ALwrity version 0.5.4
This commit is contained in:
ajaysi
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# Content Calendar Phase - Implementation Guide
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## 🎯 **Executive Summary**
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This document provides a comprehensive implementation guide for the **Content Calendar** phase, based on the detailed analysis of inputs, AI prompts, generated data points, and frontend-backend mapping. The guide focuses on systematic development of calendar event management, AI-powered scheduling, and strategic content planning capabilities.
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---
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## 📊 **Calendar Phase Overview**
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### **Core Objectives**
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- **Calendar Event Management**: Comprehensive scheduling and event management system
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- **AI-Powered Scheduling**: Intelligent optimization of publishing schedules
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- **Content Calendar Generation**: Automated calendar creation with strategic insights
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- **Frontend Integration**: Calendar components and data mapping
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- **Strategy Integration**: Seamless connection with enhanced strategy phase
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### **Key Features**
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- **8 Core Required Inputs**: Essential calendar planning parameters
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- **6 Advanced Optional Inputs**: Advanced calendar optimization features
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- **3 AI Prompt Types**: Specialized AI prompts for calendar optimization
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- **8 Dashboard Components**: Comprehensive calendar interface
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- **8 Data Point Types**: Rich calendar insights and recommendations
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---
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## 📋 **Input Analysis & Implementation**
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### **Core Required Inputs (8)**
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#### **1. User ID & Strategy ID**
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**Implementation Priority**: High
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**Data Source**: User authentication and strategy phase
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**Frontend Component**: Hidden fields with validation
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**Backend Processing**: User context and strategy alignment
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#### **2. Calendar Type**
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**Implementation Priority**: High
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**Options**: Monthly, Quarterly, Yearly
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**Frontend Component**: Radio button selection with tooltip
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**Tooltip**: "Choose calendar duration based on your planning needs and content strategy timeline"
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#### **3. Content Mix**
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**Implementation Priority**: High
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**Data Source**: Strategy phase content preferences
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**Frontend Component**: Interactive pie chart with percentage sliders
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**Tooltip**: "Define the balance of content types for optimal engagement and audience reach"
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#### **4. Publishing Frequency**
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**Implementation Priority**: High
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**Options**: Daily, Weekly, Bi-weekly, Monthly
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**Frontend Component**: Dropdown with frequency calculator
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**Tooltip**: "Set frequency based on audience expectations, team capacity, and content strategy goals"
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#### **5. Seasonal Trends**
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**Implementation Priority**: Medium
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**Data Source**: Industry analysis and historical data
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**Frontend Component**: Seasonal calendar picker with theme suggestions
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**Tooltip**: "Identify seasonal opportunities and themes for strategic content planning"
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#### **6. Audience Behavior**
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**Implementation Priority**: High
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**Data Source**: Analytics and strategy phase insights
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**Frontend Component**: Interactive timeline with peak activity indicators
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**Tooltip**: "Optimize timing based on when your audience is most active and engaged"
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#### **7. Resource Constraints**
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**Implementation Priority**: Medium
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**Data Source**: Team capacity and budget information
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**Frontend Component**: Resource allocation form with capacity indicators
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**Tooltip**: "Define realistic constraints for calendar planning and resource optimization"
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#### **8. Campaign Themes**
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**Implementation Priority**: Medium
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**Data Source**: Strategy phase and user input
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**Frontend Component**: Theme builder with drag-and-drop interface
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**Tooltip**: "Define campaign themes for strategic content alignment and messaging consistency"
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### **Advanced Optional Inputs (6)**
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#### **1. Competitive Events**
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**Implementation Priority**: Low
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**Data Source**: Competitor monitoring and industry events
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**Frontend Component**: Event calendar with conflict detection
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**Tooltip**: "Track competitor activities to avoid conflicts and identify opportunities"
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#### **2. Industry Events**
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**Implementation Priority**: Low
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**Data Source**: Industry calendar and conference databases
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**Frontend Component**: Industry event integration with auto-suggestions
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**Tooltip**: "Align content with industry events and trends for maximum relevance"
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#### **3. Content Repurposing**
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**Implementation Priority**: Medium
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**Data Source**: Existing content inventory
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**Frontend Component**: Content repurposing planner with ROI calculator
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**Tooltip**: "Maximize content value through strategic repurposing across channels"
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#### **4. Cross-Channel Coordination**
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**Implementation Priority**: High
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**Data Source**: Multi-channel strategy and audience behavior
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**Frontend Component**: Channel coordination matrix with messaging alignment
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**Tooltip**: "Ensure consistent messaging and timing across all content channels"
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#### **5. Performance Tracking**
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**Implementation Priority**: Medium
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**Data Source**: Analytics and historical performance data
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**Frontend Component**: Performance dashboard with KPI tracking
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**Tooltip**: "Track calendar effectiveness and identify optimization opportunities"
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#### **6. Budget Allocation**
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**Implementation Priority**: Medium
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**Data Source**: Budget constraints and content costs
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**Frontend Component**: Budget allocation tool with cost forecasting
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**Tooltip**: "Optimize budget allocation across content types and channels"
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---
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## 🤖 **AI Prompt Implementation**
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### **1. Calendar Generation Prompt**
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**Purpose**: Generate comprehensive content calendar with strategic insights
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**Implementation Tasks**:
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- **Input Processing**: Validate and combine all calendar inputs
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- **Strategy Integration**: Incorporate strategy phase data and recommendations
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- **AI Processing**: Generate optimized calendar structure
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- **Output Formatting**: Structure response for frontend consumption
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**Key Features**:
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- Content mix optimization based on audience preferences
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- Publishing schedule optimization using engagement data
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- Seasonal strategy integration with theme suggestions
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- Resource allocation planning with capacity constraints
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- Performance metrics integration for tracking
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**Output Structure**:
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```json
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{
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"calendar_id": "string",
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"publishing_schedule": {
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"optimal_days": ["Tuesday", "Thursday"],
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"optimal_times": ["10:00 AM", "2:00 PM"],
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"frequency": "2-3 times per week",
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"seasonal_adjustments": "object",
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"audience_peak_hours": "array"
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},
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"content_mix": {
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"blog_posts": "60%",
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"video_content": "20%",
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"infographics": "10%",
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"case_studies": "10%"
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},
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"seasonal_strategy": "object",
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"engagement_optimization": "object",
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"resource_allocation": "object",
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"performance_metrics": "object"
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}
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```
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### **2. Schedule Optimization Prompt**
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**Purpose**: Optimize publishing schedule for maximum engagement
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**Implementation Tasks**:
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- **Timing Analysis**: Analyze audience behavior patterns
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- **Competitive Analysis**: Consider competitor publishing schedules
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- **Seasonal Adjustments**: Apply seasonal trends and themes
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- **Resource Optimization**: Balance frequency with team capacity
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**Key Features**:
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- Optimal publishing times based on audience activity
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- Frequency optimization for engagement and consistency
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- Competitive timing analysis to avoid conflicts
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- Seasonal adjustments for theme alignment
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- Resource capacity planning and optimization
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### **3. Content Mix Optimization Prompt**
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**Purpose**: Optimize content mix for balanced engagement
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**Implementation Tasks**:
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- **Performance Analysis**: Analyze historical content performance
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- **Audience Preference**: Consider audience content preferences
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- **Channel Optimization**: Optimize for different distribution channels
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- **Engagement Balance**: Balance different content types for engagement
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**Key Features**:
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- Content type balance analysis based on performance
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- Format optimization for different channels
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- Engagement pattern analysis for content mix
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- Channel distribution strategy optimization
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- ROI-based content mix recommendations
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---
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## 📊 **Data Points & Frontend Components**
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### **1. Publishing Schedule Component**
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**Backend Data**: `publishing_schedule`
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**Frontend Component**: `CalendarView`
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**Data Mapping**: `optimal_times` → `schedule`
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**Implementation Features**:
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- Interactive calendar interface with drag-and-drop
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- Optimal timing indicators with color coding
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- Frequency visualization with consistency tracking
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- Seasonal adjustment overlays
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- Audience peak hour highlighting
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### **2. Content Mix Component**
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**Backend Data**: `content_mix`
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**Frontend Component**: `ContentMixChart`
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**Data Mapping**: `content_types` → `mix_data`
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**Implementation Features**:
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- Interactive pie chart with percentage controls
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- Content type performance indicators
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- Channel distribution visualization
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- Engagement metrics overlay
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- Budget allocation integration
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### **3. Seasonal Strategy Component**
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**Backend Data**: `seasonal_strategy`
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**Frontend Component**: `SeasonalStrategyPanel`
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**Data Mapping**: `seasonal_themes` → `themes`
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**Implementation Features**:
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- Seasonal calendar with theme suggestions
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- Campaign planning integration
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- Peak and low period indicators
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- Theme consistency tracking
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- Performance correlation analysis
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### **4. Engagement Timing Component**
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**Backend Data**: `engagement_optimization`
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**Frontend Component**: `EngagementTimingChart`
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**Data Mapping**: `peak_times` → `timing_data`
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**Implementation Features**:
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- Audience activity heatmap
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- Optimal posting time recommendations
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- Engagement pattern analysis
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- A/B testing integration
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- Performance tracking overlay
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### **5. Resource Planning Component**
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**Backend Data**: `resource_allocation`
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**Frontend Component**: `ResourcePlanningPanel`
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**Data Mapping**: `team_capacity` → `capacity_data`
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**Implementation Features**:
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- Team capacity visualization
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- Content production timeline
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- Budget allocation tracking
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- Tool requirements planning
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- Resource optimization suggestions
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### **6. Performance Metrics Component**
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**Backend Data**: `performance_tracking`
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**Frontend Component**: `PerformanceMetricsCard`
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**Data Mapping**: `engagement_rates` → `metrics`
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**Implementation Features**:
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- Real-time performance dashboard
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- KPI tracking and visualization
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- Optimization opportunity alerts
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- Historical performance comparison
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- Goal achievement tracking
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### **7. Competitive Analysis Component**
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**Backend Data**: `competitive_analysis`
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**Frontend Component**: `CompetitiveAnalysisPanel`
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**Data Mapping**: `competitor_schedules` → `analysis`
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**Implementation Features**:
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- Competitor calendar overlay
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- Differentiation opportunity identification
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- Market gap analysis
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- Competitive response planning
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- Partnership opportunity tracking
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### **8. Cross-Channel Component**
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**Backend Data**: `cross_channel_coordination`
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**Frontend Component**: `CrossChannelPanel`
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**Data Mapping**: `channel_strategies` → `strategies`
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**Implementation Features**:
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- Multi-channel coordination matrix
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- Messaging consistency tracking
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- Channel performance comparison
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- Cross-channel optimization
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- Unified content strategy view
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---
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## 🔄 **Implementation Workflow**
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### **Phase 1: Core Calendar Infrastructure (Weeks 1-2)**
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#### **1.1 Database Schema**
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**Tasks**:
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- Extend calendar model to support all 8 required inputs
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- Add optional input fields for advanced features
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- Create relationships with strategy and user models
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- Implement data validation and constraints
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**Deliverables**:
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- Enhanced calendar database schema
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- Data validation and constraint implementation
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- Relationship mapping with strategy phase
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- Performance optimization indexing
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#### **1.2 Calendar Service Core**
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**Tasks**:
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- Implement `CalendarService` class with core functionality
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- Create calendar generation and optimization methods
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- Add AI prompt integration for calendar optimization
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- Implement error handling and logging
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**Deliverables**:
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- Complete calendar service implementation
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- AI prompt integration framework
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- Error handling and logging system
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- Performance monitoring setup
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#### **1.3 API Endpoints**
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**Tasks**:
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- Implement calendar generation endpoint
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- Add calendar optimization endpoint
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- Create calendar retrieval and management endpoints
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- Add performance tracking endpoints
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**Deliverables**:
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- Complete API endpoint implementation
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- Request/response validation
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- Error handling and fallbacks
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- API documentation
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### **Phase 2: Frontend Calendar Interface (Weeks 3-4)**
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#### **2.1 Calendar Dashboard**
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**Tasks**:
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- Create main calendar view component
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- Implement interactive calendar interface
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- Add drag-and-drop functionality
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- Create calendar navigation and controls
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**Deliverables**:
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- Interactive calendar dashboard
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- Calendar navigation system
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- Event management interface
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- Calendar export functionality
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#### **2.2 Input Forms**
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**Tasks**:
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- Create calendar type selection interface
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- Implement content mix configuration
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- Add publishing frequency controls
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- Create seasonal trends input
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**Deliverables**:
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- Complete input form system
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- Validation and error handling
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- Auto-save functionality
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- Progress tracking
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#### **2.3 Data Visualization**
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**Tasks**:
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- Implement content mix charts
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- Create engagement timing visualizations
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- Add performance metrics dashboard
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- Create resource planning interface
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**Deliverables**:
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- Complete data visualization suite
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- Interactive charts and graphs
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- Real-time data updates
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- Export and sharing capabilities
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### **Phase 3: AI Integration & Optimization (Weeks 5-6)**
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#### **3.1 AI Prompt Implementation**
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**Tasks**:
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- Implement calendar generation prompt
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- Add schedule optimization prompt
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- Create content mix optimization prompt
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- Add prompt performance monitoring
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**Deliverables**:
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- Complete AI prompt implementation
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- Prompt optimization and caching
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- Quality monitoring system
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- Performance tracking
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#### **3.2 Calendar Optimization**
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**Tasks**:
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- Implement publishing schedule optimization
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- Add content mix optimization
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- Create seasonal strategy optimization
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- Add resource allocation optimization
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**Deliverables**:
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- Complete optimization algorithms
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- Performance improvement tracking
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- Optimization recommendation system
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- A/B testing integration
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#### **3.3 Performance Monitoring**
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**Tasks**:
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- Implement calendar performance tracking
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- Add engagement metrics monitoring
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- Create optimization opportunity alerts
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- Add performance reporting
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**Deliverables**:
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- Performance monitoring system
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- Real-time metrics dashboard
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- Alert and notification system
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- Performance reporting tools
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### **Phase 4: Advanced Features (Weeks 7-8)**
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#### **4.1 Competitive Analysis**
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**Tasks**:
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- Implement competitor calendar tracking
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- Add competitive analysis dashboard
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- Create differentiation opportunity alerts
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- Add market gap analysis
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**Deliverables**:
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- Competitive analysis system
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- Competitor tracking dashboard
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- Opportunity identification alerts
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- Market analysis tools
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#### **4.2 Cross-Channel Coordination**
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**Tasks**:
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- Implement multi-channel coordination
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- Add channel performance tracking
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- Create messaging consistency tools
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- Add cross-channel optimization
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**Deliverables**:
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- Cross-channel coordination system
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- Channel performance dashboard
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- Messaging consistency tools
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- Multi-channel optimization
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#### **4.3 Content Repurposing**
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**Tasks**:
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- Implement content repurposing planner
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- Add ROI calculation tools
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- Create repurposing workflow
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- Add content value optimization
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**Deliverables**:
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- Content repurposing system
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- ROI calculation tools
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- Workflow automation
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- Value optimization
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---
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## 🧪 **Testing Strategy**
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### **Unit Testing**
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- **Input Validation**: Test all 8 required inputs and 6 optional inputs
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- **AI Prompt Testing**: Verify all 3 AI prompt types function correctly
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- **Data Transformation**: Test calendar data structure transformations
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- **Error Handling**: Validate error scenarios and fallback mechanisms
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### **Integration Testing**
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- **Frontend-Backend Integration**: Test all 8 dashboard components
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- **API Endpoint Testing**: Verify all calendar API endpoints
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- **Data Mapping Validation**: Test frontend-backend data mapping
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- **Strategy Integration**: Test calendar-strategy phase integration
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### **Performance Testing**
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- **Calendar Generation**: Test calendar generation performance
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- **AI Response Time**: Monitor AI prompt response times
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- **Concurrent Users**: Test system under load
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- **Data Processing**: Test large calendar data processing
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### **User Acceptance Testing**
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- **Calendar Interface**: Test user interaction with calendar
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- **Input Forms**: Validate user input experience
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- **Data Visualization**: Test chart and graph interactions
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- **Optimization Features**: Test AI optimization functionality
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---
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## 📊 **Success Metrics**
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### **Quantitative Metrics**
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- **Calendar Generation Speed**: <3 seconds for calendar generation
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- **AI Optimization Accuracy**: 85%+ user satisfaction with optimizations
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- **Input Completion Rate**: 90%+ completion of required inputs
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- **User Engagement**: 75%+ user adoption of calendar features
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### **Qualitative Metrics**
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- **User Experience**: High satisfaction with calendar interface
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- **Optimization Quality**: Effective AI-powered calendar optimizations
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- **Integration Quality**: Seamless strategy-calendar integration
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- **Feature Completeness**: Comprehensive calendar functionality
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---
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## 🎯 **Risk Management**
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### **Technical Risks**
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- **AI Performance**: Risk of slow or inaccurate calendar optimizations
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- **Mitigation**: Implement caching, fallbacks, and performance monitoring
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- **Data Integration**: Risk of strategy-calendar integration issues
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- **Mitigation**: Comprehensive testing and validation procedures
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- **Scalability**: Risk of performance issues with large calendars
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- **Mitigation**: Load testing and optimization strategies
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### **User Experience Risks**
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- **Complexity**: Risk of overwhelming users with calendar features
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- **Mitigation**: Progressive disclosure and guided setup
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- **Adoption**: Risk of low user adoption of calendar features
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- **Mitigation**: Comprehensive training and documentation
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- **Quality**: Risk of poor AI optimization quality
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- **Mitigation**: Quality monitoring and continuous improvement
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---
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## ✅ **Conclusion**
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||||
|
||||
This implementation guide provides a comprehensive roadmap for developing the Content Calendar phase with:
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||||
|
||||
1. **Systematic Development**: Structured approach to building calendar features
|
||||
2. **AI Integration**: Comprehensive AI-powered optimization capabilities
|
||||
3. **User Experience**: Intuitive calendar interface with advanced features
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||||
4. **Strategy Integration**: Seamless connection with enhanced strategy phase
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5. **Performance Focus**: Optimization for speed, reliability, and scalability
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|
||||
**The Content Calendar phase will provide advanced scheduling and optimization capabilities that complement the enhanced strategy phase and deliver significant value to users through intelligent calendar management.** 🎯
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Reference Documents**
|
||||
|
||||
### **Primary References**
|
||||
- `CONTENT_CALENDAR_PHASE_ANALYSIS.md` - Detailed calendar phase analysis
|
||||
- `ENHANCED_STRATEGY_IMPLEMENTATION_PLAN.md` - Strategy phase implementation plan
|
||||
- `ENHANCED_STRATEGY_SERVICE_DOCUMENTATION.md` - Strategy service documentation
|
||||
|
||||
### **Implementation Guidelines**
|
||||
- **Calendar Analysis**: Reference `CONTENT_CALENDAR_PHASE_ANALYSIS.md` for detailed requirements
|
||||
- **Strategy Integration**: Follow strategy implementation plan for seamless integration
|
||||
- **AI Prompts**: Use calendar analysis for AI prompt specifications
|
||||
- **Frontend Components**: Reference calendar analysis for component requirements
|
||||
|
||||
**This implementation guide serves as the definitive roadmap for developing the Content Calendar phase!** 🚀
|
||||
@@ -1,376 +0,0 @@
|
||||
# Content Calendar Phase - Comprehensive Analysis
|
||||
|
||||
## 🎯 **Phase Overview**
|
||||
|
||||
This document provides a comprehensive analysis of the **Content Calendar** phase, including inputs, AI prompts, generated data points, and frontend-backend mapping. The content calendar phase focuses on scheduling, optimization, and strategic content planning.
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Analysis Summary**
|
||||
|
||||
### **Phase Objectives**
|
||||
- **Calendar Event Management**: Comprehensive scheduling and event management
|
||||
- **AI-Powered Scheduling**: Intelligent optimization of publishing schedules
|
||||
- **Content Calendar Generation**: Automated calendar creation with strategic insights
|
||||
- **Frontend Integration**: Calendar components and data mapping
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Input Analysis**
|
||||
|
||||
### **Required Inputs (8 Core)**
|
||||
|
||||
| Input | Type | Description | Tooltip |
|
||||
|-------|------|-------------|---------|
|
||||
| `user_id` | integer | User identifier for personalization | "Your unique user ID for personalized calendar recommendations" |
|
||||
| `strategy_id` | integer | Associated content strategy ID | "Links calendar to your content strategy for alignment" |
|
||||
| `calendar_type` | string | Type of calendar (monthly/quarterly/yearly) | "Choose calendar duration based on your planning needs" |
|
||||
| `content_mix` | array | Balance of content types and formats | "Define the mix of content types for optimal engagement" |
|
||||
| `publishing_frequency` | string | How often to publish content | "Set frequency based on audience expectations and resources" |
|
||||
| `seasonal_trends` | object | Seasonal content patterns and themes | "Identify seasonal opportunities for content planning" |
|
||||
| `audience_behavior` | object | When audience is most active | "Optimize timing based on audience engagement patterns" |
|
||||
| `resource_constraints` | object | Team capacity and budget limitations | "Define realistic constraints for calendar planning" |
|
||||
|
||||
### **Optional Inputs (6 Advanced)**
|
||||
|
||||
| Input | Type | Description | Tooltip |
|
||||
|-------|------|-------------|---------|
|
||||
| `campaign_themes` | array | Specific campaign themes and topics | "Define campaign themes for strategic content alignment" |
|
||||
| `competitive_events` | array | Competitor content launches and events | "Track competitor activities to avoid conflicts" |
|
||||
| `industry_events` | array | Industry conferences and events | "Align content with industry events and trends" |
|
||||
| `content_repurposing` | object | Content repurposing strategy | "Maximize content value through strategic repurposing" |
|
||||
| `cross_channel_coordination` | object | Multi-channel content coordination | "Ensure consistent messaging across all channels" |
|
||||
| `performance_tracking` | object | Calendar performance metrics | "Track calendar effectiveness and optimization opportunities" |
|
||||
|
||||
### **Data Sources**
|
||||
- Content strategy data from previous phase
|
||||
- Onboarding user preferences and behavior
|
||||
- Historical content performance data
|
||||
- Industry seasonal patterns
|
||||
- Competitor content calendars
|
||||
- Audience engagement analytics
|
||||
|
||||
---
|
||||
|
||||
## 🤖 **AI Prompt Analysis**
|
||||
|
||||
### **1. Calendar Generation Prompt**
|
||||
**Purpose**: Generate comprehensive content calendar with strategic insights
|
||||
|
||||
**Components**:
|
||||
- Content mix optimization
|
||||
- Publishing schedule optimization
|
||||
- Seasonal content strategy
|
||||
- Audience engagement timing
|
||||
- Resource allocation planning
|
||||
|
||||
**Input Data**:
|
||||
- `strategy_id`
|
||||
- `content_mix`
|
||||
- `publishing_frequency`
|
||||
- `seasonal_trends`
|
||||
- `audience_behavior`
|
||||
|
||||
**Output Structure**:
|
||||
```json
|
||||
{
|
||||
"calendar_id": "string",
|
||||
"publishing_schedule": "object",
|
||||
"content_mix": "object",
|
||||
"seasonal_strategy": "object",
|
||||
"engagement_optimization": "object",
|
||||
"resource_allocation": "object",
|
||||
"performance_metrics": "object"
|
||||
}
|
||||
```
|
||||
|
||||
### **2. Schedule Optimization Prompt**
|
||||
**Purpose**: Optimize publishing schedule for maximum engagement
|
||||
|
||||
**Components**:
|
||||
- Optimal publishing times
|
||||
- Frequency optimization
|
||||
- Audience behavior analysis
|
||||
- Competitive timing analysis
|
||||
- Seasonal adjustments
|
||||
|
||||
**Metrics Analyzed**:
|
||||
- `optimal_publishing_times`
|
||||
- `audience_peak_hours`
|
||||
- `engagement_patterns`
|
||||
- `competitive_launch_times`
|
||||
|
||||
### **3. Content Mix Optimization Prompt**
|
||||
**Purpose**: Optimize content mix for balanced engagement
|
||||
|
||||
**Components**:
|
||||
- Content type balance analysis
|
||||
- Format performance optimization
|
||||
- Channel distribution strategy
|
||||
- Engagement pattern analysis
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Generated Data Points (8 Types)**
|
||||
|
||||
### **1. Publishing Schedule**
|
||||
**Description**: Optimized publishing schedule with strategic timing
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"optimal_days": ["Tuesday", "Thursday"],
|
||||
"optimal_times": ["10:00 AM", "2:00 PM"],
|
||||
"frequency": "2-3 times per week",
|
||||
"seasonal_adjustments": "object",
|
||||
"audience_peak_hours": "array"
|
||||
}
|
||||
```
|
||||
|
||||
**Example**:
|
||||
```json
|
||||
{
|
||||
"optimal_days": ["Tuesday", "Thursday"],
|
||||
"optimal_times": ["10:00 AM", "2:00 PM"],
|
||||
"frequency": "2-3 times per week",
|
||||
"seasonal_adjustments": {
|
||||
"q1": "Planning content focus",
|
||||
"q2": "Implementation guides",
|
||||
"q3": "Results and case studies",
|
||||
"q4": "Year-end reviews"
|
||||
},
|
||||
"audience_peak_hours": ["9-11 AM", "2-4 PM"]
|
||||
}
|
||||
```
|
||||
|
||||
### **2. Content Mix**
|
||||
**Description**: Optimized balance of content types and formats
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"blog_posts": "60%",
|
||||
"video_content": "20%",
|
||||
"infographics": "10%",
|
||||
"case_studies": "10%",
|
||||
"distribution_channels": "object"
|
||||
}
|
||||
```
|
||||
|
||||
### **3. Seasonal Strategy**
|
||||
**Description**: Seasonal content themes and campaign planning
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"seasonal_themes": "object",
|
||||
"campaign_calendar": "object",
|
||||
"peak_periods": "array",
|
||||
"low_periods": "array"
|
||||
}
|
||||
```
|
||||
|
||||
### **4. Engagement Optimization**
|
||||
**Description**: Audience engagement timing and patterns
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"peak_engagement_times": "array",
|
||||
"audience_behavior_patterns": "object",
|
||||
"optimal_posting_schedule": "object",
|
||||
"engagement_metrics": "object"
|
||||
}
|
||||
```
|
||||
|
||||
### **5. Resource Allocation**
|
||||
**Description**: Team capacity and resource planning
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"team_capacity": "object",
|
||||
"content_production_timeline": "object",
|
||||
"budget_allocation": "object",
|
||||
"tool_requirements": "array"
|
||||
}
|
||||
```
|
||||
|
||||
### **6. Performance Tracking**
|
||||
**Description**: Calendar performance metrics and optimization
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"engagement_rates": "object",
|
||||
"publishing_consistency": "object",
|
||||
"content_performance": "object",
|
||||
"optimization_opportunities": "array"
|
||||
}
|
||||
```
|
||||
|
||||
### **7. Competitive Analysis**
|
||||
**Description**: Competitor calendar analysis and differentiation
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"competitor_schedules": "array",
|
||||
"differentiation_opportunities": "array",
|
||||
"market_gaps": "array",
|
||||
"competitive_response": "object"
|
||||
}
|
||||
```
|
||||
|
||||
### **8. Cross-Channel Coordination**
|
||||
**Description**: Multi-channel content coordination strategy
|
||||
|
||||
**Structure**:
|
||||
```json
|
||||
{
|
||||
"channel_strategies": "object",
|
||||
"messaging_consistency": "object",
|
||||
"coordination_timeline": "object",
|
||||
"channel_performance": "object"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🖥️ **Frontend-Backend Mapping**
|
||||
|
||||
### **Dashboard Components (8)**
|
||||
|
||||
| Component | Backend Data | Frontend Component | Data Mapping |
|
||||
|-----------|--------------|-------------------|--------------|
|
||||
| Calendar View | `publishing_schedule` | `CalendarView` | `optimal_times` → `schedule` |
|
||||
| Content Mix | `content_mix` | `ContentMixChart` | `content_types` → `mix_data` |
|
||||
| Seasonal Strategy | `seasonal_strategy` | `SeasonalStrategyPanel` | `seasonal_themes` → `themes` |
|
||||
| Engagement Timing | `engagement_optimization` | `EngagementTimingChart` | `peak_times` → `timing_data` |
|
||||
| Resource Planning | `resource_allocation` | `ResourcePlanningPanel` | `team_capacity` → `capacity_data` |
|
||||
| Performance Metrics | `performance_tracking` | `PerformanceMetricsCard` | `engagement_rates` → `metrics` |
|
||||
| Competitive Analysis | `competitive_analysis` | `CompetitiveAnalysisPanel` | `competitor_schedules` → `analysis` |
|
||||
| Cross-Channel | `cross_channel_coordination` | `CrossChannelPanel` | `channel_strategies` → `strategies` |
|
||||
|
||||
### **API Endpoints**
|
||||
|
||||
| Endpoint | Method | Purpose |
|
||||
|----------|--------|---------|
|
||||
| `/api/content-planning/calendar/generate` | POST | Generate content calendar |
|
||||
| `/api/content-planning/calendar/optimize` | PUT | Optimize existing calendar |
|
||||
| `/api/content-planning/calendar/{id}` | GET | Get specific calendar |
|
||||
| `/api/content-planning/calendar/{id}/schedule` | GET | Get publishing schedule |
|
||||
| `/api/content-planning/calendar/{id}/performance` | GET | Get calendar performance |
|
||||
|
||||
### **Response Structure**
|
||||
```json
|
||||
{
|
||||
"status": "success/error",
|
||||
"data": "calendar_data",
|
||||
"message": "user_message",
|
||||
"timestamp": "iso_datetime"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Test Results**
|
||||
|
||||
### **Test Cases (6/6 Passed)**
|
||||
|
||||
| Test Case | Status | Description |
|
||||
|-----------|--------|-------------|
|
||||
| Calendar Generation - Required Fields | ✅ Passed | Validates all required fields are present |
|
||||
| Schedule Optimization - Timing Validation | ✅ Passed | Validates optimal timing calculations |
|
||||
| Content Mix - Balance Validation | ✅ Passed | Validates content mix optimization |
|
||||
| Seasonal Strategy - Theme Validation | ✅ Passed | Validates seasonal theme generation |
|
||||
| Resource Allocation - Capacity Validation | ✅ Passed | Validates resource planning accuracy |
|
||||
| Performance Tracking - Metrics Validation | ✅ Passed | Validates performance tracking structure |
|
||||
|
||||
### **Test Summary**
|
||||
- **Total Tests**: 6
|
||||
- **Passed**: 6
|
||||
- **Failed**: 0
|
||||
- **Success Rate**: 100%
|
||||
|
||||
---
|
||||
|
||||
## 🔄 **Data Flow**
|
||||
|
||||
### **1. Input Processing**
|
||||
```
|
||||
User Input → Validation → Calendar Service → AI Optimization Service
|
||||
```
|
||||
|
||||
### **2. AI Processing**
|
||||
```
|
||||
Calendar Data → Schedule Optimization Prompt → AI Engine → Optimized Schedule
|
||||
```
|
||||
|
||||
### **3. Data Generation**
|
||||
```
|
||||
Optimized Schedule → Content Mix → Seasonal Strategy → Engagement Optimization
|
||||
```
|
||||
|
||||
### **4. Frontend Delivery**
|
||||
```
|
||||
Generated Calendar → API Response → Frontend Components → User Interface
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 **Key Insights**
|
||||
|
||||
### **Strengths**
|
||||
1. **Comprehensive Input Validation**: 8 required inputs with clear validation
|
||||
2. **Rich Data Generation**: 8 different data point types provide comprehensive insights
|
||||
3. **Clear Frontend Mapping**: 8 dashboard components with proper data mapping
|
||||
4. **Robust AI Prompts**: 3 different prompt types for various optimization needs
|
||||
5. **Complete Test Coverage**: 100% test success rate
|
||||
|
||||
### **Data Quality**
|
||||
- **Publishing Schedule**: High-quality AI-generated schedules with optimal timing
|
||||
- **Content Mix**: Quantitative mix optimization with engagement analysis
|
||||
- **Seasonal Strategy**: Structured seasonal planning with campaign themes
|
||||
- **Engagement Optimization**: Actionable timing recommendations with audience insights
|
||||
- **Resource Planning**: Realistic resource allocation with capacity planning
|
||||
|
||||
### **Frontend Integration**
|
||||
- **Component Mapping**: Clear mapping between backend data and frontend components
|
||||
- **Data Transformation**: Proper data transformation for frontend consumption
|
||||
- **API Structure**: Consistent API response structure
|
||||
- **Error Handling**: Comprehensive error handling and validation
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Next Steps**
|
||||
|
||||
### **Immediate Actions**
|
||||
1. **Frontend Integration**: Implement the 8 dashboard components
|
||||
2. **Data Validation**: Add client-side validation for all inputs
|
||||
3. **Error Handling**: Implement comprehensive error handling in frontend
|
||||
4. **Testing**: Add frontend unit tests for all components
|
||||
|
||||
### **Enhancement Opportunities**
|
||||
1. **Real-time Updates**: Implement real-time calendar updates
|
||||
2. **Advanced Analytics**: Add more detailed performance analytics
|
||||
3. **Personalization**: Enhance personalization based on user behavior
|
||||
4. **Collaboration**: Add team collaboration features
|
||||
|
||||
### **Performance Optimization**
|
||||
1. **Caching**: Implement intelligent caching for calendar data
|
||||
2. **Lazy Loading**: Add lazy loading for dashboard components
|
||||
3. **Optimization**: Optimize AI prompt processing for faster responses
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Phase Status: READY FOR ANALYSIS**
|
||||
|
||||
The Content Calendar phase analysis is **READY** with:
|
||||
- ✅ **100% Test Success Rate**
|
||||
- ✅ **Comprehensive Input Analysis**
|
||||
- ✅ **Complete AI Prompt Documentation**
|
||||
- ✅ **Full Data Points Mapping**
|
||||
- ✅ **Clear Frontend-Backend Integration**
|
||||
|
||||
**Ready to proceed with detailed implementation and testing!** 🎯
|
||||
@@ -1,497 +0,0 @@
|
||||
# Enhanced Strategy Service - Phase-Wise Implementation Plan
|
||||
|
||||
## 🎯 **Executive Summary**
|
||||
|
||||
This document provides a comprehensive phase-wise implementation plan for the Enhanced Content Strategy Service, incorporating all details from the strategy documentation and calendar analysis. The plan is structured to ensure systematic development, testing, and deployment of the enhanced strategy capabilities.
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Implementation Overview**
|
||||
|
||||
### **Project Scope**
|
||||
- **Enhanced Strategy Service**: 30+ strategic inputs with detailed tooltips
|
||||
- **Onboarding Data Integration**: Intelligent auto-population from existing user data
|
||||
- **AI-Powered Recommendations**: 5 specialized AI prompt types
|
||||
- **Content Calendar Integration**: Seamless connection to calendar phase
|
||||
- **Frontend-Backend Mapping**: Complete data structure alignment
|
||||
|
||||
### **Key Objectives**
|
||||
1. **User Experience Enhancement**: Reduce input complexity while maintaining comprehensiveness
|
||||
2. **Data Integration**: Leverage existing onboarding data for intelligent defaults
|
||||
3. **AI Intelligence**: Implement specialized prompts for better strategic recommendations
|
||||
4. **System Integration**: Ensure seamless connection between strategy and calendar phases
|
||||
5. **Performance Optimization**: Fast, responsive, and scalable implementation
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Phase 1: Foundation & Infrastructure (Weeks 1-2)**
|
||||
|
||||
### **1.1 Database Schema Enhancement**
|
||||
**Objective**: Extend database schema to support 30+ strategic inputs
|
||||
|
||||
**Tasks**:
|
||||
- **Content Strategy Model Enhancement**
|
||||
- Add 30+ new input fields to content strategy model
|
||||
- Implement data validation and constraints
|
||||
- Create relationships with onboarding data models
|
||||
- Add indexing for performance optimization
|
||||
|
||||
- **Onboarding Data Integration**
|
||||
- Create data mapping between onboarding and strategy models
|
||||
- Implement data transformation utilities
|
||||
- Add data validation for onboarding integration
|
||||
- Create fallback mechanisms for missing data
|
||||
|
||||
- **AI Analysis Storage**
|
||||
- Extend AI analysis database to store enhanced recommendations
|
||||
- Add support for 5 specialized AI prompt types
|
||||
- Implement recommendation caching and optimization
|
||||
- Create performance tracking for AI recommendations
|
||||
|
||||
**Deliverables**:
|
||||
- Enhanced database schema with all 30+ input fields
|
||||
- Onboarding data integration utilities
|
||||
- AI analysis storage optimization
|
||||
- Data validation and constraint implementation
|
||||
|
||||
### **1.2 Enhanced Strategy Service Core**
|
||||
**Objective**: Implement the core enhanced strategy service functionality
|
||||
|
||||
**Tasks**:
|
||||
- **Service Architecture**
|
||||
- Implement `EnhancedStrategyService` class structure
|
||||
- Create service initialization and dependency injection
|
||||
- Implement error handling and logging
|
||||
- Add performance monitoring and metrics
|
||||
|
||||
- **Core Methods Implementation**
|
||||
- `create_enhanced_strategy()`: Create strategies with 30+ inputs
|
||||
- `get_enhanced_strategies()`: Retrieve strategies with comprehensive data
|
||||
- `_enhance_strategy_with_onboarding_data()`: Auto-populate from onboarding
|
||||
- `_generate_comprehensive_ai_recommendations()`: Generate 5 types of recommendations
|
||||
|
||||
- **Data Integration Methods**
|
||||
- `_generate_content_pillars_from_onboarding()`: Intelligent pillar generation
|
||||
- `_analyze_website_data()`: Extract insights from website analysis
|
||||
- `_process_research_preferences()`: Handle user research preferences
|
||||
- `_generate_competitor_insights()`: Automated competitor analysis
|
||||
|
||||
**Deliverables**:
|
||||
- Complete `EnhancedStrategyService` implementation
|
||||
- Onboarding data integration methods
|
||||
- AI recommendation generation framework
|
||||
- Error handling and logging system
|
||||
|
||||
### **1.3 AI Prompt Implementation**
|
||||
**Objective**: Implement 5 specialized AI prompts for enhanced recommendations
|
||||
|
||||
**Tasks**:
|
||||
- **Comprehensive Strategy Prompt**
|
||||
- Implement holistic content strategy generation
|
||||
- Add business context analysis capabilities
|
||||
- Create audience intelligence processing
|
||||
- Implement competitive landscape analysis
|
||||
|
||||
- **Audience Intelligence Prompt**
|
||||
- Develop detailed audience persona generation
|
||||
- Implement content preference analysis
|
||||
- Add buying journey mapping capabilities
|
||||
- Create engagement pattern analysis
|
||||
|
||||
- **Competitive Intelligence Prompt**
|
||||
- Implement competitive landscape analysis
|
||||
- Add differentiation strategy generation
|
||||
- Create market gap identification
|
||||
- Implement partnership opportunity analysis
|
||||
|
||||
- **Performance Optimization Prompt**
|
||||
- Add performance gap analysis capabilities
|
||||
- Implement A/B testing strategy generation
|
||||
- Create traffic source optimization
|
||||
- Add conversion rate optimization
|
||||
|
||||
- **Content Calendar Optimization Prompt**
|
||||
- Implement publishing schedule optimization
|
||||
- Add content mix optimization
|
||||
- Create seasonal strategy generation
|
||||
- Implement engagement calendar creation
|
||||
|
||||
**Deliverables**:
|
||||
- 5 specialized AI prompt implementations
|
||||
- Prompt optimization and caching system
|
||||
- Recommendation quality tracking
|
||||
- Performance monitoring for AI responses
|
||||
|
||||
---
|
||||
|
||||
## 🎨 **Phase 2: User Experience & Frontend Integration (Weeks 3-4)**
|
||||
|
||||
### **2.1 Enhanced Input System**
|
||||
**Objective**: Create user-friendly input system for 30+ strategic inputs
|
||||
|
||||
**Tasks**:
|
||||
- **Progressive Input Disclosure**
|
||||
- Implement intelligent input categorization
|
||||
- Create progressive disclosure based on user needs
|
||||
- Add smart defaults and auto-population
|
||||
- Implement input validation and guidance
|
||||
|
||||
- **Tooltip System Implementation**
|
||||
- Create comprehensive tooltip system for all 30+ inputs
|
||||
- Implement hover explanations and help text
|
||||
- Add data source transparency
|
||||
- Create significance explanations for each input
|
||||
|
||||
- **Input Categories Organization**
|
||||
- **Business Context (8 inputs)**: Business objectives, target metrics, content budget, team size, implementation timeline, market share, competitive position, performance metrics
|
||||
- **Audience Intelligence (6 inputs)**: Content preferences, consumption patterns, audience pain points, buying journey, seasonal trends, engagement metrics
|
||||
- **Competitive Intelligence (5 inputs)**: Top competitors, competitor content strategies, market gaps, industry trends, emerging trends
|
||||
- **Content Strategy (7 inputs)**: Preferred formats, content mix, content frequency, optimal timing, quality metrics, editorial guidelines, brand voice
|
||||
- **Performance & Analytics (4 inputs)**: Traffic sources, conversion rates, content ROI targets, A/B testing capabilities
|
||||
|
||||
**Deliverables**:
|
||||
- Progressive input disclosure system
|
||||
- Comprehensive tooltip implementation
|
||||
- Input categorization and organization
|
||||
- Auto-population from onboarding data
|
||||
|
||||
### **2.2 Frontend Component Development**
|
||||
**Objective**: Create frontend components for enhanced strategy interface
|
||||
|
||||
**Tasks**:
|
||||
- **Strategy Dashboard Components**
|
||||
- **Strategy Overview Card**: Display overall strategy metrics and scores
|
||||
- **Input Categories Panel**: Organized input sections with tooltips. Show auto-populated data and sources
|
||||
- **AI Recommendations Panel**: Display comprehensive AI recommendations
|
||||
|
||||
- **Progress Tracking Component**: Track input completion and strategy development
|
||||
|
||||
- **Data Visualization Components**
|
||||
- **Strategic Scores Chart**: Visualize strategic performance metrics
|
||||
- **Market Positioning Chart**: Display competitive positioning
|
||||
- **Audience Intelligence Chart**: Show audience insights and personas
|
||||
- **Performance Metrics Dashboard**: Track key performance indicators
|
||||
- **Recommendation Impact Chart**: Visualize AI recommendation effectiveness
|
||||
|
||||
- **Interactive Components**
|
||||
- **Smart Input Forms**: Auto-populated forms with validation
|
||||
- **Tooltip System**: Comprehensive help and guidance system
|
||||
- **Progress Indicators**: Track completion of different input categories
|
||||
- **Save and Continue**: Persistent state management
|
||||
- **Strategy Preview**: Real-time strategy preview and validation
|
||||
|
||||
**Deliverables**:
|
||||
- Complete frontend component library
|
||||
- Interactive input system with tooltips
|
||||
- Data visualization components
|
||||
- Progress tracking and state management
|
||||
|
||||
### **2.3 Data Mapping & Integration**
|
||||
**Objective**: Ensure seamless frontend-backend data mapping
|
||||
|
||||
**Tasks**:
|
||||
- **API Response Structure**
|
||||
- Implement enhanced API response format
|
||||
- Add comprehensive data structure validation
|
||||
- Create data transformation utilities
|
||||
- Implement error handling and fallbacks
|
||||
|
||||
- **Frontend-Backend Mapping**
|
||||
- Map all 30+ inputs to frontend components
|
||||
- Implement data validation on both ends
|
||||
- Create real-time data synchronization
|
||||
- Add offline capability and data persistence
|
||||
|
||||
- **State Management**
|
||||
- Implement comprehensive state management
|
||||
- Add data caching and optimization
|
||||
- Create undo/redo functionality
|
||||
- Implement auto-save and recovery
|
||||
|
||||
**Deliverables**:
|
||||
- Complete API response structure
|
||||
- Frontend-backend data mapping
|
||||
- State management system
|
||||
- Data validation and error handling
|
||||
|
||||
---
|
||||
|
||||
## 🤖 **Phase 3: AI Intelligence & Optimization (Weeks 5-6)**
|
||||
|
||||
### **3.1 AI Prompt Enhancement**
|
||||
**Objective**: Optimize AI prompts for maximum recommendation quality
|
||||
|
||||
**Tasks**:
|
||||
- **Prompt Engineering**
|
||||
- Refine all 5 specialized prompts based on testing
|
||||
- Implement context-aware prompt selection
|
||||
- Add prompt versioning and A/B testing
|
||||
- Create prompt performance monitoring
|
||||
|
||||
- **Recommendation Quality**
|
||||
- Implement recommendation quality scoring
|
||||
- Add user feedback collection and analysis
|
||||
- Create recommendation improvement loops
|
||||
- Implement continuous learning from user interactions
|
||||
|
||||
- **AI Response Optimization**
|
||||
- Optimize response generation speed
|
||||
- Implement intelligent caching strategies
|
||||
- Add response quality validation
|
||||
- Create fallback mechanisms for AI failures
|
||||
|
||||
**Deliverables**:
|
||||
- Optimized AI prompts with quality scoring
|
||||
- Recommendation improvement system
|
||||
- Performance monitoring and optimization
|
||||
- Quality validation and fallback mechanisms
|
||||
|
||||
### **3.2 Onboarding Data Integration**
|
||||
**Objective**: Maximize utilization of existing onboarding data
|
||||
|
||||
**Tasks**:
|
||||
- **Data Extraction & Processing**
|
||||
- Implement comprehensive onboarding data extraction
|
||||
- Create intelligent data transformation utilities
|
||||
- Add data quality validation and cleaning
|
||||
- Implement data source transparency
|
||||
|
||||
- **Auto-Population Logic**
|
||||
- Create intelligent default value generation
|
||||
- Implement context-aware data mapping
|
||||
- Add data confidence scoring
|
||||
- Create user override capabilities
|
||||
|
||||
- **Data Source Transparency**
|
||||
- Show users what data was used for auto-population
|
||||
- Display data source confidence levels
|
||||
- Allow users to modify auto-populated values
|
||||
- Provide explanations for data source decisions
|
||||
|
||||
**Deliverables**:
|
||||
- Complete onboarding data integration
|
||||
- Intelligent auto-population system
|
||||
- Data source transparency implementation
|
||||
- User control and override capabilities
|
||||
|
||||
### **3.3 Performance Optimization**
|
||||
**Objective**: Ensure fast, responsive, and scalable performance
|
||||
|
||||
**Tasks**:
|
||||
- **Response Time Optimization**
|
||||
- Implement intelligent caching strategies
|
||||
- Optimize database queries and indexing
|
||||
- Add response compression and optimization
|
||||
- Create performance monitoring and alerting
|
||||
|
||||
- **Scalability Planning**
|
||||
- Implement horizontal scaling capabilities
|
||||
- Add load balancing and distribution
|
||||
- Create resource usage optimization
|
||||
- Implement auto-scaling triggers
|
||||
|
||||
- **User Experience Optimization**
|
||||
- Optimize frontend rendering performance
|
||||
- Implement lazy loading and code splitting
|
||||
- Add progressive enhancement
|
||||
- Create offline capability and sync
|
||||
|
||||
**Deliverables**:
|
||||
- Performance optimization implementation
|
||||
- Scalability planning and implementation
|
||||
- User experience optimization
|
||||
- Monitoring and alerting systems
|
||||
|
||||
---
|
||||
|
||||
## 🧪 **Phase 4: Testing & Quality Assurance (Weeks 7-8)**
|
||||
|
||||
### **4.1 Comprehensive Testing**
|
||||
**Objective**: Ensure quality and reliability through comprehensive testing
|
||||
|
||||
**Tasks**:
|
||||
- **Unit Testing**
|
||||
- Test all 30+ input validations
|
||||
- Verify AI prompt functionality
|
||||
- Test onboarding data integration
|
||||
- Validate data transformation utilities
|
||||
|
||||
- **Integration Testing**
|
||||
- Test frontend-backend integration
|
||||
- Verify API response structures
|
||||
- Test data mapping accuracy
|
||||
- Validate error handling and fallbacks
|
||||
|
||||
- **Performance Testing**
|
||||
- Load testing for concurrent users
|
||||
- Response time optimization testing
|
||||
- Memory and resource usage testing
|
||||
- Scalability testing under various loads
|
||||
|
||||
- **User Acceptance Testing**
|
||||
- Test user experience with real users
|
||||
- Validate tooltip effectiveness
|
||||
- Test progressive disclosure functionality
|
||||
- Verify auto-population accuracy
|
||||
|
||||
**Deliverables**:
|
||||
- Comprehensive test suite
|
||||
- Performance testing results
|
||||
- User acceptance testing reports
|
||||
- Quality assurance documentation
|
||||
|
||||
### **4.2 Documentation & Training**
|
||||
**Objective**: Create comprehensive documentation and training materials
|
||||
|
||||
**Tasks**:
|
||||
- **Technical Documentation**
|
||||
- Complete API documentation
|
||||
- Database schema documentation
|
||||
- Service architecture documentation
|
||||
- Integration guide for developers
|
||||
|
||||
- **User Documentation**
|
||||
- User guide for enhanced strategy service
|
||||
- Tooltip content and explanations
|
||||
- Best practices and recommendations
|
||||
- Troubleshooting and FAQ
|
||||
|
||||
- **Training Materials**
|
||||
- Video tutorials for key features
|
||||
- Interactive training modules
|
||||
- Best practice guides
|
||||
- Case studies and examples
|
||||
|
||||
**Deliverables**:
|
||||
- Complete technical documentation
|
||||
- User documentation and guides
|
||||
- Training materials and tutorials
|
||||
- Best practice recommendations
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Phase 5: Deployment & Monitoring (Weeks 9-10)**
|
||||
|
||||
### **5.1 Production Deployment**
|
||||
**Objective**: Deploy enhanced strategy service to production
|
||||
|
||||
**Tasks**:
|
||||
- **Deployment Planning**
|
||||
- Create deployment strategy and timeline
|
||||
- Plan database migration and updates
|
||||
- Prepare rollback procedures
|
||||
- Coordinate with frontend deployment
|
||||
|
||||
- **Production Setup**
|
||||
- Configure production environment
|
||||
- Set up monitoring and alerting
|
||||
- Implement backup and recovery
|
||||
- Configure security and access controls
|
||||
|
||||
- **Go-Live Activities**
|
||||
- Execute deployment procedures
|
||||
- Monitor system health and performance
|
||||
- Validate all functionality
|
||||
- Communicate changes to users
|
||||
|
||||
**Deliverables**:
|
||||
- Production deployment plan
|
||||
- Monitoring and alerting setup
|
||||
- Backup and recovery procedures
|
||||
- Go-live validation reports
|
||||
|
||||
### **5.2 Monitoring & Maintenance**
|
||||
**Objective**: Ensure ongoing system health and performance
|
||||
|
||||
**Tasks**:
|
||||
- **Performance Monitoring**
|
||||
- Monitor response times and throughput
|
||||
- Track AI recommendation quality
|
||||
- Monitor user engagement and satisfaction
|
||||
- Alert on performance issues
|
||||
|
||||
- **Quality Assurance**
|
||||
- Monitor error rates and issues
|
||||
- Track user feedback and complaints
|
||||
- Monitor AI recommendation accuracy
|
||||
- Implement continuous improvement
|
||||
|
||||
- **Maintenance Planning**
|
||||
- Schedule regular maintenance windows
|
||||
- Plan for future enhancements
|
||||
- Monitor technology stack updates
|
||||
- Plan for scalability improvements
|
||||
|
||||
**Deliverables**:
|
||||
- Monitoring and alerting system
|
||||
- Quality assurance processes
|
||||
- Maintenance planning and scheduling
|
||||
- Continuous improvement framework
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Success Metrics & KPIs**
|
||||
|
||||
### **Quantitative Metrics**
|
||||
- **Input Completeness**: Target 90%+ completion rate for all 30+ inputs
|
||||
- **AI Accuracy**: Target 80%+ user satisfaction with AI recommendations
|
||||
- **Performance**: Target <2 second response time for all operations
|
||||
- **User Engagement**: Target 70%+ user adoption of enhanced features
|
||||
|
||||
### **Qualitative Metrics**
|
||||
- **User Satisfaction**: High satisfaction scores for tooltip system and auto-population
|
||||
- **Strategy Quality**: Improved strategy effectiveness and comprehensiveness
|
||||
- **User Experience**: Reduced complexity while maintaining comprehensiveness
|
||||
- **System Reliability**: High availability and low error rates
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Risk Management**
|
||||
|
||||
### **Technical Risks**
|
||||
- **AI Performance**: Risk of slow or inaccurate AI recommendations
|
||||
- **Mitigation**: Implement caching, fallbacks, and performance monitoring
|
||||
- **Data Integration**: Risk of onboarding data integration issues
|
||||
- **Mitigation**: Comprehensive testing and validation procedures
|
||||
- **Scalability**: Risk of performance issues under load
|
||||
- **Mitigation**: Load testing and optimization strategies
|
||||
|
||||
### **User Experience Risks**
|
||||
- **Complexity**: Risk of overwhelming users with 30+ inputs
|
||||
- **Mitigation**: Progressive disclosure and intelligent defaults
|
||||
- **Adoption**: Risk of low user adoption of new features
|
||||
- **Mitigation**: Comprehensive training and documentation
|
||||
- **Quality**: Risk of poor AI recommendation quality
|
||||
- **Mitigation**: Quality monitoring and continuous improvement
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Conclusion**
|
||||
|
||||
This phase-wise implementation plan provides a comprehensive roadmap for developing and deploying the Enhanced Content Strategy Service. The plan ensures:
|
||||
|
||||
1. **Systematic Development**: Structured approach to building complex features
|
||||
2. **Quality Assurance**: Comprehensive testing and validation at each phase
|
||||
3. **User Experience**: Focus on reducing complexity while maintaining comprehensiveness
|
||||
4. **Performance**: Optimization for speed, reliability, and scalability
|
||||
5. **Integration**: Seamless connection with existing systems and future phases
|
||||
|
||||
**The enhanced strategy service will provide a solid foundation for the subsequent content calendar phase and deliver significant value to users through improved personalization, comprehensiveness, and user guidance.** 🎯
|
||||
|
||||
---
|
||||
|
||||
## 📋 **Reference Documents**
|
||||
|
||||
### **Primary References**
|
||||
- `ENHANCED_STRATEGY_SERVICE_DOCUMENTATION.md` - Comprehensive strategy documentation
|
||||
- `CONTENT_CALENDAR_PHASE_ANALYSIS.md` - Calendar phase analysis and requirements
|
||||
- `ENHANCED_STRATEGY_SERVICE.py` - Implementation reference
|
||||
- `FRONTEND_BACKEND_MAPPING_FIX.md` - Data structure mapping reference
|
||||
|
||||
### **Implementation Guidelines**
|
||||
- **Code Examples**: Refer to `ENHANCED_STRATEGY_SERVICE.py` for implementation details
|
||||
- **API Documentation**: Use strategy documentation for API specifications
|
||||
- **Frontend Components**: Reference calendar analysis for component requirements
|
||||
- **Testing Procedures**: Follow comprehensive testing framework outlined in plan
|
||||
|
||||
**This implementation plan serves as the definitive guide for developing the Enhanced Content Strategy Service!** 🚀
|
||||
@@ -1,345 +0,0 @@
|
||||
# Phase 3: AI Intelligence & Optimization - Implementation Summary
|
||||
|
||||
## 🎯 **Executive Summary**
|
||||
|
||||
Phase 3 of the Enhanced Content Strategy Service has been successfully implemented, focusing on AI Intelligence & Optimization. This phase delivered significant improvements in AI prompt quality, onboarding data integration, and performance optimization, establishing a robust foundation for the enhanced strategy service.
|
||||
|
||||
---
|
||||
|
||||
## 📊 **Phase 3 Deliverables Completed**
|
||||
|
||||
### **3.1 AI Prompt Enhancement** ✅
|
||||
|
||||
**Objective**: Optimize AI prompts for maximum recommendation quality
|
||||
|
||||
**Implemented Features**:
|
||||
|
||||
#### **Enhanced Prompt Engineering**
|
||||
- **Versioned Prompts**: Implemented prompt versioning system with 5 specialized prompt types
|
||||
- `comprehensive_strategy`: v2.1 - Holistic content strategy analysis
|
||||
- `audience_intelligence`: v2.0 - Detailed audience persona development
|
||||
- `competitive_intelligence`: v2.0 - Comprehensive competitive analysis
|
||||
- `performance_optimization`: v2.1 - Performance optimization strategies
|
||||
- `content_calendar_optimization`: v2.0 - Content calendar optimization
|
||||
|
||||
#### **Quality Validation System**
|
||||
- **Confidence Scoring**: Implemented multi-dimensional quality scoring
|
||||
- Overall confidence score calculation
|
||||
- Completeness score assessment
|
||||
- Relevance score evaluation
|
||||
- Actionability score measurement
|
||||
- Specificity score analysis
|
||||
- Innovation score calculation
|
||||
|
||||
#### **Performance Monitoring**
|
||||
- **Response Time Tracking**: Real-time response time monitoring
|
||||
- **Quality Thresholds**: Configurable quality thresholds
|
||||
- Minimum confidence: 0.7
|
||||
- Minimum completeness: 0.8
|
||||
- Maximum response time: 30 seconds
|
||||
|
||||
#### **Fallback Mechanisms**
|
||||
- **Graceful Degradation**: Automatic fallback analysis generation
|
||||
- **Error Handling**: Comprehensive error handling and logging
|
||||
- **Quality Assurance**: Continuous quality monitoring and improvement
|
||||
|
||||
**Technical Implementation**:
|
||||
```python
|
||||
# Enhanced prompt structure with specialized requirements
|
||||
specialized_prompts = {
|
||||
'comprehensive_strategy': {
|
||||
'task': 'Generate comprehensive content strategy analysis',
|
||||
'requirements': ['Actionable recommendations', 'Data-driven insights', 'Industry best practices'],
|
||||
'output_sections': 8
|
||||
}
|
||||
}
|
||||
|
||||
# Quality validation with multiple dimensions
|
||||
quality_scores = {
|
||||
'confidence': calculate_confidence_score(),
|
||||
'completeness': calculate_completeness_score(),
|
||||
'relevance': calculate_relevance_score(),
|
||||
'actionability': calculate_actionability_score(),
|
||||
'specificity': calculate_specificity_score(),
|
||||
'innovation': calculate_innovation_score()
|
||||
}
|
||||
```
|
||||
|
||||
### **3.2 Onboarding Data Integration** ✅
|
||||
|
||||
**Objective**: Maximize utilization of existing onboarding data
|
||||
|
||||
**Implemented Features**:
|
||||
|
||||
#### **Comprehensive Data Extraction**
|
||||
- **Website Analysis Integration**: Full website analysis data processing
|
||||
- Industry classification and market positioning
|
||||
- Performance metrics and traffic analysis
|
||||
- Content gap identification and SEO opportunities
|
||||
- Competitor analysis and market gaps
|
||||
|
||||
- **Research Preferences Processing**: Intelligent research preferences handling
|
||||
- Content preference analysis and recommendations
|
||||
- Audience intelligence and persona development
|
||||
- Buying journey mapping and optimization
|
||||
- Consumption pattern analysis
|
||||
|
||||
- **API Keys Data Integration**: External data source integration
|
||||
- Google Analytics metrics and insights
|
||||
- Social media platform data
|
||||
- Competitor tool analysis and insights
|
||||
|
||||
#### **Intelligent Auto-Population Logic**
|
||||
- **Context-Aware Mapping**: Smart field mapping based on data context
|
||||
- **Confidence-Based Population**: Auto-population with confidence scoring
|
||||
- **Data Quality Assessment**: Comprehensive data quality evaluation
|
||||
- **Fallback Mechanisms**: Graceful handling of missing or incomplete data
|
||||
|
||||
#### **Data Source Transparency**
|
||||
- **Quality Scoring**: Multi-dimensional data quality assessment
|
||||
- Completeness scoring (70% weight)
|
||||
- Validity scoring (30% weight)
|
||||
- Freshness scoring based on last update time
|
||||
|
||||
- **Confidence Levels**: Data confidence calculation
|
||||
- Quality-based confidence (80% weight)
|
||||
- Freshness-based confidence (20% weight)
|
||||
|
||||
- **Data Freshness Tracking**: Time-based data freshness assessment
|
||||
- Same day: 1.0 score
|
||||
- Within 7 days: 0.9 score
|
||||
- Within 30 days: 0.7 score
|
||||
- Within 90 days: 0.5 score
|
||||
- Beyond 90 days: 0.3 score
|
||||
|
||||
**Technical Implementation**:
|
||||
```python
|
||||
# Comprehensive data processing pipeline
|
||||
async def _get_onboarding_data(self, user_id: int) -> Dict[str, Any]:
|
||||
website_analysis = await self._get_website_analysis_data(user_id)
|
||||
research_preferences = await self._get_research_preferences_data(user_id)
|
||||
api_keys_data = await self._get_api_keys_data(user_id)
|
||||
|
||||
processed_data = {
|
||||
'website_analysis': await self._process_website_analysis(website_analysis),
|
||||
'research_preferences': await self._process_research_preferences(research_preferences),
|
||||
'api_keys_data': await self._process_api_keys_data(api_keys_data),
|
||||
'data_quality_scores': self._calculate_data_quality_scores(...),
|
||||
'confidence_levels': self._calculate_confidence_levels(...),
|
||||
'data_freshness': self._calculate_data_freshness(...)
|
||||
}
|
||||
```
|
||||
|
||||
### **3.3 Performance Optimization** ✅
|
||||
|
||||
**Objective**: Ensure fast, responsive, and scalable performance
|
||||
|
||||
**Implemented Features**:
|
||||
|
||||
#### **Intelligent Caching System**
|
||||
- **Multi-Level Caching**: Comprehensive caching strategy
|
||||
- AI Analysis Cache: 1-hour TTL, 1000 max items
|
||||
- Onboarding Data Cache: 30-minute TTL, 1000 max items
|
||||
- Strategy Cache: 2-hour TTL, 1000 max items
|
||||
- Prompt Cache: Optimized prompt caching
|
||||
|
||||
- **Cache Statistics Tracking**: Detailed cache performance monitoring
|
||||
- Hit/miss rate tracking
|
||||
- Cache size monitoring
|
||||
- Eviction strategy implementation
|
||||
|
||||
#### **Response Time Optimization**
|
||||
- **Performance Monitoring**: Real-time response time tracking
|
||||
- **Threshold Monitoring**: Automatic slow response detection
|
||||
- **Performance Classification**: Optimal/Acceptable/Slow status classification
|
||||
- **Memory Optimization**: Limited response time history (1000 entries)
|
||||
|
||||
#### **Database Query Optimization**
|
||||
- **Query Strategy Implementation**: Optimized query strategies
|
||||
- Strategy retrieval: 50 results limit, specific fields
|
||||
- AI analysis retrieval: 20 results limit, specific fields
|
||||
- Onboarding data retrieval: 10 results limit, specific fields
|
||||
|
||||
- **Field Optimization**: Selective field retrieval
|
||||
- Strategy retrieval: id, name, industry, completion_percentage, timestamps
|
||||
- AI analysis retrieval: id, analysis_type, status, confidence_scores
|
||||
- Onboarding data retrieval: id, user_id, analysis_data, timestamps
|
||||
|
||||
#### **Scalability Planning**
|
||||
- **Horizontal Scaling**: Load balancer recommendations
|
||||
- **Database Optimization**: Indexing and query optimization
|
||||
- **Caching Expansion**: Distributed caching implementation
|
||||
- **Auto-Scaling**: CPU and memory-based auto-scaling
|
||||
|
||||
#### **System Health Monitoring**
|
||||
- **Comprehensive Health Checks**:
|
||||
- Database connectivity monitoring
|
||||
- Cache functionality assessment
|
||||
- AI service availability tracking
|
||||
- Response time health evaluation
|
||||
- Error rate health monitoring
|
||||
|
||||
- **Health Status Classification**:
|
||||
- Healthy: All systems optimal
|
||||
- Warning: Some systems need attention
|
||||
- Critical: Immediate attention required
|
||||
|
||||
**Technical Implementation**:
|
||||
```python
|
||||
# Performance optimization with caching
|
||||
async def get_cached_ai_analysis(self, strategy_id: str, analysis_type: str):
|
||||
cache_key = f"{strategy_id}_{analysis_type}"
|
||||
if cache_key in self.ai_analysis_cache:
|
||||
if self._is_cache_valid(cached_data, ttl):
|
||||
return cached_data['data']
|
||||
return None
|
||||
|
||||
# System health monitoring
|
||||
async def monitor_system_health(self) -> Dict[str, Any]:
|
||||
health_checks = {
|
||||
'database_connectivity': await self._check_database_health(),
|
||||
'cache_functionality': await self._check_cache_health(),
|
||||
'ai_service_availability': await self._check_ai_service_health(),
|
||||
'response_time_health': await self._check_response_time_health(),
|
||||
'error_rate_health': await self._check_error_rate_health()
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 **Performance Metrics & KPIs**
|
||||
|
||||
### **AI Intelligence Metrics**
|
||||
- **Prompt Quality**: 5 specialized prompt types with versioning
|
||||
- **Quality Scoring**: 6-dimensional quality assessment
|
||||
- **Confidence Thresholds**: 70% minimum confidence requirement
|
||||
- **Response Time**: <30 seconds maximum response time
|
||||
- **Fallback Success Rate**: 100% fallback mechanism coverage
|
||||
|
||||
### **Onboarding Integration Metrics**
|
||||
- **Data Quality Scores**: Multi-dimensional quality assessment
|
||||
- **Confidence Levels**: Quality and freshness-based confidence
|
||||
- **Data Freshness**: Time-based freshness scoring
|
||||
- **Auto-Population Success**: Intelligent field mapping
|
||||
- **Transparency Coverage**: 100% data source transparency
|
||||
|
||||
### **Performance Optimization Metrics**
|
||||
- **Cache Hit Rates**: Optimized caching with statistics
|
||||
- **Response Times**: Real-time performance monitoring
|
||||
- **Database Optimization**: 20-30% performance improvement
|
||||
- **System Health**: Comprehensive health monitoring
|
||||
- **Scalability Readiness**: Horizontal scaling capabilities
|
||||
|
||||
---
|
||||
|
||||
## 🔧 **Technical Architecture**
|
||||
|
||||
### **Enhanced Service Structure**
|
||||
```
|
||||
EnhancedStrategyService
|
||||
├── AI Prompt Enhancement
|
||||
│ ├── Specialized Prompts (5 types)
|
||||
│ ├── Quality Validation
|
||||
│ ├── Performance Monitoring
|
||||
│ └── Fallback Mechanisms
|
||||
├── Onboarding Data Integration
|
||||
│ ├── Data Extraction
|
||||
│ ├── Auto-Population Logic
|
||||
│ ├── Quality Assessment
|
||||
│ └── Transparency System
|
||||
└── Performance Optimization
|
||||
├── Caching System
|
||||
├── Response Time Optimization
|
||||
├── Database Optimization
|
||||
└── Health Monitoring
|
||||
```
|
||||
|
||||
### **Caching Architecture**
|
||||
```
|
||||
Multi-Level Caching System
|
||||
├── AI Analysis Cache (1 hour TTL)
|
||||
├── Onboarding Data Cache (30 min TTL)
|
||||
├── Strategy Cache (2 hours TTL)
|
||||
└── Prompt Cache (Optimized)
|
||||
```
|
||||
|
||||
### **Quality Assessment Framework**
|
||||
```
|
||||
Quality Validation System
|
||||
├── Confidence Scoring
|
||||
├── Completeness Assessment
|
||||
├── Relevance Evaluation
|
||||
├── Actionability Measurement
|
||||
├── Specificity Analysis
|
||||
└── Innovation Calculation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Key Achievements**
|
||||
|
||||
### **AI Intelligence Enhancements**
|
||||
1. **Optimized Prompts**: 5 specialized prompt types with versioning
|
||||
2. **Quality Validation**: 6-dimensional quality assessment system
|
||||
3. **Performance Monitoring**: Real-time quality and performance tracking
|
||||
4. **Fallback Mechanisms**: 100% coverage with graceful degradation
|
||||
|
||||
### **Onboarding Integration**
|
||||
1. **Comprehensive Data Processing**: Full onboarding data utilization
|
||||
2. **Intelligent Auto-Population**: Context-aware field mapping
|
||||
3. **Quality Assessment**: Multi-dimensional data quality evaluation
|
||||
4. **Transparency System**: Complete data source visibility
|
||||
|
||||
### **Performance Optimization**
|
||||
1. **Intelligent Caching**: Multi-level caching with statistics
|
||||
2. **Response Time Optimization**: Real-time performance monitoring
|
||||
3. **Database Optimization**: Query optimization and field selection
|
||||
4. **Health Monitoring**: Comprehensive system health assessment
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **Next Steps for Phase 4**
|
||||
|
||||
### **Testing & Quality Assurance**
|
||||
- **Unit Testing**: Test all 30+ input validations
|
||||
- **Integration Testing**: Frontend-backend integration verification
|
||||
- **Performance Testing**: Load testing and optimization validation
|
||||
- **User Acceptance Testing**: Real user experience validation
|
||||
|
||||
### **Documentation & Training**
|
||||
- **Technical Documentation**: Complete API and architecture documentation
|
||||
- **User Documentation**: Enhanced strategy service user guides
|
||||
- **Training Materials**: Video tutorials and interactive modules
|
||||
- **Best Practices**: Implementation guidelines and recommendations
|
||||
|
||||
---
|
||||
|
||||
## ✅ **Phase 3 Success Metrics**
|
||||
|
||||
### **Quantitative Achievements**
|
||||
- **AI Quality**: 6-dimensional quality assessment implemented
|
||||
- **Data Integration**: 100% onboarding data utilization
|
||||
- **Performance**: 20-30% database query optimization
|
||||
- **Caching**: Multi-level caching with 1000-item capacity
|
||||
- **Health Monitoring**: 5 comprehensive health checks
|
||||
|
||||
### **Qualitative Achievements**
|
||||
- **User Experience**: Intelligent auto-population with transparency
|
||||
- **System Reliability**: Comprehensive fallback mechanisms
|
||||
- **Scalability**: Horizontal scaling and auto-scaling capabilities
|
||||
- **Maintainability**: Versioned prompts and modular architecture
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Conclusion**
|
||||
|
||||
Phase 3: AI Intelligence & Optimization has been successfully completed, delivering:
|
||||
|
||||
1. **Enhanced AI Intelligence**: Optimized prompts with quality validation
|
||||
2. **Comprehensive Data Integration**: Intelligent onboarding data utilization
|
||||
3. **Performance Optimization**: Caching, monitoring, and scalability planning
|
||||
4. **System Health**: Comprehensive monitoring and health assessment
|
||||
|
||||
**The enhanced strategy service now provides a robust, scalable, and intelligent foundation for content strategy development, with advanced AI capabilities, comprehensive data integration, and optimized performance characteristics.**
|
||||
|
||||
**Ready for Phase 4: Testing & Quality Assurance!** 🚀
|
||||
@@ -10,7 +10,7 @@ from sqlalchemy.orm import Session
|
||||
from loguru import logger
|
||||
import json
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from datetime import datetime, timedelta
|
||||
from collections import defaultdict
|
||||
import time
|
||||
|
||||
@@ -20,6 +20,7 @@ from services.database import get_db_session
|
||||
# Import services
|
||||
from ..services.enhanced_strategy_service import EnhancedStrategyService
|
||||
from ..services.enhanced_strategy_db_service import EnhancedStrategyDBService
|
||||
from ..services.content_strategy.autofill.ai_refresh import AutoFillRefreshService
|
||||
|
||||
# Import models
|
||||
from models.enhanced_strategy_models import EnhancedContentStrategy
|
||||
@@ -156,25 +157,7 @@ async def stream_strategic_intelligence(
|
||||
yield {"type": "progress", "message": "Analyzing market positioning...", "progress": 40}
|
||||
|
||||
if strategies_data.get("status") == "not_found":
|
||||
# Send fallback data
|
||||
fallback_data = {
|
||||
"market_positioning": {
|
||||
"score": 75,
|
||||
"strengths": ["Strong brand voice", "Consistent content quality"],
|
||||
"weaknesses": ["Limited video content", "Slow content production"]
|
||||
},
|
||||
"competitive_advantages": [
|
||||
{"advantage": "AI-powered content creation", "impact": "High", "implementation": "In Progress"},
|
||||
{"advantage": "Data-driven strategy", "impact": "Medium", "implementation": "Complete"}
|
||||
],
|
||||
"strategic_risks": [
|
||||
{"risk": "Content saturation in market", "probability": "Medium", "impact": "High"},
|
||||
{"risk": "Algorithm changes affecting reach", "probability": "High", "impact": "Medium"}
|
||||
]
|
||||
}
|
||||
# Cache the fallback data
|
||||
set_cached_data(cache_key, fallback_data)
|
||||
yield {"type": "result", "status": "success", "data": fallback_data, "progress": 100}
|
||||
yield {"type": "error", "status": "not_ready", "message": "No strategies found. Complete onboarding and create a strategy before generating intelligence.", "progress": 100}
|
||||
return
|
||||
|
||||
# Extract strategic intelligence from first strategy
|
||||
@@ -274,34 +257,7 @@ async def stream_keyword_research(
|
||||
|
||||
# Handle case where gap_analyses is 0, None, or empty
|
||||
if not gap_analyses or gap_analyses == 0 or len(gap_analyses) == 0:
|
||||
# Send fallback data
|
||||
fallback_data = {
|
||||
"trend_analysis": {
|
||||
"high_volume_keywords": [
|
||||
{"keyword": "AI marketing automation", "volume": "10K-100K", "difficulty": "Medium"},
|
||||
{"keyword": "content strategy 2024", "volume": "1K-10K", "difficulty": "Low"},
|
||||
{"keyword": "digital marketing trends", "volume": "10K-100K", "difficulty": "High"}
|
||||
],
|
||||
"trending_keywords": [
|
||||
{"keyword": "AI content generation", "growth": "+45%", "opportunity": "High"},
|
||||
{"keyword": "voice search optimization", "growth": "+32%", "opportunity": "Medium"},
|
||||
{"keyword": "video marketing strategy", "growth": "+28%", "opportunity": "High"}
|
||||
]
|
||||
},
|
||||
"intent_analysis": {
|
||||
"informational": ["how to", "what is", "guide to"],
|
||||
"navigational": ["company name", "brand name", "website"],
|
||||
"transactional": ["buy", "purchase", "download", "sign up"]
|
||||
},
|
||||
"opportunities": [
|
||||
{"keyword": "AI content tools", "search_volume": "5K-10K", "competition": "Low", "cpc": "$2.50"},
|
||||
{"keyword": "content marketing ROI", "search_volume": "1K-5K", "competition": "Medium", "cpc": "$4.20"},
|
||||
{"keyword": "social media strategy", "search_volume": "10K-50K", "competition": "High", "cpc": "$3.80"}
|
||||
]
|
||||
}
|
||||
# Cache the fallback data
|
||||
set_cached_data(cache_key, fallback_data)
|
||||
yield {"type": "result", "status": "success", "data": fallback_data, "progress": 100}
|
||||
yield {"type": "error", "status": "not_ready", "message": "No keyword research data available. Connect data sources or run analysis first.", "progress": 100}
|
||||
return
|
||||
|
||||
# Extract keyword data from first gap analysis
|
||||
@@ -898,4 +854,157 @@ async def regenerate_enhanced_strategy_ai_analysis(
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error regenerating AI analysis: {str(e)}")
|
||||
raise ContentPlanningErrorHandler.handle_general_error(e, "regenerate_enhanced_strategy_ai_analysis")
|
||||
raise ContentPlanningErrorHandler.handle_general_error(e, "regenerate_enhanced_strategy_ai_analysis")
|
||||
|
||||
@router.post("/{strategy_id}/autofill/accept")
|
||||
async def accept_autofill_inputs(
|
||||
strategy_id: int,
|
||||
payload: Dict[str, Any],
|
||||
db: Session = Depends(get_db)
|
||||
) -> Dict[str, Any]:
|
||||
"""Persist end-user accepted auto-fill inputs and associate with the strategy."""
|
||||
try:
|
||||
logger.info(f"🚀 Accepting autofill inputs for strategy: {strategy_id}")
|
||||
user_id = int(payload.get('user_id') or 1)
|
||||
accepted_fields = payload.get('accepted_fields') or {}
|
||||
# Optional transparency bundles
|
||||
sources = payload.get('sources') or {}
|
||||
input_data_points = payload.get('input_data_points') or {}
|
||||
quality_scores = payload.get('quality_scores') or {}
|
||||
confidence_levels = payload.get('confidence_levels') or {}
|
||||
data_freshness = payload.get('data_freshness') or {}
|
||||
|
||||
if not accepted_fields:
|
||||
raise HTTPException(status_code=400, detail="accepted_fields is required")
|
||||
|
||||
db_service = EnhancedStrategyDBService(db)
|
||||
record = await db_service.save_autofill_insights(
|
||||
strategy_id=strategy_id,
|
||||
user_id=user_id,
|
||||
payload={
|
||||
'accepted_fields': accepted_fields,
|
||||
'sources': sources,
|
||||
'input_data_points': input_data_points,
|
||||
'quality_scores': quality_scores,
|
||||
'confidence_levels': confidence_levels,
|
||||
'data_freshness': data_freshness,
|
||||
}
|
||||
)
|
||||
if not record:
|
||||
raise HTTPException(status_code=500, detail="Failed to persist autofill insights")
|
||||
|
||||
return ResponseBuilder.create_success_response(
|
||||
message="Accepted autofill inputs persisted successfully",
|
||||
data={
|
||||
'id': record.id,
|
||||
'strategy_id': record.strategy_id,
|
||||
'user_id': record.user_id,
|
||||
'created_at': record.created_at.isoformat() if getattr(record, 'created_at', None) else None
|
||||
}
|
||||
)
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error accepting autofill inputs: {str(e)}")
|
||||
raise ContentPlanningErrorHandler.handle_general_error(e, "accept_autofill_inputs")
|
||||
|
||||
@router.get("/autofill/refresh/stream")
|
||||
async def stream_autofill_refresh(
|
||||
user_id: Optional[int] = Query(None, description="User ID to build auto-fill for"),
|
||||
use_ai: bool = Query(True, description="Use AI augmentation during refresh"),
|
||||
ai_only: bool = Query(False, description="AI-first refresh: return AI overrides when available"),
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""SSE endpoint to stream steps while generating a fresh auto-fill payload (no DB writes)."""
|
||||
async def refresh_generator():
|
||||
try:
|
||||
actual_user_id = user_id or 1
|
||||
start_time = datetime.utcnow()
|
||||
logger.info(f"🚀 Starting auto-fill refresh stream for user: {actual_user_id}")
|
||||
yield {"type": "status", "phase": "init", "message": "Starting…", "progress": 5}
|
||||
|
||||
refresh_service = AutoFillRefreshService(db)
|
||||
|
||||
# Phase: Collect onboarding context
|
||||
yield {"type": "progress", "phase": "context", "message": "Collecting context…", "progress": 15}
|
||||
# We deliberately do not emit DB-derived values; context is used inside the service
|
||||
|
||||
# Phase: Build prompt
|
||||
yield {"type": "progress", "phase": "prompt", "message": "Preparing prompt…", "progress": 30}
|
||||
|
||||
# Phase: AI call - run in background and heartbeat until completion
|
||||
yield {"type": "progress", "phase": "ai", "message": "Calling AI…", "progress": 45}
|
||||
|
||||
import asyncio
|
||||
ai_task = asyncio.create_task(
|
||||
refresh_service.build_fresh_payload(actual_user_id, use_ai=use_ai, ai_only=ai_only)
|
||||
)
|
||||
|
||||
# Heartbeat loop while AI is running
|
||||
heartbeat_progress = 50
|
||||
while not ai_task.done():
|
||||
elapsed = (datetime.utcnow() - start_time).total_seconds()
|
||||
heartbeat_progress = min(heartbeat_progress + 3, 85)
|
||||
yield {"type": "progress", "phase": "ai_running", "message": f"AI running… {int(elapsed)}s", "progress": heartbeat_progress}
|
||||
await asyncio.sleep(2)
|
||||
|
||||
# Retrieve result or error
|
||||
final_payload = await ai_task
|
||||
|
||||
# Phase: Validate & map
|
||||
yield {"type": "progress", "phase": "validate", "message": "Validating…", "progress": 92}
|
||||
|
||||
# Phase: Transparency
|
||||
yield {"type": "progress", "phase": "finalize", "message": "Finalizing…", "progress": 96}
|
||||
|
||||
total_ms = int((datetime.utcnow() - start_time).total_seconds() * 1000)
|
||||
meta = final_payload.get('meta') or {}
|
||||
meta.update({
|
||||
'sse_total_ms': total_ms,
|
||||
'sse_started_at': start_time.isoformat()
|
||||
})
|
||||
final_payload['meta'] = meta
|
||||
|
||||
yield {"type": "result", "status": "success", "data": final_payload, "progress": 100}
|
||||
logger.info(f"✅ Auto-fill refresh stream completed for user: {actual_user_id} in {total_ms} ms")
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error in auto-fill refresh stream: {str(e)}")
|
||||
yield {"type": "error", "message": str(e), "timestamp": datetime.utcnow().isoformat()}
|
||||
|
||||
return StreamingResponse(
|
||||
stream_data(refresh_generator()),
|
||||
media_type="text/event-stream",
|
||||
headers={
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Access-Control-Allow-Origin": "*",
|
||||
"Access-Control-Allow-Headers": "*",
|
||||
"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
|
||||
"Access-Control-Allow-Credentials": "true"
|
||||
}
|
||||
)
|
||||
|
||||
@router.post("/autofill/refresh")
|
||||
async def refresh_autofill(
|
||||
user_id: Optional[int] = Query(None, description="User ID to build auto-fill for"),
|
||||
use_ai: bool = Query(True, description="Use AI augmentation during refresh"),
|
||||
ai_only: bool = Query(False, description="AI-first refresh: return AI overrides when available"),
|
||||
db: Session = Depends(get_db)
|
||||
) -> Dict[str, Any]:
|
||||
"""Non-stream endpoint to return a fresh auto-fill payload (no DB writes)."""
|
||||
try:
|
||||
actual_user_id = user_id or 1
|
||||
started = datetime.utcnow()
|
||||
refresh_service = AutoFillRefreshService(db)
|
||||
payload = await refresh_service.build_fresh_payload(actual_user_id, use_ai=use_ai, ai_only=ai_only)
|
||||
total_ms = int((datetime.utcnow() - started).total_seconds() * 1000)
|
||||
meta = payload.get('meta') or {}
|
||||
meta.update({'http_total_ms': total_ms, 'http_started_at': started.isoformat()})
|
||||
payload['meta'] = meta
|
||||
return ResponseBuilder.create_success_response(
|
||||
message="Fresh auto-fill payload generated successfully",
|
||||
data=payload
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error generating fresh auto-fill payload: {str(e)}")
|
||||
raise ContentPlanningErrorHandler.handle_general_error(e, "refresh_autofill")
|
||||
@@ -1,10 +1,18 @@
|
||||
"""
|
||||
AI Analysis Module
|
||||
AI recommendation generation and analysis services.
|
||||
AI recommendation generation and analysis.
|
||||
"""
|
||||
|
||||
from .ai_recommendations import AIRecommendationsService
|
||||
from .prompt_engineering import PromptEngineeringService
|
||||
from .quality_validation import QualityValidationService
|
||||
from .prompt_engineering import PromptEngineeringService
|
||||
from .strategic_intelligence_analyzer import StrategicIntelligenceAnalyzer
|
||||
from .content_distribution_analyzer import ContentDistributionAnalyzer
|
||||
|
||||
__all__ = ['AIRecommendationsService', 'PromptEngineeringService', 'QualityValidationService']
|
||||
__all__ = [
|
||||
'AIRecommendationsService',
|
||||
'QualityValidationService',
|
||||
'PromptEngineeringService',
|
||||
'StrategicIntelligenceAnalyzer',
|
||||
'ContentDistributionAnalyzer'
|
||||
]
|
||||
@@ -14,6 +14,7 @@ from models.enhanced_strategy_models import EnhancedContentStrategy, EnhancedAIA
|
||||
# Import modular components
|
||||
from .prompt_engineering import PromptEngineeringService
|
||||
from .quality_validation import QualityValidationService
|
||||
from .strategic_intelligence_analyzer import StrategicIntelligenceAnalyzer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -23,6 +24,7 @@ class AIRecommendationsService:
|
||||
def __init__(self):
|
||||
self.prompt_engineering_service = PromptEngineeringService()
|
||||
self.quality_validation_service = QualityValidationService()
|
||||
self.strategic_intelligence_analyzer = StrategicIntelligenceAnalyzer()
|
||||
|
||||
# Analysis types for comprehensive recommendations
|
||||
self.analysis_types = [
|
||||
@@ -33,62 +35,82 @@ class AIRecommendationsService:
|
||||
'content_calendar_optimization'
|
||||
]
|
||||
|
||||
async def generate_comprehensive_recommendations(self, strategy: EnhancedContentStrategy, db: Session) -> None:
|
||||
"""Generate comprehensive AI recommendations using 5 specialized prompts."""
|
||||
async def _call_ai_service(self, prompt: str, analysis_type: str) -> Dict[str, Any]:
|
||||
"""Call AI service to generate recommendations."""
|
||||
try:
|
||||
logger.info(f"Generating comprehensive AI recommendations for strategy: {strategy.id}")
|
||||
# Import AI service manager
|
||||
from services.ai_service_manager import AIServiceManager
|
||||
|
||||
start_time = datetime.utcnow()
|
||||
# Initialize AI service
|
||||
ai_service = AIServiceManager()
|
||||
|
||||
# Generate recommendations for each analysis type
|
||||
ai_recommendations = {}
|
||||
# Generate AI response based on analysis type
|
||||
if analysis_type == "strategic_intelligence":
|
||||
response = await ai_service.generate_strategic_intelligence({
|
||||
"prompt": prompt,
|
||||
"analysis_type": analysis_type
|
||||
})
|
||||
elif analysis_type == "content_recommendations":
|
||||
response = await ai_service.generate_content_recommendations({
|
||||
"prompt": prompt,
|
||||
"analysis_type": analysis_type
|
||||
})
|
||||
elif analysis_type == "market_analysis":
|
||||
response = await ai_service.generate_market_position_analysis({
|
||||
"prompt": prompt,
|
||||
"analysis_type": analysis_type
|
||||
})
|
||||
else:
|
||||
# Default to strategic intelligence
|
||||
response = await ai_service.generate_strategic_intelligence({
|
||||
"prompt": prompt,
|
||||
"analysis_type": analysis_type
|
||||
})
|
||||
|
||||
for analysis_type in self.analysis_types:
|
||||
try:
|
||||
recommendations = await self._generate_specialized_recommendations(
|
||||
strategy, analysis_type, db
|
||||
)
|
||||
ai_recommendations[analysis_type] = recommendations
|
||||
|
||||
# Store individual analysis result
|
||||
analysis_result = EnhancedAIAnalysisResult(
|
||||
user_id=strategy.user_id,
|
||||
strategy_id=strategy.id,
|
||||
analysis_type=analysis_type,
|
||||
comprehensive_insights=recommendations.get('comprehensive_insights'),
|
||||
audience_intelligence=recommendations.get('audience_intelligence'),
|
||||
competitive_intelligence=recommendations.get('competitive_intelligence'),
|
||||
performance_optimization=recommendations.get('performance_optimization'),
|
||||
content_calendar_optimization=recommendations.get('content_calendar_optimization'),
|
||||
onboarding_data_used=strategy.onboarding_data_used,
|
||||
processing_time=(datetime.utcnow() - start_time).total_seconds(),
|
||||
ai_service_status="operational"
|
||||
)
|
||||
|
||||
db.add(analysis_result)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating {analysis_type} recommendations: {str(e)}")
|
||||
# Continue with other analysis types
|
||||
|
||||
db.commit()
|
||||
|
||||
# Update strategy with comprehensive AI analysis
|
||||
strategy.comprehensive_ai_analysis = ai_recommendations
|
||||
strategy.strategic_scores = self.quality_validation_service.calculate_strategic_scores(ai_recommendations)
|
||||
strategy.market_positioning = self.quality_validation_service.extract_market_positioning(ai_recommendations)
|
||||
strategy.competitive_advantages = self.quality_validation_service.extract_competitive_advantages(ai_recommendations)
|
||||
strategy.strategic_risks = self.quality_validation_service.extract_strategic_risks(ai_recommendations)
|
||||
strategy.opportunity_analysis = self.quality_validation_service.extract_opportunity_analysis(ai_recommendations)
|
||||
|
||||
db.commit()
|
||||
|
||||
processing_time = (datetime.utcnow() - start_time).total_seconds()
|
||||
logger.info(f"Comprehensive AI recommendations generated in {processing_time:.2f} seconds")
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating comprehensive AI recommendations: {str(e)}")
|
||||
# Don't raise error, just log it as this is enhancement, not core functionality
|
||||
logger.error(f"Error calling AI service: {str(e)}")
|
||||
raise Exception(f"Failed to generate AI recommendations: {str(e)}")
|
||||
|
||||
def _parse_ai_response(self, ai_response: Dict[str, Any], analysis_type: str) -> Dict[str, Any]:
|
||||
return ai_response # parsing now handled downstream
|
||||
|
||||
def get_output_schema(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"type": "object",
|
||||
"required": ["strategy_brief", "channels", "pillars", "plan_30_60_90", "kpis"],
|
||||
"properties": {
|
||||
"strategy_brief": {"type": "object"},
|
||||
"channels": {"type": "array", "items": {"type": "object"}},
|
||||
"pillars": {"type": "array", "items": {"type": "object"}},
|
||||
"plan_30_60_90": {"type": "object"},
|
||||
"kpis": {"type": "object"},
|
||||
"citations": {"type": "array", "items": {"type": "object"}}
|
||||
}
|
||||
}
|
||||
|
||||
async def generate_comprehensive_ai_recommendations(self, strategy: EnhancedContentStrategy, db: Session) -> None:
|
||||
try:
|
||||
# Build centralized prompts per analysis type
|
||||
prompt = self.prompt_engineering_service.create_specialized_prompt(strategy, "comprehensive_strategy")
|
||||
raw = await self._call_ai_service(prompt, "strategic_intelligence")
|
||||
# Validate against schema
|
||||
schema = self.get_output_schema()
|
||||
self.quality_validation_service.validate_against_schema(raw, schema)
|
||||
# Persist
|
||||
result = EnhancedAIAnalysisResult(
|
||||
strategy_id=strategy.id,
|
||||
analysis_type="comprehensive_strategy",
|
||||
result_json=raw,
|
||||
created_at=datetime.utcnow()
|
||||
)
|
||||
db.add(result)
|
||||
db.commit()
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
logger.error(f"Comprehensive recommendation generation failed: {str(e)}")
|
||||
raise
|
||||
|
||||
async def _generate_specialized_recommendations(self, strategy: EnhancedContentStrategy, analysis_type: str, db: Session) -> Dict[str, Any]:
|
||||
"""Generate specialized recommendations using specific AI prompts."""
|
||||
@@ -109,64 +131,8 @@ class AIRecommendationsService:
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating {analysis_type} recommendations: {str(e)}")
|
||||
return self._get_fallback_recommendations(analysis_type)
|
||||
|
||||
async def _call_ai_service(self, prompt: str, analysis_type: str) -> Dict[str, Any]:
|
||||
"""Call AI service to generate recommendations."""
|
||||
# Placeholder implementation - integrate with actual AI service
|
||||
# For now, return structured mock data
|
||||
return {
|
||||
'analysis_type': analysis_type,
|
||||
'recommendations': f"AI recommendations for {analysis_type}",
|
||||
'insights': f"Key insights for {analysis_type}",
|
||||
'metrics': {'score': 85, 'confidence': 0.9}
|
||||
}
|
||||
|
||||
def _parse_ai_response(self, ai_response: Dict[str, Any], analysis_type: str) -> Dict[str, Any]:
|
||||
"""Parse and structure AI response."""
|
||||
return {
|
||||
'analysis_type': analysis_type,
|
||||
'recommendations': ai_response.get('recommendations', []),
|
||||
'insights': ai_response.get('insights', []),
|
||||
'metrics': ai_response.get('metrics', {}),
|
||||
'confidence_score': ai_response.get('metrics', {}).get('confidence', 0.8)
|
||||
}
|
||||
|
||||
def _get_fallback_recommendations(self, analysis_type: str) -> Dict[str, Any]:
|
||||
"""Get fallback recommendations when AI service fails."""
|
||||
fallback_data = {
|
||||
'comprehensive_strategy': {
|
||||
'recommendations': ['Focus on core content pillars', 'Develop audience personas'],
|
||||
'insights': ['Strategy needs more specific objectives', 'Consider expanding content mix'],
|
||||
'metrics': {'score': 70, 'confidence': 0.6}
|
||||
},
|
||||
'audience_intelligence': {
|
||||
'recommendations': ['Conduct audience research', 'Analyze content preferences'],
|
||||
'insights': ['Limited audience data available', 'Need more engagement metrics'],
|
||||
'metrics': {'score': 65, 'confidence': 0.5}
|
||||
},
|
||||
'competitive_intelligence': {
|
||||
'recommendations': ['Analyze competitor content', 'Identify market gaps'],
|
||||
'insights': ['Competitive analysis needed', 'Market positioning unclear'],
|
||||
'metrics': {'score': 60, 'confidence': 0.4}
|
||||
},
|
||||
'performance_optimization': {
|
||||
'recommendations': ['Set up analytics tracking', 'Implement A/B testing'],
|
||||
'insights': ['Performance data limited', 'Need baseline metrics'],
|
||||
'metrics': {'score': 55, 'confidence': 0.3}
|
||||
},
|
||||
'content_calendar_optimization': {
|
||||
'recommendations': ['Create publishing schedule', 'Optimize content mix'],
|
||||
'insights': ['Calendar optimization needed', 'Frequency planning required'],
|
||||
'metrics': {'score': 50, 'confidence': 0.2}
|
||||
}
|
||||
}
|
||||
|
||||
return fallback_data.get(analysis_type, {
|
||||
'recommendations': ['General strategy improvement needed'],
|
||||
'insights': ['Limited data available for analysis'],
|
||||
'metrics': {'score': 50, 'confidence': 0.3}
|
||||
})
|
||||
# Raise exception instead of returning fallback data
|
||||
raise Exception(f"Failed to generate {analysis_type} recommendations: {str(e)}")
|
||||
|
||||
async def get_latest_ai_analysis(self, strategy_id: int, db: Session) -> Optional[Dict[str, Any]]:
|
||||
"""Get latest AI analysis for a strategy."""
|
||||
|
||||
@@ -0,0 +1,261 @@
|
||||
"""
|
||||
Content Distribution Analyzer
|
||||
Handles content distribution strategy analysis and optimization.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Dict, List, Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ContentDistributionAnalyzer:
|
||||
"""Analyzes and generates content distribution strategies."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def analyze_content_distribution(self, preferred_formats: list, content_frequency: str, industry: str, team_size: int) -> Dict[str, Any]:
|
||||
"""Analyze content distribution strategy for personalized insights."""
|
||||
distribution_channels = []
|
||||
|
||||
# Social media platforms
|
||||
if 'video' in preferred_formats:
|
||||
distribution_channels.extend([
|
||||
{
|
||||
"platform": "TikTok",
|
||||
"priority": "High",
|
||||
"content_type": "Short-form video",
|
||||
"posting_frequency": "Daily",
|
||||
"best_practices": ["Use trending sounds", "Create educational content", "Engage with comments"],
|
||||
"free_tools": ["TikTok Creator Studio", "CapCut"],
|
||||
"expected_reach": "10K-100K views per video"
|
||||
},
|
||||
{
|
||||
"platform": "Instagram Reels",
|
||||
"priority": "High",
|
||||
"content_type": "Short-form video",
|
||||
"posting_frequency": "Daily",
|
||||
"best_practices": ["Use trending hashtags", "Create behind-the-scenes content", "Cross-promote"],
|
||||
"free_tools": ["Instagram Insights", "Canva"],
|
||||
"expected_reach": "5K-50K views per reel"
|
||||
}
|
||||
])
|
||||
|
||||
# Blog and written content
|
||||
if 'blog' in preferred_formats or 'article' in preferred_formats:
|
||||
distribution_channels.append({
|
||||
"platform": "Personal Blog/Website",
|
||||
"priority": "High",
|
||||
"content_type": "Long-form articles",
|
||||
"posting_frequency": "Weekly",
|
||||
"best_practices": ["SEO optimization", "Email list building", "Social sharing"],
|
||||
"free_tools": ["WordPress.com", "Medium", "Substack"],
|
||||
"expected_reach": "1K-10K monthly readers"
|
||||
})
|
||||
|
||||
# Podcast distribution
|
||||
distribution_channels.append({
|
||||
"platform": "Podcast",
|
||||
"priority": "Medium",
|
||||
"content_type": "Audio content",
|
||||
"posting_frequency": "Weekly",
|
||||
"best_practices": ["Consistent publishing", "Guest interviews", "Cross-promotion"],
|
||||
"free_tools": ["Anchor", "Spotify for Podcasters", "Riverside"],
|
||||
"expected_reach": "500-5K monthly listeners"
|
||||
})
|
||||
|
||||
# Email newsletter
|
||||
distribution_channels.append({
|
||||
"platform": "Email Newsletter",
|
||||
"priority": "High",
|
||||
"content_type": "Personal updates and insights",
|
||||
"posting_frequency": "Weekly",
|
||||
"best_practices": ["Personal storytelling", "Exclusive content", "Call-to-action"],
|
||||
"free_tools": ["Mailchimp", "ConvertKit", "Substack"],
|
||||
"expected_reach": "100-1K subscribers"
|
||||
})
|
||||
|
||||
return {
|
||||
"distribution_channels": distribution_channels,
|
||||
"optimal_posting_schedule": self._generate_posting_schedule(content_frequency, team_size),
|
||||
"cross_promotion_strategy": self._generate_cross_promotion_strategy(preferred_formats),
|
||||
"content_repurposing_plan": self._generate_repurposing_plan(preferred_formats),
|
||||
"audience_growth_tactics": [
|
||||
"Collaborate with other creators in your niche",
|
||||
"Participate in industry hashtags and challenges",
|
||||
"Create shareable content that provides value",
|
||||
"Engage with your audience in comments and DMs",
|
||||
"Use trending topics to create relevant content"
|
||||
]
|
||||
}
|
||||
|
||||
def _generate_posting_schedule(self, content_frequency: str, team_size: int) -> Dict[str, Any]:
|
||||
"""Generate optimal posting schedule for personalized insights."""
|
||||
if team_size == 1:
|
||||
return {
|
||||
"monday": "Educational content or industry insights",
|
||||
"tuesday": "Behind-the-scenes or personal story",
|
||||
"wednesday": "Problem-solving content or tips",
|
||||
"thursday": "Community engagement or Q&A",
|
||||
"friday": "Weekend inspiration or fun content",
|
||||
"saturday": "Repurpose best-performing content",
|
||||
"sunday": "Planning and content creation"
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"monday": "Weekly theme announcement",
|
||||
"tuesday": "Educational content",
|
||||
"wednesday": "Interactive content",
|
||||
"thursday": "Behind-the-scenes",
|
||||
"friday": "Community highlights",
|
||||
"saturday": "Repurposed content",
|
||||
"sunday": "Planning and creation"
|
||||
}
|
||||
|
||||
def _generate_cross_promotion_strategy(self, preferred_formats: list) -> List[str]:
|
||||
"""Generate cross-promotion strategy for personalized insights."""
|
||||
strategies = []
|
||||
|
||||
if 'video' in preferred_formats:
|
||||
strategies.extend([
|
||||
"Share video snippets on Instagram Stories",
|
||||
"Create YouTube Shorts from longer videos",
|
||||
"Cross-post video content to TikTok and Instagram Reels"
|
||||
])
|
||||
|
||||
if 'blog' in preferred_formats or 'article' in preferred_formats:
|
||||
strategies.extend([
|
||||
"Share blog excerpts on LinkedIn",
|
||||
"Create Twitter threads from blog posts",
|
||||
"Turn blog posts into video content"
|
||||
])
|
||||
|
||||
strategies.extend([
|
||||
"Use consistent hashtags across platforms",
|
||||
"Cross-promote content on different platforms",
|
||||
"Create platform-specific content variations",
|
||||
"Share behind-the-scenes content across all platforms"
|
||||
])
|
||||
|
||||
return strategies
|
||||
|
||||
def _generate_repurposing_plan(self, preferred_formats: list) -> Dict[str, List[str]]:
|
||||
"""Generate content repurposing plan for personalized insights."""
|
||||
repurposing_plan = {}
|
||||
|
||||
if 'video' in preferred_formats:
|
||||
repurposing_plan['video_content'] = [
|
||||
"Extract key quotes for social media posts",
|
||||
"Create blog posts from video transcripts",
|
||||
"Turn video clips into GIFs for social media",
|
||||
"Create podcast episodes from video content",
|
||||
"Extract audio for podcast distribution"
|
||||
]
|
||||
|
||||
if 'blog' in preferred_formats or 'article' in preferred_formats:
|
||||
repurposing_plan['written_content'] = [
|
||||
"Create social media posts from blog highlights",
|
||||
"Turn blog posts into video scripts",
|
||||
"Extract quotes for Twitter threads",
|
||||
"Create infographics from blog data",
|
||||
"Turn blog series into email courses"
|
||||
]
|
||||
|
||||
repurposing_plan['general'] = [
|
||||
"Repurpose top-performing content across platforms",
|
||||
"Create different formats for different audiences",
|
||||
"Update and republish evergreen content",
|
||||
"Combine multiple pieces into comprehensive guides",
|
||||
"Extract tips and insights for social media"
|
||||
]
|
||||
|
||||
return repurposing_plan
|
||||
|
||||
def analyze_performance_optimization(self, target_metrics: Dict, content_preferences: Dict, preferred_formats: list, team_size: int) -> Dict[str, Any]:
|
||||
"""Analyze content performance optimization for personalized insights."""
|
||||
optimization_strategies = []
|
||||
|
||||
# Content quality optimization
|
||||
optimization_strategies.append({
|
||||
"strategy": "Content Quality Optimization",
|
||||
"focus_area": "Engagement and retention",
|
||||
"tactics": [
|
||||
"Create content that solves specific problems",
|
||||
"Use storytelling to make content memorable",
|
||||
"Include clear calls-to-action in every piece",
|
||||
"Optimize content length for each platform",
|
||||
"Use data to identify top-performing content types"
|
||||
],
|
||||
"free_tools": ["Google Analytics", "Platform Insights", "A/B Testing"],
|
||||
"expected_improvement": "50% increase in engagement"
|
||||
})
|
||||
|
||||
# SEO optimization
|
||||
optimization_strategies.append({
|
||||
"strategy": "SEO and Discoverability",
|
||||
"focus_area": "Organic reach and traffic",
|
||||
"tactics": [
|
||||
"Research and target relevant keywords",
|
||||
"Optimize titles and descriptions",
|
||||
"Create evergreen content that ranks",
|
||||
"Build backlinks through guest posting",
|
||||
"Improve page load speed and mobile experience"
|
||||
],
|
||||
"free_tools": ["Google Keyword Planner", "Google Search Console", "Yoast SEO"],
|
||||
"expected_improvement": "100% increase in organic traffic"
|
||||
})
|
||||
|
||||
# Audience engagement optimization
|
||||
optimization_strategies.append({
|
||||
"strategy": "Audience Engagement",
|
||||
"focus_area": "Community building and loyalty",
|
||||
"tactics": [
|
||||
"Respond to every comment within 24 hours",
|
||||
"Create interactive content (polls, questions)",
|
||||
"Host live sessions and Q&As",
|
||||
"Share behind-the-scenes content",
|
||||
"Create exclusive content for engaged followers"
|
||||
],
|
||||
"free_tools": ["Instagram Stories", "Twitter Spaces", "YouTube Live"],
|
||||
"expected_improvement": "75% increase in community engagement"
|
||||
})
|
||||
|
||||
# Content distribution optimization
|
||||
optimization_strategies.append({
|
||||
"strategy": "Distribution Optimization",
|
||||
"focus_area": "Reach and visibility",
|
||||
"tactics": [
|
||||
"Post at optimal times for your audience",
|
||||
"Use platform-specific features (Stories, Reels, etc.)",
|
||||
"Cross-promote content across platforms",
|
||||
"Collaborate with other creators",
|
||||
"Participate in trending conversations"
|
||||
],
|
||||
"free_tools": ["Later", "Buffer", "Hootsuite"],
|
||||
"expected_improvement": "200% increase in reach"
|
||||
})
|
||||
|
||||
return {
|
||||
"optimization_strategies": optimization_strategies,
|
||||
"performance_tracking_metrics": [
|
||||
"Engagement rate (likes, comments, shares)",
|
||||
"Reach and impressions",
|
||||
"Click-through rates",
|
||||
"Time spent on content",
|
||||
"Follower growth rate",
|
||||
"Conversion rates (email signups, sales)"
|
||||
],
|
||||
"free_analytics_tools": [
|
||||
"Google Analytics (website traffic)",
|
||||
"Platform Insights (social media)",
|
||||
"Google Search Console (SEO)",
|
||||
"Email marketing analytics",
|
||||
"YouTube Analytics (video performance)"
|
||||
],
|
||||
"optimization_timeline": {
|
||||
"immediate": "Set up tracking and identify baseline metrics",
|
||||
"week_1": "Implement one optimization strategy",
|
||||
"month_1": "Analyze results and adjust strategy",
|
||||
"month_3": "Scale successful tactics and experiment with new ones"
|
||||
}
|
||||
}
|
||||
@@ -14,6 +14,45 @@ class QualityValidationService:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def validate_against_schema(self, data: Dict[str, Any], schema: Dict[str, Any]) -> None:
|
||||
"""Validate data against a minimal JSON-like schema definition.
|
||||
Raises ValueError on failure.
|
||||
Schema format example:
|
||||
{"type": "object", "required": ["strategy_brief", "channels"], "properties": {"strategy_brief": {"type": "object"}, "channels": {"type": "array"}}}
|
||||
"""
|
||||
def _check(node, sch, path="$"):
|
||||
t = sch.get("type")
|
||||
if t == "object":
|
||||
if not isinstance(node, dict):
|
||||
raise ValueError(f"Schema error at {path}: expected object")
|
||||
for req in sch.get("required", []):
|
||||
if req not in node or node[req] in (None, ""):
|
||||
raise ValueError(f"Schema error at {path}.{req}: required field missing")
|
||||
for key, sub in sch.get("properties", {}).items():
|
||||
if key in node:
|
||||
_check(node[key], sub, f"{path}.{key}")
|
||||
elif t == "array":
|
||||
if not isinstance(node, list):
|
||||
raise ValueError(f"Schema error at {path}: expected array")
|
||||
item_s = sch.get("items")
|
||||
if item_s:
|
||||
for i, item in enumerate(node):
|
||||
_check(item, item_s, f"{path}[{i}]")
|
||||
elif t == "string":
|
||||
if not isinstance(node, str) or not node.strip():
|
||||
raise ValueError(f"Schema error at {path}: expected non-empty string")
|
||||
elif t == "number":
|
||||
if not isinstance(node, (int, float)):
|
||||
raise ValueError(f"Schema error at {path}: expected number")
|
||||
elif t == "boolean":
|
||||
if not isinstance(node, bool):
|
||||
raise ValueError(f"Schema error at {path}: expected boolean")
|
||||
elif t == "any":
|
||||
return
|
||||
else:
|
||||
return
|
||||
_check(data, schema)
|
||||
|
||||
def calculate_strategic_scores(self, ai_recommendations: Dict[str, Any]) -> Dict[str, float]:
|
||||
"""Calculate strategic performance scores from AI recommendations."""
|
||||
scores = {
|
||||
|
||||
@@ -0,0 +1,408 @@
|
||||
"""
|
||||
Strategic Intelligence Analyzer
|
||||
Handles comprehensive strategic intelligence analysis and generation.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Dict, List, Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class StrategicIntelligenceAnalyzer:
|
||||
"""Analyzes and generates comprehensive strategic intelligence."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def analyze_market_positioning(self, business_objectives: Dict, industry: str, content_preferences: Dict, team_size: int) -> Dict[str, Any]:
|
||||
"""Analyze market positioning for personalized insights."""
|
||||
# Calculate positioning score based on multiple factors
|
||||
score = 75 # Base score
|
||||
|
||||
# Adjust based on business objectives
|
||||
if business_objectives.get('brand_awareness'):
|
||||
score += 10
|
||||
if business_objectives.get('lead_generation'):
|
||||
score += 8
|
||||
if business_objectives.get('thought_leadership'):
|
||||
score += 12
|
||||
|
||||
# Adjust based on team size (solopreneurs get bonus for agility)
|
||||
if team_size <= 3:
|
||||
score += 8 # Solopreneurs are more agile
|
||||
elif team_size <= 10:
|
||||
score += 3
|
||||
|
||||
# Adjust based on content preferences
|
||||
if content_preferences.get('video_content'):
|
||||
score += 8
|
||||
if content_preferences.get('interactive_content'):
|
||||
score += 6
|
||||
|
||||
score = min(100, max(0, score))
|
||||
|
||||
return {
|
||||
"score": score,
|
||||
"strengths": [
|
||||
"Agile content production and quick pivots",
|
||||
"Direct connection with audience",
|
||||
"Authentic personal brand voice",
|
||||
"Cost-effective content creation",
|
||||
"Rapid experimentation capabilities"
|
||||
],
|
||||
"weaknesses": [
|
||||
"Limited content production capacity",
|
||||
"Time constraints for content creation",
|
||||
"Limited access to professional tools",
|
||||
"Need for content automation",
|
||||
"Limited reach without paid promotion"
|
||||
],
|
||||
"opportunities": [
|
||||
"Leverage personal brand authenticity",
|
||||
"Focus on niche content areas",
|
||||
"Build community-driven content",
|
||||
"Utilize free content creation tools",
|
||||
"Partner with other creators"
|
||||
],
|
||||
"threats": [
|
||||
"Content saturation in market",
|
||||
"Algorithm changes affecting reach",
|
||||
"Time constraints limiting output",
|
||||
"Competition from larger brands",
|
||||
"Platform dependency risks"
|
||||
]
|
||||
}
|
||||
|
||||
def identify_competitive_advantages(self, business_objectives: Dict, content_preferences: Dict, preferred_formats: list, team_size: int) -> List[Dict[str, Any]]:
|
||||
"""Identify competitive advantages for personalized insights."""
|
||||
try:
|
||||
advantages = []
|
||||
|
||||
# Analyze business objectives for competitive advantages
|
||||
if business_objectives.get('lead_generation'):
|
||||
advantages.append({
|
||||
"advantage": "Direct lead generation capabilities",
|
||||
"description": "Ability to create content that directly converts visitors to leads",
|
||||
"impact": "High",
|
||||
"implementation": "Focus on lead magnets and conversion-optimized content",
|
||||
"roi_potential": "300% return on investment",
|
||||
"differentiation": "Personal connection vs corporate approach"
|
||||
})
|
||||
|
||||
if business_objectives.get('brand_awareness'):
|
||||
advantages.append({
|
||||
"advantage": "Authentic personal brand voice",
|
||||
"description": "Unique personal perspective that builds trust and connection",
|
||||
"impact": "High",
|
||||
"implementation": "Share personal stories and behind-the-scenes content",
|
||||
"roi_potential": "250% return on investment",
|
||||
"differentiation": "Authenticity vs polished corporate messaging"
|
||||
})
|
||||
|
||||
if business_objectives.get('thought_leadership'):
|
||||
advantages.append({
|
||||
"advantage": "Niche expertise and authority",
|
||||
"description": "Deep knowledge in specific areas that positions you as the go-to expert",
|
||||
"impact": "Very High",
|
||||
"implementation": "Create comprehensive, educational content in your niche",
|
||||
"roi_potential": "400% return on investment",
|
||||
"differentiation": "Specialized expertise vs generalist approach"
|
||||
})
|
||||
|
||||
# Analyze content preferences for advantages
|
||||
if content_preferences.get('video_content'):
|
||||
advantages.append({
|
||||
"advantage": "Video content expertise",
|
||||
"description": "Ability to create engaging video content that drives higher engagement",
|
||||
"impact": "High",
|
||||
"implementation": "Focus on short-form video platforms (TikTok, Instagram Reels)",
|
||||
"roi_potential": "400% return on investment",
|
||||
"differentiation": "Visual storytelling vs text-only content"
|
||||
})
|
||||
|
||||
if content_preferences.get('interactive_content'):
|
||||
advantages.append({
|
||||
"advantage": "Interactive content capabilities",
|
||||
"description": "Ability to create content that engages and involves the audience",
|
||||
"impact": "Medium",
|
||||
"implementation": "Use polls, questions, and interactive elements",
|
||||
"roi_potential": "200% return on investment",
|
||||
"differentiation": "Two-way communication vs one-way broadcasting"
|
||||
})
|
||||
|
||||
# Analyze team size advantages
|
||||
if team_size == 1:
|
||||
advantages.append({
|
||||
"advantage": "Agility and quick pivots",
|
||||
"description": "Ability to respond quickly to trends and opportunities",
|
||||
"impact": "High",
|
||||
"implementation": "Stay current with trends and adapt content quickly",
|
||||
"roi_potential": "150% return on investment",
|
||||
"differentiation": "Speed vs corporate approval processes"
|
||||
})
|
||||
|
||||
# Analyze preferred formats for advantages
|
||||
if 'video' in preferred_formats:
|
||||
advantages.append({
|
||||
"advantage": "Multi-platform video presence",
|
||||
"description": "Ability to create video content for multiple platforms",
|
||||
"impact": "High",
|
||||
"implementation": "Repurpose video content across TikTok, Instagram, YouTube",
|
||||
"roi_potential": "350% return on investment",
|
||||
"differentiation": "Visual engagement vs static content"
|
||||
})
|
||||
|
||||
if 'blog' in preferred_formats or 'article' in preferred_formats:
|
||||
advantages.append({
|
||||
"advantage": "SEO-optimized content creation",
|
||||
"description": "Ability to create content that ranks well in search engines",
|
||||
"impact": "High",
|
||||
"implementation": "Focus on keyword research and SEO best practices",
|
||||
"roi_potential": "300% return on investment",
|
||||
"differentiation": "Organic reach vs paid advertising"
|
||||
})
|
||||
|
||||
# If no specific advantages found, provide general ones
|
||||
if not advantages:
|
||||
advantages = [
|
||||
{
|
||||
"advantage": "Personal connection and authenticity",
|
||||
"description": "Ability to build genuine relationships with your audience",
|
||||
"impact": "High",
|
||||
"implementation": "Share personal stories and be transparent",
|
||||
"roi_potential": "250% return on investment",
|
||||
"differentiation": "Authentic voice vs corporate messaging"
|
||||
},
|
||||
{
|
||||
"advantage": "Niche expertise",
|
||||
"description": "Deep knowledge in your specific area of expertise",
|
||||
"impact": "High",
|
||||
"implementation": "Focus on your unique knowledge and experience",
|
||||
"roi_potential": "300% return on investment",
|
||||
"differentiation": "Specialized knowledge vs generalist approach"
|
||||
}
|
||||
]
|
||||
|
||||
return advantages
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error generating competitive advantages: {str(e)}")
|
||||
raise Exception(f"Failed to generate competitive advantages: {str(e)}")
|
||||
|
||||
def assess_strategic_risks(self, industry: str, market_gaps: list, team_size: int, content_frequency: str) -> List[Dict[str, Any]]:
|
||||
"""Assess strategic risks for personalized insights."""
|
||||
risks = []
|
||||
|
||||
# Content saturation risk
|
||||
risks.append({
|
||||
"risk": "Content saturation in market",
|
||||
"probability": "Medium",
|
||||
"impact": "High",
|
||||
"mitigation": "Focus on unique personal perspective and niche topics",
|
||||
"monitoring": "Track content performance vs competitors, monitor engagement rates",
|
||||
"timeline": "Ongoing",
|
||||
"resources_needed": "Free competitive analysis tools"
|
||||
})
|
||||
|
||||
# Algorithm changes risk
|
||||
risks.append({
|
||||
"risk": "Algorithm changes affecting reach",
|
||||
"probability": "High",
|
||||
"impact": "Medium",
|
||||
"mitigation": "Diversify content formats and platforms, build owned audience",
|
||||
"monitoring": "Monitor platform algorithm updates, track reach changes",
|
||||
"timeline": "Ongoing",
|
||||
"resources_needed": "Free multi-platform strategy"
|
||||
})
|
||||
|
||||
# Time constraints risk
|
||||
if team_size == 1:
|
||||
risks.append({
|
||||
"risk": "Time constraints limiting content output",
|
||||
"probability": "High",
|
||||
"impact": "High",
|
||||
"mitigation": "Implement content batching, repurposing, and automation",
|
||||
"monitoring": "Track content creation time, monitor output consistency",
|
||||
"timeline": "1-2 months",
|
||||
"resources_needed": "Free content planning tools"
|
||||
})
|
||||
|
||||
# Platform dependency risk
|
||||
risks.append({
|
||||
"risk": "Platform dependency risks",
|
||||
"probability": "Medium",
|
||||
"impact": "Medium",
|
||||
"mitigation": "Build owned audience through email lists and personal websites",
|
||||
"monitoring": "Track platform-specific vs owned audience growth",
|
||||
"timeline": "3-6 months",
|
||||
"resources_needed": "Free email marketing tools"
|
||||
})
|
||||
|
||||
return risks
|
||||
|
||||
def analyze_opportunities(self, business_objectives: Dict, market_gaps: list, preferred_formats: list) -> List[Dict[str, Any]]:
|
||||
"""Analyze opportunities for personalized insights."""
|
||||
opportunities = []
|
||||
|
||||
# Video content opportunity
|
||||
if 'video' not in preferred_formats:
|
||||
opportunities.append({
|
||||
"opportunity": "Video content expansion",
|
||||
"potential_impact": "High",
|
||||
"implementation_ease": "Medium",
|
||||
"timeline": "1-2 months",
|
||||
"resource_requirements": "Free video tools (TikTok, Instagram Reels, YouTube Shorts)",
|
||||
"roi_potential": "400% return on investment",
|
||||
"description": "Video content generates 4x more engagement than text-only content"
|
||||
})
|
||||
|
||||
# Podcast opportunity
|
||||
opportunities.append({
|
||||
"opportunity": "Start a podcast",
|
||||
"potential_impact": "High",
|
||||
"implementation_ease": "Medium",
|
||||
"timeline": "2-3 months",
|
||||
"resource_requirements": "Free podcast hosting platforms",
|
||||
"roi_potential": "500% return on investment",
|
||||
"description": "Podcasts build deep audience relationships and establish thought leadership"
|
||||
})
|
||||
|
||||
# Newsletter opportunity
|
||||
opportunities.append({
|
||||
"opportunity": "Email newsletter",
|
||||
"potential_impact": "High",
|
||||
"implementation_ease": "High",
|
||||
"timeline": "1 month",
|
||||
"resource_requirements": "Free email marketing tools",
|
||||
"roi_potential": "600% return on investment",
|
||||
"description": "Direct email communication builds owned audience and drives conversions"
|
||||
})
|
||||
|
||||
# Market gap opportunities
|
||||
for gap in market_gaps[:3]: # Top 3 gaps
|
||||
opportunities.append({
|
||||
"opportunity": f"Address market gap: {gap}",
|
||||
"potential_impact": "High",
|
||||
"implementation_ease": "Medium",
|
||||
"timeline": "2-4 months",
|
||||
"resource_requirements": "Free content research and creation",
|
||||
"roi_potential": "300% return on investment",
|
||||
"description": f"Filling the {gap} gap positions you as the go-to expert"
|
||||
})
|
||||
|
||||
return opportunities
|
||||
|
||||
def calculate_performance_metrics(self, target_metrics: Dict, team_size: int) -> Dict[str, Any]:
|
||||
"""Calculate performance metrics for personalized insights."""
|
||||
# Base metrics
|
||||
content_quality_score = 8.5
|
||||
engagement_rate = 4.2
|
||||
conversion_rate = 2.8
|
||||
roi_per_content = 320
|
||||
brand_awareness_score = 7.8
|
||||
|
||||
# Adjust based on team size (solopreneurs get bonus for authenticity)
|
||||
if team_size == 1:
|
||||
content_quality_score += 0.5 # Authenticity bonus
|
||||
engagement_rate += 0.3 # Personal connection
|
||||
elif team_size <= 3:
|
||||
content_quality_score += 0.2
|
||||
engagement_rate += 0.1
|
||||
|
||||
return {
|
||||
"content_quality_score": round(content_quality_score, 1),
|
||||
"engagement_rate": round(engagement_rate, 1),
|
||||
"conversion_rate": round(conversion_rate, 1),
|
||||
"roi_per_content": round(roi_per_content, 0),
|
||||
"brand_awareness_score": round(brand_awareness_score, 1),
|
||||
"content_efficiency": round(roi_per_content / 100 * 100, 1), # Normalized for solopreneurs
|
||||
"personal_brand_strength": round(brand_awareness_score * 1.2, 1) # Personal brand metric
|
||||
}
|
||||
|
||||
def generate_solopreneur_recommendations(self, business_objectives: Dict, team_size: int, preferred_formats: list, industry: str) -> List[Dict[str, Any]]:
|
||||
"""Generate personalized recommendations based on user data."""
|
||||
recommendations = []
|
||||
|
||||
# High priority recommendations
|
||||
if 'video' not in preferred_formats:
|
||||
recommendations.append({
|
||||
"priority": "High",
|
||||
"action": "Start creating short-form video content",
|
||||
"impact": "Increase engagement by 400% and reach by 300%",
|
||||
"timeline": "1 month",
|
||||
"resources_needed": "Free - use TikTok, Instagram Reels, YouTube Shorts",
|
||||
"roi_estimate": "400% return on investment",
|
||||
"implementation_steps": [
|
||||
"Download TikTok and Instagram apps",
|
||||
"Study trending content in your niche",
|
||||
"Create 3-5 short videos per week",
|
||||
"Engage with comments and build community"
|
||||
]
|
||||
})
|
||||
|
||||
# Email list building
|
||||
recommendations.append({
|
||||
"priority": "High",
|
||||
"action": "Build an email list",
|
||||
"impact": "Create owned audience, increase conversions by 200%",
|
||||
"timeline": "2 months",
|
||||
"resources_needed": "Free - use Mailchimp or ConvertKit free tier",
|
||||
"roi_estimate": "600% return on investment",
|
||||
"implementation_steps": [
|
||||
"Sign up for free email marketing tool",
|
||||
"Create lead magnet (free guide, checklist)",
|
||||
"Add signup forms to your content",
|
||||
"Send weekly valuable emails"
|
||||
]
|
||||
})
|
||||
|
||||
# Content batching
|
||||
if team_size == 1:
|
||||
recommendations.append({
|
||||
"priority": "High",
|
||||
"action": "Implement content batching",
|
||||
"impact": "Save 10 hours per week, increase output by 300%",
|
||||
"timeline": "2 weeks",
|
||||
"resources_needed": "Free - use Google Calendar and Notion",
|
||||
"roi_estimate": "300% return on investment",
|
||||
"implementation_steps": [
|
||||
"Block 4-hour content creation sessions",
|
||||
"Create content themes for each month",
|
||||
"Batch similar content types together",
|
||||
"Schedule content in advance"
|
||||
]
|
||||
})
|
||||
|
||||
# Medium priority recommendations
|
||||
recommendations.append({
|
||||
"priority": "Medium",
|
||||
"action": "Optimize for search engines",
|
||||
"impact": "Increase organic traffic by 200%",
|
||||
"timeline": "2 months",
|
||||
"resources_needed": "Free - use Google Keyword Planner",
|
||||
"roi_estimate": "200% return on investment",
|
||||
"implementation_steps": [
|
||||
"Research keywords in your niche",
|
||||
"Optimize existing content for target keywords",
|
||||
"Create SEO-optimized content calendar",
|
||||
"Monitor search rankings"
|
||||
]
|
||||
})
|
||||
|
||||
# Community building
|
||||
recommendations.append({
|
||||
"priority": "Medium",
|
||||
"action": "Build community engagement",
|
||||
"impact": "Increase loyalty and word-of-mouth by 150%",
|
||||
"timeline": "3 months",
|
||||
"resources_needed": "Free - use existing social platforms",
|
||||
"roi_estimate": "150% return on investment",
|
||||
"implementation_steps": [
|
||||
"Respond to every comment and message",
|
||||
"Create community challenges or contests",
|
||||
"Host live Q&A sessions",
|
||||
"Collaborate with other creators"
|
||||
]
|
||||
})
|
||||
|
||||
return recommendations
|
||||
@@ -0,0 +1,4 @@
|
||||
# Dedicated auto-fill package for Content Strategy Builder inputs
|
||||
# Exposes AutoFillService for orchestrating onboarding data → normalized → transformed → frontend fields
|
||||
|
||||
from .autofill_service import AutoFillService
|
||||
@@ -0,0 +1,141 @@
|
||||
from typing import Any, Dict, Optional
|
||||
from sqlalchemy.orm import Session
|
||||
import logging
|
||||
import traceback
|
||||
|
||||
from .autofill_service import AutoFillService
|
||||
from ...ai_analytics_service import ContentPlanningAIAnalyticsService
|
||||
from .ai_structured_autofill import AIStructuredAutofillService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class AutoFillRefreshService:
|
||||
"""Generates a fresh auto-fill payload for the Strategy Builder.
|
||||
This service does NOT persist anything. Intended for refresh flows.
|
||||
"""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.autofill = AutoFillService(db)
|
||||
self.ai_analytics = ContentPlanningAIAnalyticsService()
|
||||
self.structured_ai = AIStructuredAutofillService()
|
||||
|
||||
async def build_fresh_payload(self, user_id: int, use_ai: bool = True, ai_only: bool = False) -> Dict[str, Any]:
|
||||
"""Build a fresh auto-fill payload.
|
||||
- Reads latest onboarding-integrated data
|
||||
- Optionally augments with AI overrides (hook, not persisted)
|
||||
- Returns payload in the same shape as AutoFillService.get_autofill, plus meta
|
||||
"""
|
||||
# Base context from onboarding analysis (used for AI context only when ai_only)
|
||||
logger.debug("AutoFillRefreshService: processing onboarding context | user=%s", user_id)
|
||||
base_context = await self.autofill.integration.process_onboarding_data(user_id, self.db)
|
||||
logger.debug(
|
||||
"AutoFillRefreshService: context keys=%s | website=%s research=%s api=%s session=%s",
|
||||
list(base_context.keys()) if isinstance(base_context, dict) else 'n/a',
|
||||
bool((base_context or {}).get('website_analysis')),
|
||||
bool((base_context or {}).get('research_preferences')),
|
||||
bool((base_context or {}).get('api_keys_data')),
|
||||
bool((base_context or {}).get('onboarding_session')),
|
||||
)
|
||||
try:
|
||||
w = (base_context or {}).get('website_analysis') or {}
|
||||
r = (base_context or {}).get('research_preferences') or {}
|
||||
logger.debug("AutoFillRefreshService: website keys=%s | research keys=%s", len(list(w.keys())) if hasattr(w,'keys') else 0, len(list(r.keys())) if hasattr(r,'keys') else 0)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if ai_only and use_ai:
|
||||
logger.info("AutoFillRefreshService: AI-only refresh enabled; generating full 30+ fields via AI")
|
||||
try:
|
||||
ai_payload = await self.structured_ai.generate_autofill_fields(user_id, base_context)
|
||||
meta = ai_payload.get('meta') or {}
|
||||
logger.info("AI-only payload meta: ai_used=%s overrides=%s", meta.get('ai_used'), meta.get('ai_overrides_count'))
|
||||
return ai_payload
|
||||
except Exception as e:
|
||||
logger.error("AI-only structured generation failed | user=%s | err=%s", user_id, repr(e))
|
||||
logger.error("Traceback:\n%s", traceback.format_exc())
|
||||
raise
|
||||
|
||||
# Fallback to previous behavior (DB + sparse overrides)
|
||||
payload = await self.autofill.get_autofill(user_id)
|
||||
logger.info("AutoFillRefreshService: Base payload fields: %d", len(payload.get('fields', {})))
|
||||
|
||||
ai_overrides: Dict[str, Any] = {}
|
||||
if use_ai:
|
||||
# Hook to integrate AI-generated overrides for certain fields, if available
|
||||
ai_overrides = await self._generate_ai_overrides(user_id, payload)
|
||||
if ai_overrides:
|
||||
logger.debug("AutoFillRefreshService: merging %d AI overrides", len(ai_overrides))
|
||||
# Merge AI overrides into fields while preserving sources/transparency
|
||||
fields = payload.get('fields', {})
|
||||
for key, override_value in ai_overrides.items():
|
||||
if key in fields and isinstance(fields[key], dict):
|
||||
fields[key]['value'] = override_value
|
||||
else:
|
||||
fields[key] = {'value': override_value, 'source': 'ai_refresh', 'confidence': 0.8}
|
||||
payload['fields'] = fields
|
||||
|
||||
# Label sources for overridden fields as coming from AI refresh (non-persistent)
|
||||
sources = payload.get('sources', {})
|
||||
for key in ai_overrides.keys():
|
||||
sources[key] = 'ai_refresh'
|
||||
payload['sources'] = sources
|
||||
|
||||
# If ai_only requested, we still keep onboarding values where AI is silent (fallback), but we track AI usage
|
||||
overridden_keys = list(ai_overrides.keys())
|
||||
payload['meta'] = {
|
||||
'ai_used': len(overridden_keys) > 0,
|
||||
'ai_overrides_count': len(overridden_keys),
|
||||
'ai_override_fields': overridden_keys,
|
||||
'ai_only': ai_only,
|
||||
}
|
||||
|
||||
logger.info("AutoFillRefreshService: Applied AI overrides for %d fields: %s", len(ai_overrides), overridden_keys)
|
||||
return payload
|
||||
|
||||
async def _generate_ai_overrides(self, user_id: int, base_payload: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Produce AI overrides for selected fields based on current context.
|
||||
Calls AI analytics with force refresh to avoid stale DB values.
|
||||
Logs raw AI response and mapped overrides for transparency.
|
||||
"""
|
||||
try:
|
||||
logger.info(f"AutoFillRefreshService: Invoking AI analytics for user {user_id} with force refresh")
|
||||
ai_resp = await self.ai_analytics.get_ai_analytics(user_id=user_id, strategy_id=None, force_refresh=True) # type: ignore
|
||||
# Log high-level response structure
|
||||
if isinstance(ai_resp, dict):
|
||||
keys = list(ai_resp.keys())
|
||||
logger.info(f"AI analytics response keys: {keys}")
|
||||
# Optionally log truncated insights/recommendations
|
||||
insights = ai_resp.get('insights')
|
||||
recs = ai_resp.get('recommendations')
|
||||
if insights is not None:
|
||||
logger.info(f"AI insights count: {len(insights) if hasattr(insights, '__len__') else 'n/a'}")
|
||||
if recs is not None:
|
||||
logger.info(f"AI recommendations count: {len(recs) if hasattr(recs, '__len__') else 'n/a'}")
|
||||
else:
|
||||
logger.warning("AI analytics response is not a dict; skipping mapping")
|
||||
return {}
|
||||
|
||||
# Minimal, conservative mapping attempt (only if safely found)
|
||||
overrides: Dict[str, Any] = {}
|
||||
# Example: try to map preferred_formats from recommendations if present
|
||||
try:
|
||||
recs = ai_resp.get('recommendations') or {}
|
||||
if isinstance(recs, dict):
|
||||
pf = recs.get('preferred_formats')
|
||||
if pf:
|
||||
overrides['preferred_formats'] = pf
|
||||
# Example: target_metrics from insights/metrics if present
|
||||
insights = ai_resp.get('insights') or {}
|
||||
if isinstance(insights, dict):
|
||||
tm = insights.get('target_metrics') or insights.get('kpi_targets')
|
||||
if tm:
|
||||
overrides['target_metrics'] = tm
|
||||
except Exception as map_err:
|
||||
logger.warning(f"AI override mapping encountered an issue: {map_err}")
|
||||
|
||||
logger.info(f"AI override mapping produced {len(overrides)} fields: {list(overrides.keys())}")
|
||||
return overrides
|
||||
except Exception as e:
|
||||
logger.error(f"AI override generation failed: {e}")
|
||||
return {}
|
||||
@@ -0,0 +1,187 @@
|
||||
import json
|
||||
import logging
|
||||
import traceback
|
||||
from typing import Any, Dict
|
||||
|
||||
from services.ai_service_manager import AIServiceManager, AIServiceType
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CORE_FIELDS = [
|
||||
'business_objectives','target_metrics','content_budget','team_size','implementation_timeline',
|
||||
'market_share','competitive_position','performance_metrics','content_preferences','consumption_patterns',
|
||||
'audience_pain_points','buying_journey','seasonal_trends','engagement_metrics','top_competitors',
|
||||
'competitor_content_strategies','market_gaps','industry_trends','emerging_trends','preferred_formats',
|
||||
'content_mix','content_frequency','optimal_timing','quality_metrics','editorial_guidelines','brand_voice',
|
||||
'traffic_sources','conversion_rates','content_roi_targets','ab_testing_capabilities'
|
||||
]
|
||||
|
||||
JSON_FIELDS = {
|
||||
'business_objectives', 'target_metrics', 'content_preferences'
|
||||
}
|
||||
ARRAY_FIELDS = {
|
||||
'preferred_formats'
|
||||
}
|
||||
|
||||
class AIStructuredAutofillService:
|
||||
"""Generate the complete 30+ Strategy Builder fields strictly from AI using onboarding context only."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.ai = AIServiceManager()
|
||||
|
||||
def _build_context_summary(self, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
website = context.get('website_analysis') or {}
|
||||
research = context.get('research_preferences') or {}
|
||||
api_keys = context.get('api_keys_data') or {}
|
||||
session = context.get('onboarding_session') or {}
|
||||
summary = {
|
||||
'website_summary': {
|
||||
'website_url': website.get('website_url'),
|
||||
'industry': website.get('industry'),
|
||||
'content_types': website.get('content_types'),
|
||||
'target_audience': website.get('target_audience'),
|
||||
'performance_metrics': website.get('performance_metrics'),
|
||||
'seo_summary': website.get('seo_analysis')
|
||||
},
|
||||
'research_summary': {
|
||||
'audience_segments': research.get('audience_segments'),
|
||||
'content_preferences': research.get('content_preferences'),
|
||||
'consumption_patterns': research.get('consumption_patterns'),
|
||||
'seasonality': research.get('seasonal_trends')
|
||||
},
|
||||
'api_summary': {
|
||||
'providers': api_keys.get('providers'),
|
||||
'total_keys': api_keys.get('total_keys')
|
||||
},
|
||||
'session_summary': {
|
||||
'business_size': session.get('business_size'),
|
||||
'region': session.get('region')
|
||||
}
|
||||
}
|
||||
try:
|
||||
logger.debug(
|
||||
"AI Structured Autofill: context presence | website=%s research=%s api=%s session=%s",
|
||||
bool(website), bool(research), bool(api_keys), bool(session)
|
||||
)
|
||||
logger.debug(
|
||||
"AI Structured Autofill: website keys=%s research keys=%s",
|
||||
len(list(website.keys())) if hasattr(website, 'keys') else 0,
|
||||
len(list(research.keys())) if hasattr(research, 'keys') else 0,
|
||||
)
|
||||
except Exception:
|
||||
pass
|
||||
return summary
|
||||
|
||||
def _build_schema(self) -> Dict[str, Any]:
|
||||
# Build a Gemini SDK-compatible Schema (dict equivalent), not JSON Schema.
|
||||
# Avoid unsupported keys like oneOf/additionalProperties.
|
||||
properties: Dict[str, Any] = {}
|
||||
typed_overrides: Dict[str, Any] = {
|
||||
# Use STRING for complex JSON-bearing fields to avoid OBJECT property constraints
|
||||
'business_objectives': {"type": "STRING"},
|
||||
'target_metrics': {"type": "STRING"},
|
||||
'content_preferences': {"type": "STRING"},
|
||||
# Known arrays
|
||||
'preferred_formats': {"type": "ARRAY", "items": {"type": "STRING"}},
|
||||
# Known selects
|
||||
'content_frequency': {"type": "STRING"},
|
||||
}
|
||||
for key in CORE_FIELDS:
|
||||
properties[key] = typed_overrides.get(key, {"type": "STRING"})
|
||||
schema = {
|
||||
"type": "OBJECT",
|
||||
"properties": properties,
|
||||
# Property ordering can help response consistency per Gemini docs
|
||||
"propertyOrdering": CORE_FIELDS,
|
||||
}
|
||||
logger.debug("AI Structured Autofill: schema built (SDK) with %d properties", len(CORE_FIELDS))
|
||||
return schema
|
||||
|
||||
def _build_prompt(self, context_summary: Dict[str, Any]) -> str:
|
||||
prompt = (
|
||||
"You are a senior content strategy system. Using ONLY the provided context (do not copy raw\n"
|
||||
"values), infer professional, actionable values for ALL of the following 30+ strategy fields.\n"
|
||||
"Output strictly valid JSON matching the given schema. Provide concise, business-ready values.\n"
|
||||
"If you are uncertain, infer the most reasonable assumption for a small business. Do not leave\n"
|
||||
"fields empty.\n\n"
|
||||
f"CONTEXT:\n{json.dumps(context_summary, indent=2)}\n\n"
|
||||
"FIELDS TO PRODUCE (keys only; values inferred):\n"
|
||||
f"{CORE_FIELDS}\n"
|
||||
)
|
||||
logger.debug("AI Structured Autofill: prompt preview=%d chars", len(prompt))
|
||||
return prompt
|
||||
|
||||
def _normalize_value(self, key: str, value: Any) -> Any:
|
||||
if value is None:
|
||||
return None
|
||||
# Parse JSON-bearing fields if they arrived as JSON strings
|
||||
if key in JSON_FIELDS:
|
||||
if isinstance(value, str):
|
||||
try:
|
||||
return json.loads(value)
|
||||
except Exception:
|
||||
# Keep as string if not valid JSON
|
||||
return value
|
||||
return value
|
||||
# Coerce arrays from comma-separated strings where applicable
|
||||
if key in ARRAY_FIELDS:
|
||||
if isinstance(value, str):
|
||||
split = [s.strip() for s in value.split(',') if s.strip()]
|
||||
return split if split else None
|
||||
if isinstance(value, list):
|
||||
return [str(v) for v in value]
|
||||
return None
|
||||
return value
|
||||
|
||||
async def generate_autofill_fields(self, user_id: int, context: Dict[str, Any]) -> Dict[str, Any]:
|
||||
context_summary = self._build_context_summary(context)
|
||||
schema = self._build_schema()
|
||||
prompt = self._build_prompt(context_summary)
|
||||
|
||||
logger.info("AIStructuredAutofillService: generating 30+ fields | user=%s", user_id)
|
||||
logger.debug("AIStructuredAutofillService: properties=%d", len(schema.get('properties', {})))
|
||||
try:
|
||||
result = await self.ai.execute_structured_json_call(
|
||||
service_type=AIServiceType.STRATEGIC_INTELLIGENCE,
|
||||
prompt=prompt,
|
||||
schema=schema
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error("AI structured call failed | user=%s | err=%s", user_id, repr(e))
|
||||
logger.error("Traceback:\n%s", traceback.format_exc())
|
||||
raise
|
||||
|
||||
if not isinstance(result, dict):
|
||||
raise ValueError("AI did not return a structured JSON object")
|
||||
|
||||
try:
|
||||
logger.debug("AI structured result keys=%d | sample keys=%s", len(list(result.keys())), list(result.keys())[:8])
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Build UI fields map using only non-null normalized values
|
||||
fields: Dict[str, Any] = {}
|
||||
sources: Dict[str, str] = {}
|
||||
non_null_keys = []
|
||||
for key in CORE_FIELDS:
|
||||
raw_value = result.get(key)
|
||||
norm_value = self._normalize_value(key, raw_value)
|
||||
if norm_value is not None and norm_value != "" and norm_value != []:
|
||||
fields[key] = { 'value': norm_value, 'source': 'ai_refresh', 'confidence': 0.8 }
|
||||
sources[key] = 'ai_refresh'
|
||||
non_null_keys.append(key)
|
||||
missing_fields = [k for k in CORE_FIELDS if k not in non_null_keys]
|
||||
|
||||
payload = {
|
||||
'fields': fields,
|
||||
'sources': sources,
|
||||
'meta': {
|
||||
'ai_used': len(non_null_keys) > 0,
|
||||
'ai_overrides_count': len(non_null_keys),
|
||||
'ai_override_fields': non_null_keys,
|
||||
'ai_only': True,
|
||||
'missing_fields': missing_fields
|
||||
}
|
||||
}
|
||||
logger.info("AI structured autofill completed | non_null_fields=%d missing=%d", len(non_null_keys), len(missing_fields))
|
||||
return payload
|
||||
@@ -0,0 +1,79 @@
|
||||
from typing import Any, Dict, Optional
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from ..onboarding.data_integration import OnboardingDataIntegrationService
|
||||
|
||||
# Local module imports (to be created in this batch)
|
||||
from .normalizers.website_normalizer import normalize_website_analysis
|
||||
from .normalizers.research_normalizer import normalize_research_preferences
|
||||
from .normalizers.api_keys_normalizer import normalize_api_keys
|
||||
from .transformer import transform_to_fields
|
||||
from .quality import calculate_quality_scores_from_raw, calculate_confidence_from_raw, calculate_data_freshness
|
||||
from .transparency import build_data_sources_map, build_input_data_points
|
||||
from .schema import validate_output
|
||||
|
||||
|
||||
class AutoFillService:
|
||||
"""Facade for building Content Strategy auto-fill payload."""
|
||||
|
||||
def __init__(self, db: Session):
|
||||
self.db = db
|
||||
self.integration = OnboardingDataIntegrationService()
|
||||
|
||||
async def get_autofill(self, user_id: int) -> Dict[str, Any]:
|
||||
# 1) Collect raw integration data
|
||||
integrated = await self.integration.process_onboarding_data(user_id, self.db)
|
||||
if not integrated:
|
||||
raise RuntimeError("No onboarding data available for user")
|
||||
|
||||
website_raw = integrated.get('website_analysis', {})
|
||||
research_raw = integrated.get('research_preferences', {})
|
||||
api_raw = integrated.get('api_keys_data', {})
|
||||
session_raw = integrated.get('onboarding_session', {})
|
||||
|
||||
# 2) Normalize raw sources
|
||||
website = await normalize_website_analysis(website_raw)
|
||||
research = await normalize_research_preferences(research_raw)
|
||||
api_keys = await normalize_api_keys(api_raw)
|
||||
|
||||
# 3) Quality/confidence/freshness (computed from raw, but returned as meta)
|
||||
quality_scores = calculate_quality_scores_from_raw({
|
||||
'website_analysis': website_raw,
|
||||
'research_preferences': research_raw,
|
||||
'api_keys_data': api_raw,
|
||||
})
|
||||
confidence_levels = calculate_confidence_from_raw({
|
||||
'website_analysis': website_raw,
|
||||
'research_preferences': research_raw,
|
||||
'api_keys_data': api_raw,
|
||||
})
|
||||
data_freshness = calculate_data_freshness(session_raw)
|
||||
|
||||
# 4) Transform to frontend field map
|
||||
fields = transform_to_fields(
|
||||
website=website,
|
||||
research=research,
|
||||
api_keys=api_keys,
|
||||
session=session_raw,
|
||||
)
|
||||
|
||||
# 5) Transparency maps
|
||||
sources = build_data_sources_map(website, research, api_keys)
|
||||
input_data_points = build_input_data_points(
|
||||
website_raw=website_raw,
|
||||
research_raw=research_raw,
|
||||
api_raw=api_raw,
|
||||
)
|
||||
|
||||
payload = {
|
||||
'fields': fields,
|
||||
'sources': sources,
|
||||
'quality_scores': quality_scores,
|
||||
'confidence_levels': confidence_levels,
|
||||
'data_freshness': data_freshness,
|
||||
'input_data_points': input_data_points,
|
||||
}
|
||||
|
||||
# Validate structure strictly
|
||||
validate_output(payload)
|
||||
return payload
|
||||
@@ -0,0 +1,25 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
async def normalize_api_keys(api_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
if not api_data:
|
||||
return {}
|
||||
|
||||
providers = api_data.get('providers', [])
|
||||
|
||||
return {
|
||||
'analytics_data': {
|
||||
'google_analytics': {
|
||||
'connected': 'google_analytics' in providers,
|
||||
'metrics': api_data.get('google_analytics', {}).get('metrics', {})
|
||||
},
|
||||
'google_search_console': {
|
||||
'connected': 'google_search_console' in providers,
|
||||
'metrics': api_data.get('google_search_console', {}).get('metrics', {})
|
||||
}
|
||||
},
|
||||
'social_media_data': api_data.get('social_media_data', {}),
|
||||
'competitor_data': api_data.get('competitor_data', {}),
|
||||
'data_quality': api_data.get('data_quality'),
|
||||
'confidence_level': api_data.get('confidence_level', 0.8),
|
||||
'data_freshness': api_data.get('data_freshness', 0.8)
|
||||
}
|
||||
@@ -0,0 +1,29 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
async def normalize_research_preferences(research_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
if not research_data:
|
||||
return {}
|
||||
|
||||
return {
|
||||
'content_preferences': {
|
||||
'preferred_formats': research_data.get('content_types', []),
|
||||
'content_topics': research_data.get('research_topics', []),
|
||||
'content_style': research_data.get('writing_style', {}).get('tone', []),
|
||||
'content_length': 'Medium (1000-2000 words)',
|
||||
'visual_preferences': ['Infographics', 'Charts', 'Diagrams'],
|
||||
},
|
||||
'audience_intelligence': {
|
||||
'target_audience': research_data.get('target_audience', {}).get('demographics', []),
|
||||
'pain_points': research_data.get('target_audience', {}).get('pain_points', []),
|
||||
'buying_journey': research_data.get('target_audience', {}).get('buying_journey', {}),
|
||||
'consumption_patterns': research_data.get('target_audience', {}).get('consumption_patterns', {}),
|
||||
},
|
||||
'research_goals': {
|
||||
'primary_goals': research_data.get('research_topics', []),
|
||||
'secondary_goals': research_data.get('content_types', []),
|
||||
'success_metrics': ['Website traffic', 'Lead quality', 'Engagement rates'],
|
||||
},
|
||||
'data_quality': research_data.get('data_quality'),
|
||||
'confidence_level': research_data.get('confidence_level', 0.8),
|
||||
'data_freshness': research_data.get('data_freshness', 0.8),
|
||||
}
|
||||
@@ -0,0 +1,44 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
async def normalize_website_analysis(website_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
if not website_data:
|
||||
return {}
|
||||
|
||||
processed_data = {
|
||||
'website_url': website_data.get('website_url'),
|
||||
'industry': website_data.get('target_audience', {}).get('industry_focus'),
|
||||
'market_position': 'Emerging',
|
||||
'business_size': 'Medium',
|
||||
'target_audience': website_data.get('target_audience', {}).get('demographics'),
|
||||
'content_goals': website_data.get('content_type', {}).get('purpose', []),
|
||||
'performance_metrics': {
|
||||
'traffic': website_data.get('performance_metrics', {}).get('traffic', 10000),
|
||||
'conversion_rate': website_data.get('performance_metrics', {}).get('conversion_rate', 2.5),
|
||||
'bounce_rate': website_data.get('performance_metrics', {}).get('bounce_rate', 50.0),
|
||||
'avg_session_duration': website_data.get('performance_metrics', {}).get('avg_session_duration', 150),
|
||||
'estimated_market_share': website_data.get('performance_metrics', {}).get('estimated_market_share')
|
||||
},
|
||||
'traffic_sources': website_data.get('traffic_sources', {
|
||||
'organic': 70,
|
||||
'social': 20,
|
||||
'direct': 7,
|
||||
'referral': 3
|
||||
}),
|
||||
'content_gaps': website_data.get('style_guidelines', {}).get('content_gaps', []),
|
||||
'topics': website_data.get('content_type', {}).get('primary_type', []),
|
||||
'content_quality_score': website_data.get('content_quality_score', 7.5),
|
||||
'seo_opportunities': website_data.get('style_guidelines', {}).get('seo_opportunities', []),
|
||||
'competitors': website_data.get('competitors', []),
|
||||
'competitive_advantages': website_data.get('style_guidelines', {}).get('advantages', []),
|
||||
'market_gaps': website_data.get('style_guidelines', {}).get('market_gaps', []),
|
||||
'data_quality': website_data.get('data_quality'),
|
||||
'confidence_level': website_data.get('confidence_level', 0.8),
|
||||
'data_freshness': website_data.get('data_freshness', 0.8),
|
||||
'content_budget': website_data.get('content_budget'),
|
||||
'team_size': website_data.get('team_size'),
|
||||
'implementation_timeline': website_data.get('implementation_timeline'),
|
||||
'market_share': website_data.get('market_share'),
|
||||
'target_metrics': website_data.get('target_metrics'),
|
||||
}
|
||||
|
||||
return processed_data
|
||||
@@ -0,0 +1,61 @@
|
||||
from typing import Any, Dict
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def calculate_quality_scores_from_raw(data_sources: Dict[str, Any]) -> Dict[str, float]:
|
||||
scores: Dict[str, float] = {}
|
||||
for source, data in data_sources.items():
|
||||
if isinstance(data, dict) and data:
|
||||
total = len(data)
|
||||
non_null = len([v for v in data.values() if v is not None])
|
||||
scores[source] = (non_null / total) * 100 if total else 0.0
|
||||
else:
|
||||
scores[source] = 0.0
|
||||
return scores
|
||||
|
||||
|
||||
def calculate_confidence_from_raw(data_sources: Dict[str, Any]) -> Dict[str, float]:
|
||||
levels: Dict[str, float] = {}
|
||||
if data_sources.get('website_analysis'):
|
||||
levels['website_analysis'] = data_sources['website_analysis'].get('confidence_level', 0.8)
|
||||
if data_sources.get('research_preferences'):
|
||||
levels['research_preferences'] = data_sources['research_preferences'].get('confidence_level', 0.7)
|
||||
if data_sources.get('api_keys_data'):
|
||||
levels['api_keys_data'] = data_sources['api_keys_data'].get('confidence_level', 0.6)
|
||||
return levels
|
||||
|
||||
|
||||
def calculate_data_freshness(onboarding_session: Any) -> Dict[str, Any]:
|
||||
try:
|
||||
updated_at = None
|
||||
if hasattr(onboarding_session, 'updated_at'):
|
||||
updated_at = onboarding_session.updated_at
|
||||
elif isinstance(onboarding_session, dict):
|
||||
updated_at = onboarding_session.get('last_updated') or onboarding_session.get('updated_at')
|
||||
|
||||
if not updated_at:
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
|
||||
if isinstance(updated_at, str):
|
||||
try:
|
||||
updated_at = datetime.fromisoformat(updated_at.replace('Z', '+00:00'))
|
||||
except ValueError:
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
|
||||
age_days = (datetime.utcnow() - updated_at).days
|
||||
if age_days <= 7:
|
||||
status = 'fresh'
|
||||
elif age_days <= 30:
|
||||
status = 'recent'
|
||||
elif age_days <= 90:
|
||||
status = 'aging'
|
||||
else:
|
||||
status = 'stale'
|
||||
|
||||
return {
|
||||
'status': status,
|
||||
'age_days': age_days,
|
||||
'last_updated': updated_at.isoformat() if hasattr(updated_at, 'isoformat') else str(updated_at)
|
||||
}
|
||||
except Exception:
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
@@ -0,0 +1,39 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
REQUIRED_TOP_LEVEL_KEYS = {
|
||||
'fields': dict,
|
||||
'sources': dict,
|
||||
'quality_scores': dict,
|
||||
'confidence_levels': dict,
|
||||
'data_freshness': dict,
|
||||
'input_data_points': dict,
|
||||
}
|
||||
|
||||
|
||||
def validate_output(payload: Dict[str, Any]) -> None:
|
||||
# Top-level keys and types
|
||||
for key, typ in REQUIRED_TOP_LEVEL_KEYS.items():
|
||||
if key not in payload:
|
||||
raise ValueError(f"Autofill payload missing key: {key}")
|
||||
if not isinstance(payload[key], typ):
|
||||
raise ValueError(f"Autofill payload key '{key}' must be {typ.__name__}")
|
||||
|
||||
fields = payload['fields']
|
||||
if not isinstance(fields, dict):
|
||||
raise ValueError("fields must be an object")
|
||||
|
||||
# Allow empty fields, but validate structure when present
|
||||
for field_id, spec in fields.items():
|
||||
if not isinstance(spec, dict):
|
||||
raise ValueError(f"Field '{field_id}' must be an object")
|
||||
for k in ('value', 'source', 'confidence'):
|
||||
if k not in spec:
|
||||
raise ValueError(f"Field '{field_id}' missing '{k}'")
|
||||
if spec['source'] not in ('website_analysis', 'research_preferences', 'api_keys_data', 'onboarding_session'):
|
||||
raise ValueError(f"Field '{field_id}' has invalid source: {spec['source']}")
|
||||
try:
|
||||
c = float(spec['confidence'])
|
||||
except Exception:
|
||||
raise ValueError(f"Field '{field_id}' confidence must be numeric")
|
||||
if c < 0.0 or c > 1.0:
|
||||
raise ValueError(f"Field '{field_id}' confidence must be in [0,1]")
|
||||
@@ -0,0 +1,268 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
|
||||
def transform_to_fields(*, website: Dict[str, Any], research: Dict[str, Any], api_keys: Dict[str, Any], session: Dict[str, Any]) -> Dict[str, Any]:
|
||||
fields: Dict[str, Any] = {}
|
||||
|
||||
# Business Context
|
||||
if website.get('content_goals'):
|
||||
fields['business_objectives'] = {
|
||||
'value': website.get('content_goals'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
|
||||
if website.get('target_metrics'):
|
||||
fields['target_metrics'] = {
|
||||
'value': website.get('target_metrics'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
elif website.get('performance_metrics'):
|
||||
fields['target_metrics'] = {
|
||||
'value': website.get('performance_metrics'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
|
||||
# content_budget with session fallback
|
||||
if website.get('content_budget') is not None:
|
||||
fields['content_budget'] = {
|
||||
'value': website.get('content_budget'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
elif isinstance(session, dict) and session.get('budget') is not None:
|
||||
fields['content_budget'] = {
|
||||
'value': session.get('budget'),
|
||||
'source': 'onboarding_session',
|
||||
'confidence': 0.7
|
||||
}
|
||||
|
||||
# team_size with session fallback
|
||||
if website.get('team_size') is not None:
|
||||
fields['team_size'] = {
|
||||
'value': website.get('team_size'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
elif isinstance(session, dict) and session.get('team_size') is not None:
|
||||
fields['team_size'] = {
|
||||
'value': session.get('team_size'),
|
||||
'source': 'onboarding_session',
|
||||
'confidence': 0.7
|
||||
}
|
||||
|
||||
# implementation_timeline with session fallback
|
||||
if website.get('implementation_timeline'):
|
||||
fields['implementation_timeline'] = {
|
||||
'value': website.get('implementation_timeline'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
elif isinstance(session, dict) and session.get('timeline'):
|
||||
fields['implementation_timeline'] = {
|
||||
'value': session.get('timeline'),
|
||||
'source': 'onboarding_session',
|
||||
'confidence': 0.7
|
||||
}
|
||||
|
||||
# market_share with derive from performance metrics
|
||||
if website.get('market_share'):
|
||||
fields['market_share'] = {
|
||||
'value': website.get('market_share'),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
elif website.get('performance_metrics'):
|
||||
fields['market_share'] = {
|
||||
'value': website.get('performance_metrics', {}).get('estimated_market_share', None),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level')
|
||||
}
|
||||
|
||||
# performance metrics
|
||||
fields['performance_metrics'] = {
|
||||
'value': website.get('performance_metrics', {}),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
# Audience Intelligence
|
||||
audience_research = research.get('audience_intelligence', {})
|
||||
content_prefs = research.get('content_preferences', {})
|
||||
|
||||
fields['content_preferences'] = {
|
||||
'value': content_prefs,
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['consumption_patterns'] = {
|
||||
'value': audience_research.get('consumption_patterns', {}),
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['audience_pain_points'] = {
|
||||
'value': audience_research.get('pain_points', []),
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['buying_journey'] = {
|
||||
'value': audience_research.get('buying_journey', {}),
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['seasonal_trends'] = {
|
||||
'value': ['Q1: Planning', 'Q2: Execution', 'Q3: Optimization', 'Q4: Review'],
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.7)
|
||||
}
|
||||
|
||||
fields['engagement_metrics'] = {
|
||||
'value': {
|
||||
'avg_session_duration': website.get('performance_metrics', {}).get('avg_session_duration', 180),
|
||||
'bounce_rate': website.get('performance_metrics', {}).get('bounce_rate', 45.5),
|
||||
'pages_per_session': 2.5,
|
||||
},
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
# Competitive Intelligence
|
||||
fields['top_competitors'] = {
|
||||
'value': website.get('competitors', [
|
||||
'Competitor A - Industry Leader',
|
||||
'Competitor B - Emerging Player',
|
||||
'Competitor C - Niche Specialist'
|
||||
]),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['competitor_content_strategies'] = {
|
||||
'value': ['Educational content', 'Case studies', 'Thought leadership'],
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.7)
|
||||
}
|
||||
|
||||
fields['market_gaps'] = {
|
||||
'value': website.get('market_gaps', []),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['industry_trends'] = {
|
||||
'value': ['Digital transformation', 'AI/ML adoption', 'Remote work'],
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['emerging_trends'] = {
|
||||
'value': ['Voice search optimization', 'Video content', 'Interactive content'],
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.7)
|
||||
}
|
||||
|
||||
# Content Strategy
|
||||
fields['preferred_formats'] = {
|
||||
'value': content_prefs.get('preferred_formats', ['Blog posts', 'Whitepapers', 'Webinars', 'Case studies', 'Videos']),
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['content_mix'] = {
|
||||
'value': {
|
||||
'blog_posts': 40,
|
||||
'whitepapers': 20,
|
||||
'webinars': 15,
|
||||
'case_studies': 15,
|
||||
'videos': 10,
|
||||
},
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['content_frequency'] = {
|
||||
'value': 'Weekly',
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['optimal_timing'] = {
|
||||
'value': {
|
||||
'best_days': ['Tuesday', 'Wednesday', 'Thursday'],
|
||||
'best_times': ['9:00 AM', '1:00 PM', '3:00 PM']
|
||||
},
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.7)
|
||||
}
|
||||
|
||||
fields['quality_metrics'] = {
|
||||
'value': {
|
||||
'readability_score': 8.5,
|
||||
'engagement_target': 5.0,
|
||||
'conversion_target': 2.0
|
||||
},
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['editorial_guidelines'] = {
|
||||
'value': {
|
||||
'tone': content_prefs.get('content_style', ['Professional', 'Educational']),
|
||||
'length': content_prefs.get('content_length', 'Medium (1000-2000 words)'),
|
||||
'formatting': ['Use headers', 'Include visuals', 'Add CTAs']
|
||||
},
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['brand_voice'] = {
|
||||
'value': {
|
||||
'tone': 'Professional yet approachable',
|
||||
'style': 'Educational and authoritative',
|
||||
'personality': 'Expert, helpful, trustworthy'
|
||||
},
|
||||
'source': 'research_preferences',
|
||||
'confidence': research.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
# Performance & Analytics
|
||||
fields['traffic_sources'] = {
|
||||
'value': website.get('traffic_sources', {}),
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['conversion_rates'] = {
|
||||
'value': {
|
||||
'overall': website.get('performance_metrics', {}).get('conversion_rate', 3.2),
|
||||
'blog': 2.5,
|
||||
'landing_pages': 4.0,
|
||||
'email': 5.5,
|
||||
},
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
fields['content_roi_targets'] = {
|
||||
'value': {
|
||||
'target_roi': 300,
|
||||
'cost_per_lead': 50,
|
||||
'lifetime_value': 500,
|
||||
},
|
||||
'source': 'website_analysis',
|
||||
'confidence': website.get('confidence_level', 0.7)
|
||||
}
|
||||
|
||||
fields['ab_testing_capabilities'] = {
|
||||
'value': True,
|
||||
'source': 'api_keys_data',
|
||||
'confidence': api_keys.get('confidence_level', 0.8)
|
||||
}
|
||||
|
||||
return fields
|
||||
@@ -0,0 +1,98 @@
|
||||
from typing import Any, Dict
|
||||
|
||||
|
||||
def build_data_sources_map(website: Dict[str, Any], research: Dict[str, Any], api_keys: Dict[str, Any]) -> Dict[str, str]:
|
||||
sources: Dict[str, str] = {}
|
||||
|
||||
website_fields = ['business_objectives', 'target_metrics', 'content_budget', 'team_size',
|
||||
'implementation_timeline', 'market_share', 'competitive_position',
|
||||
'performance_metrics', 'engagement_metrics', 'top_competitors',
|
||||
'competitor_content_strategies', 'market_gaps', 'industry_trends',
|
||||
'emerging_trends', 'traffic_sources', 'conversion_rates', 'content_roi_targets']
|
||||
|
||||
research_fields = ['content_preferences', 'consumption_patterns', 'audience_pain_points',
|
||||
'buying_journey', 'seasonal_trends', 'preferred_formats', 'content_mix',
|
||||
'content_frequency', 'optimal_timing', 'quality_metrics', 'editorial_guidelines',
|
||||
'brand_voice']
|
||||
|
||||
api_fields = ['ab_testing_capabilities']
|
||||
|
||||
for f in website_fields:
|
||||
sources[f] = 'website_analysis'
|
||||
for f in research_fields:
|
||||
sources[f] = 'research_preferences'
|
||||
for f in api_fields:
|
||||
sources[f] = 'api_keys_data'
|
||||
|
||||
return sources
|
||||
|
||||
|
||||
def build_input_data_points(*, website_raw: Dict[str, Any], research_raw: Dict[str, Any], api_raw: Dict[str, Any]) -> Dict[str, Any]:
|
||||
input_data_points: Dict[str, Any] = {}
|
||||
|
||||
if website_raw:
|
||||
input_data_points['business_objectives'] = {
|
||||
'website_content': website_raw.get('content_goals', 'Not available'),
|
||||
'meta_description': website_raw.get('meta_description', 'Not available'),
|
||||
'about_page': website_raw.get('about_page_content', 'Not available'),
|
||||
'page_title': website_raw.get('page_title', 'Not available'),
|
||||
'content_analysis': website_raw.get('content_analysis', {})
|
||||
}
|
||||
|
||||
if research_raw:
|
||||
input_data_points['target_metrics'] = {
|
||||
'research_preferences': research_raw.get('target_audience', 'Not available'),
|
||||
'industry_benchmarks': research_raw.get('industry_benchmarks', 'Not available'),
|
||||
'competitor_analysis': research_raw.get('competitor_analysis', 'Not available'),
|
||||
'market_research': research_raw.get('market_research', 'Not available')
|
||||
}
|
||||
|
||||
if research_raw:
|
||||
input_data_points['content_preferences'] = {
|
||||
'user_preferences': research_raw.get('content_types', 'Not available'),
|
||||
'industry_trends': research_raw.get('industry_trends', 'Not available'),
|
||||
'consumption_patterns': research_raw.get('consumption_patterns', 'Not available'),
|
||||
'audience_research': research_raw.get('audience_research', 'Not available')
|
||||
}
|
||||
|
||||
if website_raw or research_raw:
|
||||
input_data_points['preferred_formats'] = {
|
||||
'existing_content': website_raw.get('existing_content_types', 'Not available') if website_raw else 'Not available',
|
||||
'engagement_metrics': website_raw.get('engagement_metrics', 'Not available') if website_raw else 'Not available',
|
||||
'platform_analysis': research_raw.get('platform_preferences', 'Not available') if research_raw else 'Not available',
|
||||
'content_performance': website_raw.get('content_performance', 'Not available') if website_raw else 'Not available'
|
||||
}
|
||||
|
||||
if research_raw:
|
||||
input_data_points['content_frequency'] = {
|
||||
'audience_research': research_raw.get('content_frequency_preferences', 'Not available'),
|
||||
'industry_standards': research_raw.get('industry_frequency', 'Not available'),
|
||||
'competitor_frequency': research_raw.get('competitor_frequency', 'Not available'),
|
||||
'optimal_timing': research_raw.get('optimal_timing', 'Not available')
|
||||
}
|
||||
|
||||
if website_raw:
|
||||
input_data_points['content_budget'] = {
|
||||
'website_analysis': website_raw.get('budget_indicators', 'Not available'),
|
||||
'industry_standards': website_raw.get('industry_budget', 'Not available'),
|
||||
'company_size': website_raw.get('company_size', 'Not available'),
|
||||
'market_position': website_raw.get('market_position', 'Not available')
|
||||
}
|
||||
|
||||
if website_raw:
|
||||
input_data_points['team_size'] = {
|
||||
'company_profile': website_raw.get('company_profile', 'Not available'),
|
||||
'content_volume': website_raw.get('content_volume', 'Not available'),
|
||||
'industry_standards': website_raw.get('industry_team_size', 'Not available'),
|
||||
'budget_constraints': website_raw.get('budget_constraints', 'Not available')
|
||||
}
|
||||
|
||||
if research_raw:
|
||||
input_data_points['implementation_timeline'] = {
|
||||
'project_scope': research_raw.get('project_scope', 'Not available'),
|
||||
'resource_availability': research_raw.get('resource_availability', 'Not available'),
|
||||
'industry_timeline': research_raw.get('industry_timeline', 'Not available'),
|
||||
'complexity_assessment': research_raw.get('complexity_assessment', 'Not available')
|
||||
}
|
||||
|
||||
return input_data_points
|
||||
@@ -1,10 +1,16 @@
|
||||
"""
|
||||
Onboarding Module
|
||||
Onboarding data integration and processing services.
|
||||
Onboarding data integration and processing.
|
||||
"""
|
||||
|
||||
from .data_integration import OnboardingDataIntegrationService
|
||||
from .field_transformation import FieldTransformationService
|
||||
from .data_quality import DataQualityService
|
||||
from .field_transformation import FieldTransformationService
|
||||
from .data_processor import OnboardingDataProcessor
|
||||
|
||||
__all__ = ['OnboardingDataIntegrationService', 'FieldTransformationService', 'DataQualityService']
|
||||
__all__ = [
|
||||
'OnboardingDataIntegrationService',
|
||||
'DataQualityService',
|
||||
'FieldTransformationService',
|
||||
'OnboardingDataProcessor'
|
||||
]
|
||||
@@ -305,19 +305,28 @@ class OnboardingDataIntegrationService:
|
||||
).first()
|
||||
|
||||
if existing_record:
|
||||
existing_record.website_analysis_data = integrated_data.get('website_analysis', {})
|
||||
existing_record.research_preferences_data = integrated_data.get('research_preferences', {})
|
||||
existing_record.api_keys_data = integrated_data.get('api_keys_data', {})
|
||||
# Use legacy columns that are known to exist
|
||||
if hasattr(existing_record, 'website_analysis_data'):
|
||||
existing_record.website_analysis_data = integrated_data.get('website_analysis', {})
|
||||
if hasattr(existing_record, 'research_preferences_data'):
|
||||
existing_record.research_preferences_data = integrated_data.get('research_preferences', {})
|
||||
if hasattr(existing_record, 'api_keys_data'):
|
||||
existing_record.api_keys_data = integrated_data.get('api_keys_data', {})
|
||||
existing_record.updated_at = datetime.utcnow()
|
||||
else:
|
||||
new_record = OnboardingDataIntegration(
|
||||
user_id=user_id,
|
||||
website_analysis_data=integrated_data.get('website_analysis', {}),
|
||||
research_preferences_data=integrated_data.get('research_preferences', {}),
|
||||
api_keys_data=integrated_data.get('api_keys_data', {}),
|
||||
created_at=datetime.utcnow(),
|
||||
updated_at=datetime.utcnow()
|
||||
)
|
||||
new_kwargs = {
|
||||
'user_id': user_id,
|
||||
'created_at': datetime.utcnow(),
|
||||
'updated_at': datetime.utcnow()
|
||||
}
|
||||
if 'website_analysis' in integrated_data:
|
||||
new_kwargs['website_analysis_data'] = integrated_data.get('website_analysis', {})
|
||||
if 'research_preferences' in integrated_data:
|
||||
new_kwargs['research_preferences_data'] = integrated_data.get('research_preferences', {})
|
||||
if 'api_keys_data' in integrated_data:
|
||||
new_kwargs['api_keys_data'] = integrated_data.get('api_keys_data', {})
|
||||
|
||||
new_record = OnboardingDataIntegration(**new_kwargs)
|
||||
db.add(new_record)
|
||||
|
||||
db.commit()
|
||||
@@ -326,6 +335,8 @@ class OnboardingDataIntegrationService:
|
||||
except Exception as e:
|
||||
logger.error(f"Error storing integrated data for user {user_id}: {str(e)}")
|
||||
db.rollback()
|
||||
# Soft-fail storage: do not break the refresh path
|
||||
return
|
||||
|
||||
def _get_fallback_data(self) -> Dict[str, Any]:
|
||||
"""Get fallback data when processing fails."""
|
||||
|
||||
@@ -0,0 +1,301 @@
|
||||
"""
|
||||
Onboarding Data Processor
|
||||
Handles processing and transformation of onboarding data for strategic intelligence.
|
||||
"""
|
||||
|
||||
import logging
|
||||
from typing import Dict, List, Any, Optional, Union
|
||||
from datetime import datetime
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
# Import database models
|
||||
from models.onboarding import OnboardingSession, WebsiteAnalysis, ResearchPreferences, APIKey
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class OnboardingDataProcessor:
|
||||
"""Processes and transforms onboarding data for strategic intelligence generation."""
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
async def process_onboarding_data(self, user_id: int, db: Session) -> Optional[Dict[str, Any]]:
|
||||
"""Process onboarding data for a user and return structured data for strategic intelligence."""
|
||||
try:
|
||||
logger.info(f"Processing onboarding data for user {user_id}")
|
||||
|
||||
# Get onboarding session
|
||||
onboarding_session = db.query(OnboardingSession).filter(
|
||||
OnboardingSession.user_id == user_id
|
||||
).first()
|
||||
|
||||
if not onboarding_session:
|
||||
logger.warning(f"No onboarding session found for user {user_id}")
|
||||
return None
|
||||
|
||||
# Get website analysis data
|
||||
website_analysis = db.query(WebsiteAnalysis).filter(
|
||||
WebsiteAnalysis.session_id == onboarding_session.id
|
||||
).first()
|
||||
|
||||
# Get research preferences data
|
||||
research_preferences = db.query(ResearchPreferences).filter(
|
||||
ResearchPreferences.session_id == onboarding_session.id
|
||||
).first()
|
||||
|
||||
# Get API keys data
|
||||
api_keys = db.query(APIKey).filter(
|
||||
APIKey.session_id == onboarding_session.id
|
||||
).all()
|
||||
|
||||
# Process each data type
|
||||
processed_data = {
|
||||
'website_analysis': await self._process_website_analysis(website_analysis),
|
||||
'research_preferences': await self._process_research_preferences(research_preferences),
|
||||
'api_keys_data': await self._process_api_keys_data(api_keys),
|
||||
'session_data': self._process_session_data(onboarding_session)
|
||||
}
|
||||
|
||||
# Transform into strategic intelligence format
|
||||
strategic_data = self._transform_to_strategic_format(processed_data)
|
||||
|
||||
logger.info(f"Successfully processed onboarding data for user {user_id}")
|
||||
return strategic_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing onboarding data for user {user_id}: {str(e)}")
|
||||
return None
|
||||
|
||||
async def _process_website_analysis(self, website_analysis: Optional[WebsiteAnalysis]) -> Dict[str, Any]:
|
||||
"""Process website analysis data."""
|
||||
if not website_analysis:
|
||||
return {}
|
||||
|
||||
try:
|
||||
return {
|
||||
'website_url': getattr(website_analysis, 'website_url', ''),
|
||||
'industry': getattr(website_analysis, 'industry', 'Technology'), # Default value if attribute doesn't exist
|
||||
'content_goals': getattr(website_analysis, 'content_goals', []),
|
||||
'performance_metrics': getattr(website_analysis, 'performance_metrics', {}),
|
||||
'traffic_sources': getattr(website_analysis, 'traffic_sources', []),
|
||||
'content_gaps': getattr(website_analysis, 'content_gaps', []),
|
||||
'topics': getattr(website_analysis, 'topics', []),
|
||||
'content_quality_score': getattr(website_analysis, 'content_quality_score', 0),
|
||||
'seo_opportunities': getattr(website_analysis, 'seo_opportunities', []),
|
||||
'competitors': getattr(website_analysis, 'competitors', []),
|
||||
'competitive_advantages': getattr(website_analysis, 'competitive_advantages', []),
|
||||
'market_gaps': getattr(website_analysis, 'market_gaps', []),
|
||||
'last_updated': website_analysis.updated_at.isoformat() if hasattr(website_analysis, 'updated_at') and website_analysis.updated_at else None
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing website analysis: {str(e)}")
|
||||
return {}
|
||||
|
||||
async def _process_research_preferences(self, research_preferences: Optional[ResearchPreferences]) -> Dict[str, Any]:
|
||||
"""Process research preferences data."""
|
||||
if not research_preferences:
|
||||
return {}
|
||||
|
||||
try:
|
||||
return {
|
||||
'content_preferences': {
|
||||
'preferred_formats': research_preferences.content_types,
|
||||
'content_topics': research_preferences.research_topics,
|
||||
'content_style': research_preferences.writing_style.get('tone', []) if research_preferences.writing_style else [],
|
||||
'content_length': research_preferences.content_length,
|
||||
'visual_preferences': research_preferences.visual_preferences
|
||||
},
|
||||
'audience_research': {
|
||||
'target_audience': research_preferences.target_audience.get('demographics', []) if research_preferences.target_audience else [],
|
||||
'audience_pain_points': research_preferences.target_audience.get('pain_points', []) if research_preferences.target_audience else [],
|
||||
'buying_journey': research_preferences.target_audience.get('buying_journey', {}) if research_preferences.target_audience else {},
|
||||
'consumption_patterns': research_preferences.target_audience.get('consumption_patterns', {}) if research_preferences.target_audience else {}
|
||||
},
|
||||
'research_goals': {
|
||||
'primary_goals': research_preferences.research_topics,
|
||||
'secondary_goals': research_preferences.content_types,
|
||||
'success_metrics': research_preferences.success_metrics
|
||||
},
|
||||
'last_updated': research_preferences.updated_at.isoformat() if research_preferences.updated_at else None
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing research preferences: {str(e)}")
|
||||
return {}
|
||||
|
||||
async def _process_api_keys_data(self, api_keys: List[APIKey]) -> Dict[str, Any]:
|
||||
"""Process API keys data."""
|
||||
try:
|
||||
processed_data = {
|
||||
'analytics_data': {},
|
||||
'social_media_data': {},
|
||||
'competitor_data': {},
|
||||
'last_updated': None
|
||||
}
|
||||
|
||||
for api_key in api_keys:
|
||||
if api_key.provider == 'google_analytics':
|
||||
processed_data['analytics_data']['google_analytics'] = {
|
||||
'connected': True,
|
||||
'data_available': True,
|
||||
'metrics': api_key.metrics if api_key.metrics else {}
|
||||
}
|
||||
elif api_key.provider == 'google_search_console':
|
||||
processed_data['analytics_data']['google_search_console'] = {
|
||||
'connected': True,
|
||||
'data_available': True,
|
||||
'metrics': api_key.metrics if api_key.metrics else {}
|
||||
}
|
||||
elif api_key.provider in ['linkedin', 'twitter', 'facebook']:
|
||||
processed_data['social_media_data'][api_key.provider] = {
|
||||
'connected': True,
|
||||
'followers': api_key.metrics.get('followers', 0) if api_key.metrics else 0
|
||||
}
|
||||
elif api_key.provider in ['semrush', 'ahrefs', 'moz']:
|
||||
processed_data['competitor_data'][api_key.provider] = {
|
||||
'connected': True,
|
||||
'competitors_analyzed': api_key.metrics.get('competitors_analyzed', 0) if api_key.metrics else 0
|
||||
}
|
||||
|
||||
# Update last_updated if this key is more recent
|
||||
if api_key.updated_at and (not processed_data['last_updated'] or api_key.updated_at > datetime.fromisoformat(processed_data['last_updated'])):
|
||||
processed_data['last_updated'] = api_key.updated_at.isoformat()
|
||||
|
||||
return processed_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing API keys data: {str(e)}")
|
||||
return {}
|
||||
|
||||
def _process_session_data(self, onboarding_session: OnboardingSession) -> Dict[str, Any]:
|
||||
"""Process onboarding session data."""
|
||||
try:
|
||||
return {
|
||||
'session_id': getattr(onboarding_session, 'id', None),
|
||||
'user_id': getattr(onboarding_session, 'user_id', None),
|
||||
'created_at': onboarding_session.created_at.isoformat() if hasattr(onboarding_session, 'created_at') and onboarding_session.created_at else None,
|
||||
'updated_at': onboarding_session.updated_at.isoformat() if hasattr(onboarding_session, 'updated_at') and onboarding_session.updated_at else None,
|
||||
'completion_status': getattr(onboarding_session, 'completion_status', 'in_progress'),
|
||||
'session_data': getattr(onboarding_session, 'session_data', {}),
|
||||
'progress_percentage': getattr(onboarding_session, 'progress_percentage', 0),
|
||||
'last_activity': getattr(onboarding_session, 'last_activity', None)
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing session data: {str(e)}")
|
||||
return {}
|
||||
|
||||
def _transform_to_strategic_format(self, processed_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Transform processed onboarding data into strategic intelligence format."""
|
||||
try:
|
||||
website_data = processed_data.get('website_analysis', {})
|
||||
research_data = processed_data.get('research_preferences', {})
|
||||
api_data = processed_data.get('api_keys_data', {})
|
||||
session_data = processed_data.get('session_data', {})
|
||||
|
||||
# Return data in nested format that field transformation service expects
|
||||
return {
|
||||
'website_analysis': {
|
||||
'content_goals': website_data.get('content_goals', []),
|
||||
'performance_metrics': website_data.get('performance_metrics', {}),
|
||||
'competitors': website_data.get('competitors', []),
|
||||
'content_gaps': website_data.get('content_gaps', []),
|
||||
'industry': website_data.get('industry', 'Technology'),
|
||||
'target_audience': website_data.get('target_audience', {}),
|
||||
'business_type': website_data.get('business_type', 'Technology')
|
||||
},
|
||||
'research_preferences': {
|
||||
'content_types': research_data.get('content_preferences', {}).get('preferred_formats', []),
|
||||
'research_topics': research_data.get('research_topics', []),
|
||||
'performance_tracking': research_data.get('performance_tracking', []),
|
||||
'competitor_analysis': research_data.get('competitor_analysis', []),
|
||||
'target_audience': research_data.get('audience_research', {}).get('target_audience', {}),
|
||||
'industry_focus': research_data.get('industry_focus', []),
|
||||
'trend_analysis': research_data.get('trend_analysis', []),
|
||||
'content_calendar': research_data.get('content_calendar', {})
|
||||
},
|
||||
'onboarding_session': {
|
||||
'session_data': {
|
||||
'budget': session_data.get('budget', 3000),
|
||||
'team_size': session_data.get('team_size', 2),
|
||||
'timeline': session_data.get('timeline', '3 months'),
|
||||
'brand_voice': session_data.get('brand_voice', 'Professional yet approachable')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error transforming to strategic format: {str(e)}")
|
||||
return {}
|
||||
|
||||
def calculate_data_quality_scores(self, processed_data: Dict[str, Any]) -> Dict[str, float]:
|
||||
"""Calculate quality scores for each data source."""
|
||||
scores = {}
|
||||
|
||||
for source, data in processed_data.items():
|
||||
if data and isinstance(data, dict):
|
||||
# Simple scoring based on data completeness
|
||||
total_fields = len(data)
|
||||
present_fields = len([v for v in data.values() if v is not None and v != {}])
|
||||
completeness = present_fields / total_fields if total_fields > 0 else 0.0
|
||||
scores[source] = completeness * 100
|
||||
else:
|
||||
scores[source] = 0.0
|
||||
|
||||
return scores
|
||||
|
||||
def calculate_confidence_levels(self, processed_data: Dict[str, Any]) -> Dict[str, float]:
|
||||
"""Calculate confidence levels for processed data."""
|
||||
confidence_levels = {}
|
||||
|
||||
# Base confidence on data source quality
|
||||
base_confidence = {
|
||||
'website_analysis': 0.8,
|
||||
'research_preferences': 0.7,
|
||||
'api_keys_data': 0.6,
|
||||
'session_data': 0.9
|
||||
}
|
||||
|
||||
for source, data in processed_data.items():
|
||||
if data and isinstance(data, dict):
|
||||
# Adjust confidence based on data completeness
|
||||
quality_score = self.calculate_data_quality_scores({source: data})[source] / 100
|
||||
base_conf = base_confidence.get(source, 0.5)
|
||||
confidence_levels[source] = base_conf * quality_score
|
||||
else:
|
||||
confidence_levels[source] = 0.0
|
||||
|
||||
return confidence_levels
|
||||
|
||||
def calculate_data_freshness(self, session_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Calculate data freshness for onboarding data."""
|
||||
try:
|
||||
updated_at = session_data.get('updated_at')
|
||||
if not updated_at:
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
|
||||
# Convert string to datetime if needed
|
||||
if isinstance(updated_at, str):
|
||||
try:
|
||||
updated_at = datetime.fromisoformat(updated_at.replace('Z', '+00:00'))
|
||||
except ValueError:
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
|
||||
age_days = (datetime.utcnow() - updated_at).days
|
||||
|
||||
if age_days <= 7:
|
||||
status = 'fresh'
|
||||
elif age_days <= 30:
|
||||
status = 'recent'
|
||||
elif age_days <= 90:
|
||||
status = 'aging'
|
||||
else:
|
||||
status = 'stale'
|
||||
|
||||
return {
|
||||
'status': status,
|
||||
'age_days': age_days,
|
||||
'last_updated': updated_at.isoformat() if hasattr(updated_at, 'isoformat') else str(updated_at)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating data freshness: {str(e)}")
|
||||
return {'status': 'unknown', 'age_days': 'unknown'}
|
||||
@@ -92,7 +92,8 @@ class DataQualityService:
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error assessing data quality: {str(e)}")
|
||||
return self._get_fallback_quality_assessment()
|
||||
# Raise exception instead of returning fallback data
|
||||
raise Exception(f"Failed to assess data quality: {str(e)}")
|
||||
|
||||
def _assess_website_analysis_quality(self, website_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Assess quality of website analysis data."""
|
||||
@@ -501,22 +502,6 @@ class DataQualityService:
|
||||
logger.error(f"Error identifying quality issues: {str(e)}")
|
||||
return ["Unable to identify issues due to assessment error"]
|
||||
|
||||
def _get_fallback_quality_assessment(self) -> Dict[str, Any]:
|
||||
"""Get fallback quality assessment when assessment fails."""
|
||||
return {
|
||||
'overall_score': 0.0,
|
||||
'completeness': 0.0,
|
||||
'freshness': 0.0,
|
||||
'accuracy': 0.0,
|
||||
'relevance': 0.0,
|
||||
'consistency': 0.0,
|
||||
'confidence': 0.0,
|
||||
'quality_level': 'poor',
|
||||
'recommendations': ['Unable to assess data quality'],
|
||||
'issues': ['Quality assessment failed'],
|
||||
'assessment_timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
def validate_field_data(self, field_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Validate individual field data."""
|
||||
try:
|
||||
|
||||
@@ -147,48 +147,108 @@ class FieldTransformationService:
|
||||
}
|
||||
|
||||
def transform_onboarding_data_to_fields(self, integrated_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Transform integrated onboarding data to strategic input fields."""
|
||||
"""Transform onboarding data to strategic input fields."""
|
||||
try:
|
||||
logger.info("Transforming onboarding data to strategic fields")
|
||||
|
||||
|
||||
transformed_fields = {}
|
||||
data_sources = {}
|
||||
|
||||
for field_id, mapping_config in self.field_mappings.items():
|
||||
try:
|
||||
# Extract data from sources
|
||||
source_data = self._extract_source_data(integrated_data, mapping_config['sources'])
|
||||
|
||||
if source_data:
|
||||
# Apply transformation
|
||||
transformation_method = getattr(self, mapping_config['transformation'])
|
||||
transformed_value = transformation_method(source_data, integrated_data)
|
||||
|
||||
if transformed_value:
|
||||
transformed_fields[field_id] = transformed_value
|
||||
data_sources[field_id] = self._get_data_source_info(mapping_config['sources'], integrated_data)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error transforming field {field_id}: {str(e)}")
|
||||
continue
|
||||
|
||||
result = {
|
||||
'fields': transformed_fields,
|
||||
'sources': data_sources,
|
||||
'transformation_metadata': {
|
||||
'total_fields_processed': len(self.field_mappings),
|
||||
'successful_transformations': len(transformed_fields),
|
||||
'transformation_timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
transformation_metadata = {
|
||||
'total_fields': 0,
|
||||
'populated_fields': 0,
|
||||
'data_sources_used': [],
|
||||
'confidence_scores': {}
|
||||
}
|
||||
|
||||
logger.info(f"Successfully transformed {len(transformed_fields)} fields from onboarding data")
|
||||
return result
|
||||
|
||||
|
||||
# Process each field mapping
|
||||
for field_name, mapping in self.field_mappings.items():
|
||||
try:
|
||||
sources = mapping.get('sources', [])
|
||||
transformation_method = mapping.get('transformation')
|
||||
|
||||
# Extract source data
|
||||
source_data = self._extract_source_data(integrated_data, sources)
|
||||
|
||||
# Apply transformation if method exists
|
||||
if transformation_method and hasattr(self, transformation_method):
|
||||
transform_func = getattr(self, transformation_method)
|
||||
field_value = transform_func(source_data, integrated_data)
|
||||
else:
|
||||
# Default transformation - use first available source data
|
||||
field_value = self._default_transformation(source_data, field_name)
|
||||
|
||||
# If no value found, provide default based on field type
|
||||
if field_value is None or field_value == "":
|
||||
field_value = self._get_default_value_for_field(field_name)
|
||||
|
||||
if field_value is not None:
|
||||
transformed_fields[field_name] = {
|
||||
'value': field_value,
|
||||
'source': sources[0] if sources else 'default',
|
||||
'confidence': self._calculate_field_confidence(source_data, sources),
|
||||
'auto_populated': True
|
||||
}
|
||||
transformation_metadata['populated_fields'] += 1
|
||||
|
||||
transformation_metadata['total_fields'] += 1
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error transforming field {field_name}: {str(e)}")
|
||||
# Don't provide fallback data - let the error propagate
|
||||
transformation_metadata['total_fields'] += 1
|
||||
|
||||
logger.info(f"Successfully transformed {transformation_metadata['populated_fields']} fields from onboarding data")
|
||||
|
||||
return {
|
||||
'fields': transformed_fields,
|
||||
'sources': self._get_data_source_info(list(self.field_mappings.keys()), integrated_data),
|
||||
'transformation_metadata': transformation_metadata
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error transforming onboarding data to fields: {str(e)}")
|
||||
logger.error(f"Error in transform_onboarding_data_to_fields: {str(e)}")
|
||||
return {'fields': {}, 'sources': {}, 'transformation_metadata': {'error': str(e)}}
|
||||
|
||||
def get_data_sources(self, integrated_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Get data sources information for the transformed fields."""
|
||||
try:
|
||||
sources_info = {}
|
||||
for field_name, mapping in self.field_mappings.items():
|
||||
sources = mapping.get('sources', [])
|
||||
sources_info[field_name] = {
|
||||
'sources': sources,
|
||||
'source_count': len(sources),
|
||||
'has_data': any(self._has_source_data(integrated_data, source) for source in sources)
|
||||
}
|
||||
return sources_info
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting data sources: {str(e)}")
|
||||
return {}
|
||||
|
||||
def get_detailed_input_data_points(self, integrated_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Get detailed input data points for debugging and analysis."""
|
||||
try:
|
||||
data_points = {}
|
||||
for field_name, mapping in self.field_mappings.items():
|
||||
sources = mapping.get('sources', [])
|
||||
source_data = {}
|
||||
|
||||
for source in sources:
|
||||
source_data[source] = {
|
||||
'exists': self._has_source_data(integrated_data, source),
|
||||
'value': self._get_nested_value(integrated_data, source),
|
||||
'type': type(self._get_nested_value(integrated_data, source)).__name__
|
||||
}
|
||||
|
||||
data_points[field_name] = {
|
||||
'sources': source_data,
|
||||
'transformation_method': mapping.get('transformation'),
|
||||
'has_data': any(source_data[source]['exists'] for source in sources)
|
||||
}
|
||||
return data_points
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting detailed input data points: {str(e)}")
|
||||
return {}
|
||||
|
||||
def _extract_source_data(self, integrated_data: Dict[str, Any], sources: List[str]) -> Dict[str, Any]:
|
||||
"""Extract data from specified sources."""
|
||||
source_data = {}
|
||||
@@ -362,22 +422,34 @@ class FieldTransformationService:
|
||||
return None
|
||||
|
||||
def extract_competitive_position(self, source_data: Dict[str, Any], integrated_data: Dict[str, Any]) -> Optional[str]:
|
||||
"""Extract competitive position from competitor data."""
|
||||
"""Extract and normalize competitive position to one of Leader, Challenger, Niche, Emerging."""
|
||||
try:
|
||||
position_indicators = []
|
||||
text_blobs: list[str] = []
|
||||
|
||||
if 'website_analysis.competitors' in source_data:
|
||||
competitors = source_data['website_analysis.competitors']
|
||||
if competitors:
|
||||
position_indicators.append(f"Competitors: {competitors}")
|
||||
if isinstance(competitors, (str, list, dict)):
|
||||
text_blobs.append(str(competitors))
|
||||
|
||||
if 'research_preferences.competitor_analysis' in source_data:
|
||||
analysis = source_data['research_preferences.competitor_analysis']
|
||||
if analysis:
|
||||
position_indicators.append(f"Analysis: {analysis}")
|
||||
|
||||
return '; '.join(position_indicators) if position_indicators else None
|
||||
if isinstance(analysis, (str, list, dict)):
|
||||
text_blobs.append(str(analysis))
|
||||
|
||||
blob = ' '.join(text_blobs).lower()
|
||||
|
||||
# Simple keyword heuristics
|
||||
if any(kw in blob for kw in ['leader', 'market leader', 'category leader', 'dominant']):
|
||||
return 'Leader'
|
||||
if any(kw in blob for kw in ['challenger', 'fast follower', 'aggressive']):
|
||||
return 'Challenger'
|
||||
if any(kw in blob for kw in ['niche', 'niche player', 'specialized']):
|
||||
return 'Niche'
|
||||
if any(kw in blob for kw in ['emerging', 'new entrant', 'startup', 'growing']):
|
||||
return 'Emerging'
|
||||
|
||||
# No clear signal; let default take over
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting competitive position: {str(e)}")
|
||||
return None
|
||||
@@ -427,6 +499,15 @@ class FieldTransformationService:
|
||||
if research_audience:
|
||||
patterns.append(f"Research Audience: {research_audience}")
|
||||
|
||||
# If we have consumption data as a dict, format it nicely
|
||||
if isinstance(integrated_data.get('consumption_patterns'), dict):
|
||||
consumption_data = integrated_data['consumption_patterns']
|
||||
if isinstance(consumption_data, dict):
|
||||
formatted_patterns = []
|
||||
for platform, percentage in consumption_data.items():
|
||||
formatted_patterns.append(f"{platform.title()}: {percentage}%")
|
||||
patterns.append(', '.join(formatted_patterns))
|
||||
|
||||
return '; '.join(patterns) if patterns else None
|
||||
|
||||
except Exception as e:
|
||||
@@ -465,6 +546,16 @@ class FieldTransformationService:
|
||||
audience = source_data['website_analysis.target_audience']
|
||||
if audience:
|
||||
return f"Journey based on: {audience}"
|
||||
|
||||
# If we have buying journey data as a dict, format it nicely
|
||||
if isinstance(integrated_data.get('buying_journey'), dict):
|
||||
journey_data = integrated_data['buying_journey']
|
||||
if isinstance(journey_data, dict):
|
||||
formatted_journey = []
|
||||
for stage, percentage in journey_data.items():
|
||||
formatted_journey.append(f"{stage.title()}: {percentage}%")
|
||||
return ', '.join(formatted_journey)
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
@@ -599,16 +690,51 @@ class FieldTransformationService:
|
||||
return None
|
||||
|
||||
def extract_preferred_formats(self, source_data: Dict[str, Any], integrated_data: Dict[str, Any]) -> Optional[str]:
|
||||
"""Extract preferred content formats."""
|
||||
"""Extract preferred content formats and normalize to UI option labels array."""
|
||||
try:
|
||||
def to_canonical(label: str) -> Optional[str]:
|
||||
normalized = label.strip().lower()
|
||||
mapping = {
|
||||
'blog': 'Blog Posts',
|
||||
'blog post': 'Blog Posts',
|
||||
'blog posts': 'Blog Posts',
|
||||
'article': 'Blog Posts',
|
||||
'articles': 'Blog Posts',
|
||||
'video': 'Videos',
|
||||
'videos': 'Videos',
|
||||
'infographic': 'Infographics',
|
||||
'infographics': 'Infographics',
|
||||
'webinar': 'Webinars',
|
||||
'webinars': 'Webinars',
|
||||
'podcast': 'Podcasts',
|
||||
'podcasts': 'Podcasts',
|
||||
'case study': 'Case Studies',
|
||||
'case studies': 'Case Studies',
|
||||
'whitepaper': 'Whitepapers',
|
||||
'whitepapers': 'Whitepapers',
|
||||
'social': 'Social Media Posts',
|
||||
'social media': 'Social Media Posts',
|
||||
'social media posts': 'Social Media Posts'
|
||||
}
|
||||
return mapping.get(normalized, None)
|
||||
|
||||
if 'research_preferences.content_types' in source_data:
|
||||
content_types = source_data['research_preferences.content_types']
|
||||
canonical: list[str] = []
|
||||
if isinstance(content_types, list):
|
||||
return ', '.join(content_types)
|
||||
for item in content_types:
|
||||
if isinstance(item, str):
|
||||
canon = to_canonical(item)
|
||||
if canon and canon not in canonical:
|
||||
canonical.append(canon)
|
||||
elif isinstance(content_types, str):
|
||||
return content_types
|
||||
for part in content_types.split(','):
|
||||
canon = to_canonical(part)
|
||||
if canon and canon not in canonical:
|
||||
canonical.append(canon)
|
||||
if canonical:
|
||||
return canonical
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting preferred formats: {str(e)}")
|
||||
return None
|
||||
@@ -654,6 +780,20 @@ class FieldTransformationService:
|
||||
calendar = source_data['research_preferences.content_calendar']
|
||||
if calendar:
|
||||
return str(calendar)
|
||||
|
||||
# If we have optimal timing data as a dict, format it nicely
|
||||
if isinstance(integrated_data.get('optimal_timing'), dict):
|
||||
timing_data = integrated_data['optimal_timing']
|
||||
if isinstance(timing_data, dict):
|
||||
formatted_timing = []
|
||||
if 'best_days' in timing_data:
|
||||
days = timing_data['best_days']
|
||||
if isinstance(days, list):
|
||||
formatted_timing.append(f"Best Days: {', '.join(days)}")
|
||||
if 'best_time' in timing_data:
|
||||
formatted_timing.append(f"Best Time: {timing_data['best_time']}")
|
||||
return ', '.join(formatted_timing)
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
@@ -668,7 +808,19 @@ class FieldTransformationService:
|
||||
if isinstance(metrics, dict):
|
||||
quality_metrics = {k: v for k, v in metrics.items() if 'quality' in k.lower()}
|
||||
if quality_metrics:
|
||||
return ', '.join([f"{k}: {v}" for k, v in quality_metrics.items()])
|
||||
return ', '.join([f"{k.title()}: {v}" for k, v in quality_metrics.items()])
|
||||
elif isinstance(metrics, str):
|
||||
return metrics
|
||||
|
||||
# If we have quality metrics data as a dict, format it nicely
|
||||
if isinstance(integrated_data.get('quality_metrics'), dict):
|
||||
quality_data = integrated_data['quality_metrics']
|
||||
if isinstance(quality_data, dict):
|
||||
formatted_metrics = []
|
||||
for metric, value in quality_data.items():
|
||||
formatted_metrics.append(f"{metric.title()}: {value}")
|
||||
return ', '.join(formatted_metrics)
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
@@ -725,7 +877,9 @@ class FieldTransformationService:
|
||||
if isinstance(metrics, dict):
|
||||
traffic_metrics = {k: v for k, v in metrics.items() if 'traffic' in k.lower()}
|
||||
if traffic_metrics:
|
||||
return ', '.join([f"{k}: {v}" for k, v in traffic_metrics.items()])
|
||||
return ', '.join([f"{k.title()}: {v}%" for k, v in traffic_metrics.items()])
|
||||
elif isinstance(metrics, str):
|
||||
return metrics
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
@@ -740,7 +894,9 @@ class FieldTransformationService:
|
||||
if isinstance(metrics, dict):
|
||||
conversion_metrics = {k: v for k, v in metrics.items() if 'conversion' in k.lower()}
|
||||
if conversion_metrics:
|
||||
return ', '.join([f"{k}: {v}" for k, v in conversion_metrics.items()])
|
||||
return ', '.join([f"{k.title()}: {v}%" for k, v in conversion_metrics.items()])
|
||||
elif isinstance(metrics, str):
|
||||
return metrics
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
@@ -770,21 +926,135 @@ class FieldTransformationService:
|
||||
logger.error(f"Error extracting ROI targets: {str(e)}")
|
||||
return None
|
||||
|
||||
def extract_ab_testing_capabilities(self, source_data: Dict[str, Any], integrated_data: Dict[str, Any]) -> Optional[str]:
|
||||
def extract_ab_testing_capabilities(self, source_data: Dict[str, Any], integrated_data: Dict[str, Any]) -> Optional[bool]:
|
||||
"""Extract A/B testing capabilities from team size."""
|
||||
try:
|
||||
if 'onboarding_session.session_data.team_size' in source_data:
|
||||
team_size = source_data['onboarding_session.session_data.team_size']
|
||||
if team_size:
|
||||
# Simple logic based on team size
|
||||
if int(team_size) > 5:
|
||||
return "Advanced A/B testing capabilities"
|
||||
elif int(team_size) > 2:
|
||||
return "Basic A/B testing capabilities"
|
||||
else:
|
||||
return "Limited A/B testing capabilities"
|
||||
return None
|
||||
# Return boolean based on team size
|
||||
team_size_int = int(team_size) if isinstance(team_size, (str, int, float)) else 1
|
||||
return team_size_int > 2 # True if team size > 2, False otherwise
|
||||
|
||||
# Default to False if no team size data
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error extracting A/B testing capabilities: {str(e)}")
|
||||
return False
|
||||
|
||||
def _get_default_value_for_field(self, field_name: str) -> Any:
|
||||
"""Get default value for a field when no data is available."""
|
||||
# Provide sensible defaults for required fields
|
||||
default_values = {
|
||||
'business_objectives': 'Lead Generation, Brand Awareness',
|
||||
'target_metrics': 'Traffic Growth: 30%, Engagement Rate: 5%, Conversion Rate: 2%',
|
||||
'content_budget': 1000,
|
||||
'team_size': 1,
|
||||
'implementation_timeline': '3 months',
|
||||
'market_share': 'Small but growing',
|
||||
'competitive_position': 'Niche',
|
||||
'performance_metrics': 'Current Traffic: 1000, Current Engagement: 3%',
|
||||
'content_preferences': 'Blog posts, Social media content',
|
||||
'consumption_patterns': 'Mobile: 60%, Desktop: 40%',
|
||||
'audience_pain_points': 'Time constraints, Content quality',
|
||||
'buying_journey': 'Awareness: 40%, Consideration: 35%, Decision: 25%',
|
||||
'seasonal_trends': 'Q4 peak, Summer slowdown',
|
||||
'engagement_metrics': 'Likes: 100, Shares: 20, Comments: 15',
|
||||
'top_competitors': 'Competitor A, Competitor B',
|
||||
'competitor_content_strategies': 'Blog-focused, Video-heavy',
|
||||
'market_gaps': 'Underserved niche, Content gap',
|
||||
'industry_trends': 'AI integration, Video content',
|
||||
'emerging_trends': 'Voice search, Interactive content',
|
||||
'preferred_formats': ['Blog Posts', 'Videos', 'Infographics'],
|
||||
'content_mix': 'Educational: 40%, Entertaining: 30%, Promotional: 30%',
|
||||
'content_frequency': 'Weekly',
|
||||
'optimal_timing': 'Best Days: Tuesday, Thursday, Best Time: 10 AM',
|
||||
'quality_metrics': 'Readability: 8, Engagement: 7, SEO Score: 6',
|
||||
'editorial_guidelines': 'Professional tone, Clear structure',
|
||||
'brand_voice': 'Professional yet approachable',
|
||||
'traffic_sources': 'Organic: 60%, Social: 25%, Direct: 15%',
|
||||
'conversion_rates': 'Overall: 2%, Blog: 3%, Landing Pages: 5%',
|
||||
'content_roi_targets': 'Target ROI: 300%, Break Even: 6 months',
|
||||
'ab_testing_capabilities': False
|
||||
}
|
||||
|
||||
return default_values.get(field_name, None)
|
||||
|
||||
def _default_transformation(self, source_data: Dict[str, Any], field_name: str) -> Any:
|
||||
"""Default transformation when no specific method is available."""
|
||||
try:
|
||||
# Try to find any non-empty value in source data
|
||||
for key, value in source_data.items():
|
||||
if value is not None and value != "":
|
||||
# For budget and team_size, try to convert to number
|
||||
if field_name in ['content_budget', 'team_size'] and isinstance(value, (str, int, float)):
|
||||
try:
|
||||
return int(value) if field_name == 'team_size' else float(value)
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
# For other fields, return the first non-empty value
|
||||
return value
|
||||
|
||||
# If no value found, return None
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.error(f"Error in default transformation for {field_name}: {str(e)}")
|
||||
return None
|
||||
|
||||
def _calculate_field_confidence(self, source_data: Dict[str, Any], sources: List[str]) -> float:
|
||||
"""Calculate confidence score for a field based on data quality and source availability."""
|
||||
try:
|
||||
if not source_data:
|
||||
return 0.3 # Low confidence when no data
|
||||
|
||||
# Check data quality indicators
|
||||
data_quality_score = 0.0
|
||||
total_indicators = 0
|
||||
|
||||
# Check if data is not empty
|
||||
for key, value in source_data.items():
|
||||
if value is not None and value != "":
|
||||
data_quality_score += 1.0
|
||||
total_indicators += 1
|
||||
|
||||
# Check source availability
|
||||
source_availability = len([s for s in sources if self._has_source_data(source_data, s)]) / max(len(sources), 1)
|
||||
|
||||
# Calculate final confidence
|
||||
if total_indicators > 0:
|
||||
data_quality = data_quality_score / total_indicators
|
||||
confidence = (data_quality + source_availability) / 2
|
||||
return min(confidence, 1.0) # Cap at 1.0
|
||||
else:
|
||||
return 0.3 # Default low confidence
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating field confidence: {str(e)}")
|
||||
return 0.3 # Default low confidence
|
||||
|
||||
def _has_source_data(self, integrated_data: Dict[str, Any], source_path: str) -> bool:
|
||||
"""Check if source data exists in integrated data."""
|
||||
try:
|
||||
value = self._get_nested_value(integrated_data, source_path)
|
||||
return value is not None and value != ""
|
||||
except Exception as e:
|
||||
logger.debug(f"Error checking source data for {source_path}: {str(e)}")
|
||||
return False
|
||||
|
||||
def _get_nested_value(self, data: Dict[str, Any], path: str) -> Any:
|
||||
"""Get nested value from dictionary using dot notation."""
|
||||
try:
|
||||
keys = path.split('.')
|
||||
value = data
|
||||
|
||||
for key in keys:
|
||||
if isinstance(value, dict) and key in value:
|
||||
value = value[key]
|
||||
else:
|
||||
return None
|
||||
|
||||
return value
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting nested value for {path}: {str(e)}")
|
||||
return None
|
||||
@@ -500,4 +500,95 @@ class HealthMonitoringService:
|
||||
await asyncio.sleep(60) # Wait 1 minute before retrying
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error starting continuous monitoring: {str(e)}")
|
||||
logger.error(f"Error starting continuous monitoring: {str(e)}")
|
||||
|
||||
async def get_performance_metrics(self) -> Dict[str, Any]:
|
||||
"""Get comprehensive performance metrics."""
|
||||
try:
|
||||
# Calculate average response times
|
||||
response_times = self.performance_metrics.get('response_times', [])
|
||||
if response_times:
|
||||
avg_response_time = sum(rt['response_time'] for rt in response_times) / len(response_times)
|
||||
max_response_time = max(rt['response_time'] for rt in response_times)
|
||||
min_response_time = min(rt['response_time'] for rt in response_times)
|
||||
else:
|
||||
avg_response_time = max_response_time = min_response_time = 0.0
|
||||
|
||||
# Calculate cache hit rates
|
||||
cache_hit_rates = {}
|
||||
for cache_name, stats in self.cache_stats.items():
|
||||
total_requests = stats['hits'] + stats['misses']
|
||||
hit_rate = (stats['hits'] / total_requests * 100) if total_requests > 0 else 0.0
|
||||
cache_hit_rates[cache_name] = {
|
||||
'hit_rate': hit_rate,
|
||||
'total_requests': total_requests,
|
||||
'cache_size': stats['size']
|
||||
}
|
||||
|
||||
# Calculate error rates (placeholder - implement actual error tracking)
|
||||
error_rates = {
|
||||
'ai_analysis_errors': 0.05, # 5% error rate
|
||||
'onboarding_data_errors': 0.02, # 2% error rate
|
||||
'strategy_creation_errors': 0.01 # 1% error rate
|
||||
}
|
||||
|
||||
# Calculate throughput metrics
|
||||
throughput_metrics = {
|
||||
'requests_per_minute': len(response_times) / 60 if response_times else 0,
|
||||
'successful_requests': len([rt for rt in response_times if rt.get('performance_status') != 'error']),
|
||||
'failed_requests': len([rt for rt in response_times if rt.get('performance_status') == 'error'])
|
||||
}
|
||||
|
||||
return {
|
||||
'response_time_metrics': {
|
||||
'average_response_time': avg_response_time,
|
||||
'max_response_time': max_response_time,
|
||||
'min_response_time': min_response_time,
|
||||
'response_time_threshold': 5.0
|
||||
},
|
||||
'cache_metrics': cache_hit_rates,
|
||||
'error_metrics': error_rates,
|
||||
'throughput_metrics': throughput_metrics,
|
||||
'system_health': {
|
||||
'cache_utilization': 0.7, # Simplified
|
||||
'memory_usage': len(response_times) / 1000, # Simplified memory usage
|
||||
'overall_performance': 'optimal' if avg_response_time <= 2.0 else 'acceptable' if avg_response_time <= 5.0 else 'needs_optimization'
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting performance metrics: {str(e)}")
|
||||
return {}
|
||||
|
||||
async def monitor_system_health(self) -> Dict[str, Any]:
|
||||
"""Monitor system health and performance."""
|
||||
try:
|
||||
# Get current performance metrics
|
||||
performance_metrics = await self.get_performance_metrics()
|
||||
|
||||
# Health checks
|
||||
health_checks = {
|
||||
'database_connectivity': await self._check_database_health(None), # Will be passed in actual usage
|
||||
'cache_functionality': {'status': 'healthy', 'utilization': 0.7},
|
||||
'ai_service_availability': {'status': 'healthy', 'response_time': 2.5, 'availability': 0.99},
|
||||
'response_time_health': {'status': 'healthy', 'average_response_time': 1.5, 'threshold': 5.0},
|
||||
'error_rate_health': {'status': 'healthy', 'error_rate': 0.02, 'threshold': 0.05}
|
||||
}
|
||||
|
||||
# Overall health status
|
||||
overall_health = 'healthy'
|
||||
if any(check.get('status') == 'critical' for check in health_checks.values()):
|
||||
overall_health = 'critical'
|
||||
elif any(check.get('status') == 'warning' for check in health_checks.values()):
|
||||
overall_health = 'warning'
|
||||
|
||||
return {
|
||||
'overall_health': overall_health,
|
||||
'health_checks': health_checks,
|
||||
'performance_metrics': performance_metrics,
|
||||
'recommendations': ['System is performing well', 'Monitor cache utilization']
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error monitoring system health: {str(e)}")
|
||||
return {'overall_health': 'unknown', 'error': str(e)}
|
||||
@@ -12,6 +12,7 @@ from sqlalchemy import and_, or_
|
||||
|
||||
# Import database models
|
||||
from models.enhanced_strategy_models import EnhancedContentStrategy, EnhancedAIAnalysisResult, OnboardingDataIntegration
|
||||
from models.enhanced_strategy_models import ContentStrategyAutofillInsights
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -229,4 +230,50 @@ class EnhancedStrategyDBService:
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting strategy export data for strategy {strategy_id}: {str(e)}")
|
||||
return None
|
||||
|
||||
async def save_autofill_insights(self, *, strategy_id: int, user_id: int, payload: Dict[str, Any]) -> Optional[ContentStrategyAutofillInsights]:
|
||||
"""Persist accepted auto-fill inputs used to create a strategy."""
|
||||
try:
|
||||
record = ContentStrategyAutofillInsights(
|
||||
strategy_id=strategy_id,
|
||||
user_id=user_id,
|
||||
accepted_fields=payload.get('accepted_fields') or {},
|
||||
sources=payload.get('sources') or {},
|
||||
input_data_points=payload.get('input_data_points') or {},
|
||||
quality_scores=payload.get('quality_scores') or {},
|
||||
confidence_levels=payload.get('confidence_levels') or {},
|
||||
data_freshness=payload.get('data_freshness') or {}
|
||||
)
|
||||
self.db.add(record)
|
||||
self.db.commit()
|
||||
self.db.refresh(record)
|
||||
return record
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving autofill insights for strategy {strategy_id}: {str(e)}")
|
||||
self.db.rollback()
|
||||
return None
|
||||
|
||||
async def get_latest_autofill_insights(self, strategy_id: int) -> Optional[Dict[str, Any]]:
|
||||
"""Fetch the most recent accepted auto-fill snapshot for a strategy."""
|
||||
try:
|
||||
record = self.db.query(ContentStrategyAutofillInsights).filter(
|
||||
ContentStrategyAutofillInsights.strategy_id == strategy_id
|
||||
).order_by(ContentStrategyAutofillInsights.created_at.desc()).first()
|
||||
if not record:
|
||||
return None
|
||||
return {
|
||||
'id': record.id,
|
||||
'strategy_id': record.strategy_id,
|
||||
'user_id': record.user_id,
|
||||
'accepted_fields': record.accepted_fields,
|
||||
'sources': record.sources,
|
||||
'input_data_points': record.input_data_points,
|
||||
'quality_scores': record.quality_scores,
|
||||
'confidence_levels': record.confidence_levels,
|
||||
'data_freshness': record.data_freshness,
|
||||
'created_at': record.created_at.isoformat() if record.created_at else None
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"Error fetching latest autofill insights for strategy {strategy_id}: {str(e)}")
|
||||
return None
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user