431 lines
16 KiB
Python
431 lines
16 KiB
Python
import json
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from datetime import datetime, timezone
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from typing import Any, Dict, List, Optional
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from sqlalchemy.orm import Session
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from models.daily_workflow_models import DailyWorkflowPlan, DailyWorkflowTask
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from models.agent_activity_models import AgentAlert
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from services.agent_activity_service import AgentActivityService
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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.data_integration import OnboardingDataIntegrationService
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from loguru import logger
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PILLAR_IDS = ["plan", "generate", "publish", "analyze", "engage", "remarket"]
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def _today_date_str() -> str:
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return datetime.now(timezone.utc).date().isoformat()
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def _coerce_priority(value: Any) -> str:
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v = str(value or "medium").lower().strip()
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return v if v in {"high", "medium", "low"} else "medium"
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def _coerce_status(value: Any) -> str:
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v = str(value or "pending").lower().strip()
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if v in {"pending", "in_progress", "completed", "skipped", "dismissed"}:
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return "skipped" if v == "dismissed" else v
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return "pending"
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def _proposal_priority_rank(priority: str) -> int:
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return {"low": 0, "medium": 1, "high": 2}.get(str(priority or "").lower(), 1)
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def _proposal_order_key(proposal: Any) -> tuple:
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return (
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str(getattr(proposal, "source_agent", "") or "").lower(),
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str(getattr(proposal, "title", "") or "").lower(),
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str(getattr(proposal, "description", "") or "").lower(),
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str(getattr(proposal, "action_url", "") or "").lower(),
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)
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def _fallback_tasks(date: str) -> List[Dict[str, Any]]:
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return [
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{
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"pillarId": "plan",
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"title": "Review today’s plan",
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"description": "Confirm priorities and adjust the content calendar for today.",
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"priority": "high",
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"estimatedTime": 15,
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"actionType": "navigate",
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"actionUrl": "/content-planning-dashboard",
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"enabled": True,
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},
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{
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"pillarId": "generate",
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"title": "Generate one core content asset",
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"description": "Create a draft aligned with your current strategy and voice.",
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"priority": "high",
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"estimatedTime": 45,
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"actionType": "navigate",
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"actionUrl": "/blog-writer",
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"enabled": True,
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},
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{
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"pillarId": "publish",
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"title": "Publish or schedule today’s content",
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"description": "Publish or schedule content across the selected channel(s).",
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"priority": "medium",
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"estimatedTime": 20,
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"actionType": "navigate",
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"actionUrl": "/content-planning-dashboard",
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"enabled": True,
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},
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{
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"pillarId": "analyze",
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"title": "Check semantic health and performance",
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"description": "Review semantic health metrics and key performance indicators.",
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"priority": "medium",
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"estimatedTime": 15,
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"actionType": "navigate",
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"actionUrl": "/seo-dashboard",
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"enabled": True,
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},
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{
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"pillarId": "engage",
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"title": "Engage on one channel",
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"description": "Respond to comments and share one post to keep momentum.",
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"priority": "medium",
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"estimatedTime": 15,
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"actionType": "navigate",
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"actionUrl": "/linkedin-writer",
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"enabled": True,
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},
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{
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"pillarId": "remarket",
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"title": "Repurpose and remarket content",
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"description": "Create one repurposed snippet and distribute it to increase reach.",
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"priority": "low",
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"estimatedTime": 20,
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"actionType": "navigate",
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"actionUrl": "/facebook-writer",
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"enabled": True,
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},
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]
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def build_grounding_context(db: Session, user_id: str, date: str) -> Dict[str, Any]:
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# 1. Fetch unread alerts
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unread_agent_alerts = (
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db.query(AgentAlert)
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.filter(AgentAlert.user_id == user_id, AgentAlert.read_at.is_(None))
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.order_by(AgentAlert.created_at.desc())
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.limit(10)
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.all()
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)
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# 2. Fetch comprehensive onboarding data (SIF)
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onboarding_context = {}
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try:
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svc = OnboardingDataIntegrationService()
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integrated = svc.get_integrated_data_sync(user_id, db) or {}
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canonical = integrated.get("canonical_profile", {})
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website_analysis = integrated.get("website_analysis", {})
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onboarding_context = {
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"website_url": website_analysis.get("website_url"),
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"business_type": website_analysis.get("business_type"),
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"industry": canonical.get("industry") or website_analysis.get("industry"),
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"target_audience": canonical.get("target_audience") or website_analysis.get("target_audience"),
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"content_pillars": canonical.get("content_pillars", []),
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"competitors": [c.get("domain") for c in website_analysis.get("competitors", [])[:3]] if website_analysis.get("competitors") else []
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}
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except Exception as e:
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logger.warning(f"Failed to fetch onboarding data for workflow generation: {e}")
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return {
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"date": date,
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"user_id": user_id,
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"pillars": PILLAR_IDS,
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"onboarding_data": onboarding_context,
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"recent_agent_alerts": [
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{"type": a.alert_type, "severity": a.severity, "title": a.title, "message": a.message}
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for a in unread_agent_alerts
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],
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}
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import asyncio
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from services.intelligence.agents.agent_orchestrator import AgentOrchestrationService
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from services.task_memory_service import TaskMemoryService
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# Initialize orchestration service (singleton)
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orchestration_service = AgentOrchestrationService()
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async def generate_agent_enhanced_plan(db: Session, user_id: str, date: str) -> Dict[str, Any]:
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activity = AgentActivityService(db, user_id)
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grounding = build_grounding_context(db, user_id, date)
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memory_service = TaskMemoryService(user_id, db)
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# 1. Get Orchestrator
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try:
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orchestrator = await orchestration_service.get_or_create_orchestrator(user_id)
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except Exception as e:
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logger.error(f"Failed to get orchestrator: {e}")
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return {"date": date, "tasks": _fallback_tasks(date)}
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# 2. Parallel "Committee" Proposal Gathering
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logger.info(f"Gathering daily task proposals from agent committee for user {user_id}")
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agent_tasks = []
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try:
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# Define agents to poll
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agents_to_poll = [
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orchestrator.agents.get('content'), # ContentStrategyAgent
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orchestrator.agents.get('seo'), # SEOOptimizationAgent
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orchestrator.agents.get('social'), # SocialAmplificationAgent
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orchestrator.agents.get('competitor'), # CompetitorResponseAgent
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orchestrator.agents.get('strategy'), # StrategyArchitectAgent
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]
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# Filter out None agents (disabled/failed init)
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active_agents = [a for a in agents_to_poll if a]
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# Execute propose_daily_tasks in parallel
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results = await asyncio.gather(
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*[a.propose_daily_tasks(grounding) for a in active_agents],
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return_exceptions=True
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)
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# Collect successful proposals
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raw_proposals = []
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for res in results:
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if isinstance(res, list):
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raw_proposals.extend(res)
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elif isinstance(res, Exception):
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logger.warning(f"Agent proposal failed: {res}")
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# 3. Filter Redundant Proposals (Self-Learning)
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# Note: We need to ensure we don't filter out essential recurring tasks if they were completed long ago
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# But for now, we filter exact duplicates from recent history (last 7 days)
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# We can implement semantic filtering later
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# Simple deduplication based on title+pillar
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unique_map = {}
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for p in raw_proposals:
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key = f"{p.pillar_id}:{p.title}"
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if key not in unique_map:
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unique_map[key] = p
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continue
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existing = unique_map[key]
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if _proposal_priority_rank(p.priority) > _proposal_priority_rank(existing.priority):
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unique_map[key] = p
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continue
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# Deterministic tie-breaker for equal priority proposals.
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if (
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_proposal_priority_rank(p.priority) == _proposal_priority_rank(existing.priority)
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and _proposal_order_key(p) < _proposal_order_key(existing)
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):
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unique_map[key] = p
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agent_tasks = list(unique_map.values())
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# Phase 3: Check memory for rejections (Semantic Filter)
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# For now, we rely on exact match logic in memory service if implemented fully
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# agent_tasks = await memory_service.filter_redundant_proposals(agent_tasks)
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except Exception as e:
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logger.error(f"Committee proposal phase failed: {e}")
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# Continue to fallback or LLM generation if committee fails
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# 4. Final Selection
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# If we have agent tasks, use them. Otherwise fall back to LLM generation.
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if agent_tasks:
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logger.info(f"Generated {len(agent_tasks)} tasks via Agent Committee")
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# Convert TaskProposal objects to dicts for frontend
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final_tasks = []
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for prop in agent_tasks:
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final_tasks.append({
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"pillarId": prop.pillar_id,
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"title": prop.title,
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"description": prop.description,
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"priority": prop.priority,
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"estimatedTime": prop.estimated_time,
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"actionType": prop.action_type,
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"actionUrl": prop.action_url,
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"enabled": True,
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"metadata": {
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"source_agent": prop.source_agent,
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"reasoning": prop.reasoning,
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"context_data": prop.context_data
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}
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})
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# Ensure we have coverage for all pillars (fill gaps with fallback/LLM if needed)
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# For now, let's just return what the agents proposed
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return {
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"date": date,
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"tasks": final_tasks
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}
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# Fallback to original LLM generation if agents returned nothing
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logger.info("Agent committee returned no tasks, falling back to LLM generation")
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schema = {
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"type": "object",
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"properties": {
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"date": {"type": "string"},
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"tasks": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"pillarId": {"type": "string"},
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"title": {"type": "string"},
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"description": {"type": "string"},
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"priority": {"type": "string"},
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"estimatedTime": {"type": "number"},
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"actionType": {"type": "string"},
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"actionUrl": {"type": "string"},
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"enabled": {"type": "boolean"},
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"dependencies": {"type": "array", "items": {"type": "string"}},
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"metadata": {"type": "object"},
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},
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},
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},
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},
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}
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prompt = (
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"Generate a personalized Today workflow plan for ALwrity with exactly 6 lifecycle pillars: "
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"plan, generate, publish, analyze, engage, remarket.\n\n"
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"User Context (Onboarding & Strategy):\n"
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f"{json.dumps(grounding.get('onboarding_data', {}), indent=2)}\n\n"
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"Rules:\n"
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"- Produce JSON only that matches the schema.\n"
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"- Include 1-3 tasks per pillar.\n"
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"- Each task must have pillarId in {plan, generate, publish, analyze, engage, remarket}.\n"
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"- Customize tasks based on the user's industry, business type, and content pillars found in User Context.\n"
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"- If competitors are listed, include a task to analyze one of them.\n"
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"- Prefer actionable tasks that can be completed today.\n"
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"- Use these common actionUrl routes when relevant: "
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"/content-planning-dashboard, /blog-writer, /linkedin-writer, /facebook-writer, /seo-dashboard, /scheduler-dashboard.\n"
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"- Keep descriptions concise.\n\n"
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f"Grounding context (Alerts):\n{json.dumps(grounding.get('recent_agent_alerts', []), indent=2)}\n"
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)
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run = activity.start_run(agent_type="TodayWorkflowGenerator", prompt=prompt[:4000])
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activity.log_event(
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event_type="plan",
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severity="info",
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message="Building grounded daily workflow plan",
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payload={"grounding": grounding},
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run_id=run.id,
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agent_type="TodayWorkflowGenerator",
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)
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try:
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raw = llm_text_gen(prompt=prompt, json_struct=schema, user_id=user_id)
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if isinstance(raw, dict):
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result = raw
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else:
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try:
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result = json.loads(raw)
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except Exception:
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result = {"date": date, "tasks": _fallback_tasks(date)}
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except Exception as e:
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activity.log_event(
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event_type="warning",
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severity="warning",
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message=str(e)[:2000],
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payload={"fallback": True},
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run_id=run.id,
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agent_type="TodayWorkflowGenerator",
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)
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result = {"date": date, "tasks": _fallback_tasks(date)}
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tasks = result.get("tasks") if isinstance(result, dict) else None
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if not isinstance(tasks, list) or not tasks:
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result = {"date": date, "tasks": _fallback_tasks(date)}
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activity.log_event(
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event_type="final_summary",
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severity="info",
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message="Daily workflow plan generated",
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payload={"date": date, "task_count": len(result.get("tasks", []))},
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run_id=run.id,
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agent_type="TodayWorkflowGenerator",
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)
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activity.finish_run(run.id, success=True, result_summary=json.dumps({"date": date, "tasks": result.get("tasks", [])})[:4000])
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return result
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async def get_or_create_daily_workflow_plan(db: Session, user_id: str, date: Optional[str] = None) -> tuple[DailyWorkflowPlan, bool]:
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date_str = date or _today_date_str()
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existing = (
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db.query(DailyWorkflowPlan)
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.filter(DailyWorkflowPlan.user_id == user_id, DailyWorkflowPlan.date == date_str)
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.first()
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)
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if existing:
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return existing, False
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plan_data = await generate_agent_enhanced_plan(db, user_id, date_str)
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tasks = plan_data.get("tasks", [])
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plan = DailyWorkflowPlan(
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user_id=user_id,
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date=date_str,
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source="agent",
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plan_json=plan_data,
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created_at=datetime.utcnow(),
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updated_at=datetime.utcnow(),
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)
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db.add(plan)
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db.commit()
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db.refresh(plan)
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for t in tasks:
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pillar_id = str(t.get("pillarId") or "").lower().strip()
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if pillar_id not in PILLAR_IDS:
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continue
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task = DailyWorkflowTask(
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plan_id=plan.id,
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user_id=user_id,
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pillar_id=pillar_id,
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title=str(t.get("title") or "Task").strip()[:255],
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description=str(t.get("description") or "").strip(),
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status=_coerce_status(t.get("status")),
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priority=_coerce_priority(t.get("priority")),
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estimated_time=int(t.get("estimatedTime") or 15),
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action_type=str(t.get("actionType") or "navigate").strip()[:20],
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action_url=str(t.get("actionUrl") or "").strip() or None,
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enabled=bool(t.get("enabled", True)),
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dependencies=t.get("dependencies") if isinstance(t.get("dependencies"), list) else None,
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metadata_json=t.get("metadata") if isinstance(t.get("metadata"), dict) else None,
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created_at=datetime.utcnow(),
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updated_at=datetime.utcnow(),
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)
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db.add(task)
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db.commit()
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db.refresh(plan)
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return plan, True
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def update_task_status(
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db: Session,
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user_id: str,
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task_id: int,
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status: str,
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completion_notes: Optional[str] = None,
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) -> Optional[DailyWorkflowTask]:
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task = db.query(DailyWorkflowTask).filter(DailyWorkflowTask.id == task_id, DailyWorkflowTask.user_id == user_id).first()
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if not task:
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return None
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task.status = _coerce_status(status)
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task.decided_at = datetime.utcnow()
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if completion_notes is not None:
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task.completion_notes = completion_notes[:4000]
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db.add(task)
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db.commit()
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db.refresh(task)
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return task
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