Alwrity version 0.5.4
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ajaysi
@@ -60,7 +60,7 @@ class AIStrategyGenerator:
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strategy_name: Optional custom strategy name
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Returns:
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Comprehensive strategy with all components
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Comprehensive strategy with all components (EXCLUDING content calendar)
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Raises:
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RuntimeError: If any AI component fails to generate
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@@ -77,19 +77,16 @@ class AIStrategyGenerator:
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# Step 3: Generate competitive analysis
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competitive_analysis = await self._generate_competitive_analysis(base_strategy, context)
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# Step 4: Generate content calendar
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content_calendar = await self._generate_content_calendar(base_strategy, context)
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# Step 5: Generate performance predictions
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# Step 4: Generate performance predictions
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performance_predictions = await self._generate_performance_predictions(base_strategy, context)
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# Step 6: Generate implementation roadmap
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# Step 5: Generate implementation roadmap
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implementation_roadmap = await self._generate_implementation_roadmap(base_strategy, context)
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# Step 7: Generate risk assessment
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# Step 6: Generate risk assessment
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risk_assessment = await self._generate_risk_assessment(base_strategy, context)
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# Step 8: Compile comprehensive strategy
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# Step 7: Compile comprehensive strategy (NO CONTENT CALENDAR)
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comprehensive_strategy = {
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"strategy_metadata": {
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"generated_at": datetime.utcnow().isoformat(),
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@@ -99,21 +96,21 @@ class AIStrategyGenerator:
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"ai_model": "gemini-pro",
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"personalization_level": "high",
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"ai_generated": True,
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"comprehensive": True
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"comprehensive": True,
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"content_calendar_ready": False # Indicates calendar needs to be generated separately
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},
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"base_strategy": base_strategy,
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"strategic_insights": strategic_insights,
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"competitive_analysis": competitive_analysis,
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"content_calendar": content_calendar,
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"performance_predictions": performance_predictions,
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"implementation_roadmap": implementation_roadmap,
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"risk_assessment": risk_assessment,
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"summary": {
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"total_content_pieces": len(content_calendar.get("content_pieces", [])),
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"estimated_roi": performance_predictions.get("estimated_roi", "15-25%"),
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"implementation_timeline": implementation_roadmap.get("total_duration", "12 months"),
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"risk_level": risk_assessment.get("overall_risk_level", "Medium"),
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"success_probability": performance_predictions.get("success_probability", "85%")
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"success_probability": performance_predictions.get("success_probability", "85%"),
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"next_step": "Review strategy and generate content calendar"
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}
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}
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@@ -334,8 +331,55 @@ class AIStrategyGenerator:
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}
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},
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"themes": {"type": "array", "items": {"type": "string"}},
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"schedule": {"type": "object"},
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"distribution_strategy": {"type": "object"}
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"schedule": {
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"type": "object",
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"properties": {
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"publishing_frequency": {"type": "string"},
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"optimal_times": {"type": "array", "items": {"type": "string"}},
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"content_mix": {
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"type": "object",
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"properties": {
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"blog_posts": {"type": "string"},
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"social_media": {"type": "string"},
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"videos": {"type": "string"},
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"infographics": {"type": "string"},
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"newsletters": {"type": "string"}
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}
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},
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"seasonal_adjustments": {
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"type": "object",
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"properties": {
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"holiday_content": {"type": "array", "items": {"type": "string"}},
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"seasonal_themes": {"type": "array", "items": {"type": "string"}},
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"peak_periods": {"type": "array", "items": {"type": "string"}}
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}
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}
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}
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},
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"distribution_strategy": {
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"type": "object",
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"properties": {
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"primary_platforms": {"type": "array", "items": {"type": "string"}},
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"cross_posting_strategy": {"type": "string"},
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"platform_specific_content": {
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"type": "object",
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"properties": {
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"linkedin_content": {"type": "array", "items": {"type": "string"}},
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"twitter_content": {"type": "array", "items": {"type": "string"}},
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"instagram_content": {"type": "array", "items": {"type": "string"}},
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"facebook_content": {"type": "array", "items": {"type": "string"}}
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}
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},
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"engagement_timing": {
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"type": "object",
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"properties": {
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"best_times": {"type": "array", "items": {"type": "string"}},
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"frequency": {"type": "string"},
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"timezone_considerations": {"type": "string"}
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}
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}
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}
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}
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}
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}
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@@ -483,8 +527,33 @@ class AIStrategyGenerator:
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}
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}
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},
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"timeline": {"type": "object"},
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"resource_allocation": {"type": "object"},
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"timeline": {
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"type": "object",
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"properties": {
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"start_date": {"type": "string"},
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"end_date": {"type": "string"},
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"key_milestones": {"type": "array", "items": {"type": "string"}},
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"critical_path": {"type": "array", "items": {"type": "string"}}
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}
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},
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"resource_allocation": {
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"type": "object",
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"properties": {
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"team_requirements": {"type": "array", "items": {"type": "string"}},
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"budget_allocation": {
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"type": "object",
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"properties": {
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"total_budget": {"type": "string"},
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"content_creation": {"type": "string"},
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"technology_tools": {"type": "string"},
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"marketing_promotion": {"type": "string"},
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"external_resources": {"type": "string"}
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}
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},
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"technology_needs": {"type": "array", "items": {"type": "string"}},
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"external_resources": {"type": "array", "items": {"type": "string"}}
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}
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},
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"success_metrics": {"type": "array", "items": {"type": "string"}},
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"total_duration": {"type": "string"}
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}
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@@ -552,9 +621,25 @@ class AIStrategyGenerator:
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}
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},
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"overall_risk_level": {"type": "string"},
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"risk_categories": {"type": "object"},
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"risk_categories": {
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"type": "object",
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"properties": {
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"technical_risks": {"type": "array", "items": {"type": "string"}},
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"market_risks": {"type": "array", "items": {"type": "string"}},
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"operational_risks": {"type": "array", "items": {"type": "string"}},
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"financial_risks": {"type": "array", "items": {"type": "string"}}
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}
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},
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"mitigation_strategies": {"type": "array", "items": {"type": "string"}},
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"monitoring_framework": {"type": "object"}
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"monitoring_framework": {
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"type": "object",
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"properties": {
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"key_indicators": {"type": "array", "items": {"type": "string"}},
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"monitoring_frequency": {"type": "string"},
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"escalation_procedures": {"type": "array", "items": {"type": "string"}},
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"review_schedule": {"type": "string"}
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}
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}
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}
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}
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@@ -506,7 +506,7 @@ class EnhancedStrategyService:
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def _merge_strategy_with_onboarding(self, strategy_data: Dict[str, Any], field_transformations: Dict[str, Any]) -> Dict[str, Any]:
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"""Merge strategy data with onboarding data."""
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merged_data = strategy_data.copy()
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merged_data = strategy_data.copy()
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for field, transformation in field_transformations.items():
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if field not in merged_data or merged_data[field] is None:
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