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"""
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try:
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# Get strategy data from database directly
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from services.content_planning_db import ContentPlanningDBService
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db_service = ContentPlanningDBService()
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strategy_data = await db_service.get_strategy_data(strategy_id)
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self.mark_updated()
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logger.info(f"Retrieved content strategy data for strategy {strategy_id}")
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return strategy_data
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except Exception as e:
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logger.error(f"Error getting content strategy data: {e}")
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return {}
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async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate content strategy data quality.
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Args:
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data: Strategy data to validate
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Returns:
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Validation result dictionary
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"""
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result = DataSourceValidationResult()
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# Required fields for content strategy
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required_fields = [
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"strategy_id", "strategy_name", "industry", "target_audience",
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"content_pillars", "business_objectives", "content_preferences"
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]
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# Check for missing fields
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for field in required_fields:
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if not data.get(field):
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result.add_missing_field(field)
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# Enhanced fields validation
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enhanced_fields = [
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"brand_voice", "editorial_guidelines", "content_frequency",
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"preferred_formats", "content_mix", "ai_recommendations"
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]
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enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
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enhanced_score = enhanced_count / len(enhanced_fields)
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# Calculate overall quality score
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required_count = len(required_fields) - len(result.missing_fields)
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required_score = required_count / len(required_fields)
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# Weighted quality score (70% required, 30% enhanced)
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result.quality_score = (required_score * 0.7) + (enhanced_score * 0.3)
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# Add recommendations
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if result.quality_score < 0.8:
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result.add_recommendation("Consider adding more enhanced strategy fields for better calendar generation")
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if not data.get("brand_voice"):
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result.add_recommendation("Add brand voice guidelines for consistent content tone")
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if not data.get("editorial_guidelines"):
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result.add_recommendation("Add editorial guidelines for content standards")
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self.update_quality_score(result.quality_score)
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return result.to_dict()
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async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Enhance strategy data with AI insights.
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Args:
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data: Original strategy data
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Returns:
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Enhanced strategy data
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"""
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enhanced_data = data.copy()
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# Add AI-generated insights if not present
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if "ai_recommendations" not in enhanced_data:
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enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
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if "strategy_analysis" not in enhanced_data:
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enhanced_data["strategy_analysis"] = await self._analyze_strategy(data)
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# Add enhancement metadata
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enhanced_data["enhancement_metadata"] = {
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"enhanced_at": datetime.utcnow().isoformat(),
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"enhancement_version": self.version,
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"enhancement_source": "ContentStrategyDataSource"
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}
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logger.info(f"Enhanced content strategy data with AI insights")
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return enhanced_data
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async def _generate_ai_recommendations(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate AI recommendations for content strategy."""
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# Implementation for AI recommendations
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return {
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"content_opportunities": [],
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"optimization_suggestions": [],
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"trend_recommendations": [],
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"performance_insights": []
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}
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async def _analyze_strategy(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze strategy completeness and quality."""
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# Implementation for strategy analysis
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return {
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"completeness_score": 0.0,
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"coherence_analysis": {},
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"gap_identification": [],
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"optimization_opportunities": []
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}
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class GapAnalysisDataSource(DataSourceInterface):
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"""
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Enhanced gap analysis data source with AI-powered insights.
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Provides comprehensive gap analysis including content gaps, keyword opportunities,
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competitor analysis, and market positioning insights.
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"""
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def __init__(self):
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super().__init__(
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source_id="gap_analysis",
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source_type=DataSourceType.ANALYSIS,
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priority=DataSourcePriority.HIGH
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)
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self.version = "1.5.0"
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async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""
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Get enhanced gap analysis data.
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Args:
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user_id: User identifier
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strategy_id: Strategy identifier
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Returns:
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Dictionary containing gap analysis data
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"""
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try:
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gap_data = await self._get_enhanced_gap_analysis(user_id, strategy_id)
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self.mark_updated()
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logger.info(f"Retrieved gap analysis data for strategy {strategy_id}")
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return gap_data
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except Exception as e:
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logger.error(f"Error getting gap analysis data: {e}")
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return {}
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async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate gap analysis data quality.
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Args:
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data: Gap analysis data to validate
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Returns:
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Validation result dictionary
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"""
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result = DataSourceValidationResult()
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# Required fields for gap analysis
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required_fields = [
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"content_gaps", "keyword_opportunities", "competitor_insights"
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]
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# Check for missing fields
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for field in required_fields:
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if not data.get(field):
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result.add_missing_field(field)
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# Enhanced fields validation
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enhanced_fields = [
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"market_trends", "content_opportunities", "performance_insights",
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"ai_recommendations", "gap_prioritization"
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]
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enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
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enhanced_score = enhanced_count / len(enhanced_fields)
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# Calculate overall quality score
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required_count = len(required_fields) - len(result.missing_fields)
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required_score = required_count / len(required_fields)
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# Weighted quality score (60% required, 40% enhanced)
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result.quality_score = (required_score * 0.6) + (enhanced_score * 0.4)
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# Add recommendations
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if result.quality_score < 0.7:
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result.add_recommendation("Enhance gap analysis with AI-powered insights")
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if not data.get("market_trends"):
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result.add_recommendation("Add market trend analysis for better content opportunities")
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self.update_quality_score(result.quality_score)
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return result.to_dict()
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async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Enhance gap analysis data with AI insights.
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Args:
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data: Original gap analysis data
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Returns:
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Enhanced gap analysis data
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"""
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enhanced_data = data.copy()
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# Add AI enhancements
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if "ai_recommendations" not in enhanced_data:
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enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
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if "gap_prioritization" not in enhanced_data:
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enhanced_data["gap_prioritization"] = await self._prioritize_gaps(data)
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# Add enhancement metadata
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enhanced_data["enhancement_metadata"] = {
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"enhanced_at": datetime.utcnow().isoformat(),
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"enhancement_version": self.version,
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"enhancement_source": "GapAnalysisDataSource"
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}
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logger.info(f"Enhanced gap analysis data with AI insights")
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return enhanced_data
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async def _get_enhanced_gap_analysis(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""Get enhanced gap analysis with AI insights."""
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# Implementation for enhanced gap analysis
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return {
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"content_gaps": [],
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"keyword_opportunities": [],
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"competitor_insights": [],
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"market_trends": [],
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"content_opportunities": [],
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"performance_insights": []
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}
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async def _generate_ai_recommendations(self, gap_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate AI recommendations for gap analysis."""
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return {
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"gap_prioritization": [],
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"content_opportunities": [],
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"optimization_suggestions": []
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}
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async def _prioritize_gaps(self, gap_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Prioritize content gaps based on impact and effort."""
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return []
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class KeywordsDataSource(DataSourceInterface):
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"""
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Enhanced keywords data source with dynamic research capabilities.
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Provides comprehensive keyword data including research, trending keywords,
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competitor analysis, and difficulty scoring.
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"""
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def __init__(self):
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super().__init__(
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source_id="keywords",
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source_type=DataSourceType.RESEARCH,
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priority=DataSourcePriority.HIGH
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)
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self.version = "1.5.0"
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async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""
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Get enhanced keywords data with dynamic research.
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Args:
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user_id: User identifier
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strategy_id: Strategy identifier
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Returns:
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Dictionary containing keywords data
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"""
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try:
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keywords_data = await self._get_enhanced_keywords(user_id, strategy_id)
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self.mark_updated()
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logger.info(f"Retrieved keywords data for strategy {strategy_id}")
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return keywords_data
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except Exception as e:
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logger.error(f"Error getting keywords data: {e}")
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return {}
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async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate keywords data quality.
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Args:
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data: Keywords data to validate
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Returns:
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Validation result dictionary
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"""
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result = DataSourceValidationResult()
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# Required fields for keywords
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required_fields = [
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"primary_keywords", "secondary_keywords", "keyword_research"
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]
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# Check for missing fields
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for field in required_fields:
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if not data.get(field):
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result.add_missing_field(field)
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# Enhanced fields validation
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enhanced_fields = [
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"trending_keywords", "competitor_keywords", "keyword_difficulty",
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"search_volume", "keyword_opportunities", "ai_recommendations"
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]
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enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
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enhanced_score = enhanced_count / len(enhanced_fields)
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# Calculate overall quality score
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required_count = len(required_fields) - len(result.missing_fields)
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required_score = required_count / len(required_fields)
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# Weighted quality score (50% required, 50% enhanced)
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result.quality_score = (required_score * 0.5) + (enhanced_score * 0.5)
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# Add recommendations
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if result.quality_score < 0.7:
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result.add_recommendation("Enhance keyword research with trending and competitor analysis")
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if not data.get("keyword_difficulty"):
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result.add_recommendation("Add keyword difficulty scoring for better content planning")
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self.update_quality_score(result.quality_score)
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return result.to_dict()
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async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Enhance keywords data with AI insights.
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Args:
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data: Original keywords data
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Returns:
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Enhanced keywords data
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"""
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enhanced_data = data.copy()
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# Add AI enhancements
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if "ai_recommendations" not in enhanced_data:
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enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
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if "keyword_optimization" not in enhanced_data:
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enhanced_data["keyword_optimization"] = await self._optimize_keywords(data)
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# Add enhancement metadata
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enhanced_data["enhancement_metadata"] = {
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"enhanced_at": datetime.utcnow().isoformat(),
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"enhancement_version": self.version,
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"enhancement_source": "KeywordsDataSource"
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}
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logger.info(f"Enhanced keywords data with AI insights")
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return enhanced_data
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async def _get_enhanced_keywords(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""Get enhanced keywords with dynamic research."""
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# Implementation for enhanced keywords
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return {
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"primary_keywords": [],
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"secondary_keywords": [],
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"keyword_research": {},
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"trending_keywords": [],
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"competitor_keywords": [],
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"keyword_difficulty": {},
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"search_volume": {},
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"keyword_opportunities": []
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}
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async def _generate_ai_recommendations(self, keywords_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate AI recommendations for keywords."""
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return {
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"keyword_opportunities": [],
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"optimization_suggestions": [],
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"trend_recommendations": []
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}
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async def _optimize_keywords(self, keywords_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Optimize keywords based on performance and trends."""
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return {
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"optimized_keywords": [],
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"performance_insights": {},
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"optimization_recommendations": []
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}
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class ContentPillarsDataSource(DataSourceInterface):
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"""
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Enhanced content pillars data source with AI-generated dynamic pillars.
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Provides comprehensive content pillar data including AI-generated pillars,
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market-based optimization, and performance-based adjustment.
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"""
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def __init__(self):
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super().__init__(
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source_id="content_pillars",
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source_type=DataSourceType.STRATEGY,
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priority=DataSourcePriority.MEDIUM
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)
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self.version = "1.5.0"
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async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
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"""
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Get enhanced content pillars data.
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Args:
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user_id: User identifier
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strategy_id: Strategy identifier
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Returns:
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Dictionary containing content pillars data
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"""
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try:
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pillars_data = await self._get_enhanced_pillars(user_id, strategy_id)
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self.mark_updated()
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logger.info(f"Retrieved content pillars data for strategy {strategy_id}")
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return pillars_data
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except Exception as e:
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logger.error(f"Error getting content pillars data: {e}")
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return {}
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async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Validate content pillars data quality.
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Args:
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data: Content pillars data to validate
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Returns:
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Validation result dictionary
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"""
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result = DataSourceValidationResult()
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# Required fields for content pillars
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required_fields = [
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"content_pillars", "pillar_topics", "pillar_keywords"
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]
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# Check for missing fields
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for field in required_fields:
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if not data.get(field):
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result.add_missing_field(field)
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# Enhanced fields validation
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enhanced_fields = [
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"ai_generated_pillars", "market_optimization", "performance_adjustment",
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"audience_preferences", "pillar_prioritization", "ai_recommendations"
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]
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enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
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enhanced_score = enhanced_count / len(enhanced_fields)
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# Calculate overall quality score
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required_count = len(required_fields) - len(result.missing_fields)
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required_score = required_count / len(required_fields)
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# Weighted quality score (60% required, 40% enhanced)
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result.quality_score = (required_score * 0.6) + (enhanced_score * 0.4)
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# Add recommendations
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if result.quality_score < 0.7:
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result.add_recommendation("Enhance content pillars with AI-generated insights")
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if not data.get("pillar_prioritization"):
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result.add_recommendation("Add pillar prioritization for better content planning")
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self.update_quality_score(result.quality_score)
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return result.to_dict()
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async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Enhance content pillars data with AI insights.
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Args:
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data: Original content pillars data
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Returns:
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Enhanced content pillars data
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"""
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enhanced_data = data.copy()
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# Add AI enhancements
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if "ai_recommendations" not in enhanced_data:
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enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
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if "pillar_optimization" not in enhanced_data:
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enhanced_data["pillar_optimization"] = await self._optimize_pillars(data)
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# Add enhancement metadata
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enhanced_data["enhancement_metadata"] = {
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"enhanced_at": datetime.utcnow().isoformat(),
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"enhancement_version": self.version,
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"enhancement_source": "ContentPillarsDataSource"
|
|
}
|
|
|
|
logger.info(f"Enhanced content pillars data with AI insights")
|
|
return enhanced_data
|
|
|
|
async def _get_enhanced_pillars(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
|
|
"""Get enhanced content pillars with AI generation."""
|
|
# Implementation for enhanced content pillars
|
|
return {
|
|
"content_pillars": [],
|
|
"pillar_topics": {},
|
|
"pillar_keywords": {},
|
|
"ai_generated_pillars": [],
|
|
"market_optimization": {},
|
|
"performance_adjustment": {},
|
|
"audience_preferences": {},
|
|
"pillar_prioritization": []
|
|
}
|
|
|
|
async def _generate_ai_recommendations(self, pillars_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generate AI recommendations for content pillars."""
|
|
return {
|
|
"pillar_opportunities": [],
|
|
"optimization_suggestions": [],
|
|
"trend_recommendations": []
|
|
}
|
|
|
|
async def _optimize_pillars(self, pillars_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Optimize content pillars based on performance and market trends."""
|
|
return {
|
|
"optimized_pillars": [],
|
|
"performance_insights": {},
|
|
"optimization_recommendations": []
|
|
}
|
|
|
|
|
|
class PerformanceDataSource(DataSourceInterface):
|
|
"""
|
|
Enhanced performance data source with real-time tracking capabilities.
|
|
|
|
Provides comprehensive performance data including conversion rates,
|
|
engagement metrics, ROI calculations, and optimization insights.
|
|
"""
|
|
|
|
def __init__(self):
|
|
super().__init__(
|
|
source_id="performance_data",
|
|
source_type=DataSourceType.PERFORMANCE,
|
|
priority=DataSourcePriority.MEDIUM
|
|
)
|
|
self.version = "1.0.0"
|
|
|
|
async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
|
|
"""
|
|
Get enhanced performance data.
|
|
|
|
Args:
|
|
user_id: User identifier
|
|
strategy_id: Strategy identifier
|
|
|
|
Returns:
|
|
Dictionary containing performance data
|
|
"""
|
|
try:
|
|
performance_data = await self._get_enhanced_performance(user_id, strategy_id)
|
|
self.mark_updated()
|
|
logger.info(f"Retrieved performance data for strategy {strategy_id}")
|
|
return performance_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting performance data: {e}")
|
|
return {}
|
|
|
|
async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Validate performance data quality.
|
|
|
|
Args:
|
|
data: Performance data to validate
|
|
|
|
Returns:
|
|
Validation result dictionary
|
|
"""
|
|
result = DataSourceValidationResult()
|
|
|
|
# Required fields for performance data
|
|
required_fields = [
|
|
"engagement_metrics", "conversion_rates", "performance_insights"
|
|
]
|
|
|
|
# Check for missing fields
|
|
for field in required_fields:
|
|
if not data.get(field):
|
|
result.add_missing_field(field)
|
|
|
|
# Enhanced fields validation
|
|
enhanced_fields = [
|
|
"roi_calculations", "optimization_insights", "trend_analysis",
|
|
"predictive_analytics", "ai_recommendations", "performance_forecasting"
|
|
]
|
|
|
|
enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
|
|
enhanced_score = enhanced_count / len(enhanced_fields)
|
|
|
|
# Calculate overall quality score
|
|
required_count = len(required_fields) - len(result.missing_fields)
|
|
required_score = required_count / len(required_fields)
|
|
|
|
# Weighted quality score (50% required, 50% enhanced)
|
|
result.quality_score = (required_score * 0.5) + (enhanced_score * 0.5)
|
|
|
|
# Add recommendations
|
|
if result.quality_score < 0.6:
|
|
result.add_recommendation("Enhance performance tracking with real-time metrics")
|
|
|
|
if not data.get("roi_calculations"):
|
|
result.add_recommendation("Add ROI calculations for better performance measurement")
|
|
|
|
self.update_quality_score(result.quality_score)
|
|
return result.to_dict()
|
|
|
|
async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Enhance performance data with AI insights.
|
|
|
|
Args:
|
|
data: Original performance data
|
|
|
|
Returns:
|
|
Enhanced performance data
|
|
"""
|
|
enhanced_data = data.copy()
|
|
|
|
# Add AI enhancements
|
|
if "ai_recommendations" not in enhanced_data:
|
|
enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
|
|
|
|
if "performance_optimization" not in enhanced_data:
|
|
enhanced_data["performance_optimization"] = await self._optimize_performance(data)
|
|
|
|
# Add enhancement metadata
|
|
enhanced_data["enhancement_metadata"] = {
|
|
"enhanced_at": datetime.utcnow().isoformat(),
|
|
"enhancement_version": self.version,
|
|
"enhancement_source": "PerformanceDataSource"
|
|
}
|
|
|
|
logger.info(f"Enhanced performance data with AI insights")
|
|
return enhanced_data
|
|
|
|
async def _get_enhanced_performance(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
|
|
"""Get enhanced performance data with real-time tracking."""
|
|
# Implementation for enhanced performance data
|
|
return {
|
|
"engagement_metrics": {},
|
|
"conversion_rates": {},
|
|
"performance_insights": {},
|
|
"roi_calculations": {},
|
|
"optimization_insights": {},
|
|
"trend_analysis": {},
|
|
"predictive_analytics": {},
|
|
"performance_forecasting": {}
|
|
}
|
|
|
|
async def _generate_ai_recommendations(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generate AI recommendations for performance optimization."""
|
|
return {
|
|
"optimization_opportunities": [],
|
|
"performance_suggestions": [],
|
|
"trend_recommendations": []
|
|
}
|
|
|
|
async def _optimize_performance(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Optimize performance based on analytics and trends."""
|
|
return {
|
|
"optimization_strategies": [],
|
|
"performance_insights": {},
|
|
"optimization_recommendations": []
|
|
}
|
|
|
|
|
|
class AIAnalysisDataSource(DataSourceInterface):
|
|
"""
|
|
Enhanced AI analysis data source with strategic intelligence generation.
|
|
|
|
Provides comprehensive AI analysis including strategic insights,
|
|
market intelligence, competitive analysis, and predictive analytics.
|
|
"""
|
|
|
|
def __init__(self):
|
|
super().__init__(
|
|
source_id="ai_analysis",
|
|
source_type=DataSourceType.AI,
|
|
priority=DataSourcePriority.HIGH
|
|
)
|
|
self.version = "2.0.0"
|
|
|
|
async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
|
|
"""
|
|
Get enhanced AI analysis data.
|
|
|
|
Args:
|
|
user_id: User identifier
|
|
strategy_id: Strategy identifier
|
|
|
|
Returns:
|
|
Dictionary containing AI analysis data
|
|
"""
|
|
try:
|
|
ai_data = await self._get_enhanced_ai_analysis(user_id, strategy_id)
|
|
self.mark_updated()
|
|
logger.info(f"Retrieved AI analysis data for strategy {strategy_id}")
|
|
return ai_data
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting AI analysis data: {e}")
|
|
return {}
|
|
|
|
async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Validate AI analysis data quality.
|
|
|
|
Args:
|
|
data: AI analysis data to validate
|
|
|
|
Returns:
|
|
Validation result dictionary
|
|
"""
|
|
result = DataSourceValidationResult()
|
|
|
|
# Required fields for AI analysis
|
|
required_fields = [
|
|
"strategic_insights", "market_intelligence", "competitive_analysis"
|
|
]
|
|
|
|
# Check for missing fields
|
|
for field in required_fields:
|
|
if not data.get(field):
|
|
result.add_missing_field(field)
|
|
|
|
# Enhanced fields validation
|
|
enhanced_fields = [
|
|
"predictive_analytics", "trend_forecasting", "opportunity_identification",
|
|
"risk_assessment", "ai_recommendations", "strategic_recommendations"
|
|
]
|
|
|
|
enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
|
|
enhanced_score = enhanced_count / len(enhanced_fields)
|
|
|
|
# Calculate overall quality score
|
|
required_count = len(required_fields) - len(result.missing_fields)
|
|
required_score = required_count / len(required_fields)
|
|
|
|
# Weighted quality score (40% required, 60% enhanced)
|
|
result.quality_score = (required_score * 0.4) + (enhanced_score * 0.6)
|
|
|
|
# Add recommendations
|
|
if result.quality_score < 0.8:
|
|
result.add_recommendation("Enhance AI analysis with predictive analytics and trend forecasting")
|
|
|
|
if not data.get("opportunity_identification"):
|
|
result.add_recommendation("Add opportunity identification for better strategic planning")
|
|
|
|
self.update_quality_score(result.quality_score)
|
|
return result.to_dict()
|
|
|
|
async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Enhance AI analysis data with additional insights.
|
|
|
|
Args:
|
|
data: Original AI analysis data
|
|
|
|
Returns:
|
|
Enhanced AI analysis data
|
|
"""
|
|
enhanced_data = data.copy()
|
|
|
|
# Add AI enhancements
|
|
if "ai_recommendations" not in enhanced_data:
|
|
enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
|
|
|
|
if "strategic_optimization" not in enhanced_data:
|
|
enhanced_data["strategic_optimization"] = await self._optimize_strategy(data)
|
|
|
|
# Add enhancement metadata
|
|
enhanced_data["enhancement_metadata"] = {
|
|
"enhanced_at": datetime.utcnow().isoformat(),
|
|
"enhancement_version": self.version,
|
|
"enhancement_source": "AIAnalysisDataSource"
|
|
}
|
|
|
|
logger.info(f"Enhanced AI analysis data with additional insights")
|
|
return enhanced_data
|
|
|
|
async def _get_enhanced_ai_analysis(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
|
|
"""Get enhanced AI analysis with strategic intelligence."""
|
|
# Implementation for enhanced AI analysis
|
|
return {
|
|
"strategic_insights": {},
|
|
"market_intelligence": {},
|
|
"competitive_analysis": {},
|
|
"predictive_analytics": {},
|
|
"trend_forecasting": {},
|
|
"opportunity_identification": [],
|
|
"risk_assessment": {},
|
|
"strategic_recommendations": []
|
|
}
|
|
|
|
async def _generate_ai_recommendations(self, ai_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Generate AI recommendations for strategic optimization."""
|
|
return {
|
|
"strategic_opportunities": [],
|
|
"optimization_suggestions": [],
|
|
"trend_recommendations": []
|
|
}
|
|
|
|
async def _optimize_strategy(self, ai_data: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""Optimize strategy based on AI analysis and insights."""
|
|
return {
|
|
"optimization_strategies": [],
|
|
"strategic_insights": {},
|
|
"optimization_recommendations": []
|
|
}
|