Alwrity version 0.5.4
This commit is contained in:
ajaysi
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"""
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AI Generation Module
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AI-powered content strategy generation with comprehensive insights and recommendations.
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"""
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from .strategy_generator import AIStrategyGenerator, StrategyGenerationConfig
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__all__ = ["AIStrategyGenerator", "StrategyGenerationConfig"]
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@@ -0,0 +1,699 @@
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"""
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AI-Powered Strategy Generation Service
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Generates comprehensive content strategies using AI with enhanced insights and recommendations.
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"""
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import json
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import logging
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from typing import Any, Dict, List, Optional
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from datetime import datetime
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from dataclasses import dataclass
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from services.ai_service_manager import AIServiceManager, AIServiceType
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from ..autofill.ai_structured_autofill import AIStructuredAutofillService
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logger = logging.getLogger(__name__)
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@dataclass
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class StrategyGenerationConfig:
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"""Configuration for strategy generation."""
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include_competitive_analysis: bool = True
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include_content_calendar: bool = True
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include_performance_predictions: bool = True
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include_implementation_roadmap: bool = True
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include_risk_assessment: bool = True
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max_content_pieces: int = 50
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timeline_months: int = 12
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class AIStrategyGenerator:
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"""
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AI-Powered Content Strategy Generator
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Generates comprehensive content strategies including:
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- Strategic field autofill (leveraging existing 100% success system)
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- Competitive analysis and positioning
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- Content calendar and publishing schedule
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- Performance predictions and KPIs
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- Implementation roadmap
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- Risk assessment and mitigation
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"""
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def __init__(self, config: Optional[StrategyGenerationConfig] = None):
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"""Initialize the AI strategy generator."""
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self.config = config or StrategyGenerationConfig()
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self.ai_manager = AIServiceManager()
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self.autofill_service = AIStructuredAutofillService()
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self.logger = logger
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async def generate_comprehensive_strategy(
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self,
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user_id: int,
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context: Dict[str, Any],
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strategy_name: Optional[str] = None
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) -> Dict[str, Any]:
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"""
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Generate a comprehensive content strategy using AI.
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Args:
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user_id: User ID for personalization
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context: User context and onboarding data
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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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Raises:
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RuntimeError: If any AI component fails to generate
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"""
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try:
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self.logger.info(f"🚀 Generating comprehensive AI strategy for user: {user_id}")
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# Step 1: Generate base strategy fields (using existing autofill system)
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base_strategy = await self._generate_base_strategy_fields(user_id, context)
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# Step 2: Generate strategic insights and recommendations
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strategic_insights = await self._generate_strategic_insights(base_strategy, context)
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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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performance_predictions = await self._generate_performance_predictions(base_strategy, context)
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# Step 6: 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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risk_assessment = await self._generate_risk_assessment(base_strategy, context)
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# Step 8: Compile comprehensive strategy
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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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"user_id": user_id,
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"strategy_name": strategy_name or f"AI-Generated Strategy {datetime.utcnow().strftime('%Y-%m-%d')}",
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"generation_version": "2.0",
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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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},
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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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}
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}
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self.logger.info(f"✅ Comprehensive AI strategy generated successfully for user: {user_id}")
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return comprehensive_strategy
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except Exception as e:
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self.logger.error(f"❌ Error generating comprehensive strategy: {str(e)}")
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raise RuntimeError(f"Failed to generate comprehensive strategy: {str(e)}")
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async def _generate_base_strategy_fields(
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self,
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user_id: int,
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context: Dict[str, Any]
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) -> Dict[str, Any]:
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"""Generate base strategy fields using existing autofill system."""
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try:
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self.logger.info(f"Generating base strategy fields for user: {user_id}")
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# Use existing autofill service (100% success rate)
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autofill_result = await self.autofill_service.generate_autofill_fields(user_id, context)
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# Extract the fields from autofill result
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base_strategy = autofill_result.get("fields", {})
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# Add generation metadata
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base_strategy["generation_metadata"] = {
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"generated_by": "ai_autofill_system",
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"success_rate": autofill_result.get("success_rate", 100),
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"personalized": autofill_result.get("personalized", True),
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"data_sources": autofill_result.get("data_sources", [])
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}
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return base_strategy
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except Exception as e:
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self.logger.error(f"Error generating base strategy fields: {str(e)}")
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raise
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async def _generate_strategic_insights(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
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"""Generate strategic insights using AI."""
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try:
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logger.info("🧠 Generating strategic insights...")
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# Use provided AI manager or create default one
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if ai_manager is None:
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from services.ai_service_manager import AIServiceManager
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ai_manager = AIServiceManager()
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prompt = f"""
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Generate comprehensive strategic insights for content strategy based on the following context:
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CONTEXT:
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{json.dumps(context, indent=2)}
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BASE STRATEGY:
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{json.dumps(base_strategy, indent=2)}
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Please provide strategic insights including:
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1. Market positioning analysis
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2. Content opportunity identification
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3. Competitive advantage mapping
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4. Growth potential assessment
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5. Strategic recommendations
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Format as structured JSON with insights, reasoning, and confidence levels.
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"""
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schema = {
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"type": "object",
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"properties": {
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"insights": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"insight": {"type": "string"},
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"reasoning": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"},
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"confidence_level": {"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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response = await ai_manager.execute_structured_json_call(
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AIServiceType.STRATEGIC_INTELLIGENCE,
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prompt,
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schema
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)
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if not response or not response.get("data"):
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raise RuntimeError("AI service returned empty strategic insights")
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logger.info("✅ Strategic insights generated successfully")
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return response.get("data", {})
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except Exception as e:
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logger.error(f"❌ Error generating strategic insights: {str(e)}")
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raise RuntimeError(f"Failed to generate strategic insights: {str(e)}")
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async def _generate_competitive_analysis(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
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"""Generate competitive analysis using AI."""
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try:
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logger.info("🔍 Generating competitive analysis...")
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# Use provided AI manager or create default one
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if ai_manager is None:
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from services.ai_service_manager import AIServiceManager
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ai_manager = AIServiceManager()
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prompt = f"""
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Generate comprehensive competitive analysis for content strategy based on the following context:
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CONTEXT:
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{json.dumps(context, indent=2)}
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BASE STRATEGY:
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{json.dumps(base_strategy, indent=2)}
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Please provide competitive analysis including:
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1. Competitor identification and analysis
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2. Market gap identification
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3. Differentiation opportunities
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4. Competitive positioning
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5. Strategic recommendations
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Format as structured JSON with detailed analysis and recommendations.
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"""
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schema = {
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"type": "object",
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"properties": {
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"competitors": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"name": {"type": "string"},
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"strengths": {"type": "array", "items": {"type": "string"}},
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"weaknesses": {"type": "array", "items": {"type": "string"}},
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"content_strategy": {"type": "string"},
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"market_position": {"type": "string"}
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}
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}
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},
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"market_gaps": {"type": "array", "items": {"type": "string"}},
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"opportunities": {"type": "array", "items": {"type": "string"}},
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"recommendations": {"type": "array", "items": {"type": "string"}}
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}
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}
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response = await ai_manager.execute_structured_json_call(
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AIServiceType.MARKET_POSITION_ANALYSIS,
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prompt,
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schema
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)
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if not response or not response.get("data"):
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raise RuntimeError("AI service returned empty competitive analysis")
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logger.info("✅ Competitive analysis generated successfully")
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return response.get("data", {})
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except Exception as e:
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logger.error(f"❌ Error generating competitive analysis: {str(e)}")
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raise RuntimeError(f"Failed to generate competitive analysis: {str(e)}")
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async def _generate_content_calendar(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
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"""Generate content calendar using AI."""
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try:
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logger.info("📅 Generating content calendar...")
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# Use provided AI manager or create default one
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if ai_manager is None:
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from services.ai_service_manager import AIServiceManager
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ai_manager = AIServiceManager()
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prompt = f"""
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Generate comprehensive content calendar for content strategy based on the following context:
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CONTEXT:
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{json.dumps(context, indent=2)}
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BASE STRATEGY:
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{json.dumps(base_strategy, indent=2)}
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Please provide content calendar including:
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1. Content pieces with titles and descriptions
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2. Publishing schedule and timing
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3. Content types and formats
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4. Platform distribution strategy
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5. Content themes and pillars
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Format as structured JSON with detailed content schedule.
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"""
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schema = {
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"type": "object",
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"properties": {
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"content_pieces": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"description": {"type": "string"},
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"content_type": {"type": "string"},
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"platform": {"type": "string"},
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"publishing_date": {"type": "string"},
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"theme": {"type": "string"},
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"priority": {"type": "string"}
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}
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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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}
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}
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response = await ai_manager.execute_structured_json_call(
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AIServiceType.CONTENT_SCHEDULE_GENERATION,
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prompt,
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schema
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)
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if not response or not response.get("data"):
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raise RuntimeError("AI service returned empty content calendar")
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logger.info("✅ Content calendar generated successfully")
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return response.get("data", {})
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except Exception as e:
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logger.error(f"❌ Error generating content calendar: {str(e)}")
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raise RuntimeError(f"Failed to generate content calendar: {str(e)}")
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async def _generate_performance_predictions(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
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"""Generate performance predictions using AI."""
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try:
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logger.info("📊 Generating performance predictions...")
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# Use provided AI manager or create default one
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if ai_manager is None:
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from services.ai_service_manager import AIServiceManager
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ai_manager = AIServiceManager()
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prompt = f"""
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Generate comprehensive performance predictions for content strategy based on the following context:
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CONTEXT:
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{json.dumps(context, indent=2)}
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BASE STRATEGY:
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{json.dumps(base_strategy, indent=2)}
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Please provide performance predictions including:
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1. Traffic growth projections
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2. Engagement rate predictions
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3. Conversion rate estimates
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4. ROI projections
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5. Success probability assessment
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Format as structured JSON with detailed predictions and confidence levels.
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"""
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schema = {
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"type": "object",
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"properties": {
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"traffic_predictions": {
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"type": "object",
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"properties": {
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"monthly_traffic": {"type": "string"},
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"growth_rate": {"type": "string"},
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"peak_traffic": {"type": "string"}
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}
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},
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"engagement_predictions": {
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"type": "object",
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"properties": {
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"engagement_rate": {"type": "string"},
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"time_on_page": {"type": "string"},
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"bounce_rate": {"type": "string"}
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}
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},
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"conversion_predictions": {
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"type": "object",
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"properties": {
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"conversion_rate": {"type": "string"},
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"lead_generation": {"type": "string"},
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"sales_impact": {"type": "string"}
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}
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},
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"roi_predictions": {
|
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"type": "object",
|
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"properties": {
|
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"estimated_roi": {"type": "string"},
|
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"cost_benefit": {"type": "string"},
|
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"payback_period": {"type": "string"}
|
||||
}
|
||||
}
|
||||
}
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||||
}
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|
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response = await ai_manager.execute_structured_json_call(
|
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AIServiceType.PERFORMANCE_PREDICTION,
|
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prompt,
|
||||
schema
|
||||
)
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|
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if not response or not response.get("data"):
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raise RuntimeError("AI service returned empty performance predictions")
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||||
|
||||
logger.info("✅ Performance predictions generated successfully")
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return response.get("data", {})
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|
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except Exception as e:
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logger.error(f"❌ Error generating performance predictions: {str(e)}")
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raise RuntimeError(f"Failed to generate performance predictions: {str(e)}")
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|
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async def _generate_implementation_roadmap(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
|
||||
"""Generate implementation roadmap using AI."""
|
||||
try:
|
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logger.info("🗺️ Generating implementation roadmap...")
|
||||
|
||||
# Use provided AI manager or create default one
|
||||
if ai_manager is None:
|
||||
from services.ai_service_manager import AIServiceManager
|
||||
ai_manager = AIServiceManager()
|
||||
|
||||
prompt = f"""
|
||||
Generate comprehensive implementation roadmap for content strategy based on the following context:
|
||||
|
||||
CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
BASE STRATEGY:
|
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{json.dumps(base_strategy, indent=2)}
|
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|
||||
Please provide implementation roadmap including:
|
||||
1. Phase-by-phase breakdown
|
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2. Timeline with milestones
|
||||
3. Resource allocation
|
||||
4. Success metrics
|
||||
5. Risk mitigation strategies
|
||||
|
||||
Format as structured JSON with detailed implementation plan.
|
||||
"""
|
||||
|
||||
schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"phases": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"phase": {"type": "string"},
|
||||
"duration": {"type": "string"},
|
||||
"tasks": {"type": "array", "items": {"type": "string"}},
|
||||
"milestones": {"type": "array", "items": {"type": "string"}},
|
||||
"resources": {"type": "array", "items": {"type": "string"}}
|
||||
}
|
||||
}
|
||||
},
|
||||
"timeline": {"type": "object"},
|
||||
"resource_allocation": {"type": "object"},
|
||||
"success_metrics": {"type": "array", "items": {"type": "string"}},
|
||||
"total_duration": {"type": "string"}
|
||||
}
|
||||
}
|
||||
|
||||
response = await ai_manager.execute_structured_json_call(
|
||||
AIServiceType.STRATEGIC_INTELLIGENCE,
|
||||
prompt,
|
||||
schema
|
||||
)
|
||||
|
||||
if not response or not response.get("data"):
|
||||
raise RuntimeError("AI service returned empty implementation roadmap")
|
||||
|
||||
logger.info("✅ Implementation roadmap generated successfully")
|
||||
return response.get("data", {})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error generating implementation roadmap: {str(e)}")
|
||||
raise RuntimeError(f"Failed to generate implementation roadmap: {str(e)}")
|
||||
|
||||
async def _generate_risk_assessment(self, base_strategy: Dict[str, Any], context: Dict[str, Any], ai_manager: Optional[Any] = None) -> Dict[str, Any]:
|
||||
"""Generate risk assessment using AI."""
|
||||
try:
|
||||
logger.info("⚠️ Generating risk assessment...")
|
||||
|
||||
# Use provided AI manager or create default one
|
||||
if ai_manager is None:
|
||||
from services.ai_service_manager import AIServiceManager
|
||||
ai_manager = AIServiceManager()
|
||||
|
||||
prompt = f"""
|
||||
Generate comprehensive risk assessment for content strategy based on the following context:
|
||||
|
||||
CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
BASE STRATEGY:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
Please provide risk assessment including:
|
||||
1. Risk identification and analysis
|
||||
2. Probability and impact assessment
|
||||
3. Mitigation strategies
|
||||
4. Contingency planning
|
||||
5. Risk monitoring framework
|
||||
|
||||
Format as structured JSON with detailed risk analysis and mitigation plans.
|
||||
"""
|
||||
|
||||
schema = {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"risks": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"risk": {"type": "string"},
|
||||
"probability": {"type": "string"},
|
||||
"impact": {"type": "string"},
|
||||
"mitigation": {"type": "string"},
|
||||
"contingency": {"type": "string"}
|
||||
}
|
||||
}
|
||||
},
|
||||
"overall_risk_level": {"type": "string"},
|
||||
"risk_categories": {"type": "object"},
|
||||
"mitigation_strategies": {"type": "array", "items": {"type": "string"}},
|
||||
"monitoring_framework": {"type": "object"}
|
||||
}
|
||||
}
|
||||
|
||||
response = await ai_manager.execute_structured_json_call(
|
||||
AIServiceType.STRATEGIC_INTELLIGENCE,
|
||||
prompt,
|
||||
schema
|
||||
)
|
||||
|
||||
if not response or not response.get("data"):
|
||||
raise RuntimeError("AI service returned empty risk assessment")
|
||||
|
||||
logger.info("✅ Risk assessment generated successfully")
|
||||
return response.get("data", {})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error generating risk assessment: {str(e)}")
|
||||
raise RuntimeError(f"Failed to generate risk assessment: {str(e)}")
|
||||
|
||||
def _build_strategic_insights_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for strategic insights generation."""
|
||||
return f"""
|
||||
As an expert content strategy consultant with 15+ years of experience, analyze this content strategy and provide strategic insights:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Provide comprehensive strategic insights covering:
|
||||
1. Key insights about the strategy's strengths and opportunities
|
||||
2. Strategic recommendations with priority levels
|
||||
3. Identified opportunity areas for growth
|
||||
4. Competitive advantages to leverage
|
||||
|
||||
Focus on actionable, data-driven insights that will drive content strategy success.
|
||||
"""
|
||||
|
||||
def _build_competitive_analysis_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for competitive analysis generation."""
|
||||
return f"""
|
||||
As a competitive intelligence expert, analyze the competitive landscape for this content strategy:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Provide comprehensive competitive analysis covering:
|
||||
1. Competitive landscape analysis with key players
|
||||
2. Positioning strategy and differentiation factors
|
||||
3. Market gaps and opportunities
|
||||
4. Competitive advantages and unique value propositions
|
||||
|
||||
Focus on actionable competitive intelligence that will inform strategic positioning.
|
||||
"""
|
||||
|
||||
def _build_content_calendar_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for content calendar generation."""
|
||||
return f"""
|
||||
As a content strategy expert, create a comprehensive content calendar for this strategy:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Generate a {self.config.max_content_pieces}-piece content calendar covering {self.config.timeline_months} months including:
|
||||
1. Diverse content pieces (blog posts, social media, videos, etc.)
|
||||
2. Publishing schedule with optimal timing
|
||||
3. Content mix distribution
|
||||
4. Topic clusters and content pillars
|
||||
5. Target audience alignment
|
||||
|
||||
Ensure content aligns with business objectives and audience preferences.
|
||||
"""
|
||||
|
||||
def _build_performance_predictions_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for performance predictions generation."""
|
||||
return f"""
|
||||
As a data-driven content strategist, predict performance outcomes for this content strategy:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Provide realistic performance predictions covering:
|
||||
1. Traffic growth projections (3, 6, 12 months)
|
||||
2. Engagement metrics predictions
|
||||
3. Conversion and lead generation forecasts
|
||||
4. ROI estimates and success probability
|
||||
5. Key performance indicators with targets
|
||||
|
||||
Base predictions on industry benchmarks and strategy characteristics.
|
||||
"""
|
||||
|
||||
def _build_implementation_roadmap_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for implementation roadmap generation."""
|
||||
return f"""
|
||||
As a project management expert, create an implementation roadmap for this content strategy:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Create a detailed implementation roadmap covering:
|
||||
1. Phased implementation approach
|
||||
2. Resource requirements and budget allocation
|
||||
3. Timeline with milestones and deliverables
|
||||
4. Critical path and dependencies
|
||||
5. Success metrics and evaluation criteria
|
||||
|
||||
Ensure roadmap is realistic and achievable given available resources.
|
||||
"""
|
||||
|
||||
def _build_risk_assessment_prompt(self, base_strategy: Dict[str, Any], context: Dict[str, Any]) -> str:
|
||||
"""Build prompt for risk assessment generation."""
|
||||
return f"""
|
||||
As a risk management expert, assess potential risks for this content strategy:
|
||||
|
||||
STRATEGY CONTEXT:
|
||||
{json.dumps(base_strategy, indent=2)}
|
||||
|
||||
USER CONTEXT:
|
||||
{json.dumps(context, indent=2)}
|
||||
|
||||
Provide comprehensive risk assessment covering:
|
||||
1. Identified risks with probability and impact
|
||||
2. Risk categorization (market, operational, competitive, resource)
|
||||
3. Mitigation strategies for each risk
|
||||
4. Contingency plans for high-impact scenarios
|
||||
5. Overall risk level assessment
|
||||
|
||||
Focus on practical risk mitigation strategies.
|
||||
"""
|
||||
Reference in New Issue
Block a user