ALwrity persona system
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ajaysi
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backend/services/persona/facebook/facebook_persona_prompts.py
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backend/services/persona/facebook/facebook_persona_prompts.py
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"""
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Facebook Persona Prompts
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Contains Facebook-specific persona prompt generation logic.
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"""
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from typing import Dict, Any
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from loguru import logger
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class FacebookPersonaPrompts:
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"""Facebook-specific persona prompt generation."""
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@staticmethod
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def build_facebook_system_prompt(core_persona: Dict[str, Any]) -> str:
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"""
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Build optimized system prompt with core persona for Facebook generation.
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This moves the core persona to system prompt to free up context window.
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"""
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import json
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return f"""You are an expert Facebook content strategist specializing in community engagement and social sharing optimization.
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CORE PERSONA FOUNDATION:
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{json.dumps(core_persona, indent=2)}
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TASK: Create Facebook-optimized persona adaptations that maintain core identity while maximizing community engagement and Facebook algorithm performance.
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FOCUS AREAS:
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- Community-focused tone and engagement strategies
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- Facebook algorithm optimization (engagement, reach, timing)
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- Social sharing and viral content potential
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- Facebook-specific features (Stories, Reels, Live, Groups, Events)
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- Audience interaction and community building"""
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@staticmethod
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def build_focused_facebook_prompt(onboarding_data: Dict[str, Any]) -> str:
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"""
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Build focused Facebook prompt without core persona JSON to optimize context usage.
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"""
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# Extract audience context
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audience_context = FacebookPersonaPrompts._extract_audience_context(onboarding_data)
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target_audience = audience_context.get("target_audience", "general")
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content_goals = audience_context.get("content_goals", "engagement")
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business_type = audience_context.get("business_type", "general")
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return f"""FACEBOOK OPTIMIZATION TASK: Create Facebook-specific adaptations for the core persona.
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AUDIENCE CONTEXT:
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- Target: {target_audience} | Goals: {content_goals} | Business: {business_type}
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- Demographics: {audience_context.get('demographics', [])}
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- Interests: {audience_context.get('interests', [])}
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- Behaviors: {audience_context.get('behaviors', [])}
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FACEBOOK SPECS:
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- Character Limit: 63,206 | Optimal Length: 40-80 words
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- Algorithm Priority: Engagement, meaningful interactions, community building
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- Content Types: Posts, Stories, Reels, Live, Events, Groups, Carousels, Polls
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- Hashtag Strategy: 1-2 recommended (max 30)
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- Link Strategy: Native content performs better
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OPTIMIZATION REQUIREMENTS:
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1. COMMUNITY-FOCUSED TONE:
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- Authentic, conversational, approachable language
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- Balance professionalism with relatability
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- Incorporate storytelling and personal anecdotes
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- Community-building elements
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2. CONTENT STRATEGY FOR {business_type.upper()}:
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- Community engagement content for {target_audience}
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- Social sharing optimization for {content_goals}
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- Facebook-specific content formats
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- Audience interaction strategies
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- Viral content potential
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3. FACEBOOK-SPECIFIC ADAPTATIONS:
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- Algorithm optimization (engagement, reach, timing)
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- Platform-specific vocabulary and terminology
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- Engagement patterns for Facebook audience
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- Community interaction strategies
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- Facebook feature optimization (Stories, Reels, Live, Events, Groups)
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4. AUDIENCE TARGETING:
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- Demographic-specific positioning
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- Interest-based content adaptation
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- Behavioral targeting considerations
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- Community building strategies
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- Engagement optimization tactics
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Generate comprehensive Facebook-optimized persona maintaining core identity while maximizing community engagement and social sharing potential."""
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@staticmethod
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def _extract_audience_context(onboarding_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Extract audience context from onboarding data."""
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try:
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# Get enhanced analysis data
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enhanced_analysis = onboarding_data.get("enhanced_analysis", {})
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website_analysis = onboarding_data.get("website_analysis", {}) or {}
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research_prefs = onboarding_data.get("research_preferences", {}) or {}
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# Extract audience intelligence
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audience_intel = enhanced_analysis.get("audience_intelligence", {})
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# Extract target audience from website analysis
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target_audience_data = website_analysis.get("target_audience", {}) or {}
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# Build audience context
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audience_context = {
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"target_audience": target_audience_data.get("primary_audience", "general"),
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"content_goals": research_prefs.get("content_goals", "engagement"),
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"business_type": website_analysis.get("business_type", "general"),
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"demographics": audience_intel.get("demographics", []),
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"interests": audience_intel.get("interests", []),
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"behaviors": audience_intel.get("behaviors", []),
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"psychographic_profile": audience_intel.get("psychographic_profile", "general"),
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"pain_points": audience_intel.get("pain_points", []),
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"engagement_level": audience_intel.get("engagement_level", "moderate")
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}
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return audience_context
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except Exception as e:
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logger.warning(f"Error extracting audience context: {str(e)}")
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return {
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"target_audience": "general",
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"content_goals": "engagement",
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"business_type": "general",
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"demographics": [],
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"interests": [],
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"behaviors": [],
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"psychographic_profile": "general",
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"pain_points": [],
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"engagement_level": "moderate"
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}
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@staticmethod
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def build_facebook_validation_prompt(persona_data: Dict[str, Any]) -> str:
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"""Build optimized prompt for validating Facebook persona data."""
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return f"""FACEBOOK PERSONA VALIDATION TASK: Validate Facebook persona data for completeness and quality.
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PERSONA DATA:
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{persona_data}
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VALIDATION REQUIREMENTS:
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1. COMPLETENESS CHECK:
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- Verify all required Facebook-specific fields are present
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- Check for missing algorithm optimization strategies
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- Validate engagement strategy completeness
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- Ensure content format rules are defined
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2. QUALITY ASSESSMENT:
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- Evaluate Facebook algorithm optimization quality
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- Assess engagement strategy effectiveness
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- Check content format optimization
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- Validate audience targeting strategies
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3. FACEBOOK-SPECIFIC VALIDATION:
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- Verify Facebook platform constraints are respected
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- Check for Facebook-specific best practices
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- Validate community building strategies
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- Ensure Facebook feature optimization
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4. RECOMMENDATIONS:
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- Provide specific improvement suggestions
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- Identify missing optimization opportunities
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- Suggest Facebook-specific enhancements
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- Recommend engagement strategy improvements
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Generate comprehensive validation report with scores, recommendations, and specific improvement suggestions for Facebook optimization."""
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@staticmethod
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def build_facebook_optimization_prompt(persona_data: Dict[str, Any]) -> str:
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"""Build optimized prompt for optimizing Facebook persona data."""
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return f"""FACEBOOK PERSONA OPTIMIZATION TASK: Optimize Facebook persona data for maximum algorithm performance and community engagement.
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CURRENT PERSONA DATA:
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{persona_data}
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OPTIMIZATION REQUIREMENTS:
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1. ALGORITHM OPTIMIZATION:
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- Enhance Facebook algorithm performance strategies
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- Optimize for Facebook's engagement metrics
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- Improve content timing and frequency
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- Enhance audience targeting precision
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2. ENGAGEMENT OPTIMIZATION:
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- Strengthen community building strategies
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- Enhance social sharing potential
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- Improve audience interaction tactics
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- Optimize content for viral potential
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3. CONTENT FORMAT OPTIMIZATION:
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- Optimize for Facebook's content formats
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- Enhance visual content strategies
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- Improve video content optimization
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- Optimize for Facebook Stories and Reels
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4. AUDIENCE TARGETING OPTIMIZATION:
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- Refine demographic targeting
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- Enhance interest-based targeting
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- Improve behavioral targeting
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- Optimize for Facebook's audience insights
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5. COMMUNITY BUILDING OPTIMIZATION:
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- Enhance group management strategies
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- Improve event management tactics
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- Optimize live streaming strategies
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- Enhance community interaction methods
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Generate optimized Facebook persona data with enhanced algorithm performance, engagement strategies, and community building tactics."""
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