chore: bulk commit of local changes across blog writer, SEO dashboard, scheduler, docs-site, and frontend
This commit is contained in:
ajaysi
@@ -30,6 +30,7 @@ from services.seo_tools.on_page_seo_service import OnPageSEOService
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from services.seo_tools.technical_seo_service import TechnicalSEOService
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from services.seo_tools.enterprise_seo_service import EnterpriseSEOService
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from services.seo_tools.gsc_analyzer_service import GSCAnalyzerService
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from services.seo_tools.gsc_strategy_insights_service import GSCStrategyInsightsService
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from services.seo_tools.content_strategy_service import ContentStrategyService
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from services.seo_tools.llm_insights_service import LLMInsightsService
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from services.database import get_session_for_user
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@@ -199,6 +200,34 @@ class KeywordExpansionRequest(BaseModel):
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content_analysis: Dict[str, Any] = Field(..., description="Content analysis data")
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target_difficulty: Optional[str] = Field(None, description="Target difficulty (low/medium/high)")
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# ==================== GSC STRATEGY INSIGHTS REQUEST MODELS ====================
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class GSCStrategyInsightsRequest(BaseModel):
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"""Request model for GSC strategy insights (dashboard context)"""
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site_url: HttpUrl = Field(..., description="Website URL registered in GSC")
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include_trends: bool = Field(default=True, description="Include trend analysis")
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include_competitive: bool = Field(default=False, description="Include competitive analysis (Phase 2)")
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top_n: int = Field(default=20, ge=5, le=100, description="Number of top opportunities to return")
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class GSCOpportunityRankingRequest(BaseModel):
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"""Request model for ROI-ranked opportunities"""
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site_url: HttpUrl = Field(..., description="Website URL registered in GSC")
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ranking_metric: str = Field(default="roi_score", description="Metric to rank by (roi_score/effort/impact/timeline)")
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severity_filter: Optional[str] = Field(None, description="Filter by severity (critical/high/medium/low/watch)")
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limit: int = Field(default=20, ge=5, le=100, description="Number of opportunities to return")
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class GSCTrendAnalysisRequest(BaseModel):
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"""Request model for performance trend analysis"""
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site_url: HttpUrl = Field(..., description="Website URL registered in GSC")
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metric: str = Field(default="all", description="Metric to analyze (position/impressions/clicks/ctr/all)")
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days_back: int = Field(default=90, ge=7, le=365, description="Days of historical data to analyze")
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class GSCHealthMetricsRequest(BaseModel):
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"""Request model for health metrics calculation"""
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site_url: HttpUrl = Field(..., description="Website URL registered in GSC")
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include_distribution: bool = Field(default=True, description="Include keyword distribution breakdown")
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include_trends: bool = Field(default=True, description="Include trend comparison")
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# Exception Handler
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async def handle_seo_tool_exception(func_name: str, error: Exception, request_data: Dict) -> ErrorResponse:
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"""Handle exceptions from SEO tools with intelligent logging"""
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@@ -1102,6 +1131,236 @@ async def get_content_opportunities_report(
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return await handle_seo_tool_exception("get_content_opportunities_report", e, request.dict())
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# ==================== GSC STRATEGY INSIGHTS ENDPOINTS (Dashboard-Focused) ====================
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@router.post("/gsc/strategy-insights", response_model=BaseResponse)
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@log_api_call
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async def get_gsc_strategy_insights(
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request: GSCStrategyInsightsRequest,
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current_user: dict = Depends(get_current_user)
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) -> Union[BaseResponse, ErrorResponse]:
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"""
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Get comprehensive strategy insights from GSC data for SEO Dashboard.
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Provides strategic insights optimized for dashboard display:
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- Ranked opportunities by ROI score (0-100)
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- Health metrics with trend comparison
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- Quick summary of key insights
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- Optional: Performance trends and competitive positioning
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ROI Scoring Formula:
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ROI = 0.40×traffic_impact + 0.30×ease + 0.20×competitive + 0.10×momentum
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Severity Levels:
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- CRITICAL: 80-100 (immediate action)
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- HIGH: 60-79 (high priority)
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- MEDIUM: 40-59 (medium priority)
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- LOW: 20-39 (low priority)
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- WATCH: <20 (monitoring)
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"""
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start_time = datetime.utcnow()
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try:
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user_id = str(current_user.get("id")) if current_user else None
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service = GSCStrategyInsightsService()
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insights = await service.get_dashboard_strategy(
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user_id=user_id,
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site_url=str(request.site_url),
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include_trends=request.include_trends,
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include_competitive=request.include_competitive,
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top_n=request.top_n
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)
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execution_time = (datetime.utcnow() - start_time).total_seconds()
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return BaseResponse(
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success=True,
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message="GSC strategy insights generated successfully",
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execution_time=execution_time,
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data=insights
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)
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except Exception as e:
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logger.error(f"GSC strategy insights failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("get_gsc_strategy_insights", e, request.dict())
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@router.post("/gsc/opportunity-ranking", response_model=BaseResponse)
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@log_api_call
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async def get_ranked_opportunities(
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request: GSCOpportunityRankingRequest,
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current_user: dict = Depends(get_current_user)
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) -> Union[BaseResponse, ErrorResponse]:
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"""
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Get ROI-ranked opportunities from GSC data.
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Returns opportunities sorted by specified metric:
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- roi_score: ROI-weighted score (recommended)
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- effort: Easiest to implement first
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- impact: Highest traffic impact first
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- timeline: Fastest results first
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Optional filtering by severity level:
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- critical: 80-100 ROI (immediate action required)
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- high: 60-79 ROI (high priority)
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- medium: 40-59 ROI (medium priority)
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- low: 20-39 ROI (low priority)
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- watch: <20 ROI (monitoring)
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Each opportunity includes:
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- ROI score and severity level
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- Implementation effort (hours)
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- Timeline to impact (weeks)
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- Recommendations
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- Related keywords
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"""
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start_time = datetime.utcnow()
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try:
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user_id = str(current_user.get("id")) if current_user else None
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service = GSCStrategyInsightsService()
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opportunities = await service._get_ranked_opportunities(
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site_url=str(request.site_url),
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top_n=request.limit
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)
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# Filter by severity if specified
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if request.severity_filter and opportunities.get('status') == 'success':
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filtered = [
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opp for opp in opportunities.get('opportunities', [])
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if opp.get('severity') == request.severity_filter
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]
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opportunities['opportunities'] = filtered
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# Sort by metric
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if opportunities.get('status') == 'success' and request.ranking_metric != 'roi_score':
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opps = opportunities.get('opportunities', [])
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if request.ranking_metric == 'effort':
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opps.sort(key=lambda x: x.get('effort_hours', 0))
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elif request.ranking_metric == 'impact':
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opps.sort(key=lambda x: x.get('estimated_impact', 0), reverse=True)
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elif request.ranking_metric == 'timeline':
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opps.sort(key=lambda x: x.get('timeline_weeks', 0))
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opportunities['opportunities'] = opps
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execution_time = (datetime.utcnow() - start_time).total_seconds()
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return BaseResponse(
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success=True,
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message="Ranked opportunities retrieved successfully",
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execution_time=execution_time,
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data=opportunities
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)
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except Exception as e:
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logger.error(f"Ranked opportunities failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("get_ranked_opportunities", e, request.dict())
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@router.post("/gsc/health-metrics", response_model=BaseResponse)
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@log_api_call
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async def get_health_metrics(
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request: GSCHealthMetricsRequest,
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current_user: dict = Depends(get_current_user)
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) -> Union[BaseResponse, ErrorResponse]:
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"""
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Get comprehensive health metrics for SEO Dashboard.
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Returns overall SEO health with:
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- Health score (0-100)
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- Health trend (up/down/stable)
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- Keyword position distribution
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- Average metrics (position, CTR, etc.)
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- Optional: Trend comparison vs period ago
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Health Score Calculation:
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Score = 0.60×(Page1_Keywords%) + 0.30×CTR_vs_Benchmark + 0.10×Growth_Rate
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Interpretation:
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- 80-100: Excellent SEO health
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- 60-79: Good SEO health
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- 40-59: Needs improvement
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- 0-39: Critical issues
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"""
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start_time = datetime.utcnow()
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try:
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user_id = str(current_user.get("id")) if current_user else None
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service = GSCStrategyInsightsService()
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metrics = await service._calculate_health_metrics(
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site_url=str(request.site_url)
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)
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execution_time = (datetime.utcnow() - start_time).total_seconds()
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return BaseResponse(
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success=True,
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message="Health metrics calculated successfully",
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execution_time=execution_time,
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data=metrics
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)
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except Exception as e:
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logger.error(f"Health metrics calculation failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("get_health_metrics", e, request.dict())
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@router.post("/gsc/trend-analysis", response_model=BaseResponse)
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@log_api_call
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async def analyze_gsc_trends(
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request: GSCTrendAnalysisRequest,
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current_user: dict = Depends(get_current_user)
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) -> Union[BaseResponse, ErrorResponse]:
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"""
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Analyze performance trends from GSC data.
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Returns trend analysis for specified metrics:
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- position: Ranking trend for keywords
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- impressions: Search volume trends
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- clicks: Click trend
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- ctr: Click-through rate trend
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- all: All metrics combined
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For each metric includes:
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- Current value
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- Value from 30/90 days ago
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- Trend direction (up/down/stable)
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- Trend percentage change
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- Momentum (acceleration of trend)
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- Seasonal patterns
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- Anomalies detected
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Note: This feature requires historical data collection.
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Phase 1: Manual trend calculation from snapshots.
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Phase 2: Automated historical tracking.
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"""
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start_time = datetime.utcnow()
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try:
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user_id = str(current_user.get("id")) if current_user else None
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service = GSCStrategyInsightsService()
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trends = await service._analyze_performance_trends(
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site_url=str(request.site_url)
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)
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execution_time = (datetime.utcnow() - start_time).total_seconds()
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return BaseResponse(
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success=True,
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message="Trend analysis completed",
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execution_time=execution_time,
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data=trends
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)
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except Exception as e:
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logger.error(f"Trend analysis failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("analyze_gsc_trends", e, request.dict())
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@router.get("/enterprise/health", response_model=BaseResponse)
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@log_api_call
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async def check_enterprise_services_health() -> BaseResponse:
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