Base code

This commit is contained in:
Kunthawat Greethong
2026-01-08 22:39:53 +07:00
parent 697115c61a
commit c35fa52117
2169 changed files with 626670 additions and 0 deletions

View File

@@ -0,0 +1,536 @@
# CopilotKit Implementation Plan for Alwrity
## 🎯 **Executive Summary**
This document provides a detailed, phase-wise implementation plan for integrating CopilotKit into Alwrity's AI content platform. The plan focuses on transforming Alwrity's complex form-based interfaces into an intelligent, conversational AI assistant that democratizes content strategy creation.
---
## 📋 **Implementation Overview**
### **Technology Stack**
- **Frontend**: React + TypeScript + CopilotKit React components
- **Backend**: Python FastAPI + CopilotKit Python SDK
- **AI/ML**: OpenAI GPT-4, Anthropic Claude, Custom fine-tuned models
- **Database**: PostgreSQL + Redis for caching
- **Infrastructure**: Docker + Kubernetes
---
## 🚀 **Phase 1: Foundation (Weeks 1-4)**
### **Week 1: Core Setup & Infrastructure**
#### **Day 1-2: Environment Setup**
- **Task 1.1**: Install CopilotKit dependencies
- Add `@copilotkit/react-core` and `@copilotkit/react-ui` to frontend
- Add `copilotkit` Python package to backend
- Configure environment variables for API keys
- **Task 1.2**: Create CopilotKit configuration
- Set up CopilotKit provider in main App component
- Configure API endpoints for backend communication
- Implement basic error handling and logging
- **Task 1.3**: Database schema updates
- Add `copilot_sessions` table for conversation history
- Add `user_preferences` table for personalization
- Add `workflow_states` table for multi-step processes
#### **Day 3-4: Basic Chat Interface**
- **Task 1.4**: Implement CopilotSidebar component
- Integrate `CopilotSidebar` from `@copilotkit/react-ui`
- Style to match Alwrity's design system
- Add basic message handling and display
- **Task 1.5**: Create backend chat endpoint
- Implement `/api/copilot/chat` endpoint
- Add basic message processing pipeline
- Implement session management and persistence
- **Task 1.6**: Add context management
- Create user context provider
- Implement business context extraction
- Add active strategy and preferences tracking
#### **Day 5: Testing & Documentation**
- **Task 1.7**: Unit tests for core components
- **Task 1.8**: API documentation for chat endpoints
- **Task 1.9**: Basic user acceptance testing
### **Week 2: Intent Recognition & Basic Tools**
#### **Day 1-2: Intent Recognition System**
- **Task 2.1**: Implement intent classification
- Create intent detection using OpenAI embeddings
- Define core intents: strategy_creation, calendar_generation, seo_analysis, content_creation, analytics
- Add confidence scoring and fallback handling
- **Task 2.2**: Create intent handlers
- Implement `ContentStrategyIntentHandler`
- Implement `CalendarGenerationIntentHandler`
- Implement `SEOAnalysisIntentHandler`
- Add intent routing and delegation
#### **Day 3-4: Basic Tool Integration**
- **Task 2.3**: Create CopilotKit tools
- Implement `ContentStrategyTool` using `useCopilotAction`
- Implement `CalendarGenerationTool` using `useCopilotAction`
- Add tool registration and discovery
- **Task 2.4**: Connect to existing Alwrity services
- Integrate with `ContentStrategyService`
- Integrate with `CalendarGenerationService`
- Add service abstraction layer for copilot access
#### **Day 5: Context Enhancement**
- **Task 2.5**: Implement `useCopilotReadable` for context
- Add user profile context
- Add active strategy context
- Add business information context
### **Week 3: Workflow Automation**
#### **Day 1-2: Multi-Step Workflows**
- **Task 3.1**: Create workflow orchestrator
- Implement `WorkflowOrchestrator` class
- Add workflow state management
- Create progress tracking system
- **Task 3.2**: Implement strategy-to-calendar workflow
- Create "Create Strategy + Generate Calendar" workflow
- Add intermediate validation steps
- Implement rollback and error recovery
#### **Day 3-4: Progress Tracking**
- **Task 3.3**: Add progress indicators
- Implement progress bar component
- Add step-by-step status updates
- Create workflow completion notifications
- **Task 3.4**: Add workflow templates
- Create "Product Launch" workflow template
- Create "Content Audit" workflow template
- Add customizable workflow builder
#### **Day 5: Testing & Optimization**
- **Task 3.5**: End-to-end workflow testing
- **Task 3.6**: Performance optimization
- **Task 3.7**: Error handling improvements
### **Week 4: User Experience & Polish**
#### **Day 1-2: Enhanced UI/UX**
- **Task 4.1**: Improve chat interface
- Add typing indicators
- Implement message threading
- Add rich message formatting (markdown, tables, charts)
- **Task 4.2**: Add quick actions
- Implement quick action buttons
- Add suggested responses
- Create action shortcuts
#### **Day 3-4: Personalization**
- **Task 4.3**: Implement user preferences
- Add business type detection
- Implement industry-specific defaults
- Create personalized recommendations
- **Task 4.4**: Add learning system
- Implement user behavior tracking
- Add preference learning
- Create adaptive responses
#### **Day 5: Phase 1 Review**
- **Task 4.5**: User testing and feedback collection
- **Task 4.6**: Performance metrics analysis
- **Task 4.7**: Phase 1 documentation and handoff
---
## 🎨 **Phase 2: Enhancement (Weeks 5-8)**
### **Week 5: Advanced AI Features**
#### **Day 1-2: Intelligent Recommendations**
- **Task 5.1**: Implement recommendation engine
- Create `RecommendationEngine` using ML models
- Add content performance prediction
- Implement A/B testing for recommendations
- **Task 5.2**: Add proactive suggestions
- Implement "smart suggestions" system
- Add contextual recommendations
- Create opportunity detection
#### **Day 3-4: Advanced Context Management**
- **Task 5.3**: Enhanced context awareness
- Add real-time data context
- Implement competitor analysis context
- Add market trends context
- **Task 5.4**: Implement context persistence
- Add long-term memory system
- Implement context learning
- Create context optimization
#### **Day 5: AI Model Integration**
- **Task 5.5**: Fine-tune models for Alwrity
- **Task 5.6**: Add model performance monitoring
- **Task 5.7**: Implement model fallback strategies
### **Week 6: Multi-Modal Support**
#### **Day 1-2: Voice Input**
- **Task 6.1**: Implement voice recognition
- Add Web Speech API integration
- Implement voice-to-text conversion
- Add voice command recognition
- **Task 6.2**: Voice response system
- Implement text-to-speech
- Add voice feedback for actions
- Create voice navigation
#### **Day 3-4: Image Analysis**
- **Task 6.3**: Image upload and processing
- Add image upload component
- Implement image analysis using Vision API
- Add competitor content analysis
- **Task 6.4**: Visual content generation
- Implement image-based content suggestions
- Add visual trend analysis
- Create image optimization recommendations
#### **Day 5: Document Processing**
- **Task 6.5**: PDF and document analysis
- **Task 6.6**: Business plan processing
- **Task 6.7**: Content audit automation
### **Week 7: Educational Integration**
#### **Day 1-2: Adaptive Learning System**
- **Task 7.1**: Create learning path generator
- Implement skill assessment
- Add personalized learning paths
- Create progress tracking
- **Task 7.2**: Interactive tutorials
- Add guided walkthroughs
- Implement interactive exercises
- Create practice scenarios
#### **Day 3-4: Contextual Help**
- **Task 7.3**: Smart help system
- Implement contextual help triggers
- Add concept explanations
- Create FAQ integration
- **Task 7.4**: Educational content generation
- Add concept explanation generation
- Implement example creation
- Create best practice suggestions
#### **Day 5: Knowledge Base Integration**
- **Task 7.5**: Connect to Alwrity knowledge base
- **Task 7.6**: Add external resource integration
- **Task 7.7**: Implement knowledge validation
### **Week 8: Advanced Workflows**
#### **Day 1-2: Complex Workflow Orchestration**
- **Task 8.1**: Advanced workflow builder
- Create visual workflow designer
- Add conditional logic
- Implement parallel processing
- **Task 8.2**: Workflow templates
- Add industry-specific templates
- Create custom template builder
- Implement template sharing
#### **Day 3-4: Integration with External Tools**
- **Task 8.3**: Social media integration
- Add platform-specific workflows
- Implement cross-platform optimization
- Create scheduling automation
- **Task 8.4**: Analytics integration
- Add real-time analytics
- Implement performance tracking
- Create optimization suggestions
#### **Day 5: Phase 2 Review**
- **Task 8.5**: Advanced feature testing
- **Task 8.6**: Performance optimization
- **Task 8.7**: User feedback integration
---
## 🚀 **Phase 3: Optimization (Weeks 9-12)**
### **Week 9: Predictive Analytics**
#### **Day 1-2: Performance Prediction**
- **Task 9.1**: Implement prediction models
- Create content performance predictor
- Add engagement forecasting
- Implement conversion prediction
- **Task 9.2**: Trend analysis
- Add market trend detection
- Implement seasonal analysis
- Create competitive intelligence
#### **Day 3-4: Automated Optimization**
- **Task 9.3**: Smart optimization engine
- Implement automatic strategy updates
- Add performance-based recommendations
- Create optimization scheduling
- **Task 9.4**: A/B testing framework
- Add automated testing
- Implement result analysis
- Create optimization loops
#### **Day 5: Analytics Dashboard**
- **Task 9.5**: Create copilot analytics dashboard
- **Task 9.6**: Add performance metrics
- **Task 9.7**: Implement reporting automation
### **Week 10: Enterprise Features**
#### **Day 1-2: Team Collaboration**
- **Task 10.1**: Multi-user support
- Add team member management
- Implement role-based access
- Create collaboration workflows
- **Task 10.2**: Shared workspaces
- Add workspace management
- Implement resource sharing
- Create team analytics
#### **Day 3-4: Advanced Permissions**
- **Task 10.3**: Permission system
- Implement granular permissions
- Add approval workflows
- Create audit trails
- **Task 10.4**: White-label capabilities
- Add branding customization
- Implement custom domains
- Create white-label deployment
#### **Day 5: Enterprise Integration**
- **Task 10.5**: SSO integration
- **Task 10.6**: API rate limiting
- **Task 10.7**: Enterprise security features
### **Week 11: Performance & Scalability**
#### **Day 1-2: Performance Optimization**
- **Task 11.1**: Response time optimization
- Implement caching strategies
- Add request optimization
- Create performance monitoring
- **Task 11.2**: Scalability improvements
- Add load balancing
- Implement horizontal scaling
- Create auto-scaling policies
#### **Day 3-4: Reliability & Monitoring**
- **Task 11.3**: Error handling
- Implement comprehensive error handling
- Add retry mechanisms
- Create error recovery
- **Task 11.4**: Monitoring and alerting
- Add performance monitoring
- Implement alert systems
- Create health checks
#### **Day 5: Security Enhancements**
- **Task 11.5**: Security audit
- **Task 11.6**: Data protection
- **Task 11.7**: Compliance features
### **Week 12: Final Integration & Launch**
#### **Day 1-2: End-to-End Testing**
- **Task 12.1**: Comprehensive testing
- Add integration testing
- Implement user acceptance testing
- Create performance testing
- **Task 12.2**: Bug fixes and optimization
- Address critical issues
- Optimize performance bottlenecks
- Improve user experience
#### **Day 3-4: Documentation & Training**
- **Task 12.3**: Complete documentation
- Update API documentation
- Create user guides
- Add developer documentation
- **Task 12.4**: Training materials
- Create training videos
- Add interactive tutorials
- Prepare support materials
#### **Day 5: Launch Preparation**
- **Task 12.5**: Production deployment
- **Task 12.6**: Monitoring setup
- **Task 12.7**: Launch announcement
---
## 🔧 **Technical Specifications**
### **Frontend Architecture**
#### **Core Components**
- **CopilotProvider**: Main context provider for copilot state
- **CopilotSidebar**: Primary chat interface component
- **IntentHandler**: Routes user intents to appropriate tools
- **WorkflowOrchestrator**: Manages multi-step workflows
- **ContextManager**: Handles user and business context
#### **Key Hooks**
- **useCopilotAction**: For tool execution and workflow automation
- **useCopilotReadable**: For context sharing and state management
- **useCopilotContext**: For accessing copilot state and functions
#### **State Management**
- **CopilotState**: Manages conversation history and current state
- **UserContext**: Stores user preferences and business information
- **WorkflowState**: Tracks multi-step workflow progress
### **Backend Architecture**
#### **Core Services**
- **CopilotService**: Main service for copilot operations
- **IntentService**: Handles intent recognition and classification
- **ToolService**: Manages tool registration and execution
- **WorkflowService**: Orchestrates complex workflows
- **ContextService**: Manages user and business context
#### **API Endpoints**
- **POST /api/copilot/chat**: Main chat endpoint
- **POST /api/copilot/intent**: Intent recognition endpoint
- **POST /api/copilot/tools**: Tool execution endpoint
- **GET /api/copilot/context**: Context retrieval endpoint
- **POST /api/copilot/workflow**: Workflow management endpoint
#### **Database Schema**
```sql
-- Copilot sessions and conversations
copilot_sessions (id, user_id, session_data, created_at, updated_at)
copilot_messages (id, session_id, message_type, content, metadata, timestamp)
-- User preferences and context
user_preferences (id, user_id, business_type, industry, goals, preferences)
business_context (id, user_id, company_info, target_audience, competitors)
-- Workflow management
workflow_states (id, user_id, workflow_type, current_step, state_data, status)
workflow_templates (id, name, description, steps, conditions, metadata)
```
### **AI/ML Integration**
#### **Intent Recognition**
- **Model**: OpenAI GPT-4 for intent classification
- **Training Data**: Alwrity-specific intent examples
- **Accuracy Target**: >95% intent recognition accuracy
- **Fallback**: Rule-based classification for edge cases
#### **Context Understanding**
- **Embeddings**: OpenAI text-embedding-ada-002
- **Vector Database**: Pinecone for context storage
- **Similarity Search**: For finding relevant context
- **Context Window**: 8K tokens for conversation history
#### **Recommendation Engine**
- **Model**: Custom fine-tuned model on Alwrity data
- **Features**: User behavior, content performance, market trends
- **Output**: Personalized recommendations and suggestions
- **Update Frequency**: Real-time with batch optimization
---
## 📊 **Success Metrics & KPIs**
### **Technical Metrics**
- **Response Time**: <2 seconds for all interactions
- **Uptime**: 99.9% availability
- **Error Rate**: <1% for copilot interactions
- **Intent Accuracy**: >95% recognition accuracy
- **Context Relevance**: >90% context accuracy
### **User Experience Metrics**
- **Adoption Rate**: 85% of users use copilot within 30 days
- **Session Duration**: 25 minutes average (vs 15 minutes current)
- **Feature Discovery**: 80% of features discovered through copilot
- **User Satisfaction**: 9.1/10 satisfaction score
- **Support Reduction**: 80% reduction in support tickets
---
## 🚨 **Risk Mitigation**
### **Technical Risks**
- **API Rate Limits**: Implement caching and request optimization
- **Model Performance**: Add fallback models and human-in-the-loop
- **Scalability Issues**: Design for horizontal scaling from day one
- **Data Privacy**: Implement end-to-end encryption and GDPR compliance
### **User Experience Risks**
- **Adoption Resistance**: Provide clear value proposition and gradual rollout
- **Learning Curve**: Implement progressive disclosure and contextual help
- **Performance Issues**: Optimize for speed and add loading indicators
- **Error Handling**: Comprehensive error messages and recovery options
### **Business Risks**
- **Competition**: Focus on unique value propositions and rapid iteration
- **Market Fit**: Continuous user feedback and feature validation
- **Resource Constraints**: Prioritize high-impact features and iterative development
- **Timeline Pressure**: Maintain quality while meeting deadlines
---
## 📋 **Resource Requirements**
### **Development Team**
- **Frontend Developer**: React/TypeScript, CopilotKit expertise
- **Backend Developer**: Python/FastAPI, AI/ML integration
- **AI/ML Engineer**: Model fine-tuning, recommendation systems
- **DevOps Engineer**: Infrastructure, monitoring, deployment
---
## ✅ **Conclusion**
This implementation plan provides a comprehensive roadmap for integrating CopilotKit into Alwrity's platform. The phased approach ensures:
1. **Foundation First**: Core functionality and user experience
2. **Progressive Enhancement**: Advanced features and capabilities
3. **Production Ready**: Performance, scalability, and reliability
The plan focuses on delivering maximum value to users while maintaining technical excellence and business impact. Each phase builds upon the previous one, ensuring a smooth transition and continuous improvement.
**Next Steps**:
1. Review and approve the implementation plan
2. Assemble the development team
3. Set up development environment and infrastructure
4. Begin Phase 1 implementation
5. Establish regular review and feedback cycles
The CopilotKit integration will transform Alwrity into the most user-friendly and intelligent content strategy platform in the market, providing significant competitive advantages and business growth opportunities.