merge: resolve conflicts favoring local for frontend packages
This commit is contained in:
ajaysi
@@ -177,7 +177,7 @@ class AIQualityAnalysisService:
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Focus on strategic depth, clarity, and measurability.
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"""
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ai_response = await gemini_structured_json_response(
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ai_response = gemini_structured_json_response(
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prompt=prompt,
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schema=QUALITY_ANALYSIS_SCHEMA,
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temperature=0.3,
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@@ -78,9 +78,15 @@ class PromptChainOrchestrator:
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self.comprehensive_user_processor = ComprehensiveUserDataProcessor()
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# Inject database service if available
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if hasattr(self.comprehensive_user_processor, 'content_planning_db_service') and db_session:
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self.comprehensive_user_processor.content_planning_db_service = db_session
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logger.info("✅ Database service injected into comprehensive user processor")
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if db_session:
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try:
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from services.content_planning_db import ContentPlanningDBService
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db_service = ContentPlanningDBService(db_session)
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self.comprehensive_user_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into comprehensive user processor")
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except Exception as e:
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logger.error(f"❌ Failed to inject database service: {e}")
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self.comprehensive_user_processor.content_planning_db_service = None
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# 12-step configuration
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self.steps = self._initialize_steps()
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@@ -92,21 +98,78 @@ class PromptChainOrchestrator:
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"""Initialize all 12 steps of the prompt chain."""
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steps = {}
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# Create database service if available
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db_service = None
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if self.db_session:
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try:
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from services.content_planning_db import ContentPlanningDBService
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db_service = ContentPlanningDBService(self.db_session)
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logger.info("✅ Database service created for step injection")
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except Exception as e:
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logger.error(f"❌ Failed to create database service for steps: {e}")
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# Phase 1: Foundation (Steps 1-3) - REAL IMPLEMENTATIONS
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steps["step_01"] = ContentStrategyAnalysisStep()
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steps["step_02"] = GapAnalysisStep()
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steps["step_03"] = AudiencePlatformStrategyStep()
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# Inject database service into Phase 1 steps
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if db_service:
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# Step 1: Content Strategy Analysis
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if hasattr(steps["step_01"], 'strategy_processor'):
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steps["step_01"].strategy_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 1 strategy processor")
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# Step 2: Gap Analysis
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if hasattr(steps["step_02"], 'gap_processor'):
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steps["step_02"].gap_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 2 gap processor")
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# Step 3: Audience Platform Strategy
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if hasattr(steps["step_03"], 'comprehensive_processor'):
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steps["step_03"].comprehensive_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 3 comprehensive processor")
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# Phase 2: Structure (Steps 4-6) - REAL IMPLEMENTATIONS
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steps["step_04"] = CalendarFrameworkStep()
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steps["step_05"] = ContentPillarDistributionStep()
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steps["step_06"] = PlatformSpecificStrategyStep()
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# Inject database service into Phase 2 steps
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if db_service:
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# Step 4: Calendar Framework
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if hasattr(steps["step_04"], 'comprehensive_user_processor'):
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steps["step_04"].comprehensive_user_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 4 comprehensive processor")
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# Step 5: Content Pillar Distribution
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if hasattr(steps["step_05"], 'comprehensive_user_processor'):
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steps["step_05"].comprehensive_user_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 5 comprehensive processor")
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# Step 6: Platform Specific Strategy
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if hasattr(steps["step_06"], 'comprehensive_user_processor'):
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steps["step_06"].comprehensive_user_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 6 comprehensive processor")
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# Phase 3: Content (Steps 7-9) - REAL IMPLEMENTATIONS
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steps["step_07"] = WeeklyThemeDevelopmentStep()
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steps["step_08"] = DailyContentPlanningStep()
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steps["step_09"] = ContentRecommendationsStep()
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# Inject database service into Phase 3 steps
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if db_service:
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# Step 7: Weekly Theme Development
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if hasattr(steps["step_07"], 'comprehensive_user_processor'):
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steps["step_07"].comprehensive_user_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 7 comprehensive processor")
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if hasattr(steps["step_07"], 'strategy_processor'):
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steps["step_07"].strategy_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 7 strategy processor")
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if hasattr(steps["step_07"], 'gap_analysis_processor'):
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steps["step_07"].gap_analysis_processor.content_planning_db_service = db_service
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logger.info("✅ Database service injected into Step 7 gap analysis processor")
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# Phase 4: Optimization (Steps 10-12) - REAL IMPLEMENTATIONS
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steps["step_10"] = PerformanceOptimizationStep()
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steps["step_11"] = StrategyAlignmentValidationStep()
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@@ -169,7 +169,7 @@ def gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_promp
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# FIXME: Expose model_name in main_config
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try:
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response = client.models.generate_content(
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model='gemini-2.5-pro',
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model='gemini-2.0-flash-lite',
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contents=prompt,
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config=types.GenerateContentConfig(
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system_instruction=system_prompt,
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359
backend/services/monitoring_data_service.py
Normal file
359
backend/services/monitoring_data_service.py
Normal file
@@ -0,0 +1,359 @@
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"""
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Monitoring Data Service
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Handles saving and retrieving monitoring data from database and cache.
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"""
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import logging
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from sqlalchemy.orm import Session
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from sqlalchemy import and_, desc
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from models.monitoring_models import (
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StrategyMonitoringPlan, MonitoringTask, TaskExecutionLog,
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StrategyPerformanceMetrics, StrategyActivationStatus
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)
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from models.enhanced_strategy_models import EnhancedContentStrategy
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logger = logging.getLogger(__name__)
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class MonitoringDataService:
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"""Service for managing monitoring data in database and cache."""
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def __init__(self, db_session: Session):
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self.db = db_session
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async def save_monitoring_data(self, strategy_id: int, monitoring_plan: Dict[str, Any]) -> bool:
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"""Save monitoring plan and tasks to database."""
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try:
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logger.info(f"Saving monitoring data for strategy {strategy_id}")
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logger.info(f"Monitoring plan received: {monitoring_plan}")
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# Save the complete monitoring plan
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monitoring_plan_record = StrategyMonitoringPlan(
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strategy_id=strategy_id,
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plan_data=monitoring_plan
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)
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self.db.add(monitoring_plan_record)
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# Save individual monitoring tasks
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monitoring_tasks = monitoring_plan.get('monitoringTasks', [])
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logger.info(f"Found {len(monitoring_tasks)} monitoring tasks to save")
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for i, task_data in enumerate(monitoring_tasks):
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logger.info(f"Saving task {i+1}: {task_data.get('title', 'Unknown')}")
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task = MonitoringTask(
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strategy_id=strategy_id,
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component_name=task_data.get('component', ''),
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task_title=task_data.get('title', ''),
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task_description=task_data.get('description', ''),
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assignee=task_data.get('assignee', 'ALwrity'),
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frequency=task_data.get('frequency', 'Monthly'),
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metric=task_data.get('metric', ''),
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measurement_method=task_data.get('measurementMethod', ''),
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success_criteria=task_data.get('successCriteria', ''),
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alert_threshold=task_data.get('alertThreshold', ''),
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status='active'
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)
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self.db.add(task)
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# Save activation status
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activation_status = StrategyActivationStatus(
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strategy_id=strategy_id,
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user_id=1, # Default user ID
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activation_date=datetime.utcnow(),
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status='active'
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)
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self.db.add(activation_status)
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# Save initial performance metrics
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performance_metrics = StrategyPerformanceMetrics(
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strategy_id=strategy_id,
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user_id=1, # Default user ID
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metric_date=datetime.utcnow(),
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data_source='monitoring_plan',
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confidence_score=85 # High confidence for monitoring plan data
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)
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self.db.add(performance_metrics)
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self.db.commit()
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logger.info(f"Successfully saved monitoring data for strategy {strategy_id}")
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return True
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except Exception as e:
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logger.error(f"Error saving monitoring data for strategy {strategy_id}: {e}")
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self.db.rollback()
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return False
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async def get_monitoring_data(self, strategy_id: int) -> Optional[Dict[str, Any]]:
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"""Get monitoring data from database."""
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try:
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logger.info(f"Retrieving monitoring data for strategy {strategy_id}")
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# Get the monitoring plan
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monitoring_plan = self.db.query(StrategyMonitoringPlan).filter(
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StrategyMonitoringPlan.strategy_id == strategy_id
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).order_by(desc(StrategyMonitoringPlan.created_at)).first()
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if not monitoring_plan:
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logger.warning(f"No monitoring plan found for strategy {strategy_id}")
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return None
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# Get monitoring tasks
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tasks = self.db.query(MonitoringTask).filter(
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MonitoringTask.strategy_id == strategy_id
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).all()
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# Get activation status
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activation_status = self.db.query(StrategyActivationStatus).filter(
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StrategyActivationStatus.strategy_id == strategy_id
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).first()
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# Get performance metrics
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performance_metrics = self.db.query(StrategyPerformanceMetrics).filter(
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StrategyPerformanceMetrics.strategy_id == strategy_id
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).order_by(desc(StrategyPerformanceMetrics.metric_date)).first()
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# Build comprehensive monitoring data
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monitoring_data = {
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'strategy_id': strategy_id,
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'monitoring_plan': monitoring_plan.plan_data,
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'monitoring_tasks': [
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{
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'id': task.id,
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'component': task.component_name,
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'title': task.task_title,
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'description': task.task_description,
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'assignee': task.assignee,
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'frequency': task.frequency,
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'metric': task.metric,
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'measurementMethod': task.measurement_method,
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'successCriteria': task.success_criteria,
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'alertThreshold': task.alert_threshold,
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'status': task.status,
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'last_executed': task.last_executed.isoformat() if task.last_executed else None,
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'next_execution': task.next_execution.isoformat() if task.next_execution else None
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}
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for task in tasks
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],
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'activation_status': {
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'activation_date': activation_status.activation_date.isoformat() if activation_status else None,
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'status': activation_status.status if activation_status else 'unknown',
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'performance_score': activation_status.performance_score if activation_status else None
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},
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'performance_metrics': {
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'traffic_growth': performance_metrics.traffic_growth_percentage if performance_metrics else None,
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'engagement_rate': performance_metrics.engagement_rate_percentage if performance_metrics else None,
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'conversion_rate': performance_metrics.conversion_rate_percentage if performance_metrics else None,
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'roi_ratio': performance_metrics.roi_ratio if performance_metrics else None,
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'content_quality_score': performance_metrics.content_quality_score if performance_metrics else None,
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'data_source': performance_metrics.data_source if performance_metrics else None,
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'confidence_score': performance_metrics.confidence_score if performance_metrics else None
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},
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'created_at': monitoring_plan.created_at.isoformat(),
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'updated_at': monitoring_plan.updated_at.isoformat()
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}
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logger.info(f"Successfully retrieved monitoring data for strategy {strategy_id}")
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return monitoring_data
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except Exception as e:
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logger.error(f"Error retrieving monitoring data for strategy {strategy_id}: {e}")
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return None
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async def get_analytics_data(self, strategy_id: int) -> Dict[str, Any]:
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"""Get analytics data from monitoring data (no external API calls)."""
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try:
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logger.info(f"Generating analytics data for strategy {strategy_id}")
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# Get monitoring data from database
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monitoring_data = await self.get_monitoring_data(strategy_id)
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if not monitoring_data:
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logger.warning(f"No monitoring data found for strategy {strategy_id}")
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return self._get_empty_analytics_data()
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# Extract analytics from monitoring data
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monitoring_plan = monitoring_data['monitoring_plan']
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tasks = monitoring_data['monitoring_tasks']
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performance_metrics = monitoring_data['performance_metrics']
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# Always use monitoring tasks from the plan for rich data, fallback to database tasks
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monitoring_tasks = []
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if monitoring_plan.get('monitoringTasks'):
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# Use rich data from monitoring plan
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monitoring_tasks = [
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{
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'id': i + 1,
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'component': task.get('component', ''),
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'title': task.get('title', ''),
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'description': task.get('description', ''),
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'assignee': task.get('assignee', 'ALwrity'),
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'frequency': task.get('frequency', 'Monthly'),
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'metric': task.get('metric', ''),
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'measurementMethod': task.get('measurementMethod', ''),
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'successCriteria': task.get('successCriteria', ''),
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'alertThreshold': task.get('alertThreshold', ''),
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'actionableInsights': task.get('actionableInsights', ''),
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'status': 'active',
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'last_executed': None,
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'next_execution': None
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}
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for i, task in enumerate(monitoring_plan.get('monitoringTasks', []))
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]
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elif tasks:
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# Fallback to database tasks if plan doesn't have them
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monitoring_tasks = [
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{
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'id': task.id,
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'component': task.component_name,
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'title': task.task_title,
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'description': task.task_description,
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'assignee': task.assignee,
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'frequency': task.frequency,
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'metric': task.metric,
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'measurementMethod': task.measurement_method,
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'successCriteria': task.success_criteria,
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'alertThreshold': task.alert_threshold,
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'actionableInsights': '',
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'status': task.status,
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'last_executed': task.last_executed.isoformat() if task.last_executed else None,
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'next_execution': task.next_execution.isoformat() if task.next_execution else None
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}
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for task in tasks
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]
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# Always use performance metrics from success metrics for rich data
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extracted_metrics = {}
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if monitoring_plan.get('successMetrics'):
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success_metrics = monitoring_plan['successMetrics']
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extracted_metrics = {
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'traffic_growth': self._extract_percentage(success_metrics.get('trafficGrowth', {}).get('current', '0%')),
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'engagement_rate': self._extract_percentage(success_metrics.get('engagementRate', {}).get('current', '0%')),
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'conversion_rate': self._extract_percentage(success_metrics.get('conversionRate', {}).get('current', '0%')),
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'roi_ratio': self._extract_ratio(success_metrics.get('roi', {}).get('current', '0:1')),
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'content_quality_score': self._extract_percentage(success_metrics.get('contentQuality', {}).get('current', '0%')),
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'data_source': 'monitoring_plan',
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'confidence_score': 85
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}
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else:
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# Fallback to database metrics if plan doesn't have them
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extracted_metrics = {
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'traffic_growth': performance_metrics.get('traffic_growth', 0),
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'engagement_rate': performance_metrics.get('engagement_rate', 0),
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'conversion_rate': performance_metrics.get('conversion_rate', 0),
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'roi_ratio': performance_metrics.get('roi_ratio', 0),
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'content_quality_score': performance_metrics.get('content_quality_score', 0),
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'data_source': performance_metrics.get('data_source', 'database'),
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'confidence_score': performance_metrics.get('confidence_score', 70)
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}
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# Build analytics data from monitoring plan
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analytics_data = {
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'performance_trends': {
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'traffic_growth': extracted_metrics.get('traffic_growth', 0),
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'engagement_rate': extracted_metrics.get('engagement_rate', 0),
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'conversion_rate': extracted_metrics.get('conversion_rate', 0),
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'roi_ratio': extracted_metrics.get('roi_ratio', 0),
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'content_quality_score': extracted_metrics.get('content_quality_score', 0)
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},
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'content_evolution': {
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'content_pillars': monitoring_plan.get('contentPillars', []),
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'content_mix': monitoring_plan.get('contentMix', {}),
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'publishing_frequency': monitoring_plan.get('publishingFrequency', ''),
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'quality_metrics': monitoring_plan.get('qualityMetrics', [])
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},
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'engagement_patterns': {
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'audience_segments': monitoring_plan.get('audienceSegments', []),
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'engagement_metrics': monitoring_plan.get('engagementMetrics', {}),
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'optimal_timing': monitoring_plan.get('optimalTiming', {}),
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'platform_performance': monitoring_plan.get('platformPerformance', {})
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},
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'recommendations': monitoring_plan.get('recommendations', []),
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'insights': monitoring_plan.get('insights', []),
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'monitoring_data': monitoring_data,
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'monitoring_tasks': monitoring_tasks,
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'monitoring_plan': monitoring_plan, # Include full monitoring plan for rich data
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'success_metrics': monitoring_plan.get('successMetrics', {}), # Include success metrics
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'monitoring_schedule': monitoring_plan.get('monitoringSchedule', {}), # Include monitoring schedule
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'_source': 'database_monitoring',
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'data_freshness': monitoring_data['updated_at'],
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'confidence_score': extracted_metrics.get('confidence_score', 85)
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}
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logger.info(f"Successfully generated analytics data for strategy {strategy_id}")
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return analytics_data
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except Exception as e:
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logger.error(f"Error generating analytics data for strategy {strategy_id}: {e}")
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return self._get_empty_analytics_data()
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def _get_empty_analytics_data(self) -> Dict[str, Any]:
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"""Return empty analytics data structure."""
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return {
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'performance_trends': {},
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'content_evolution': {},
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||||
'engagement_patterns': {},
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'recommendations': [],
|
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'insights': [],
|
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'monitoring_data': None,
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'monitoring_tasks': [],
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'_source': 'empty',
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'data_freshness': datetime.utcnow().isoformat(),
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'confidence_score': 0
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||||
}
|
||||
|
||||
def _extract_percentage(self, value: str) -> float:
|
||||
"""Extract percentage value from string like '15%'."""
|
||||
try:
|
||||
if isinstance(value, str) and '%' in value:
|
||||
return float(value.replace('%', ''))
|
||||
elif isinstance(value, (int, float)):
|
||||
return float(value)
|
||||
else:
|
||||
return 0.0
|
||||
except (ValueError, TypeError):
|
||||
return 0.0
|
||||
|
||||
def _extract_ratio(self, value: str) -> float:
|
||||
"""Extract ratio value from string like '3:1'."""
|
||||
try:
|
||||
if isinstance(value, str) and ':' in value:
|
||||
parts = value.split(':')
|
||||
if len(parts) == 2:
|
||||
return float(parts[0]) / float(parts[1])
|
||||
elif isinstance(value, (int, float)):
|
||||
return float(value)
|
||||
else:
|
||||
return 0.0
|
||||
except (ValueError, TypeError):
|
||||
return 0.0
|
||||
|
||||
async def update_performance_metrics(self, strategy_id: int, metrics: Dict[str, Any]) -> bool:
|
||||
"""Update performance metrics for a strategy."""
|
||||
try:
|
||||
logger.info(f"Updating performance metrics for strategy {strategy_id}")
|
||||
|
||||
performance_metrics = StrategyPerformanceMetrics(
|
||||
strategy_id=strategy_id,
|
||||
user_id=1, # Default user ID
|
||||
metric_date=datetime.utcnow(),
|
||||
traffic_growth_percentage=metrics.get('traffic_growth'),
|
||||
engagement_rate_percentage=metrics.get('engagement_rate'),
|
||||
conversion_rate_percentage=metrics.get('conversion_rate'),
|
||||
roi_ratio=metrics.get('roi_ratio'),
|
||||
content_quality_score=metrics.get('content_quality_score'),
|
||||
data_source='manual_update',
|
||||
confidence_score=metrics.get('confidence_score', 70)
|
||||
)
|
||||
|
||||
self.db.add(performance_metrics)
|
||||
self.db.commit()
|
||||
|
||||
logger.info(f"Successfully updated performance metrics for strategy {strategy_id}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error updating performance metrics for strategy {strategy_id}: {e}")
|
||||
self.db.rollback()
|
||||
return False
|
||||
389
backend/services/strategy_copilot_service.py
Normal file
389
backend/services/strategy_copilot_service.py
Normal file
@@ -0,0 +1,389 @@
|
||||
from typing import Dict, Any, List, Optional
|
||||
from sqlalchemy.orm import Session
|
||||
from loguru import logger
|
||||
from services.onboarding_data_service import OnboardingDataService
|
||||
from services.user_data_service import UserDataService
|
||||
from services.llm_providers.gemini_provider import gemini_text_response, gemini_structured_json_response
|
||||
|
||||
class StrategyCopilotService:
|
||||
"""Service for CopilotKit strategy assistance using Gemini."""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.onboarding_service = OnboardingDataService()
|
||||
self.user_data_service = UserDataService(db)
|
||||
|
||||
async def generate_category_data(
|
||||
self,
|
||||
category: str,
|
||||
user_description: str,
|
||||
current_form_data: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
"""Generate data for a specific category."""
|
||||
try:
|
||||
# Get user onboarding data
|
||||
user_id = 1 # TODO: Get from auth context
|
||||
onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
|
||||
|
||||
# Build prompt for category generation
|
||||
prompt = self._build_category_generation_prompt(
|
||||
category, user_description, current_form_data, onboarding_data
|
||||
)
|
||||
|
||||
# Generate response using Gemini
|
||||
response = gemini_text_response(
|
||||
prompt=prompt,
|
||||
temperature=0.3,
|
||||
top_p=0.9,
|
||||
n=1,
|
||||
max_tokens=2048,
|
||||
system_prompt="You are ALwrity's Strategy Assistant. Generate appropriate values for strategy fields."
|
||||
)
|
||||
|
||||
# Parse and validate response
|
||||
generated_data = self._parse_category_response(response, category)
|
||||
|
||||
return generated_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating category data: {str(e)}")
|
||||
raise
|
||||
|
||||
async def validate_field(self, field_id: str, value: Any) -> Dict[str, Any]:
|
||||
"""Validate a specific strategy field."""
|
||||
try:
|
||||
# Get field definition
|
||||
field_definition = self._get_field_definition(field_id)
|
||||
|
||||
# Build validation prompt
|
||||
prompt = self._build_validation_prompt(field_definition, value)
|
||||
|
||||
# Generate validation response using Gemini
|
||||
response = gemini_text_response(
|
||||
prompt=prompt,
|
||||
temperature=0.2,
|
||||
top_p=0.9,
|
||||
n=1,
|
||||
max_tokens=1024,
|
||||
system_prompt="You are ALwrity's Strategy Assistant. Validate field values and provide suggestions."
|
||||
)
|
||||
|
||||
# Parse validation result
|
||||
validation_result = self._parse_validation_response(response)
|
||||
|
||||
return validation_result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error validating field {field_id}: {str(e)}")
|
||||
raise
|
||||
|
||||
async def analyze_strategy(self, form_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Analyze complete strategy for completeness and coherence."""
|
||||
try:
|
||||
# Get user data for context
|
||||
user_id = 1 # TODO: Get from auth context
|
||||
onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
|
||||
|
||||
# Build analysis prompt
|
||||
prompt = self._build_analysis_prompt(form_data, onboarding_data)
|
||||
|
||||
# Generate analysis using Gemini
|
||||
response = gemini_text_response(
|
||||
prompt=prompt,
|
||||
temperature=0.3,
|
||||
top_p=0.9,
|
||||
n=1,
|
||||
max_tokens=2048,
|
||||
system_prompt="You are ALwrity's Strategy Assistant. Analyze strategies for completeness and coherence."
|
||||
)
|
||||
|
||||
# Parse analysis result
|
||||
analysis_result = self._parse_analysis_response(response)
|
||||
|
||||
return analysis_result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error analyzing strategy: {str(e)}")
|
||||
raise
|
||||
|
||||
async def generate_field_suggestions(
|
||||
self,
|
||||
field_id: str,
|
||||
current_form_data: Dict[str, Any]
|
||||
) -> Dict[str, Any]:
|
||||
"""Generate suggestions for a specific field."""
|
||||
try:
|
||||
# Get field definition
|
||||
field_definition = self._get_field_definition(field_id)
|
||||
|
||||
# Get user data
|
||||
user_id = 1 # TODO: Get from auth context
|
||||
onboarding_data = self.onboarding_service.get_personalized_ai_inputs(user_id)
|
||||
|
||||
# Build suggestions prompt
|
||||
prompt = self._build_suggestions_prompt(
|
||||
field_definition, current_form_data, onboarding_data
|
||||
)
|
||||
|
||||
# Generate suggestions using Gemini
|
||||
response = gemini_text_response(
|
||||
prompt=prompt,
|
||||
temperature=0.4,
|
||||
top_p=0.9,
|
||||
n=1,
|
||||
max_tokens=1024,
|
||||
system_prompt="You are ALwrity's Strategy Assistant. Generate helpful suggestions for strategy fields."
|
||||
)
|
||||
|
||||
# Parse suggestions
|
||||
suggestions = self._parse_suggestions_response(response)
|
||||
|
||||
return suggestions
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating suggestions for {field_id}: {str(e)}")
|
||||
raise
|
||||
|
||||
def _build_category_generation_prompt(
|
||||
self,
|
||||
category: str,
|
||||
user_description: str,
|
||||
current_form_data: Dict[str, Any],
|
||||
onboarding_data: Dict[str, Any]
|
||||
) -> str:
|
||||
"""Build prompt for category data generation."""
|
||||
return f"""
|
||||
You are ALwrity's Strategy Assistant. Generate data for the {category} category based on the user's description.
|
||||
|
||||
User Description: {user_description}
|
||||
|
||||
Current Form Data: {current_form_data}
|
||||
|
||||
Onboarding Data: {onboarding_data}
|
||||
|
||||
Category Fields: {self._get_category_fields(category)}
|
||||
|
||||
Generate appropriate values for all fields in the {category} category. Return only valid JSON with field IDs as keys.
|
||||
|
||||
Example response format:
|
||||
{{
|
||||
"field_id": "value",
|
||||
"another_field": "value"
|
||||
}}
|
||||
"""
|
||||
|
||||
def _build_validation_prompt(self, field_definition: Dict[str, Any], value: Any) -> str:
|
||||
"""Build prompt for field validation."""
|
||||
return f"""
|
||||
Validate the following field value:
|
||||
|
||||
Field: {field_definition['label']}
|
||||
Description: {field_definition['description']}
|
||||
Required: {field_definition['required']}
|
||||
Type: {field_definition['type']}
|
||||
Value: {value}
|
||||
|
||||
Return JSON with: {{"isValid": boolean, "suggestion": string, "confidence": number}}
|
||||
|
||||
Example response:
|
||||
{{
|
||||
"isValid": true,
|
||||
"suggestion": "This looks good!",
|
||||
"confidence": 0.95
|
||||
}}
|
||||
"""
|
||||
|
||||
def _build_analysis_prompt(
|
||||
self,
|
||||
form_data: Dict[str, Any],
|
||||
onboarding_data: Dict[str, Any]
|
||||
) -> str:
|
||||
"""Build prompt for strategy analysis."""
|
||||
return f"""
|
||||
Analyze the following content strategy for completeness, coherence, and alignment:
|
||||
|
||||
Form Data: {form_data}
|
||||
Onboarding Data: {onboarding_data}
|
||||
|
||||
Return JSON with: {{
|
||||
"completeness": number,
|
||||
"coherence": number,
|
||||
"alignment": number,
|
||||
"suggestions": [string],
|
||||
"missingFields": [string],
|
||||
"improvements": [string]
|
||||
}}
|
||||
|
||||
Example response:
|
||||
{{
|
||||
"completeness": 85,
|
||||
"coherence": 90,
|
||||
"alignment": 88,
|
||||
"suggestions": ["Consider adding more specific metrics"],
|
||||
"missingFields": ["content_budget"],
|
||||
"improvements": ["Add timeline details"]
|
||||
}}
|
||||
"""
|
||||
|
||||
def _build_suggestions_prompt(
|
||||
self,
|
||||
field_definition: Dict[str, Any],
|
||||
current_form_data: Dict[str, Any],
|
||||
onboarding_data: Dict[str, Any]
|
||||
) -> str:
|
||||
"""Build prompt for field suggestions."""
|
||||
return f"""
|
||||
Generate suggestions for the following field:
|
||||
|
||||
Field: {field_definition['label']}
|
||||
Description: {field_definition['description']}
|
||||
Required: {field_definition['required']}
|
||||
Type: {field_definition['type']}
|
||||
|
||||
Current Form Data: {current_form_data}
|
||||
Onboarding Data: {onboarding_data}
|
||||
|
||||
Return JSON with: {{
|
||||
"suggestions": [string],
|
||||
"reasoning": string,
|
||||
"confidence": number
|
||||
}}
|
||||
|
||||
Example response:
|
||||
{{
|
||||
"suggestions": ["Focus on measurable outcomes", "Align with business goals"],
|
||||
"reasoning": "Based on your business context, measurable outcomes will be most effective",
|
||||
"confidence": 0.92
|
||||
}}
|
||||
"""
|
||||
|
||||
def _get_field_definition(self, field_id: str) -> Dict[str, Any]:
|
||||
"""Get field definition from STRATEGIC_INPUT_FIELDS."""
|
||||
# This would be imported from the frontend field definitions
|
||||
# For now, return a basic structure
|
||||
return {
|
||||
"id": field_id,
|
||||
"label": field_id.replace("_", " ").title(),
|
||||
"description": f"Description for {field_id}",
|
||||
"required": True,
|
||||
"type": "text"
|
||||
}
|
||||
|
||||
def _get_category_fields(self, category: str) -> List[str]:
|
||||
"""Get fields for a specific category."""
|
||||
# This would be imported from the frontend field definitions
|
||||
category_fields = {
|
||||
"business_context": [
|
||||
"business_objectives", "target_metrics", "content_budget", "team_size",
|
||||
"implementation_timeline", "market_share", "competitive_position", "performance_metrics"
|
||||
],
|
||||
"audience_intelligence": [
|
||||
"content_preferences", "consumption_patterns", "audience_pain_points",
|
||||
"buying_journey", "seasonal_trends", "engagement_metrics"
|
||||
],
|
||||
"competitive_intelligence": [
|
||||
"top_competitors", "competitor_content_strategies", "market_gaps",
|
||||
"industry_trends", "emerging_trends"
|
||||
],
|
||||
"content_strategy": [
|
||||
"preferred_formats", "content_mix", "content_frequency", "optimal_timing",
|
||||
"quality_metrics", "editorial_guidelines", "brand_voice"
|
||||
],
|
||||
"performance_analytics": [
|
||||
"traffic_sources", "conversion_rates", "content_roi_targets", "ab_testing_capabilities"
|
||||
]
|
||||
}
|
||||
return category_fields.get(category, [])
|
||||
|
||||
def _parse_category_response(self, response: str, category: str) -> Dict[str, Any]:
|
||||
"""Parse LLM response for category data."""
|
||||
try:
|
||||
import json
|
||||
# Clean up the response to extract JSON
|
||||
response = response.strip()
|
||||
if response.startswith("```json"):
|
||||
response = response[7:]
|
||||
if response.endswith("```"):
|
||||
response = response[:-3]
|
||||
response = response.strip()
|
||||
|
||||
parsed_data = json.loads(response)
|
||||
|
||||
# Validate that we have actual data
|
||||
if not isinstance(parsed_data, dict) or len(parsed_data) == 0:
|
||||
raise Exception("Invalid or empty response data")
|
||||
|
||||
return parsed_data
|
||||
except Exception as e:
|
||||
logger.error(f"Error parsing category response: {str(e)}")
|
||||
raise Exception(f"Failed to parse category response: {str(e)}")
|
||||
|
||||
def _parse_validation_response(self, response: str) -> Dict[str, Any]:
|
||||
"""Parse LLM response for validation."""
|
||||
try:
|
||||
import json
|
||||
# Clean up the response to extract JSON
|
||||
response = response.strip()
|
||||
if response.startswith("```json"):
|
||||
response = response[7:]
|
||||
if response.endswith("```"):
|
||||
response = response[:-3]
|
||||
response = response.strip()
|
||||
|
||||
parsed_data = json.loads(response)
|
||||
|
||||
# Validate required fields
|
||||
if not isinstance(parsed_data, dict) or 'isValid' not in parsed_data:
|
||||
raise Exception("Invalid validation response format")
|
||||
|
||||
return parsed_data
|
||||
except Exception as e:
|
||||
logger.error(f"Error parsing validation response: {str(e)}")
|
||||
raise Exception(f"Failed to parse validation response: {str(e)}")
|
||||
|
||||
def _parse_analysis_response(self, response: str) -> Dict[str, Any]:
|
||||
"""Parse LLM response for analysis."""
|
||||
try:
|
||||
import json
|
||||
# Clean up the response to extract JSON
|
||||
response = response.strip()
|
||||
if response.startswith("```json"):
|
||||
response = response[7:]
|
||||
if response.endswith("```"):
|
||||
response = response[:-3]
|
||||
response = response.strip()
|
||||
|
||||
parsed_data = json.loads(response)
|
||||
|
||||
# Validate required fields
|
||||
required_fields = ['completeness', 'coherence', 'alignment']
|
||||
if not isinstance(parsed_data, dict) or not all(field in parsed_data for field in required_fields):
|
||||
raise Exception("Invalid analysis response format")
|
||||
|
||||
return parsed_data
|
||||
except Exception as e:
|
||||
logger.error(f"Error parsing analysis response: {str(e)}")
|
||||
raise Exception(f"Failed to parse analysis response: {str(e)}")
|
||||
|
||||
def _parse_suggestions_response(self, response: str) -> Dict[str, Any]:
|
||||
"""Parse LLM response for suggestions."""
|
||||
try:
|
||||
import json
|
||||
# Clean up the response to extract JSON
|
||||
response = response.strip()
|
||||
if response.startswith("```json"):
|
||||
response = response[7:]
|
||||
if response.endswith("```"):
|
||||
response = response[:-3]
|
||||
response = response.strip()
|
||||
|
||||
parsed_data = json.loads(response)
|
||||
|
||||
# Validate required fields
|
||||
if not isinstance(parsed_data, dict) or 'suggestions' not in parsed_data:
|
||||
raise Exception("Invalid suggestions response format")
|
||||
|
||||
return parsed_data
|
||||
except Exception as e:
|
||||
logger.error(f"Error parsing suggestions response: {str(e)}")
|
||||
raise Exception(f"Failed to parse suggestions response: {str(e)}")
|
||||
Reference in New Issue
Block a user