feat: Sprint 1 - Deep discovery, lead persistence, and dashboard nav
- Add BacklinkOutreachScraper (Exa + DuckDuckGo deep scraping) - Extend DB and Pydantic models for lead enrichment columns - Add StorageService methods for lead CRUD with auto-migration - Add backend endpoints: deep discover, campaign detail, lead management - Extend frontend API client and store with discovery + lead actions - Create BacklinkOutreachDashboard component with campaigns/discover/leads tabs - Register route at /backlink-outreach under SEO feature flag - Add nav entry under Enterprise & Advanced in tool categories
This commit is contained in:
ajaysi
@@ -29,6 +29,7 @@ from services.seo_tools.opengraph_service import OpenGraphService
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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.content_strategy_service import ContentStrategyService
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from services.database import get_session_for_user
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from api.content_planning.services.content_strategy.onboarding import OnboardingDataIntegrationService
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@@ -128,6 +129,28 @@ class CompetitiveSitemapBenchmarkingRunRequest(BaseModel):
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max_competitors: int = Field(default=5, ge=1, le=10, description="Max competitors to analyze")
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competitors: Optional[List[HttpUrl]] = Field(None, description="Optional explicit competitor URLs")
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class EnterpriseAuditRequest(BaseModel):
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"""Request model for complete enterprise SEO audit"""
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website_url: HttpUrl = Field(..., description="Primary website URL to audit")
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competitors: Optional[List[HttpUrl]] = Field(None, description="Competitor URLs for benchmarking (max 5)")
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target_keywords: Optional[List[str]] = Field(None, description="Target keywords for analysis")
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include_content_analysis: bool = Field(default=True, description="Include content strategy analysis")
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include_competitive_analysis: bool = Field(default=True, description="Include competitive benchmarking")
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generate_executive_report: bool = Field(default=True, description="Generate executive summary")
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class GSCAnalysisRequest(BaseModel):
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"""Request model for advanced GSC analysis"""
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site_url: HttpUrl = Field(..., description="Website URL registered in Google Search Console")
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date_range_days: int = Field(default=90, ge=7, le=365, description="Number of days to analyze")
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include_opportunities: bool = Field(default=True, description="Include content opportunity analysis")
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include_competitive: bool = Field(default=True, description="Include competitive positioning")
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class ContentOpportunitiesRequest(BaseModel):
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"""Request model for content opportunities report"""
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site_url: HttpUrl = Field(..., description="Website URL registered in GSC")
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min_impressions: int = Field(default=100, ge=10, description="Minimum impressions threshold")
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date_range_days: int = Field(default=90, ge=7, le=365, description="Number of days to analyze")
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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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@@ -836,3 +859,225 @@ async def get_tools_status() -> BaseResponse:
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"timestamp": datetime.utcnow().isoformat()
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}
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)
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# ==================== ENTERPRISE AUDIT ENDPOINTS ====================
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@router.post("/enterprise/complete-audit", response_model=BaseResponse)
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@log_api_call
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async def execute_enterprise_audit(
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request: EnterpriseAuditRequest,
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background_tasks: BackgroundTasks,
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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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Execute comprehensive enterprise SEO audit with full orchestration.
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Combines multiple SEO analysis tools into an intelligent workflow:
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- Technical SEO audit with issue severity classification
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- On-page SEO analysis with keyword optimization
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- PageSpeed Insights with Core Web Vitals analysis
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- Sitemap analysis with trend detection
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- Content strategy with competitive comparison
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- Competitive benchmarking across specified competitors
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- AI-powered insights and recommendations
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Returns prioritized action items with implementation roadmap.
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"""
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start_time = datetime.utcnow()
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try:
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logger.info(f"Starting enterprise audit for {request.website_url}")
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# Initialize service
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enterprise_service = EnterpriseSEOService()
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# Execute audit
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audit_result = await enterprise_service.execute_complete_audit(
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website_url=str(request.website_url),
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competitors=[str(c) for c in request.competitors] if request.competitors else [],
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target_keywords=request.target_keywords or [],
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include_content_analysis=request.include_content_analysis,
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include_competitive_analysis=request.include_competitive_analysis,
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generate_executive_report=request.generate_executive_report
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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="Complete enterprise audit executed successfully",
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execution_time=execution_time,
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data=audit_result
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)
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except Exception as e:
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logger.error(f"Enterprise audit failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("execute_enterprise_audit", e, request.dict())
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@router.post("/enterprise/quick-audit", response_model=BaseResponse)
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@log_api_call
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async def execute_quick_enterprise_audit(
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website_url: HttpUrl,
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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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Execute quick 5-minute enterprise audit focusing on critical issues.
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Provides rapid assessment of most critical SEO problems:
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- Technical SEO critical issues
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- PageSpeed performance bottlenecks
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- Top 3 actionable recommendations
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- Estimated business impact
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"""
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start_time = datetime.utcnow()
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try:
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logger.info(f"Starting quick audit for {website_url}")
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enterprise_service = EnterpriseSEOService()
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audit_result = await enterprise_service.execute_quick_audit(str(website_url))
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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="Quick audit completed",
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execution_time=execution_time,
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data=audit_result
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)
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except Exception as e:
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return await handle_seo_tool_exception("execute_quick_enterprise_audit", e, {"website_url": str(website_url)})
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# ==================== ADVANCED GSC ANALYSIS ENDPOINTS ====================
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@router.post("/gsc/analyze-search-performance", response_model=BaseResponse)
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@log_api_call
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async def analyze_gsc_search_performance(
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request: GSCAnalysisRequest,
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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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Advanced Google Search Console analysis with comprehensive insights.
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Provides deep dive into search performance:
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- Performance overview with aggregated metrics
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- Keyword analysis with trend detection
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- Page-level performance breakdown
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- Content opportunity identification (15+ opportunities scored)
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- Technical SEO signal analysis
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- Competitive positioning assessment
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- AI-powered strategic recommendations
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Each analysis component includes:
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- Current metrics and trends
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- Performance scores (0-100)
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- Actionable recommendations
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- Implementation priority
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"""
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start_time = datetime.utcnow()
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try:
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logger.info(f"Starting GSC analysis for {request.site_url}")
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user_id = str(current_user.get("id")) if current_user else None
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gsc_service = GSCAnalyzerService()
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analysis_result = await gsc_service.analyze_search_performance(
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site_url=str(request.site_url),
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date_range_days=request.date_range_days,
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user_id=user_id
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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 search performance analysis completed",
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execution_time=execution_time,
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data=analysis_result
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)
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except Exception as e:
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logger.error(f"GSC analysis failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("analyze_gsc_search_performance", e, request.dict())
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@router.post("/gsc/content-opportunities", response_model=BaseResponse)
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@log_api_call
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async def get_content_opportunities_report(
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request: ContentOpportunitiesRequest,
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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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Generate detailed content opportunities report from GSC data.
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Identifies high-priority content gaps and optimization opportunities:
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- Queries with high volume but low CTR (meta/title optimization)
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- Keywords ranking 4-10 (ready for ranking improvement)
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- Long-tail keywords with expansion potential
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- Competitive white space analysis
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For each opportunity includes:
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- Current position and metrics
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- Estimated traffic gain
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- Optimization strategy
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- Implementation difficulty
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- Phased roadmap (Phase 1, 2, 3)
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"""
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start_time = datetime.utcnow()
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try:
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logger.info(f"Generating content opportunities for {request.site_url}")
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gsc_service = GSCAnalyzerService()
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report = await gsc_service.get_content_opportunities_report(
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site_url=str(request.site_url),
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min_impressions=request.min_impressions,
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date_range_days=request.date_range_days
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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="Content opportunities report generated",
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execution_time=execution_time,
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data=report
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)
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except Exception as e:
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logger.error(f"Content opportunities report failed: {str(e)}", exc_info=True)
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return await handle_seo_tool_exception("get_content_opportunities_report", 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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"""Health check for enterprise services"""
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try:
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enterprise_service = EnterpriseSEOService()
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gsc_service = GSCAnalyzerService()
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enterprise_health = await enterprise_service.health_check()
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gsc_health = await gsc_service.health_check()
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return BaseResponse(
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success=True,
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message="Enterprise services health check completed",
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data={
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"enterprise_seo_service": enterprise_health,
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"gsc_analyzer_service": gsc_health,
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"timestamp": datetime.utcnow().isoformat()
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}
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)
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except Exception as e:
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logger.error(f"Enterprise health check failed: {str(e)}")
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return BaseResponse(
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success=False,
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message="Enterprise health check failed",
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data={"error": str(e)}
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)
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