151 lines
8.1 KiB
Python
151 lines
8.1 KiB
Python
from typing import Any, Dict, List, Optional
|
|
|
|
|
|
def build_data_sources_map(website: Dict[str, Any], research: Dict[str, Any], api_keys: Dict[str, Any], persona: Dict[str, Any] = None, competitor: Dict[str, Any] = None, analytics: Dict[str, Any] = None) -> Dict[str, str]:
|
|
sources: Dict[str, str] = {}
|
|
|
|
website_fields = ['business_objectives', 'target_metrics', 'content_budget', 'team_size',
|
|
'implementation_timeline', 'market_share', 'competitive_position',
|
|
'conversion_rates', 'content_roi_targets']
|
|
|
|
analytics_fields = ['performance_metrics', 'engagement_metrics', 'traffic_sources']
|
|
|
|
research_fields = ['content_preferences', 'consumption_patterns', 'audience_pain_points',
|
|
'buying_journey', 'seasonal_trends', 'preferred_formats', 'content_mix',
|
|
'content_frequency', 'optimal_timing', 'quality_metrics', 'editorial_guidelines']
|
|
|
|
competitor_fields = ['top_competitors', 'competitor_content_strategies', 'market_gaps',
|
|
'industry_trends', 'emerging_trends']
|
|
|
|
persona_fields = ['brand_voice']
|
|
|
|
api_fields = ['ab_testing_capabilities']
|
|
|
|
for f in website_fields:
|
|
sources[f] = 'website_analysis'
|
|
for f in research_fields:
|
|
sources[f] = 'research_preferences'
|
|
for f in competitor_fields:
|
|
sources[f] = 'competitor_analysis' if competitor else 'onboarding_session'
|
|
for f in persona_fields:
|
|
sources[f] = 'persona_data' if persona else 'research_preferences'
|
|
for f in analytics_fields:
|
|
sources[f] = 'analytics_data' if analytics else 'website_analysis'
|
|
for f in api_fields:
|
|
sources[f] = 'api_keys_data'
|
|
|
|
return sources
|
|
|
|
|
|
def build_input_data_points(*, website_raw: Dict[str, Any], research_raw: Dict[str, Any], api_raw: Dict[str, Any], persona_raw: Dict[str, Any] = None, competitor_raw: List[Dict[str, Any]] = None, gsc_raw: Dict[str, Any] = None, bing_raw: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
input_data_points: Dict[str, Any] = {}
|
|
|
|
if website_raw:
|
|
input_data_points['business_objectives'] = {
|
|
'website_content': website_raw.get('content_goals', 'Not available'),
|
|
'meta_description': website_raw.get('meta_description', 'Not available'),
|
|
'about_page': website_raw.get('about_page_content', 'Not available'),
|
|
'page_title': website_raw.get('page_title', 'Not available'),
|
|
'content_analysis': website_raw.get('content_analysis', {})
|
|
}
|
|
|
|
if research_raw:
|
|
input_data_points['target_metrics'] = {
|
|
'research_preferences': research_raw.get('target_audience', 'Not available'),
|
|
'industry_benchmarks': research_raw.get('industry_benchmarks', 'Not available'),
|
|
'competitor_analysis': research_raw.get('competitor_analysis', 'Not available'),
|
|
'market_research': research_raw.get('market_research', 'Not available')
|
|
}
|
|
|
|
if research_raw:
|
|
input_data_points['content_preferences'] = {
|
|
'user_preferences': research_raw.get('content_types', 'Not available'),
|
|
'industry_trends': research_raw.get('industry_trends', 'Not available'),
|
|
'consumption_patterns': research_raw.get('consumption_patterns', 'Not available'),
|
|
'audience_research': research_raw.get('audience_research', 'Not available')
|
|
}
|
|
|
|
if website_raw or research_raw:
|
|
input_data_points['preferred_formats'] = {
|
|
'existing_content': website_raw.get('existing_content_types', 'Not available') if website_raw else 'Not available',
|
|
'engagement_metrics': website_raw.get('engagement_metrics', 'Not available') if website_raw else 'Not available',
|
|
'platform_analysis': research_raw.get('platform_preferences', 'Not available') if research_raw else 'Not available',
|
|
'content_performance': website_raw.get('content_performance', 'Not available') if website_raw else 'Not available'
|
|
}
|
|
|
|
if research_raw:
|
|
input_data_points['content_frequency'] = {
|
|
'audience_research': research_raw.get('content_frequency_preferences', 'Not available'),
|
|
'industry_standards': research_raw.get('industry_frequency', 'Not available'),
|
|
'competitor_frequency': research_raw.get('competitor_frequency', 'Not available'),
|
|
'optimal_timing': research_raw.get('optimal_timing', 'Not available')
|
|
}
|
|
|
|
if website_raw:
|
|
input_data_points['content_budget'] = {
|
|
'website_analysis': website_raw.get('budget_indicators', 'Not available'),
|
|
'industry_standards': website_raw.get('industry_budget', 'Not available'),
|
|
'company_size': website_raw.get('company_size', 'Not available'),
|
|
'market_position': website_raw.get('market_position', 'Not available')
|
|
}
|
|
|
|
if website_raw:
|
|
input_data_points['team_size'] = {
|
|
'company_profile': website_raw.get('company_profile', 'Not available'),
|
|
'content_volume': website_raw.get('content_volume', 'Not available'),
|
|
'industry_standards': website_raw.get('industry_team_size', 'Not available'),
|
|
'budget_constraints': website_raw.get('budget_constraints', 'Not available')
|
|
}
|
|
|
|
if research_raw:
|
|
input_data_points['implementation_timeline'] = {
|
|
'project_scope': research_raw.get('project_scope', 'Not available'),
|
|
'resource_availability': research_raw.get('resource_availability', 'Not available'),
|
|
'industry_timeline': research_raw.get('industry_timeline', 'Not available'),
|
|
'complexity_assessment': research_raw.get('complexity_assessment', 'Not available')
|
|
}
|
|
|
|
if competitor_raw:
|
|
input_data_points['top_competitors'] = {
|
|
'competitor_analysis': competitor_raw,
|
|
'analysis_count': len(competitor_raw),
|
|
'competitor_urls': [c.get('competitor_url') or c.get('url', '') for c in competitor_raw]
|
|
}
|
|
|
|
if persona_raw:
|
|
input_data_points['brand_voice'] = {
|
|
'core_persona': persona_raw.get('core_persona') or persona_raw.get('corePersona', 'Not available'),
|
|
'platform_personas': persona_raw.get('platform_personas') or persona_raw.get('platformPersonas', 'Not available'),
|
|
'quality_metrics': persona_raw.get('quality_metrics') or persona_raw.get('qualityMetrics', 'Not available')
|
|
}
|
|
|
|
if gsc_raw:
|
|
input_data_points['traffic_sources'] = {
|
|
'gsc_analytics': gsc_raw.get('data', 'Not available'),
|
|
'gsc_metrics': gsc_raw.get('metrics', 'Not available'),
|
|
'gsc_date_range': gsc_raw.get('date_range', 'Not available')
|
|
}
|
|
input_data_points['performance_metrics'] = {
|
|
'gsc_clicks': gsc_raw.get('metrics', {}).get('total_clicks', 'Not available') if isinstance(gsc_raw.get('metrics'), dict) else 'Not available',
|
|
'gsc_impressions': gsc_raw.get('metrics', {}).get('total_impressions', 'Not available') if isinstance(gsc_raw.get('metrics'), dict) else 'Not available',
|
|
'gsc_ctr': gsc_raw.get('metrics', {}).get('avg_ctr', 'Not available') if isinstance(gsc_raw.get('metrics'), dict) else 'Not available'
|
|
}
|
|
|
|
if bing_raw:
|
|
bing_summary = bing_raw.get('summary', {})
|
|
if bing_summary and not bing_summary.get('error'):
|
|
input_data_points['traffic_sources'] = {
|
|
**input_data_points.get('traffic_sources', {}),
|
|
'bing_analytics': bing_summary,
|
|
'bing_total_clicks': bing_summary.get('total_clicks', 'Not available'),
|
|
'bing_total_impressions': bing_summary.get('total_impressions', 'Not available'),
|
|
'bing_avg_ctr': bing_summary.get('avg_ctr', 'Not available')
|
|
}
|
|
input_data_points['performance_metrics'] = {
|
|
**input_data_points.get('performance_metrics', {}),
|
|
'bing_clicks': bing_summary.get('total_clicks', 'Not available'),
|
|
'bing_impressions': bing_summary.get('total_impressions', 'Not available'),
|
|
'bing_ctr': bing_summary.get('avg_ctr', 'Not available')
|
|
}
|
|
|
|
return input_data_points |