feat: ContentGuardianAgent, onboarding UX, Team Activity action wiring, docs, agent help modal
ContentGuardianAgent consolidation:
- Merge 3 duplicate classes into single source in specialized/content_guardian.py
- Watchdog audit_committee() with heuristic scoring, coverage gaps, overlaps, alerts
- Remove misleading rejection_rate() helper; use acceptance_rate directly
- Integrate audit + alerts + trend signals into today_workflow_service.py
Team Activity page:
- QualityAuditPanel: health ring, per-agent critiques, coverage gaps, overlaps
- TrendSignalsPanel: opportunity cards with urgency/impact/coverage bars
- AlertBanner: persistent dismiss via POST /alerts/{id}/mark-read
- AgentHelpModal: dialog showing all 8 agents with descriptions, tools, schedule
- QualityAuditPanel action buttons: Fill gap -> /content-planning, Resolve overlap, View CTA on alerts/issues
- TrendSignalsPanel action buttons: Create content from this trend -> /blog-writer with trend context state
Onboarding system:
- Step 4 validation: no auto-pass via basic_ready; requires persona data or explicit progression
- Step 5 validation: logs warning on auto-pass without integration data
- OnboardingCompletionService: single DB session, transactional task creation, upsert pattern
- Business-without-website: nullable website_url on SIFIndexingTask and MarketTrendsTask
- DeepCompetitorAnalysisExecutor: 5-min timeout, 10-competitor cap, asyncio.wait_for
- Persona generation: async with 30s timeout, falls back to scheduler
- OnboardingProgressService.reset_onboarding(): resets session + pauses all DB tasks
- OnboardingControlService.reset_onboarding(): also cancels APScheduler jobs
- FinalStep TaskSchedulingPanel: shows scheduled/failed tasks after completion, 8s auto-redirect
- onboarding_completed agent activity event logged to feed
Documentation:
- docs-site/features/onboarding/: overview, steps, scheduler-tasks, technical-reference (4 pages)
- docs-site/mkdocs.yml: added Onboarding System nav section
- docs-site/features/sif-agents/: overview, agent-directory, committee-system, content-guardian (4 pages)
- docs-site/features/team-activity/: overview, quality-audit, trend-signals, alert-system (4 pages)
- docs-site/features/todays-workflow/: updated overview, technical-architecture, workflow-guide, api-reference
This commit is contained in:
ajaysi
@@ -19,7 +19,7 @@ from services.seo import SEODashboardService
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from middleware.auth_middleware import get_current_user
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from services.llm_providers.main_text_generation import llm_text_gen
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from api.content_planning.services.content_strategy.onboarding import OnboardingDataIntegrationService
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from models.onboarding import SEOPageAudit, WebsiteAnalysis, OnboardingSession
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from models.onboarding import SEOPageAudit, WebsiteAnalysis, OnboardingSession, CompetitorAnalysis
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from sqlalchemy.orm.attributes import flag_modified
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from sqlalchemy import desc
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@@ -752,6 +752,391 @@ async def get_keyword_gaps(
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raise HTTPException(status_code=500, detail=f"Failed to get keyword gaps: {str(e)}")
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async def get_serp_gaps(
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current_user: dict = Depends(get_current_user),
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topics: Optional[List[str]] = None,
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) -> Dict[str, Any]:
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"""
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Get SERP gap analysis — detect which competitors rank for given topics.
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Uses Google Custom Search `site:` queries per competitor domain to detect
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ranking presence. Topics can be provided explicitly or derived from the
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user's latest SIF semantic gap analysis.
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Args:
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topics: Optional list of topic phrases. If omitted, uses the user's
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latest SIF semantic gaps (up to 12 topics).
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Returns:
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Dict with gaps list and metadata.
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"""
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try:
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user_id = str(current_user.get("id"))
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# If no topics provided, fetch from SIF semantic gaps
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if not topics:
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try:
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from services.intelligence.agents.specialized import StrategyArchitectAgent
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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integration = OnboardingDataIntegrationService()
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db_session = get_session_for_user(user_id)
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if db_session:
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try:
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integrated = integration.get_integrated_data_sync(
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user_id, db_session
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)
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competitor_indices = []
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if integrated and integrated.get("competitor_analysis"):
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competitor_indices = [
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i
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for i, _ in enumerate(
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integrated["competitor_analysis"]
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)
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]
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agent = StrategyArchitectAgent(
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TxtaiIntelligenceService(user_id), user_id
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)
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gaps = await agent.find_semantic_gaps(competitor_indices)
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topics = [g["topic"] for g in gaps[:12]]
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finally:
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db_session.close()
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except Exception as e:
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logger.warning(
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f"Could not derive topics from SIF gaps: {e}. "
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"Pass topics explicitly."
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)
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return {
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"gaps": [],
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"message": "No topics provided and unable to derive from SIF gaps.",
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}
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if not topics:
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return {
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"gaps": [],
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"message": "No topics to analyze. Complete onboarding and SIF indexing first.",
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}
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# Get competitor domains from onboarding
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competitor_domains = []
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db_session = get_session_for_user(user_id)
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if db_session:
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try:
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analyses = (
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db_session.query(CompetitorAnalysis)
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.join(
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OnboardingSession,
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CompetitorAnalysis.session_id == OnboardingSession.id,
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)
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.filter(OnboardingSession.user_id == user_id)
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.filter(CompetitorAnalysis.competitor_domain.isnot(None))
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.all()
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)
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competitor_domains = list(
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set(a.competitor_domain for a in analyses if a.competitor_domain)
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)
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finally:
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db_session.close()
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if not competitor_domains:
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return {
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"gaps": [],
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"message": "No competitor domains found. Complete onboarding Step 3.",
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}
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# Run SERP gap analysis
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from services.seo_tools.serp_gap_service import SerpGapService
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service = SerpGapService()
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result = await service.analyze_topic_gaps(topics, competitor_domains)
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return result
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except Exception as e:
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logger.error(f"Failed to get SERP gaps: {e}")
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raise HTTPException(
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status_code=500, detail=f"Failed to get SERP gaps: {str(e)}"
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)
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async def get_competitor_content(
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current_user: dict = Depends(get_current_user),
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topics: Optional[List[str]] = None,
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) -> Dict[str, Any]:
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"""
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Get competitor content deep-dive for gap topics using Exa.
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Scopes Exa neural search to known competitor domains (from onboarding Step 3)
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and returns full text, highlights, and summaries for competitive analysis.
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Args:
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topics: Optional list of topic phrases. If omitted, uses the user's
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latest SIF semantic gaps (up to 6 topics — Exa is paid).
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Returns:
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Dict with per-topic competitor content results.
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"""
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try:
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user_id = str(current_user.get("id"))
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# If no topics provided, fetch from SIF semantic gaps
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if not topics:
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try:
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from services.intelligence.agents.specialized import StrategyArchitectAgent
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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integration = OnboardingDataIntegrationService()
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db_session = get_session_for_user(user_id)
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if db_session:
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try:
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integrated = integration.get_integrated_data_sync(
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user_id, db_session
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)
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competitor_indices = []
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if integrated and integrated.get("competitor_analysis"):
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competitor_indices = [
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i
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for i, _ in enumerate(
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integrated["competitor_analysis"]
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)
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]
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agent = StrategyArchitectAgent(
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TxtaiIntelligenceService(user_id), user_id
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)
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gaps = await agent.find_semantic_gaps(competitor_indices)
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# Fewer topics for Exa (paid API)
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topics = [g["topic"] for g in gaps[:6]]
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finally:
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db_session.close()
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except Exception as e:
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logger.warning(
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f"Could not derive topics from SIF gaps: {e}. "
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"Pass topics explicitly."
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)
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return {
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"results": [],
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"message": "No topics provided and unable to derive from SIF gaps.",
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}
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if not topics:
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return {
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"results": [],
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"message": "No topics to analyze. Complete onboarding and SIF indexing first.",
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}
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# Get competitor domains from onboarding
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competitor_domains = []
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db_session = get_session_for_user(user_id)
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if db_session:
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try:
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analyses = (
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db_session.query(CompetitorAnalysis)
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.join(
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OnboardingSession,
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CompetitorAnalysis.session_id == OnboardingSession.id,
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)
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.filter(OnboardingSession.user_id == user_id)
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.filter(CompetitorAnalysis.competitor_domain.isnot(None))
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.all()
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)
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competitor_domains = list(
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set(a.competitor_domain for a in analyses if a.competitor_domain)
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)
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finally:
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db_session.close()
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if not competitor_domains:
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return {
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"results": [],
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"message": "No competitor domains found. Complete onboarding Step 3.",
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}
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# Run Exa competitor deep-dive
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from services.seo_tools.competitor_content_service import (
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CompetitorContentService,
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)
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service = CompetitorContentService()
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result = await service.deep_dive(topics, competitor_domains)
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return result
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except Exception as e:
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logger.error(f"Failed to get competitor content: {e}")
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raise HTTPException(
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status_code=500, detail=f"Failed to get competitor content: {str(e)}"
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)
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async def get_content_gap_radar(
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current_user: dict = Depends(get_current_user),
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bypass_cache: bool = False,
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) -> Dict[str, Any]:
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"""
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Run the Content Gap Radar pipeline — the full Phase 3 agent.
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Orchestrates SIF semantic gap analysis, SERP ranking presence detection,
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Exa competitor content deep-dive, and trend momentum scoring into a
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single ROI-ranked list of content opportunities.
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Returns scored gaps with per-topic evidence and a summary.
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"""
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try:
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user_id = str(current_user.get("id"))
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# Fetch competitor domains + indices from onboarding data
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competitor_domains = []
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competitor_indices = []
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db_session = get_session_for_user(user_id)
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if db_session:
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try:
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# Competitor domains
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analyses = (
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db_session.query(CompetitorAnalysis)
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.join(
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OnboardingSession,
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CompetitorAnalysis.session_id == OnboardingSession.id,
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)
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.filter(OnboardingSession.user_id == user_id)
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.filter(CompetitorAnalysis.competitor_domain.isnot(None))
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.all()
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)
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competitor_domains = list(
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set(
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a.competitor_domain
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for a in analyses
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if a.competitor_domain
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)
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)
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# Competitor indices from integrated data
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integration = OnboardingDataIntegrationService()
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integrated = integration.get_integrated_data_sync(
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user_id, db_session
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)
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if integrated and integrated.get("competitor_analysis"):
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competitor_indices = [
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i
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for i, _ in enumerate(
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integrated["competitor_analysis"]
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)
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]
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finally:
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db_session.close()
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if not competitor_domains:
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return {
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"gaps": [],
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"summary": {},
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"message": "No competitor domains found. Complete onboarding Step 3.",
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}
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# Run the agent
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from services.intelligence.agents import ContentGapRadarAgent
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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agent = ContentGapRadarAgent(
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TxtaiIntelligenceService(user_id), user_id
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)
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result = await agent.analyze(
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competitor_domains=competitor_domains,
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competitor_indices=competitor_indices,
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bypass_cache=bypass_cache,
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)
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return result
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except Exception as e:
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logger.error(f"Failed to run content gap radar: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to run content gap radar: {str(e)}",
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)
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class GenerateContentRequest(BaseModel):
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topic: str
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recommended_action: str = ""
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scoring: Optional[Dict[str, float]] = None
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serp_evidence: Optional[Dict[str, Any]] = None
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sif_gap: Optional[Dict[str, Any]] = None
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async def generate_content_from_gap(
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request: GenerateContentRequest,
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current_user: dict = Depends(get_current_user),
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) -> Dict[str, Any]:
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"""
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Generate a content brief from a content gap radar item and save it
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as a blog ContentAsset so the user can resume in the Blog Writer.
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"""
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try:
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user_id = str(current_user.get("id"))
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from services.intelligence.agents import ContentGapRadarAgent
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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agent = ContentGapRadarAgent(
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TxtaiIntelligenceService(user_id), user_id
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)
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brief_result = await agent.generate_content_brief(
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topic=request.topic,
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recommended_action=request.recommended_action,
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scoring=request.scoring,
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serp_evidence=request.serp_evidence,
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sif_gap=request.sif_gap,
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)
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# Create blog ContentAsset so user can resume in Blog Writer
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from services.content_asset_service import ContentAssetService
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from models.content_asset_models import AssetType, AssetSource
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from services.database import get_db_session
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session = get_db_session()
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asset_id = None
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if session:
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try:
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svc = ContentAssetService(session)
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asset = svc.create_asset(
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user_id=user_id,
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asset_type=AssetType.TEXT,
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source_module=AssetSource.BLOG_WRITER,
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filename=f"gap_{int(time.time())}.md",
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file_url=f"/api/blog/content/pending",
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title=request.topic,
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description=f"Content brief from gap analysis: {request.topic}",
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tags=["content-gap", "seo-dashboard"],
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asset_metadata={
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"phase": "research",
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"research_keywords": request.topic,
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"topic": request.topic,
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"research_data": brief_result,
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"outline_data": None,
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"content_data": None,
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"seo_data": None,
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"publish_data": None,
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},
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)
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asset_id = asset.id
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logger.info(
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f"Created blog asset {asset_id} for gap topic '{request.topic}'"
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)
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except Exception as e:
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logger.warning(f"Failed to create blog asset: {e}")
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finally:
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session.close()
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return {
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"success": True,
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"brief": brief_result["brief"],
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"asset_id": asset_id,
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}
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except Exception as e:
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logger.error(f"Failed to generate content from gap: {e}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to generate content brief: {str(e)}",
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)
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async def get_onboarding_task_health(
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current_user: dict = Depends(get_current_user),
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site_url: Optional[str] = None,
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Reference in New Issue
Block a user