ALwrity/backend/services/calendar_generation_datasource_framework/data_processing

Data Processing Modules for 12-Step Calendar Generation

📋 Overview

This directory contains the data processing modules that provide real data exclusively to the 12-step calendar generation process. These modules connect to actual services and databases to retrieve comprehensive user data, strategy information, and analysis results.

NO MOCK DATA - Only real data sources allowed.

🎯 12-Step Calendar Generation Data Flow

Phase 1: Foundation (Steps 1-3)

Step 1: Content Strategy Analysis

Data Processing Module: strategy_data.py
Function: StrategyDataProcessor.get_strategy_data(strategy_id)
Real Data Sources:

• ContentPlanningDBService.get_content_strategy(strategy_id) - Real strategy data from database
• EnhancedStrategyDBService.get_enhanced_strategy(strategy_id) - Real enhanced strategy fields
• StrategyQualityAssessor.analyze_strategy_completeness() - Real strategy analysis

Expected Data Points (from prompt chaining document):

• Content pillars and target audience preferences
• Business goals and success metrics
• Market positioning and competitive landscape
• KPI mapping and alignment validation
• Brand voice and editorial guidelines

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase1/phase1_steps.py
Class: ContentStrategyAnalysisStep

Step 2: Gap Analysis and Opportunity Identification

Data Processing Module: gap_analysis_data.py
Function: GapAnalysisDataProcessor.get_gap_analysis_data(user_id)
Real Data Sources:

• ContentPlanningDBService.get_user_content_gap_analyses(user_id) - Real gap analysis results
• ContentGapAnalyzer.analyze_content_gaps() - Real content gap analysis
• CompetitorAnalyzer.analyze_competitors() - Real competitor insights

Expected Data Points (from prompt chaining document):

• Prioritized content gaps with impact scores
• High-value keyword opportunities
• Competitor differentiation strategies
• Opportunity implementation timeline
• Keyword distribution and uniqueness validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase1/phase1_steps.py
Class: GapAnalysisStep

Step 3: Audience and Platform Strategy

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• OnboardingDataService.get_personalized_ai_inputs(user_id) - Real onboarding data
• ActiveStrategyService.get_active_strategy(user_id) - Real active strategy
• AIAnalyticsService.generate_strategic_intelligence(strategy_id) - Real AI analysis

Expected Data Points (from prompt chaining document):

• Audience personas and preferences
• Platform performance analysis
• Content mix recommendations
• Optimal timing strategies
• Enterprise-level strategy validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase1/phase1_steps.py
Class: AudiencePlatformStrategyStep

Phase 2: Structure (Steps 4-6)

Step 4: Calendar Framework and Timeline

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• Phase 1 outputs (real strategy analysis, gap analysis, audience strategy)
• strategy_data from comprehensive user data
• gap_analysis from comprehensive user data

Expected Data Points (from prompt chaining document):

• Calendar framework and timeline
• Content frequency and distribution
• Theme structure and focus areas
• Timeline optimization recommendations
• Duration accuracy validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase2/step4_implementation.py
Class: CalendarFrameworkStep

Step 5: Content Pillar Distribution

Data Processing Module: strategy_data.py
Function: StrategyDataProcessor.get_strategy_data(strategy_id)
Real Data Sources:

• strategy_data.content_pillars from comprehensive user data
• strategy_analysis from enhanced strategy data
• Phase 1 outputs (real strategy analysis)

Expected Data Points (from prompt chaining document):

• Content pillar distribution plan
• Theme variations and content types
• Engagement level balancing
• Strategic alignment validation
• Content diversity and uniqueness validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase2/step5_implementation.py
Class: ContentPillarDistributionStep

Step 6: Platform-Specific Strategy

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• onboarding_data from comprehensive user data
• performance_data from comprehensive user data
• competitor_analysis from comprehensive user data

Expected Data Points (from prompt chaining document):

• Platform-specific content strategies
• Content adaptation guidelines
• Platform timing optimization
• Cross-platform coordination plan
• Platform uniqueness validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase2/step6_implementation.py
Class: PlatformSpecificStrategyStep

Phase 3: Content (Steps 7-9)

Step 7: Weekly Theme Development

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• Phase 2 outputs (real calendar framework, content pillars)
• gap_analysis from comprehensive user data
• strategy_data from comprehensive user data

Expected Data Points (from prompt chaining document):

• Weekly theme structure
• Content opportunity integration
• Strategic alignment validation
• Engagement level planning
• Theme uniqueness and progression validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase3/step7_implementation.py
Class: WeeklyThemeDevelopmentStep

Step 8: Daily Content Planning

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• Phase 3 outputs (real weekly themes)
• performance_data from comprehensive user data
• keyword_analysis from comprehensive user data

Expected Data Points (from prompt chaining document):

• Daily content schedule
• Timing optimization
• Keyword integration plan
• Content variety strategy
• Content uniqueness and keyword distribution validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase3/step8_implementation.py
Class: DailyContentPlanningStep

Step 9: Content Recommendations

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• recommendations_data from comprehensive user data
• gap_analysis from comprehensive user data
• strategy_data from comprehensive user data

Expected Data Points (from prompt chaining document):

• Specific content recommendations
• Gap-filling content ideas
• Implementation guidance
• Quality assurance metrics
• Enterprise-level content validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase3/step9_implementation.py
Class: ContentRecommendationsStep

Phase 4: Optimization (Steps 10-12)

Step 10: Performance Optimization

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• All previous phase outputs
• performance_data from comprehensive user data
• ai_analysis_results from comprehensive user data

Expected Data Points (from prompt chaining document):

• Performance optimization recommendations
• Quality improvement suggestions
• Strategic alignment validation
• Performance metric validation
• KPI achievement and ROI validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase4/step10_implementation.py
Class: PerformanceOptimizationStep

Step 11: Strategy Alignment Validation

Data Processing Module: strategy_data.py
Function: StrategyDataProcessor.get_strategy_data(strategy_id)
Real Data Sources:

• All previous phase outputs
• strategy_data from comprehensive user data
• strategy_analysis from enhanced strategy data

Expected Data Points (from prompt chaining document):

• Strategy alignment validation
• Goal achievement assessment
• Content pillar verification
• Audience targeting confirmation
• Strategic objective achievement validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase4/step11_implementation.py
Class: StrategyAlignmentValidationStep

Step 12: Final Calendar Assembly

Data Processing Module: comprehensive_user_data.py
Function: ComprehensiveUserDataProcessor.get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• All previous phase outputs
• Complete comprehensive user data
• All data sources summary

Expected Data Points (from prompt chaining document):

• Complete content calendar
• Quality assurance report
• Data utilization summary
• Final recommendations and insights
• Enterprise-level quality validation

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase4/step12_implementation.py
Class: FinalCalendarAssemblyStep

📊 Data Processing Modules Details

1. comprehensive_user_data.py

Purpose: Central data aggregator for all real user data
Main Function: get_comprehensive_user_data(user_id, strategy_id)
Real Data Sources:

• OnboardingDataService.get_personalized_ai_inputs(user_id) - Real onboarding data
• AIAnalyticsService.generate_strategic_intelligence(strategy_id) - Real AI analysis
• AIEngineService.generate_content_recommendations(onboarding_data) - Real AI recommendations
• ActiveStrategyService.get_active_strategy(user_id) - Real active strategy

Data Structure:

{
    "user_id": user_id,
    "onboarding_data": onboarding_data,  # Real onboarding data
    "ai_analysis_results": ai_analysis_results,  # Real AI analysis
    "gap_analysis": {
        "content_gaps": gap_analysis_data,  # Real gap analysis
        "keyword_opportunities": onboarding_data.get("keyword_analysis", {}).get("high_value_keywords", []),
        "competitor_insights": onboarding_data.get("competitor_analysis", {}).get("top_performers", []),
        "recommendations": gap_analysis_data,
        "opportunities": onboarding_data.get("gap_analysis", {}).get("content_opportunities", [])
    },
    "strategy_data": strategy_data,  # Real strategy data
    "recommendations_data": recommendations_data,
    "performance_data": performance_data,
    "industry": strategy_data.get("industry") or onboarding_data.get("website_analysis", {}).get("industry_focus", "technology"),
    "target_audience": strategy_data.get("target_audience") or onboarding_data.get("website_analysis", {}).get("target_audience", []),
    "business_goals": strategy_data.get("business_objectives") or ["Increase brand awareness", "Generate leads", "Establish thought leadership"],
    "website_analysis": onboarding_data.get("website_analysis", {}),
    "competitor_analysis": onboarding_data.get("competitor_analysis", {}),
    "keyword_analysis": onboarding_data.get("keyword_analysis", {}),
    "strategy_analysis": strategy_data.get("strategy_analysis", {}),
    "quality_indicators": strategy_data.get("quality_indicators", {})
}

2. strategy_data.py

Purpose: Process and enhance real strategy data
Main Function: get_strategy_data(strategy_id)
Real Data Sources:

• ContentPlanningDBService.get_content_strategy(strategy_id) - Real database strategy
• EnhancedStrategyDBService.get_enhanced_strategy(strategy_id) - Real enhanced strategy
• StrategyQualityAssessor.analyze_strategy_completeness() - Real quality assessment

Data Structure:

{
    "strategy_id": strategy_dict.get("id"),
    "strategy_name": strategy_dict.get("name"),
    "industry": strategy_dict.get("industry", "technology"),
    "target_audience": strategy_dict.get("target_audience", {}),
    "content_pillars": strategy_dict.get("content_pillars", []),
    "ai_recommendations": strategy_dict.get("ai_recommendations", {}),
    "strategy_analysis": await quality_assessor.analyze_strategy_completeness(strategy_dict, enhanced_strategy_data),
    "quality_indicators": await quality_assessor.calculate_strategy_quality_indicators(strategy_dict, enhanced_strategy_data),
    "data_completeness": await quality_assessor.calculate_data_completeness(strategy_dict, enhanced_strategy_data),
    "strategic_alignment": await quality_assessor.assess_strategic_alignment(strategy_dict, enhanced_strategy_data)
}

3. gap_analysis_data.py

Purpose: Process real gap analysis data
Main Function: get_gap_analysis_data(user_id)
Real Data Sources:

• ContentPlanningDBService.get_user_content_gap_analyses(user_id) - Real database gap analysis

Data Structure:

{
    "content_gaps": latest_analysis.get("analysis_results", {}).get("content_gaps", []),
    "keyword_opportunities": latest_analysis.get("analysis_results", {}).get("keyword_opportunities", []),
    "competitor_insights": latest_analysis.get("analysis_results", {}).get("competitor_insights", []),
    "recommendations": latest_analysis.get("recommendations", []),
    "opportunities": latest_analysis.get("opportunities", [])
}

🔗 Integration Points

Orchestrator Integration

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/orchestrator.py
Function: _get_comprehensive_user_data(user_id, strategy_id)
Usage:

# Line 35: Import
from calendar_generation_datasource_framework.data_processing import ComprehensiveUserDataProcessor

# Line 220+: Usage
user_data = await self.comprehensive_user_processor.get_comprehensive_user_data(user_id, strategy_id)

Step Integration

File: backend/services/calendar_generation_datasource_framework/prompt_chaining/steps/phase1/phase1_steps.py
Usage:

# Line 27-30: Imports
from calendar_generation_datasource_framework.data_processing import (
    ComprehensiveUserDataProcessor,
    StrategyDataProcessor,
    GapAnalysisDataProcessor
)

# Usage in steps
strategy_processor = StrategyDataProcessor()
processed_strategy = await strategy_processor.get_strategy_data(strategy_id)

✅ Real Data Source Validation

Real Data Sources Confirmed

• ✅ OnboardingDataService - Real onboarding data
• ✅ AIAnalyticsService - Real AI analysis
• ✅ AIEngineService - Real AI engine
• ✅ ActiveStrategyService - Real active strategy
• ✅ ContentPlanningDBService - Real database service
• ✅ EnhancedStrategyDBService - Real enhanced strategy
• ✅ StrategyQualityAssessor - Real quality assessment

No Mock Data Policy

• ❌ No hardcoded mock data in data_processing modules
• ❌ No fallback mock responses when services fail
• ❌ No silent failures that mask real issues
• ✅ All data comes from real services and databases
• ✅ Proper error handling for missing data
• ✅ Clear error messages when services are unavailable

🚀 Usage in 12-Step Process

Step Execution Flow

1. Orchestrator calls ComprehensiveUserDataProcessor.get_comprehensive_user_data()
2. Individual Steps receive real data through context from orchestrator
3. Step-specific processors (StrategyDataProcessor, GapAnalysisDataProcessor) provide additional real data
4. All data is real - no mock data used in the 12-step process

Data Flow by Phase

• Phase 1: Uses ComprehensiveUserDataProcessor + StrategyDataProcessor + GapAnalysisDataProcessor
• Phase 2: Uses Phase 1 outputs + ComprehensiveUserDataProcessor
• Phase 3: Uses Phase 2 outputs + ComprehensiveUserDataProcessor
• Phase 4: Uses all previous outputs + ComprehensiveUserDataProcessor

🛡️ Error Handling & Quality Assurance

Real Data Error Handling

• Service Unavailable: Clear error messages with service name
• Data Validation Failed: Specific field validation errors
• Quality Gate Failed: Detailed quality score breakdown
• No Silent Failures: All failures are explicit and traceable

Quality Validation

• Data Completeness: All required fields present and valid
• Service Availability: All required services responding
• Data Quality: Real data meets quality thresholds
• Strategic Alignment: Output aligns with business goals

📝 Notes

• All data processing modules use real services - no mock data
• Comprehensive error handling for missing or invalid data
• Proper validation mechanisms that fail gracefully
• Data validation ensures data quality and completeness
• Integration with 12-step orchestrator is clean and efficient
• Real data integrity maintained throughout the pipeline

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Last Updated: January 2025
Status: ✅ Production Ready - Real Data Only
Quality: Enterprise Grade - No Mock Data