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Advanced Content Hyper-Personalization Implementation

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ajaysi
2025-09-04 22:49:15 +05:30
parent d57f7feb4a
commit ccbdc9e8c6
18 changed files with 1452 additions and 974 deletions
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10
backend/services/persona_analysis_service.py
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@@ -317,8 +317,8 @@ class PersonaAnalysisService:
def _build_persona_analysis_prompt(self, onboarding_data: Dict[str, Any]) -> str:
"""Build the main persona analysis prompt."""
website_analysis = onboarding_data.get("website_analysis", {})
research_prefs = onboarding_data.get("research_preferences", {})
website_analysis = onboarding_data.get("website_analysis", {}) or {}
research_prefs = onboarding_data.get("research_preferences", {}) or {}
prompt = f"""
PERSONA GENERATION TASK: Create a comprehensive writing persona based on user onboarding data.
@@ -328,7 +328,7 @@ ONBOARDING DATA ANALYSIS:
Website Analysis:
- URL: {website_analysis.get('website_url', 'Not provided')}
- Writing Style: {json.dumps(website_analysis.get('writing_style', {}), indent=2)}
- Content Characteristics: {json.dumps(website_analysis.get('content_characteristics', {}), indent=2)}
- Content Characteristics: {json.dumps(website_analysis.get('content_characteristics', {}) or {}, indent=2)}
- Target Audience: {json.dumps(website_analysis.get('target_audience', {}), indent=2)}
- Content Type: {json.dumps(website_analysis.get('content_type', {}), indent=2)}
- Style Patterns: {json.dumps(website_analysis.get('style_patterns', {}), indent=2)}
@@ -521,8 +521,8 @@ Generate a platform-optimized persona adaptation that maintains brand consistenc
linguistic_fingerprint=core_persona.get("linguistic_fingerprint", {}),
platform_adaptations={"platforms": list(platform_personas.keys())},
onboarding_session_id=onboarding_data.get("session_info", {}).get("session_id"),
source_website_analysis=onboarding_data.get("website_analysis"),
source_research_preferences=onboarding_data.get("research_preferences"),
source_website_analysis=onboarding_data.get("website_analysis") or {},
source_research_preferences=onboarding_data.get("research_preferences") or {},
ai_analysis_version="gemini_v1.0",
confidence_score=core_persona.get("confidence_score", 0.0)
)